A method and system for intelligently monitoring the operation status of park AGVs, and electronic equipment

Abstract

The present invention provides a method and system for intelligently monitoring the running status of an AGV in a park, and an electronic device. The method obtains the motor drive current data, vibration frequency data, load data, and current speed data of the sample AGV during its running at preset time intervals, and then uses the coupling relationship among the motor drive current data, vibration frequency data, load data, and current speed data to train an intelligent recognition model for abnormal running status of the AGV. When the model training is completed, the trained intelligent recognition model for abnormal running status of the AGV is used to perform a qualitative analysis on the running status of the target AGV, and then determine whether the running status of the target AGV at the current moment is abnormal. This solves the problem in the prior art that it is difficult to accurately determine whether the running status of the AGV is normal through quantitative analysis due to differences in running speed, load, road condition, and aging status of various components of the vehicle body.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent monitoring of the running status of park AGVs, and in particular to an intelligent monitoring method and system for the running status of park AGVs, and electronic equipment. Background Art

[0002] At present, the monitoring of the operating status of the park AGV is often carried out by calibrating and testing the sensors of the AGV to ensure that it can accurately perceive the environment. For example, the laser sensors, infrared sensors, ultrasonic sensors, etc. of the AGV are tested to ensure that the vehicle can correctly perceive and avoid obstacles by testing whether the sensors are working properly. However, the accurate evaluation of the operating status of the AGV is affected by many factors, such as operating speed, load capacity, road conditions, and aging status of various parts of the vehicle body, which makes it difficult to accurately judge whether the operating status of the AGV is normal through quantitative analysis. Therefore, during the movement of the AGV, there is a lack of effective solutions for accurately evaluating the operating status of the AGV, and it is impossible to continuously and qualitatively accurately evaluate and monitor the operating status of the AGV. The existing technology urgently needs a solution that can solve the above technical problems.

[0003] Therefore, the prior art needs to be further developed. Summary of the invention

[0004] The purpose of the present invention is to overcome the above technical deficiencies and provide a method and system for intelligently monitoring the operating status of a park AGV, and electronic equipment to solve the problems existing in the prior art.

[0005] To achieve the above technical objectives, according to a first aspect of the present invention, the present invention provides a method for intelligently monitoring the running status of a park AGV, the method comprising: S100. During the operation of the sample AGV, the motor drive current data, vibration frequency data, and load data of the sample AGV during operation are obtained at preset time intervals using the current sensor, vibration frequency data, and weight sensor provided on the sample AGV, and the current speed data of the sample AGV is simultaneously obtained, the motor drive current data, vibration frequency data, load data, and current speed data of the sample AGV obtained are recorded as the AGV operation status data set at the current moment, a preset number of AGV operation status data sets are obtained, and the collected AGV operation status data sets are divided into a training data set and a verification data set according to a first preset ratio; S200, using the collected training data set to train the AGV running state abnormality intelligent recognition model, using the verification data set to optimize the AGV running state abnormality intelligent recognition model, optimize the model parameters, and complete the AGV running state abnormality intelligent recognition model training; S300. After the model training is completed, during the operation of the target AGV, the AGV operation status data set of the target AGV at the current moment is obtained, and the AGV operation status data set of the target AGV at the current moment is input into the AGV operation status abnormality intelligent recognition model, and then it is determined whether the operation status of the target AGV at the current moment is abnormal, and according to the determination result, it is determined whether to output an alarm signal regarding the abnormal operation status of the target AGV at the current moment.

[0006] Specifically, the step S100 further includes: The preset number of AGV operation status data sets collected need to be confirmed by relevant management personnel to be normal, and the model and specification of the sample AGV and the target AGV are the same.

[0007] Specifically, the method uses the collected training data set to train the AGV running state abnormality intelligent recognition model, uses the verification data set to optimize the AGV running state abnormality intelligent recognition model, optimizes the model parameters, and completes the AGV running state abnormality intelligent recognition model training, including: The training data set is input into the preset network layer in batches for training, and the preset network layer includes a Transformer network layer, and the Transformer network layer is used to predict the AGV operation status data set at the next moment based on the forward propagation of the AGV operation status data set at the current moment, and then obtain the predicted loss value; the loss value of the preset network layer is calculated and input into the optimizer for optimization, and the direction in which the parameter gradient of the AGV operation status abnormality intelligent recognition model decreases fastest is determined; the AGV operation status abnormality intelligent recognition model performs back propagation according to the loss value and the parameter gradient of the model, and optimizes the parameters of the AGV operation status abnormality intelligent recognition model.

