Assistive tool type visual identification method and device based on telescopic boom forklift loader and medium

Abstract

The invention discloses an auxiliary tool type visual identification method and device based on a telescopic arm forklift loader and a medium. When it is detected that the assistive device of the target telescopic boom forklift loader is changed, image collection is conducted on the assistive device of the target telescopic boom forklift loader through a double-spectrum vision device, and a current collection image is obtained; processing the currently acquired image through a data processing mode to obtain a current key point mathematical descriptor and a current region feature code; inputting the current key point mathematical descriptor and the current region feature code into a pre-trained assistive device type visual identification model for identification to obtain an assistive device type identification result; and inputting the auxiliary tool type identification result into an auxiliary tool flag bit receiving control module corresponding to the target telescopic boom forklift loader so as to realize adjustment processing operation on the working state of the vehicle. The problem that the auxiliary tool type is determined mistakenly due to the fact that people need to participate in auxiliary tool change of the telescopic arm forklift loader is solved, and the accuracy of auxiliary tool type identification is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular, to a visual recognition method, device and medium for attachment types based on a telescopic forklift loader. Background Art

[0002] Changing the attachment type of a telescopic forklift loader mainly involves three steps: attachment replacement, attachment recognition, and adjustment of the attachment gear of the whole vehicle. Among them, attachment recognition and adjustment of the attachment type of the whole vehicle need to be completed with manual participation. Whenever the attachment type is changed, the driver needs to manually adjust the attachment knob switch and adjust it to the gear corresponding to the attachment type to complete the attachment gear switching.

[0003] In the process of implementing the present invention, the inventor found that the prior art has the following defects: Currently, since different degrees of human-machine interaction are required in each step of changing the attachment of a telescopic forklift loader, some equipment failures and safety accidents caused by human factors are inevitable. For example, problems such as forgetting to adjust the attachment type gear of the whole vehicle after attachment change, adjusting the attachment type gear of the whole machine incorrectly after attachment change, and the driver being unable to observe the specific attachment type. These problems may all cause damage to the vehicle and even major construction accidents. Summary of the Invention

[0004] The present invention provides a visual recognition method, device and medium for attachment types based on a telescopic forklift loader to improve the accuracy of attachment type recognition and effectively ensure the life and property safety of users.

[0005] According to one aspect of the present invention, there is provided a visual recognition method for attachment types based on a telescopic forklift loader, which includes:

[0006] When it is detected that the attachment of the target telescopic forklift loader has changed, an image of the target telescopic forklift loader attachment is collected through a pre-set dual-spectrum vision device to obtain the current collected image;

[0007] Wherein, a dual-spectrum vision device is added to the end of the telescopic arm corresponding to the target telescopic forklift loader, and a mechanical anti-shake device is installed between the end of the boom corresponding to the target telescopic forklift loader and the dual-spectrum vision device;

[0008] The current collected image is processed through a pre-set data processing method to obtain the current key point mathematical descriptor and the current region feature code;

[0009] The current key point mathematical descriptor and the current region feature code are input into a pre-trained attachment type visual recognition model for recognition to obtain the attachment type recognition result;

[0010] Input the auxiliary tool type recognition result into the auxiliary tool flag bit receiving and control module corresponding to the target telescopic forklift truck, so as to realize the adjustment and processing operation of the vehicle working state according to the received auxiliary tool type recognition result.

[0011] According to another aspect of the present invention, there is provided a visual recognition device for the auxiliary tool type of a telescopic forklift truck, which includes:

[0012] A current acquisition image determination module, configured to, when it is detected that the auxiliary tool of the target telescopic forklift truck changes, collect an image of the auxiliary tool of the target telescopic forklift truck through a pre-set dual-spectrum vision device to obtain a current acquisition image;

[0013] Wherein, a dual-spectrum vision device is added to the end of the telescopic arm corresponding to the target telescopic forklift truck, and a mechanical anti-shake device is installed between the end of the boom corresponding to the target telescopic forklift truck and the dual-spectrum vision device;

[0014] A current key point mathematical descriptor and current region feature encoding determination module, configured to process the current acquisition image through a pre-set data processing method to obtain a current key point mathematical descriptor and a current region feature encoding;

[0015] An auxiliary tool type recognition result determination module, configured to input the current key point mathematical descriptor and the current region feature encoding into a pre-trained visual recognition model for the auxiliary tool type to obtain an auxiliary tool type recognition result;

[0016] An auxiliary tool type recognition result input module, configured to input the auxiliary tool type recognition result into the auxiliary tool flag bit receiving and control module corresponding to the target telescopic forklift truck, so as to realize the adjustment and processing operation of the vehicle working state according to the received auxiliary tool type recognition result.

[0017] According to another aspect of the present invention, there is provided an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. Wherein, when the processor executes the computer program, it implements the visual recognition method for the auxiliary tool type of the telescopic forklift truck according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, there is provided a computer-readable storage medium, which stores computer instructions for causing a processor to implement the visual recognition method for the auxiliary tool type of the telescopic forklift truck according to any embodiment of the present invention when executed.