[0008] Specifically, the method uses the collected training data set to train the AGV running state abnormality intelligent recognition model, uses the verification data set to optimize the AGV running state abnormality intelligent recognition model, optimizes the model parameters, and completes the AGV running state abnormality intelligent recognition model training, and also includes: After each training, the verification data set is input into the preset network layer of the previous training in batches for model parameter verification, and cyclic training is performed, with the total number of training rounds set to the first preset round; the loss value of the preset network layer is recorded, and it is determined whether the loss value meets the first preset condition, and whether to end the training and output the model parameters based on the judgment result.

[0009] Specifically, the determining whether the loss value satisfies the first preset condition, and determining whether to end the training and output the model parameters according to the determination result, includes: If the loss value meets the first preset condition, the training is terminated and the parameters of the current AGV operating status abnormality intelligent recognition model are output; if the loss value does not meet the first preset condition, the training continues.

[0010] Specifically, the first preset condition is: After the second preset round of training, the loss values ​​obtained in the next round of training are greater than or equal to the loss values ​​that occurred during the second preset round of training.

[0011] Specifically, the model output result of the AGV abnormal operation status intelligent recognition model includes the abnormal operation status of the target AGV at the current moment and the normal operation status of the target AGV at the current moment.

[0012] Specifically, the AGV operation status data set of the target AGV at the current moment is input into the AGV operation status abnormality intelligent recognition model, and then determine whether the operation status of the target AGV at the current moment is abnormal, and determine whether to output an alarm signal related to the abnormal operation status of the target AGV at the current moment according to the determination result, including: If it is determined that the current operating state of the target AGV is abnormal, an alarm signal related to the abnormal current operating state of the target AGV is output; If it is determined that the current operating state of the target AGV is normal, a prompt signal indicating that the current operating state of the target AGV is normal is output.

[0013] According to a second aspect of the present invention, there is provided a park AGV operation status intelligent monitoring system, comprising: An acquisition module is used to acquire the motor drive current data, vibration frequency data, and load data of the sample AGV during operation by using the current sensor, vibration sensor, and weight sensor provided on the sample AGV at preset time intervals, and synchronously acquire the current speed data of the sample AGV, record the acquired motor drive current data, vibration frequency data, load data, and current speed data of the sample AGV as the AGV operation status data set at the current moment, and acquire a preset number of AGV operation status data sets; A control module is used to divide the collected AGV operation status data set into a training data set and a verification data set according to a first preset ratio; to use the collected training data set to train the AGV operation status abnormality intelligent recognition model, and to use the verification data set to optimize the AGV operation status abnormality intelligent recognition model, optimize the model parameters, and complete the AGV operation status abnormality intelligent recognition model training; and to obtain the AGV operation status data set of the target AGV at the current moment during the operation of the target AGV after the model training is completed, input the AGV operation status data set of the target AGV at the current moment into the AGV operation status abnormality intelligent recognition model, and then determine whether the operation status of the target AGV at the current moment is abnormal, and determine whether to output an alarm signal regarding the abnormal operation status of the target AGV at the current moment according to the determination result.

[0014] According to a third aspect of the present invention, there is provided an electronic device, comprising: a memory; and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the above-mentioned method for intelligently monitoring the operating status of a park AGV is implemented.