[0019] In the technical solution of the embodiment of the present invention, when it is detected that the auxiliary tool of the target telescopic forklift truck changes, the dual-spectrum vision device set in advance is used to collect images of the auxiliary tool of the target telescopic forklift truck to obtain the currently collected image; through the data processing method set in advance, the currently collected image is processed to obtain the current key point mathematical descriptor and the current region feature code; the current key point mathematical descriptor and the current region feature code are input into the pre-trained auxiliary tool type vision recognition model for recognition to obtain the auxiliary tool type recognition result; the auxiliary tool type recognition result is input into the auxiliary tool flag receiving control module corresponding to the target telescopic forklift truck to implement the adjustment processing operation of the vehicle working state according to the received auxiliary tool type recognition result. This solves the problem of incorrect determination of the auxiliary tool type caused by the need for manual participation in the change of the auxiliary tool of the telescopic forklift truck, improves the accuracy of the auxiliary tool type recognition, and ensures the life safety and property safety of users.

[0020] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0022] Figure 1 is a flowchart of a method for visually recognizing the type of auxiliary tool based on a telescopic forklift truck according to Embodiment 1 of the present invention;

[0023] Figure 2 is a flowchart of the training of the auxiliary tool type vision recognition model in the method according to Embodiment 2 of the present invention;

[0024] Figure 3 is a schematic structural diagram of a device for visually recognizing the type of auxiliary tool based on a telescopic forklift truck according to Embodiment 3 of the present invention;

[0025] Figure 4 is a schematic structural diagram of an electronic device according to Embodiment 4 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] To enable those skilled in the art to better understand the solution of the present invention, the following will clearly and completely describe the technical solution in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0027] It should be noted that the terms "target", "current", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0028] Embodiment 1

[0029] Figure 1 A flowchart of a visual recognition method for the accessory type of a telescopic forklift truck is provided for Embodiment 1 of the present invention. This embodiment is applicable to the situation of recognizing the accessory type of a telescopic forklift truck. This method can be executed by a visual recognition device for the accessory type of a telescopic forklift truck, and the visual recognition device for the accessory type of a telescopic forklift truck can be implemented in the form of hardware and / or software.

[0030] Correspondingly, as Figure 1 shown, the method includes:

[0031] S110. When it is detected that the accessory of the target telescopic forklift truck has changed, use a pre-set dual-spectrum vision device to collect an image of the target telescopic forklift truck accessory to obtain a currently collected image.

[0032] Among them, a dual-spectrum vision device is added to the end of the telescopic arm corresponding to the target telescopic forklift truck, and a mechanical anti-shake device is installed between the end of the boom corresponding to the target telescopic forklift truck and the dual-spectrum vision device.

[0033] Among them, the dual-spectrum vision device can be composed of a visible light spectrum camera and a near-infrared spectrum camera. The accessories of the target telescopic forklift truck can include accessory types such as forks, hooks, manned platforms, or buckets. The currently collected image can be an image collected by the dual-spectrum vision device.

[0034] In this embodiment, a dual-spectrum vision device can be used to capture the surface texture, color, and morphological features of the auxiliary tool on the target telescopic boom forklift.

[0035] Additionally, a mechanical anti-shake device is installed between the end of the boom corresponding to the target telescopic boom forklift and the dual-spectrum vision device, and the mechanical anti-shake device uses a three-axis gyroscope stabilized gimbal.

[0036] The advantage of such a setting is that: by setting the mechanical anti-shake device, the vibration occurring at the end of the boom during vehicle operation can be alleviated or avoided; by using a three-axis gyroscope stabilized gimbal, it can be used to compensate for the vibration at the end of the boom during vehicle driving or during boom telescoping, so that the dual-spectrum vision device can capture stable and clear image information.

[0037] In this embodiment, when it is detected that the auxiliary tool of the target telescopic boom forklift has changed, it means that a processing operation for adjusting the corresponding vehicle working state according to the change of the auxiliary tool is required. Therefore, it is necessary to collect images through the dual-spectrum vision device to obtain the corresponding currently collected images.

[0038] In another preferred implementation manner in this embodiment, for the dual-spectrum vision device, it can also be a visible light spectrum camera and a lidar, and corresponding three-dimensional image point clouds can be generated according to the collected images, and the function of predicting the type of auxiliary tool for multi-modal learning recognition can be realized.

[0039] S120. Process the currently collected image through a pre-set data processing method to obtain a current key point mathematical descriptor and a current region feature encoding.

[0040] Among them, the data processing method is a method of obtaining the characteristic data corresponding to the image through a variety of image processing methods. The current key point mathematical descriptor can be a descriptor that describes the image with a multi-dimensional vector. The current region feature encoding can be a feature encoding obtained by dividing the image into different regions and extracting features.

[0041] Specifically, the process of processing the currently collected image through a pre-set data processing method to obtain a current key point mathematical descriptor includes: through a pre-set RGB conversion formula for the morphological features of the auxiliary tool: , process the currently collected image to obtain a current single-channel grayscale image of the morphological features of the auxiliary tool; where It is a single-channel grayscale image of the current assistive device's morphological features; R is the red pixel value of the currently acquired image; G is the green pixel value of the currently acquired image; B is the blue pixel value of the currently acquired image; through a pre-set non-local means denoising method, the single-channel grayscale image of the assistive device's morphological features is denoised to obtain the current assistive device's morphological feature map; through a pre-set SIFT algorithm, the corner key point information and edge intersection key point information of different assistive devices in the current assistive device's morphological feature map are detected to obtain the current key point mathematical descriptor.