[0015] Beneficial effects: The present invention obtains the motor drive current data, vibration frequency data, load data and current speed data of the sample AGV during its operation at preset time intervals, and then uses the coupling relationship among the motor drive current data, vibration frequency data, load data and current speed data to train an intelligent recognition model for abnormal AGV operation status. When the model training is completed, the trained intelligent recognition model for abnormal AGV operation status is used to perform a qualitative analysis on the operation status of the target AGV, and then determine whether the operation status of the target AGV at the current moment is abnormal. This solves the problem in the prior art that it is difficult to accurately determine whether the operation status of the AGV is normal through quantitative analysis due to differences in operation speed, load, road condition and aging status of various components of the vehicle body, and greatly improves the intelligence, safety and reliability of intelligent monitoring of the operation status of park AGVs. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the method for intelligent monitoring of the operation status of the park AGV provided in a specific embodiment of the present invention; Figure 2 It is a schematic diagram of the system composition of the park AGV operation status intelligent monitoring system provided in a specific embodiment of the present invention; Figure 3 It is a structural diagram of the AGV operating status abnormality intelligent recognition model proposed in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is clearly and completely described below in conjunction with the accompanying drawings of the present invention. Based on the embodiments in this application, other similar embodiments obtained by ordinary technicians in this field without making creative work should all fall within the scope of protection of this application. In addition, the directional words mentioned in the following embodiments, such as "up", "down", "left", "right", etc., are only reference to the directions of the accompanying drawings. Therefore, the directional words used are used to illustrate rather than limit the invention.

[0018] The present invention will be further described below in conjunction with the accompanying drawings and preferred embodiments.

[0019] See also Figure 1 The present invention provides a method for intelligently monitoring the running status of an AGV in a park, comprising: S100. During the operation of the sample AGV, the motor drive current data, vibration frequency data, and load data of the sample AGV during operation are obtained at preset time intervals using the current sensor, vibration frequency data, and weight sensor provided on the sample AGV, and the current speed data of the sample AGV is simultaneously obtained. The motor drive current data, vibration frequency data, load data, and current speed data of the sample AGV obtained are recorded as the AGV operation status data set at the current moment, a preset number of AGV operation status data sets are obtained, and the collected AGV operation status data sets are divided into a training data set and a verification data set according to a first preset ratio.

[0020] Specifically, the step S100 further includes: The preset number of AGV operation status data sets collected need to be confirmed by relevant management personnel to be normal.

[0021] It is understandable that the relevant management personnel confirm whether the preset number of AGV operation status data sets collected are abnormal according to industry standards, and the present invention does not make any limitation thereto.

[0022] It should be noted here that the model and specifications of the sample AGV and the target AGV are the same.

[0023] It should be noted that the step before step S100 includes: A preset number, a preset time interval, a first preset ratio, a first preset round, and a second preset round are preset in the control module.

[0024] It can be understood that the preset number, preset time interval, first preset ratio, first preset round, and second preset round can be specifically set according to the actual needs of the user of the present invention. The present invention does not limit the specific values ​​of the above parameters, and there is no regularity in setting the specific values ​​of the above parameters, as long as it is applicable to the intelligent monitoring method of the operating status of the park AGV proposed in the present invention.

[0025] Preferably, the present invention sets the preset time interval to 1 second, the first preset number to 2000, the present invention sets the first preset ratio to 8:2, the present invention sets the first preset round to 20000 times, and the present invention sets the second preset round to 300. The above settings can further improve the performance of the model and the efficiency of model training, and greatly improve the accuracy and reliability of the prediction of the AGV operating status abnormality intelligent identification model of the present invention.

[0026] It can be understood that when the operating state of the AGV is normal, there is a certain coupling relationship between the motor drive current data, vibration frequency data, load data, and current speed data of the AGV. For example, when the aging degree, load data, motor drive current, and current speed data of each part of the AGV are the same, the vibration frequency of the AGV should be roughly the same. When the operating state of the AGV is abnormal, for example, a part of the AGV is damaged, the coupling relationship between the motor drive current data, vibration frequency data, load data, and current speed data of the AGV will be broken, such as abnormal changes in vibration frequency, or abnormal changes in motor drive current data, etc. Based on this, the present invention designs and trains an intelligent recognition model for abnormal AGV operating status.

[0027] S200, using the collected training data set to train the AGV running status abnormality intelligent recognition model, using the verification data set to optimize the AGV running status abnormality intelligent recognition model, optimize the model parameters, and complete the AGV running status abnormality intelligent recognition model training.

[0028] Specifically, the method uses the collected training data set to train the AGV running state abnormality intelligent recognition model, uses the verification data set to optimize the AGV running state abnormality intelligent recognition model, optimizes the model parameters, and completes the AGV running state abnormality intelligent recognition model training, including: The training data set is input into the preset network layer in batches for training, and the preset network layer includes a Transformer network layer, and the Transformer network layer is used to predict the AGV operation status data set at the next moment based on the forward propagation of the AGV operation status data set at the current moment, and then obtain the predicted loss value; the loss value of the preset network layer is calculated and input into the optimizer for optimization, and the direction in which the parameter gradient of the AGV operation status abnormality intelligent recognition model decreases fastest is determined; the AGV operation status abnormality intelligent recognition model performs back propagation according to the loss value and the parameter gradient of the model, and optimizes the parameters of the AGV operation status abnormality intelligent recognition model.