[0042] Among them, the SIFT (Scale Invariant Feature Transform) algorithm is a local feature description algorithm in the field of image processing.

[0043] In this embodiment, the currently acquired image is an RGB three-channel image, and it can be processed through the assistive device morphological feature RGB conversion formula: , to process the RGB three-channel image to obtain the single-channel grayscale image of the current assistive device's morphological features. Exemplarily, if the red pixel value of the currently acquired image is 128, the green pixel value is 64, and the blue pixel value is 32, the pixel value of the single-channel grayscale image of the current assistive device's morphological features can be calculated to be 80. This can greatly reduce the data volume and improve the calculation speed. Through the assistive device morphological feature RGB conversion formula, the morphological features of the assistive device can be highlighted, key feature information such as the outline and texture of the object can be retained, and the interference of light color temperature can be weakened.

[0044] Furthermore, through the non-local means denoising method, the single-channel grayscale image of the assistive device's morphological features is denoised. During the denoising process, median filtering is mainly used to eliminate random noise in the image. The non-local means denoising method can also retain the sharpness of the assistive device's edges, thereby avoiding the blurring of image information at key parts such as the tips of the teeth, forklift tines, and hooks of the assistive device, and the current assistive device's morphological feature map can be further obtained.

[0045] Correspondingly, the SIFT algorithm can also be used to detect the corner key point information and edge intersection key point information of different assistive devices in the current assistive device's morphological feature map to obtain the current key point mathematical descriptor, where the current key point mathematical descriptor can be a 128-dimensional vector descriptor.

[0046] Specifically, the currently acquired image is processed through a pre-set data processing method to obtain the current region feature encoding, including: through a pre-set feature region detection algorithm, the current assistive device's morphological feature map is divided into feature regions to obtain at least one current feature region; the region of interest is extracted from each current feature region to obtain the current region feature encoding.

[0047] Among them, the feature area detection algorithm can be an algorithm generated based on a convolutional neural network or a capsule network. Assuming an algorithm generated by a capsule network, information can be passed through vectorized capsules to retain attributes such as the pose and rotation of different auxiliary tools. Moreover, when the collected images of the auxiliary tools change, such as translation or rotation, the data changes more stably.

[0048] In this embodiment, since different auxiliary tools have different features. Specifically, the feature of the forklift attachment is that it has two prominent tips, and the feature of the hook attachment is a relatively small hook, etc. Therefore, it is necessary to use the feature area detection algorithm to divide the feature areas of the current auxiliary tool morphology feature map, extract the regions of interest for each current feature area, and obtain the current region feature encoding. This can concentrate the computing power on the key feature areas and reduce the influence of factors such as background, dirt, damage, and occlusion.

[0049] S130. Input the current key point mathematical descriptor and the current region feature encoding into a pre-trained auxiliary tool type visual recognition model for recognition to obtain an auxiliary tool type recognition result.

[0050] Among them, the auxiliary tool type visual recognition model can be a model for recognizing the auxiliary tool type. The auxiliary tool type recognition result can be the type of the specific auxiliary tool that has changed.

[0051] In this embodiment, the auxiliary tool type visual recognition model can perform recognition processing operations based on the current key point mathematical descriptor and the current region feature encoding to obtain the specific auxiliary tool type.

[0052] In addition, assuming that the specific auxiliary tool type recognition result is not recognized by the auxiliary tool type visual recognition model, the current collected image needs to be fed back to the human for recognition processing operations, and the auxiliary tool type visual recognition model can be retrained through the manually labeled auxiliary tool type tags.

[0053] S140. Input the auxiliary tool type recognition result into the auxiliary tool flag bit receiving and control module corresponding to the target telescopic forklift to implement the adjustment processing operation of the vehicle working state according to the received auxiliary tool type recognition result.

[0054] In this embodiment, assuming that the recognized auxiliary tool type recognition result is a hook, the hook needs to be input into the auxiliary tool flag bit receiving and control module corresponding to the target telescopic forklift, which also means that the hook has changed. Therefore, it is necessary to adjust the vehicle working state of the target telescopic forklift.

[0055] The technical solution of the embodiment of the present invention is to collect images of the auxiliary equipment of the target telescopic forklift through a pre-set dual-spectrum visual device when a change in the auxiliary equipment of the target telescopic forklift is detected, so as to obtain the current collected image; the current collected image is processed through a pre-set data processing method to obtain the current key point mathematical descriptor and the current area feature code; the current key point mathematical descriptor and the current area feature code are input into a pre-trained auxiliary equipment type visual recognition model for recognition to obtain the auxiliary equipment type recognition result; the auxiliary equipment type recognition result is input into the auxiliary equipment flag receiving control module corresponding to the target telescopic forklift, so as to realize the adjustment and processing operation of the vehicle working state according to the received auxiliary equipment type recognition result. The problem of error in determining the auxiliary equipment type caused by the need for human participation in the change of the auxiliary equipment of the telescopic forklift is solved, the accuracy of auxiliary equipment type recognition is improved, and the life and property safety of users is guaranteed.

[0056] Embodiment 2

[0057] Figure 2 A flowchart of training an auxiliary tool type visual recognition model in an auxiliary tool type visual recognition method based on a telescopic forklift is provided for the second embodiment of the present invention. This embodiment is optimized based on the above embodiments. In this embodiment, before the image of the target telescopic forklift is collected by a pre-set dual-spectrum visual device to obtain the current collected image, a training operation of the auxiliary tool type visual recognition model is also included.

[0058] Correspondingly, such as Figure 2 As shown, the method includes:

[0059] S210: Acquire a plurality of historical assistive tool samples and a historical assistive tool type corresponding to each of the historical assistive tool image samples.

[0060] The historical assistive tool samples may be image samples of multiple different assistive tool types. The historical assistive tool types may be labeled for each historical assistive tool sample, and the specific assistive tool type may be determined by the label.

[0061] S220, processing each of the historical auxiliary tool image samples by a preset data processing method to obtain corresponding historical key point mathematical descriptors and historical area feature codes respectively.

[0062] The historical key point mathematical descriptor may be a descriptor that describes each historical auxiliary tool sample using a multi-dimensional vector. The current region feature code may be a feature code obtained by dividing each historical auxiliary tool sample into different regions and extracting features.

[0063] S230. Process each of the historical key-point mathematical descriptors through a pre-set feature integration and mapping processing method to obtain historical key-point integrated mapping mathematical descriptors.

[0064] Among them, the feature integration and mapping processing method can be a method for integrating and mapping the extracted features of the historical key-point mathematical descriptors.

[0065] Optionally, the step of "processing each of the historical key-point mathematical descriptors through a pre-set feature integration and mapping processing method to obtain historical key-point integrated mapping mathematical descriptors" includes: performing weighted summation and activation function processing on each historical key-point mathematical descriptor through the neurons in the hidden layer of the feature integration and mapping processing method to obtain processed mathematical descriptors for each historical key-point; performing feature integration and mapping processing on the processed mathematical descriptors for each historical key-point to obtain historical key-point integrated mapping mathematical descriptors.

[0066] Exemplarily, assuming that the historical key-point mathematical descriptor is a 128-dimensional vector descriptor, first, linear and non-linear transformation operations need to be performed on the 128-dimensional vector descriptor. Then, through the neurons in the hidden layer of the feature integration and mapping processing method, weighted summation and activation function are used to process the input 128-dimensional vector descriptor, and high-level features in the data are gradually extracted. Different hidden layers can learn features at different levels and degrees of abstraction. Then, the features extracted by the hidden layer are integrated and mapped to obtain historical key-point integrated mapping mathematical descriptors. This can be better used for the training operation of the assistive device type visual recognition model.

[0067] S240. Input the historical key-point integrated mapping mathematical descriptors, the historical region feature encoding, and the historical assistive device type into a pre-constructed initial assistive device type visual recognition model for training to obtain the currently trained assistive device type visual recognition model.

[0068] S250. Randomly obtain a set of historical assistive device verification samples and historical assistive device test types, input the historical assistive device verification samples into the currently trained assistive device type visual recognition model to obtain the model-predicted assistive device type, and combine it with the historical assistive device test type to determine whether the training of the assistive device type visual recognition model is completely completed.

[0069] In this embodiment, by randomly obtaining a set of historical assistive device verification samples and historical assistive device test types from the test set samples and using them to verify whether the assistive device type visual recognition model is trained, that is, to determine whether the recognition accuracy of the assistive device type visual recognition model meets the requirements.

[0070] Optionally, inputting the historical assistive device verification sample into the currently trained assistive device type visual recognition model to obtain the model-predicted assistive device type, and combining with the historical assistive device test type to determine whether the training of the assistive device type visual recognition model is completed in full, includes: inputting the historical assistive device verification sample into the currently trained assistive device type visual recognition model to obtain the model-predicted assistive device type, and combining with the historical assistive device test type to calculate the input-output mean square error value; obtaining a preset input-output mean square error value threshold, and determining whether the input-output mean square error value meets the requirement of the input-output mean square error value threshold. If it meets, it is determined that the training of the assistive device type visual recognition model is completed in full, and the target parameter weight value and target parameter threshold corresponding to the assistive device type visual recognition model are determined.

[0071] Among them, the input-output mean square error value can be used to describe the difference between the model-predicted assistive device type and the historical assistive device test type, and to reflect the accuracy of the assistive device type visual recognition model in predicting the assistive device type. The input-output mean square error value threshold can be a preset threshold size for the input-output mean square error value.

[0072] In this embodiment, if the input-output mean square error value meets the requirement of the input-output mean square error value threshold, it indicates that the prediction accuracy of the trained assistive device type visual recognition model meets the requirement. Therefore, the training of the assistive device type visual recognition model can be stopped, that is, it is determined that the training of the assistive device type visual recognition model is completed in full.

[0073] Furthermore, after determining that the training of the assistive device type visual recognition model is completed, it is also necessary to determine the target parameter weight value and target parameter threshold corresponding to the assistive device type visual recognition model.

[0074] Optionally, after determining whether the input-output mean square error value meets the requirement of the input-output mean square error value threshold, it further includes: if it does not meet, respectively adjusting the target parameter weight value and target parameter threshold through a preset backpropagation method, and returning the operation of inputting the integrated mapping mathematical descriptor of the historical key points, the historical region feature encoding, and the historical assistive device type into the pre-constructed initial assistive device type visual recognition model for training until it is determined that the training of the assistive device type visual recognition model is completed in full.