[0029] It can be understood that Transformer is a deep learning model based on the self-attention mechanism, which consists of a multi-head attention mechanism and a feedforward neural network. It can process sequence data and capture long-distance dependencies. The Transformer model does not contain traditional convolutional layers, but processes each element in the input sequence through an attention layer. The typical structure of Transformer includes an encoder and a decoder, wherein the encoder is composed of multiple identical layers stacked together, and the decoder adds a self-attention layer on the basis of the encoder to process sequence generation tasks. The present invention uses Transformer to supplement global context information, enhances the model's capabilities in feature extraction and global understanding, greatly improves the performance of the model, and greatly reduces the computational cost.

[0030] Specifically, the preset network layer also includes a ResNet network layer, which is used to classify and predict the output results based on the collected AGV operation status data set, and the classification results include abnormal operation status of the target AGV at the current moment and normal operation status of the target AGV at the current moment.

[0031] It should be noted here that ResNet is a deep convolutional neural network that solves the gradient vanishing problem in deep network training by introducing residual connections. The core component of ResNet is the residual block, which usually contains two or three convolutional layers and a jump connection, which allows the gradient to bypass these convolutional layers directly. The network structure of ResNet can be very deep. These networks extract the features of the AGV operating status data at the current moment by stacking multiple residual blocks, and classify and predict the output results through global average pooling and fully connected layers. The classification results include the abnormal operating status of the target AGV at the current moment and the normal operating status of the target AGV at the current moment.

[0032] See also Figure 3The present invention combines the ResNet network layer and the Transformer network layer. ResNet is good at extracting the local features of the AGV operation status data at the current moment in the training data set, while Transformer can supplement the global context information. This combination can enhance the model's ability in feature extraction and global understanding, greatly improve the performance of the model, and greatly reduce the computational cost.

[0033] It can be understood that the ResNet network layer is used to classify and predict the output results through the AGV running status data set collected in the training data set and the verification data set. The specific steps are: (1) First, put the training data set into the Backbones network layer of the model. Backbone refers to a series of convolutional layers that constitute the skeleton of the neural network. The main function of these layers is to extract the features of the input data. The Backbone network is usually composed of multiple convolutional layers, pooling layers, and activation functions. It can extract meaningful feature representations from the original data. Backbones is composed of multiple Backbones, and the core network layer in Backbone is the Resnet network layer. After the data set is input into the model, the AGV operation status data set will be input into Backbones for training; (2) When building Backbone, the Resnet network layer and AGV number were added. After the training data set was input, Resnet began to train layer by layer. The training extracted features from the AGV operation status data set and assisted Transformer in predicting whether the current operation status of the target AGV was abnormal. The extracted features were used with the help of the AGV number to obtain the target AGV number information, which facilitated the relevant management personnel to locate the target AGV.

[0034] Specifically, the method uses the collected training data set to train the AGV running state abnormality intelligent recognition model, uses the verification data set to optimize the AGV running state abnormality intelligent recognition model, optimizes the model parameters, and completes the AGV running state abnormality intelligent recognition model training, and also includes: After each training, the verification data set is input into the preset network layer of the previous training in batches for model parameter verification, and cyclic training is performed, with the total number of training rounds set to the first preset round; the loss value of the preset network layer is recorded, and it is determined whether the loss value meets the first preset condition, and whether to end the training and output the model parameters based on the judgment result.

[0035] Specifically, after each training, the verification data set is input into the preset network layer of the previous training in batches to verify the model parameters, and the cyclic training is performed, including the following methods: When the number of training times is not enough and the model needs to be optimized, the training can only be performed from the beginning. Therefore, the present invention develops a method for continuing the following training based on the previously trained model parameter file; (1) Provide custom parameters, which can be set according to user needs to continue training based on any training parameters; (2) Load the parameters of the custom model file; (3) Put the validation data set into the custom parameter network layer for validation, obtain the loss value and record the model parameters of the model corresponding to the minimum loss value during the training process; (4) Based on this set of model parameters, forward propagation training and back propagation are performed, and the optimizer optimizes the parameters to obtain a new set of data; (5) Put the verification data into the new data network layer for inference to obtain the loss value; (6) Perform cyclic training to obtain the best model and complete model optimization.