[0075] In this embodiment, if the input-output mean square error value does not meet the requirement of the input-output mean square error value threshold, it indicates that the prediction accuracy of the trained assistive device type visual recognition model does not meet the requirement. Then, it is necessary to respectively adjust the target parameter weight value and target parameter threshold through the backpropagation method, and perform retraining on the assistive device type visual recognition model.

[0076] Specifically, during the execution of the backpropagation method, it is necessary to analyze the target parameter weights and target parameter thresholds, and also perform steps such as hierarchical gradient normalization, adaptive gradient clipping, and updating the target parameter weights and target parameter thresholds.

[0077] In detail, analyzing the target parameter weights and target parameter thresholds is to calculate the gradients of the mean squared error value of the input and output of the assistive device type visual recognition model with respect to these parameters under the current parameter settings, and will guide how to update the target parameter weights and target parameter thresholds according to the mean squared error value of the input and output, so that the mean squared error value of the model's input and output meets the requirements of the mean squared error value threshold.

[0078] Furthermore, during the hierarchical gradient normalization process, it is necessary to normalize the gradients of different layers so that the gradients of each layer are within an expected range. This can avoid the problems of gradient disappearance or gradient explosion as much as possible, accelerate the convergence process of the assistive device type visual recognition model, and ensure the stability of training.

[0079] Correspondingly, during the adaptive gradient clipping process, the gradients are clipped to prevent the parameter update amplitude from being too large due to too large gradients, making the training process unstable. The adaptive method can dynamically adjust the clipping threshold according to the actual situation during the training process.

[0080] During the process of updating the target parameter weights and target parameter thresholds, according to the calculated gradient information, the stochastic gradient descent algorithm is used to update the target parameter weights and target parameter thresholds of the assistive device type visual recognition model, so that the assistive device type visual recognition model can reduce the difference between the predicted assistive device type and the historical assistive device test type in subsequent predictions, and gradually improve the prediction accuracy.

[0081] S260. When it is detected that the assistive device of the target telescopic boom forklift changes, the image of the target telescopic boom forklift assistive device is collected through a pre-set dual-spectrum vision device to obtain the current collected image.

[0082] Among them, a dual-spectrum vision device is added to the end of the telescopic boom corresponding to the target telescopic boom forklift, and a mechanical anti-shake device is installed between the end of the boom corresponding to the target telescopic boom forklift and the dual-spectrum vision device.

[0083] S270. The current collected image is processed through a pre-set data processing method to obtain the current key point mathematical descriptor and the current region feature encoding.

[0084] S280. The current key point mathematical descriptor and the current region feature encoding are input into a pre-trained assistive device type visual recognition model for recognition to obtain the assistive device type recognition result.

[0085] S290. Input the auxiliary tool type recognition result into the auxiliary tool flag bit receiving and control module corresponding to the target telescopic forklift truck, so as to realize the adjustment and processing operation of the vehicle working state according to the received auxiliary tool type recognition result.

[0086] In the technical solution of the embodiment of the present invention, by training the auxiliary tool type visual recognition model, and when it is detected that the auxiliary tool of the target telescopic forklift truck has changed, the currently collected image is collected through the trained auxiliary tool type visual recognition model for the recognition of the auxiliary tool type. According to the obtained auxiliary tool type recognition result, it is input into the auxiliary tool flag bit receiving and control module corresponding to the target telescopic forklift truck, so as to realize the adjustment and processing operation of the vehicle working state according to the received auxiliary tool type recognition result. It solves the problem of incorrect determination of the auxiliary tool type caused by the need for manual participation in the change of the auxiliary tool of the telescopic forklift truck. By using the trained auxiliary tool type visual recognition model, the accuracy of the auxiliary tool type recognition is improved, thereby reducing the labor cost, improving the efficiency and accuracy of the adjustment of the vehicle working state by the auxiliary tool change, and ensuring the life safety and property safety of users.

[0087] Embodiment III

[0088] Figure 3 It is a schematic structural diagram of an auxiliary tool type visual recognition device based on a telescopic forklift truck provided by Embodiment III of the present invention. The auxiliary tool type visual recognition device provided in this embodiment can be implemented by software and / or hardware, and can be configured in a terminal device or a server to implement an auxiliary tool type visual recognition method based on a telescopic forklift truck in the embodiment of the present invention. As Figure 3 shown, the device includes: a currently collected image determination module 310, a currently key point mathematical descriptor and a currently region feature encoding determination module 320, an auxiliary tool type recognition result determination module 330, and an auxiliary tool type recognition result input module 340.

[0089] Among them, the currently collected image determination module 310 is used to collect an image of the auxiliary tool of the target telescopic forklift truck through a pre-set dual-spectrum vision device when it is detected that the auxiliary tool of the target telescopic forklift truck has changed, so as to obtain the currently collected image;

[0090] Among them, a dual-spectrum vision device is added to the end of the telescopic arm corresponding to the target telescopic forklift truck, and a mechanical anti-shake device is installed between the end of the boom corresponding to the target telescopic forklift truck and the dual-spectrum vision device;

[0091] The current key-point mathematical descriptor and current region feature encoding determination module 320 is configured to process the currently acquired image through a preset data processing method to obtain a current key-point mathematical descriptor and a current region feature encoding;

[0092] The auxiliary tool type recognition result determination module 330 is configured to input the current key-point mathematical descriptor and the current region feature encoding into a pre-trained auxiliary tool type visual recognition model for recognition to obtain an auxiliary tool type recognition result;

[0093] The auxiliary tool type recognition result input module 340 is configured to input the auxiliary tool type recognition result into the auxiliary tool flag bit receiving and control module corresponding to the target telescopic boom forklift truck, so as to implement an adjustment processing operation of the vehicle working state according to the received auxiliary tool type recognition result.