[0036] It is understandable that the training method adopted by the present invention is batch training, which means that each time the model parameters are updated, only a part of the samples in the validation data set is used, which is called a batch. The advantages of batch training are that it can reduce memory consumption, speed up training, increase randomness, and facilitate the generalization of the model.

[0037] It should be noted here that, in the present invention, batch_size=4, and the batch_size is the batch size. For example, when batch_size=4, it means that two AGV operation status data sets are selected from the verification data set each time and put into the model for verification.

[0038] It can be understood that the present invention preferably sets the maximum round of cyclic training to 20,000 times, which can effectively ensure the accuracy of the model prediction of the present invention and record the loss value of the model training. The present invention sets the second preset round to 300, that is, when the loss value of the training no longer decreases within 300 rounds of training, the round with the smallest loss value in the 300 rounds of training is saved as the best verification round parameter to generate the best model file. The above setting makes the training rounds no longer rely on manual judgment, but uses the deep learning model for automatic training, which improves the accuracy of the model prediction, and automatically ends the training when the accuracy meets the requirements, which greatly saves the training time, and can also effectively prevent overfitting, which greatly improves the intelligence level of the present invention and the efficiency of model training.

[0039] Furthermore, the specific process of training the AGV operation status abnormal intelligent recognition model of the present invention is as follows: (1) Obtain the latent vector mean mu and logarithmic variance logvar in the forward propagation process of the AGV operation status abnormality intelligent recognition model; (2) Calculate a reasonable loss value representing the training effect: Calculate the divergence: k1=-0.5*(1+logvar-mu^2-(e^logvar)); Calculate the loss between the AGV running status data set and the predicted value in the validation data set: loss=L1+k1*k1_weight; Among them, loss is the loss value, k1 is the divergence, k1_weight is the proportion of k1, the value is 10, L1 is the absolute difference between the predicted value and the true value, and the calculation formula of L1 is as follows: ; Among them, A is the L1 loss in pytorch, which calculates the average or sum of the absolute differences between two tensors; the shape of A is N*C*H*W, which indicates that it is a four-dimensional tensor, N represents the number of samples (Batch Size), indicating that there are N data points, C represents the number of channels (Channels), H and W represent the height and width respectively, which are usually the two-dimensional spatial dimensions of the input features; P represents the padding value, which is a Boolean type, and the shape of P is c*h*w*1. The dimensions of P are the same as those of A in terms of channel, height, and width, but there is no sample number N, which means that P is a mask or padding information for a feature map of each sample, and the Boolean value of P may be used to mark whether the elements in A are filled (True) or valid (False).

[0040] (3) The loss value when using the validation data set to validate the current model network; (4) If the validation loss of this round is less than that of the previous round, the best model parameters are replaced with the training model of this round; (5) Put the rounds and loss values ​​into the dictionary one by one; (6) Compare the loss value of the current round with the loss values ​​of the 300 rounds before this round (parameters are provided and can be set according to actual needs, the default is 300), that is, traverse the loss values ​​from epoch-300 to epoch-1 (epoch is the training round, and comparison is performed when epoch is greater than 300). If the loss values ​​of the current round of epoch are all greater than the loss values ​​from (epoch-300) to (epoch-1), stop training.

[0041] (7) Save the best model parameters in the cyclic validation as a ckpt model file.

[0042] Specifically, the determining whether the loss value satisfies the first preset condition, and determining whether to end the training and output the model parameters according to the determination result, includes: If the loss value meets the first preset condition, the training is terminated and the parameters of the current AGV operating status abnormality intelligent recognition model are output; if the loss value does not meet the first preset condition, the training continues.

[0043] Specifically, the first preset condition is: After the second preset round of training, the loss values ​​obtained in the next round of training are greater than or equal to the loss values ​​that occurred during the second preset round of training.

[0044] Specifically, the model output result of the AGV abnormal operation status intelligent recognition model includes the abnormal operation status of the target AGV at the current moment and the normal operation status of the target AGV at the current moment.

[0045] Specifically, the AGV operation status data set of the target AGV at the current moment is input into the AGV operation status abnormality intelligent recognition model, and then determine whether the operation status of the target AGV at the current moment is abnormal, and determine whether to output an alarm signal related to the abnormal operation status of the target AGV at the current moment according to the determination result, including: If it is determined that the current operating state of the target AGV is abnormal, an alarm signal related to the abnormal current operating state of the target AGV is output; If it is determined that the current operating state of the target AGV is normal, a prompt signal indicating that the current operating state of the target AGV is normal is output.

[0046] S300. After the model training is completed, during the operation of the target AGV, the AGV operation status data set of the target AGV at the current moment is obtained, and the AGV operation status data set of the target AGV at the current moment is input into the AGV operation status abnormality intelligent recognition model, and then it is determined whether the operation status of the target AGV at the current moment is abnormal, and according to the determination result, it is determined whether to output an alarm signal regarding the abnormal operation status of the target AGV at the current moment.

[0047] It can be understood that the present invention obtains the motor drive current data, vibration frequency data, load data and current speed data of the sample AGV during its operation at preset time intervals, and then uses the coupling relationship among the motor drive current data, vibration frequency data, load data and current speed data to train an intelligent recognition model for abnormal AGV operation status. When the model training is completed, the trained intelligent recognition model for abnormal AGV operation status is used to perform a qualitative analysis on the operation status of the target AGV, and then determine whether the operation status of the target AGV at the current moment is abnormal. This solves the problem in the prior art that it is difficult to accurately determine whether the operation status of the AGV is normal through quantitative analysis due to differences in operation speed, load, road condition and aging status of various components of the vehicle body, and greatly improves the intelligence, safety and reliability of intelligent monitoring of the operation status of park AGVs.

[0048] See also Figure 2 The present invention provides another embodiment, which provides an intelligent monitoring system for the operation status of a park AGV, and the intelligent monitoring system for the operation status of a park AGV includes: The acquisition module 100 is used to acquire the motor drive current data, vibration frequency data, and load data of the sample AGV during operation by using the current sensor, vibration sensor, and weight sensor provided on the sample AGV at preset time intervals, and synchronously acquire the current speed data of the sample AGV, record the acquired motor drive current data, vibration frequency data, load data, and current speed data of the sample AGV as the AGV operation status data set at the current moment, and acquire a preset number of AGV operation status data sets; The control module 200 is used to divide the collected AGV operation status data set into a training data set and a verification data set according to a first preset ratio; to use the collected training data set to train the AGV operation status abnormality intelligent recognition model, and to use the verification data set to optimize the AGV operation status abnormality intelligent recognition model, optimize the model parameters, and complete the AGV operation status abnormality intelligent recognition model training; and to obtain the AGV operation status data set of the target AGV at the current moment during the operation of the target AGV after the model training is completed, input the AGV operation status data set of the target AGV at the current moment into the AGV operation status abnormality intelligent recognition model, and then determine whether the operation status of the target AGV at the current moment is abnormal, and determine whether to output an alarm signal regarding the abnormal operation status of the target AGV at the current moment according to the determination result.

[0049] It should be noted here that the present invention obtains the motor drive current data, vibration frequency data, load data and current speed data of the sample AGV during its operation at preset time intervals, and then uses the coupling relationship among the motor drive current data, vibration frequency data, load data and current speed data to train an intelligent recognition model for abnormal AGV operation status. When the model training is completed, the trained intelligent recognition model for abnormal AGV operation status is used to perform a qualitative analysis on the operation status of the target AGV, and then determine whether the operation status of the target AGV at the current moment is abnormal. This solves the problem in the prior art that it is difficult to accurately determine whether the operation status of the AGV is normal through quantitative analysis due to differences in operation speed, load, road condition and aging status of various components of the vehicle body. This greatly improves the intelligence, safety and reliability of intelligent monitoring of the operation status of park AGVs.