[0094] In the technical solution of the embodiment of the present invention, when it is detected that the auxiliary tool of the target telescopic boom forklift truck changes, an image of the auxiliary tool of the target telescopic boom forklift truck is collected through a preset dual-spectrum vision device to obtain a currently acquired image; the currently acquired image is processed through a preset data processing method to obtain a current key-point mathematical descriptor and a current region feature encoding; the current key-point mathematical descriptor and the current region feature encoding are input into a pre-trained auxiliary tool type visual recognition model for recognition to obtain an auxiliary tool type recognition result; the auxiliary tool type recognition result is input into the auxiliary tool flag bit receiving and control module corresponding to the target telescopic boom forklift truck, so as to implement an adjustment processing operation of the vehicle working state according to the received auxiliary tool type recognition result. The problem of incorrect determination of the auxiliary tool type caused by the need for manual participation in the change of the auxiliary tool of the telescopic boom forklift truck is solved, the accuracy of the auxiliary tool type recognition is improved, and the life safety and property safety of users are guaranteed.

[0095] Based on the above embodiments, the current key-point mathematical descriptor and current region feature encoding determination module 320 may specifically be configured to: through a preset RGB conversion formula for the auxiliary tool morphological feature: , process the currently acquired image to obtain a current single-channel grayscale image of the auxiliary tool morphological feature; where is the current single-channel grayscale image of the auxiliary tool morphological feature; R is the red pixel value of the currently acquired image; G is the green pixel value of the currently acquired image; B is the blue pixel value of the currently acquired image; perform denoising processing on the single-channel grayscale image of the auxiliary tool morphological feature through a preset non-local means denoising method to obtain a current auxiliary tool morphological feature map; detect the corner key-point information and edge intersection key-point information of different auxiliary tools in the current auxiliary tool morphological feature map through a preset SIFT algorithm to obtain a current key-point mathematical descriptor.

[0096] Based on the above embodiments, the current key-point mathematical descriptor and current region feature encoding determination module 320 may specifically be configured to: divide the current assistive device morphological feature map into feature regions through a pre-set feature region detection algorithm to obtain at least one current feature region; extract regions of interest from each of the current feature regions to obtain the current region feature encoding.

[0097] Based on the above embodiments, there is further included an assistive device type visual recognition model training module, which may specifically include: a historical assistive device sample and historical assistive device type acquisition unit, configured to obtain a plurality of historical assistive device samples and historical assistive device types corresponding to each of the historical assistive device image samples before collecting an image of the target telescopic forklift truck through a pre-set dual-spectrum vision device to obtain the current collected image; a historical key-point mathematical descriptor and historical region feature encoding obtaining unit, configured to process each of the historical assistive device image samples through a pre-set data processing method to respectively obtain corresponding historical key-point mathematical descriptors and historical region feature encodings; a historical key-point integrated mapping mathematical descriptor determination unit, configured to process each of the historical key-point mathematical descriptors through a pre-set feature integration and mapping processing method to obtain a historical key-point integrated mapping mathematical descriptor; an assistive device type visual recognition model training unit, configured to input the historical key-point integrated mapping mathematical descriptor, the historical region feature encoding, and the historical assistive device type into a pre-constructed initial assistive device type visual recognition model for training to obtain the currently trained assistive device type visual recognition model; an assistive device type visual recognition model training completion unit, configured to randomly obtain a set of historical assistive device verification samples and historical assistive device test types, input the historical assistive device verification samples into the currently trained assistive device type visual recognition model to obtain a model-predicted assistive device type, and combine with the historical assistive device test types to determine whether the training of the assistive device type visual recognition model is all completed.

[0098] Based on the above embodiments, the historical key-point integrated mapping mathematical descriptor determination unit may specifically be configured to: perform weighted summation and activation function processing on each historical key-point mathematical descriptor through neurons in the hidden layer of the feature integration and mapping processing method to obtain each historical key-point processed mathematical descriptor; perform feature integration and mapping processing on each of the historical key-point processed mathematical descriptors to obtain a historical key-point integrated mapping mathematical descriptor.

[0099] Based on the above embodiments, the unit for completing the training of the auxiliary tool type visual recognition model may specifically be configured to: input the historical auxiliary tool verification sample into the currently trained auxiliary tool type visual recognition model to obtain the model-predicted auxiliary tool type, and combine the historical auxiliary tool test type to calculate the input-output mean square error value; obtain a preset input-output mean square error value threshold, and determine whether the input-output mean square error value meets the requirement of the input-output mean square error value threshold. If it meets, it is determined that the training of the auxiliary tool type visual recognition model is completely completed, and the target parameter weight and target parameter threshold corresponding to the auxiliary tool type visual recognition model are determined.