[0050] In a preferred embodiment, the present application further provides an electronic device, the electronic device comprising: A memory; and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method for intelligent monitoring of the operation status of the park AGV is implemented. The computer device can be broadly a server, a terminal, or any other electronic device with necessary computing and / or processing capabilities. In one embodiment, the computer device may include a processor, a memory, a network interface, a communication interface, etc. connected via a system bus. The processor of the computer device can be used to provide necessary computing, processing and / or control capabilities. The memory of the computer device may include a non-volatile storage medium and an internal memory. An operating system, a computer program, etc. may be stored in or on the non-volatile storage medium. The internal memory can provide an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface and the communication interface of the computer device can be used to connect and communicate with external devices through a network. When the computer program is executed by the processor, the steps of the method of the present invention are performed.

[0051] The present invention may be implemented as a computer-readable storage medium having a computer program stored thereon, which causes the steps of the method of an embodiment of the present invention to be executed when executed by a processor. In one embodiment, the computer program is distributed on a plurality of computer devices or processors coupled to a network so that the computer program is stored, accessed and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be performed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be performed by one or more computer devices or processors, and one or more other method steps / operations may be performed by one or more other computer devices or processors. One or more computer devices or processors may perform a single method step / operation, or perform two or more method steps / operations.

[0052] It will be appreciated by a person skilled in the art that the method steps of the present invention may be performed by instructing related hardware such as a computer device or a processor through a computer program, and the computer program may be stored in a non-temporary computer-readable storage medium, which causes the steps of the present invention to be performed when the computer program is executed. Depending on the circumstances, any reference to memory, storage, database, or other media herein may include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0053] It can be understood that the present invention obtains the motor drive current data, vibration frequency data, load data and current speed data of the sample AGV during its operation at preset time intervals, and then uses the coupling relationship among the motor drive current data, vibration frequency data, load data and current speed data to train an intelligent recognition model for abnormal AGV operation status. When the model training is completed, the trained intelligent recognition model for abnormal AGV operation status is used to perform a qualitative analysis on the operation status of the target AGV, and then determine whether the operation status of the target AGV at the current moment is abnormal. This solves the problem in the prior art that it is difficult to accurately determine whether the operation status of the AGV is normal through quantitative analysis due to differences in operation speed, load, road condition and aging status of various components of the vehicle body, and greatly improves the intelligence, safety and reliability of intelligent monitoring of the operation status of park AGVs.

[0054] The various technical features described above can be combined arbitrarily. Although all possible combinations of these technical features are not described, any combination of these technical features should be considered to be covered by this specification as long as there is no contradiction in such combination.

[0055] The specific implementation of the present invention described above does not constitute a limitation on the protection scope of the present invention. Any other corresponding changes and modifications made based on the technical concept of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for intelligent monitoring of the operation status of an AGV in a park, characterized in that: The method comprises: S100. During the operation of the sample AGV, the motor drive current data, vibration frequency data, and load data of the sample AGV during operation are obtained at preset time intervals using the current sensor, vibration frequency data, and weight sensor provided on the sample AGV, and the current speed data of the sample AGV is simultaneously obtained, the motor drive current data, vibration frequency data, load data, and current speed data of the sample AGV obtained are recorded as the AGV operation status data set at the current moment, a preset number of AGV operation status data sets are obtained, and the collected AGV operation status data sets are divided into a training data set and a verification data set according to a first preset ratio; S200, using the collected training data set to train the AGV running state abnormality intelligent recognition model, using the verification data set to optimize the AGV running state abnormality intelligent recognition model, optimize the model parameters, and complete the AGV running state abnormality intelligent recognition model training; S300. After the model training is completed, during the operation of the target AGV, the AGV operation status data set of the target AGV at the current moment is obtained, and the AGV operation status data set of the target AGV at the current moment is input into the AGV operation status abnormality intelligent recognition model, and then it is determined whether the operation status of the target AGV at the current moment is abnormal, and according to the determination result, it is determined whether to output an alarm signal regarding the abnormal operation status of the target AGV at the current moment.

2. The method for intelligent monitoring of the operation status of the park AGV according to claim 1 is characterized in that: The step S100 further includes: The preset number of AGV operation status data sets collected need to be confirmed by relevant management personnel to be normal, and the model and specification of the sample AGV and the target AGV are the same.

3. The method for intelligent monitoring of the operation status of the park AGV according to claim 1 is characterized in that: The method uses the collected training data set to train the AGV running state abnormality intelligent recognition model, uses the verification data set to optimize the AGV running state abnormality intelligent recognition model, optimizes the model parameters, and completes the AGV running state abnormality intelligent recognition model training, including: The training data set is input into the preset network layer in batches for training, and the preset network layer includes a Transformer network layer, and the Transformer network layer is used to predict the AGV operation status data set at the next moment based on the forward propagation of the AGV operation status data set at the current moment, and then obtain the predicted loss value; the loss value of the preset network layer is calculated and input into the optimizer for optimization, and the direction in which the parameter gradient of the AGV operation status abnormality intelligent recognition model decreases fastest is determined; the AGV operation status abnormality intelligent recognition model performs back propagation according to the loss value and the parameter gradient of the model, and optimizes the parameters of the AGV operation status abnormality intelligent recognition model.

4. The method for intelligent monitoring of the operation status of the park AGV according to claim 1 is characterized in that: The method of using the collected training data set to train the AGV running state abnormality intelligent recognition model, using the verification data set to optimize the AGV running state abnormality intelligent recognition model, optimizing the model parameters, and completing the AGV running state abnormality intelligent recognition model training also includes: After each training, the verification data set is input into the preset network layer of the previous training in batches for model parameter verification, and cyclic training is performed, with the total number of training rounds set to the first preset round; the loss value of the preset network layer is recorded, and it is determined whether the loss value meets the first preset condition, and whether to end the training and output the model parameters based on the judgment result.

5. The method for intelligently monitoring the operation status of a park AGV according to claim 4 is characterized in that: The determining whether the loss value satisfies the first preset condition, and determining whether to end the training and output the model parameters according to the determination result, includes: If the loss value meets the first preset condition, the training is terminated and the parameters of the current AGV operating status abnormality intelligent recognition model are output; if the loss value does not meet the first preset condition, the training continues.

6. The method for intelligently monitoring the operation status of a park AGV according to claim 5 is characterized in that: The first preset condition is: After the second preset round of training, the loss values ​​obtained in the next round of training are greater than or equal to the loss values ​​that occurred during the second preset round of training.

7. The method for intelligently monitoring the operation status of a park AGV according to claim 1, characterized in that: The model output result of the AGV operation status abnormality intelligent recognition model includes the abnormal operation status of the target AGV at the current moment and the normal operation status of the target AGV at the current moment.

8. The method for intelligently monitoring the operation status of a park AGV according to claim 1 is characterized in that: The method includes inputting the AGV operation status data set of the target AGV at the current moment into the AGV operation status abnormality intelligent recognition model, and then determining whether the operation status of the target AGV at the current moment is abnormal, and determining whether to output an alarm signal related to the abnormal operation status of the target AGV at the current moment according to the determination result, including: If it is determined that the current operating state of the target AGV is abnormal, an alarm signal related to the abnormal current operating state of the target AGV is output; If it is determined that the current operating state of the target AGV is normal, a prompt signal indicating that the current operating state of the target AGV is normal is output.

9. An intelligent monitoring system for the operation status of AGVs in a park, characterized in that: include: An acquisition module is used to acquire the motor drive current data, vibration frequency data, and load data of the sample AGV during operation by using the current sensor, vibration sensor, and weight sensor provided on the sample AGV at preset time intervals, and synchronously acquire the current speed data of the sample AGV, record the acquired motor drive current data, vibration frequency data, load data, and current speed data of the sample AGV as the AGV operation status data set at the current moment, and acquire a preset number of AGV operation status data sets; A control module is used to divide the collected AGV operation status data set into a training data set and a verification data set according to a first preset ratio; to use the collected training data set to train the AGV operation status abnormality intelligent recognition model, and to use the verification data set to optimize the AGV operation status abnormality intelligent recognition model, optimize the model parameters, and complete the AGV operation status abnormality intelligent recognition model training; and to obtain the AGV operation status data set of the target AGV at the current moment during the operation of the target AGV after the model training is completed, input the AGV operation status data set of the target AGV at the current moment into the AGV operation status abnormality intelligent recognition model, and then determine whether the operation status of the target AGV at the current moment is abnormal, and determine whether to output an alarm signal regarding the abnormal operation status of the target AGV at the current moment according to the determination result.

10. An electronic device, characterized in that: include: Memory; and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method for intelligently monitoring the operating status of a park AGV according to any one of claims 1 to 8 is implemented.

Images