[0100] Based on the above embodiments, the unit for completing the training of the auxiliary tool type visual recognition model may also specifically be configured to: after determining whether the input-output mean square error value meets the requirement of the input-output mean square error value threshold, if it does not meet, adjust the target parameter weight and target parameter threshold respectively through a pre-set backpropagation method, and return to the operation of inputting the historical key point integration mapping mathematical descriptor, the historical region feature encoding, and the historical auxiliary tool type into the pre-constructed initial auxiliary tool type visual recognition model for training until it is determined that the training of the auxiliary tool type visual recognition model is completely completed.

[0101] The auxiliary tool type visual recognition device based on the telescopic forklift truck provided by the embodiments of the present invention can execute the auxiliary tool type visual recognition method based on the telescopic forklift truck provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0102] Embodiment 4

[0103] Figure 4 FIG. shows a schematic structural diagram of an electronic device 10 that can be used to implement Embodiment 4 of the present invention. The electronic device is intended to represent various forms of digital computers, such as, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0104] As Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. The memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0105] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0106] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the vision recognition method for the attachment type of a telescopic forklift truck.

[0107] In some embodiments, the vision recognition method for the attachment type of a telescopic forklift truck can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the vision recognition method for the attachment type of a telescopic forklift truck described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the vision recognition method for the attachment type of a telescopic forklift truck in any other appropriate manner (e.g., by means of firmware).

[0108] The method includes: when it is detected that the attachment of the target telescopic forklift truck changes, the image of the attachment of the target telescopic forklift truck is collected through a pre-set dual-spectrum vision device to obtain the current collected image; wherein, a dual-spectrum vision device is added to the end of the telescopic arm corresponding to the target telescopic forklift truck, and a mechanical anti-shake device is installed between the end of the boom corresponding to the target telescopic forklift truck and the dual-spectrum vision device; the current collected image is processed through a pre-set data processing method to obtain the current key-point mathematical descriptor and the current region feature encoding; the current key-point mathematical descriptor and the current region feature encoding are input into a pre-trained attachment type vision recognition model for recognition to obtain the attachment type recognition result; the attachment type recognition result is input into the attachment flag receiving and control module corresponding to the target telescopic forklift truck to implement the adjustment processing operation of the vehicle working state according to the received attachment type recognition result.

[0109] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems on a chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: implemented in one or more computer programs, the one or more computer programs can be executed and / or interpreted on a programmable system including at least one programmable processor, the programmable processor can be a dedicated or general programmable processor, and can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0110] The computer programs for implementing the method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a dedicated computer, or other programmable data processing devices, such that when the computer programs are executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, as an independent software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0111] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0112] To provide for interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic, speech, or tactile input).

[0113] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0114] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The relationship between the client and the server is created by computer programs running on respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0115] It should be understood that various forms of the processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0116] The above specific embodiments do not constitute a limitation to the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

[0117] Embodiment Five

[0118] Embodiment Five of the present invention further provides a computer-readable storage medium, and the computer-readable instructions are used to execute a vision recognition method for attachment types based on a telescopic forklift when executed by a computer processor. The method includes: when it is detected that the attachment of the target telescopic forklift has changed, collecting an image of the target telescopic forklift attachment through a pre-set dual-spectrum vision device to obtain the current collected image; wherein, a dual-spectrum vision device is added to the end of the telescopic arm corresponding to the target telescopic forklift, and a mechanical anti-shake device is installed between the end of the boom corresponding to the target telescopic forklift and the dual-spectrum vision device; processing the current collected image through a pre-set data processing method to obtain the current key point mathematical descriptor and the current region feature encoding; inputting the current key point mathematical descriptor and the current region feature encoding into a pre-trained vision recognition model for attachment types to obtain an attachment type recognition result; and inputting the attachment type recognition result into the attachment flag receiving and control module corresponding to the target telescopic forklift to implement an adjustment processing operation for the vehicle working state according to the received attachment type recognition result.

[0119] Of course, the computer-readable storage medium provided by the embodiments of the present invention has computer-executable instructions that are not limited to the method operations described above, and can also execute related operations in the visual recognition of the accessory types based on the telescopic forklift truck provided by any embodiment of the present invention.

[0120] From the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software and necessary general-purpose hardware. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk, or optical disc of a computer, and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0121] It should be noted that in the above embodiments of the visual recognition of the accessory types based on the telescopic forklift truck, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0122] The above specific embodiments do not constitute a limitation to the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for visually identifying the type of auxiliary equipment of a telescopic forklift, characterized in that: include: When a change is detected in the auxiliary equipment of the target telescopic forklift, an image of the auxiliary equipment of the target telescopic forklift is collected by a pre-set dual-spectrum vision device to obtain a current collected image; Wherein, a dual-spectrum vision device is added to the end of the telescopic arm corresponding to the target telescopic forklift, and a mechanical anti-shake device is installed between the end of the arm corresponding to the target telescopic forklift and the dual-spectrum vision device; Processing the current captured image by a preset data processing method to obtain a current key point mathematical descriptor and a current region feature code; Input the current key point mathematical descriptor and the current region feature code into a pre-trained assistive device type visual recognition model for recognition, thereby obtaining an assistive device type recognition result; The auxiliary tool type identification result is input into the auxiliary tool flag receiving control module corresponding to the target telescopic arm forklift, so as to implement the adjustment processing operation of the vehicle working state according to the received auxiliary tool type identification result.

2. The method according to claim 1, characterized in that: The currently acquired image is processed by a preset data processing method to obtain a current key point mathematical descriptor, including: Through the preset auxiliary shape feature RGB conversion formula: , processing the current acquired image to obtain a single-channel grayscale image of the current assistive device morphological features; in, is a single-channel grayscale image of the current auxiliary device morphological features; R is the red pixel value of the current acquired image; G is the green pixel value of the current acquired image; B is the blue pixel value of the current acquired image; The single-channel grayscale image of the assistive device morphology feature is denoised by a pre-set non-local mean denoising method to obtain a current assistive device morphology feature image; The key point information of the corner points and the key point information of the edge intersection points of different assistive devices in the current assistive device morphological feature map are detected by using a preset scale-invariant feature transform (SIFT) algorithm to obtain a current key point mathematical descriptor.

3. The method according to claim 2, characterized in that The currently acquired image is processed by a preset data processing method to obtain a current region feature code, including: Performing feature region division on the current assistive device morphology feature map using a preset feature region detection algorithm to obtain at least one current feature region; The region of interest is extracted from each of the current feature regions to obtain a current region feature code.

4. The method according to claim 1, characterized in that Before acquiring an image of the target telescopic forklift auxiliary device by using the pre-set dual-spectrum vision device to obtain the current acquired image, the method further includes: Acquire multiple historical assistive tool samples and the historical assistive tool type corresponding to each of the historical assistive tool image samples; Processing each of the historical auxiliary tool image samples by a preset data processing method to obtain corresponding historical key point mathematical descriptors and historical area feature codes; Through a preset feature integration and mapping processing method, each of the historical key point mathematical descriptors is processed respectively to obtain a historical key point integrated mapping mathematical descriptor; Inputting the historical key point integrated mapping mathematical descriptors, the historical area feature codes, and the historical assistive device types into a pre-built initial assistive device type visual recognition model for training, thereby obtaining a currently trained assistive device type visual recognition model; A group of historical assistive device verification samples and historical assistive device test types are randomly obtained, and the historical assistive device verification samples are input into the currently trained assistive device type visual recognition model to obtain the model-predicted assistive device type, and combined with the historical assistive device test type, it is determined whether the training of the assistive device type visual recognition model has been completed.

5. The method according to claim 4, characterized in that The method of pre-setting feature integration and mapping processing is used to process the mathematical descriptors of the historical key points respectively to obtain the integrated mapping mathematical descriptors of the historical key points, including: Through the neurons of the hidden layer in the feature integration and mapping processing method, weighted summation and activation function processing are performed on each historical key point mathematical descriptor to obtain the processed mathematical descriptors of each historical key point; The feature integration and mapping processing are performed on each of the historical key point processing mathematical descriptors to obtain the historical key point integrated mapping mathematical descriptors.

6. The method according to claim 5, characterized in that The inputting the historical assistive device verification sample into the currently trained assistive device type visual recognition model to obtain the model predicted assistive device type, and combining the historical assistive device test type to determine whether the training of the assistive device type visual recognition model is fully completed, including: Input the historical assistive device verification sample into the currently trained assistive device type visual recognition model to obtain the model predicted assistive device type, and combine the historical assistive device test type to calculate the input-output mean square error value; Obtain a preset input-output mean square error value threshold, and determine whether the input-output mean square error value meets the requirement of the input-output mean square error value threshold; if so, determine that the training of the assistive device type visual recognition model is completed, and determine the target parameter weights and target parameter thresholds corresponding to the assistive device type visual recognition model.

7. The method according to claim 6, characterized in that After determining whether the input-output mean square error value meets the requirement of the input-output mean square error value threshold, the method further includes: If not satisfied, the target parameter weight and target parameter threshold are adjusted respectively through a preset back propagation method, and the operation of integrating the historical key point mapping mathematical descriptor and the historical area feature coding, as well as the historical assistive device type, is returned to be input into a pre-built initial assistive device type visual recognition model for training, until it is determined that the training of the assistive device type visual recognition model is completed.

8. A visual recognition device for the type of auxiliary equipment based on a telescopic arm forklift, characterized in that: include: A current captured image determination module is used to capture an image of the target telescopic forklift auxiliary equipment through a pre-set dual-spectrum vision device to obtain a current captured image when a change in the auxiliary equipment of the target telescopic forklift is detected; Wherein, a dual-spectrum vision device is added to the end of the telescopic arm corresponding to the target telescopic forklift, and a mechanical anti-shake device is installed between the end of the arm corresponding to the target telescopic forklift and the dual-spectrum vision device; A current key point mathematical descriptor and current region feature code determination module, used to process the current captured image by a preset data processing method to obtain a current key point mathematical descriptor and a current region feature code; An assistive device type recognition result determination module is used to input the current key point mathematical descriptor and the current region feature code into a pre-trained assistive device type visual recognition model for recognition, thereby obtaining an assistive device type recognition result; The auxiliary tool type identification result input module is used to input the auxiliary tool type identification result into the auxiliary tool flag receiving control module corresponding to the target telescopic arm forklift, so as to implement the adjustment processing operation of the vehicle working state according to the received auxiliary tool type identification result.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, a method for visually identifying the type of auxiliary equipment based on a telescopic forklift according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement a method for visually identifying the type of auxiliary equipment based on a telescopic forklift according to any one of claims 1 to 7.

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