Cargo detection method of mobile equipment and cargo detection system of mobile equipment

Abstract

The invention provides a cargo detection method of mobile equipment and a cargo detection system of the mobile equipment, the method is applied to a controller in the cargo detection system, and the method comprises the following steps: obtaining size information and weight information of a cargo; determining whether the size of the cargo is qualified or not according to the size information of the cargo, and determining whether the weight of the cargo is qualified or not according to the weight information of the cargo; and controlling the mobile equipment to transport the goods at least under the condition that the size of the goods is qualified and the weight of the goods is qualified, and controlling the mobile equipment to return the goods under the condition that the size of the goods is unqualified and / or the weight of the goods is unqualified. According to the method, the goods are subjected to advanced commodity type detection before being carried, and the goods are carried after being detected to be qualified, so that the time for carrying unqualified goods back and forth is shortened, and the problems that in the prior art, when a mobile device carries the goods, the size and the weight of the goods are not detected, the unqualified goods are carried, and the carrying error rate is high are solved.

Description

Technical Field

[0001] The present application relates to the field of cargo detection, and in particular, to a cargo detection method for a mobile device, a cargo detection system for a mobile device, and an electronic device. Background Art

[0002] With the continuous development of economy, a large number of logistics warehouses have emerged in various places. Most of them use mobile equipment (such as automatic guided vehicles AGV) to transport goods. However, when transporting goods, the existing mobile equipment does not detect the size and weight of the goods, which may lead to the transport of unqualified goods, resulting in a high handling error rate. Summary of the invention

[0003] The main purpose of the present application is to provide a cargo detection method for a mobile device, a cargo detection system for a mobile device and an electronic device, so as to at least solve the problem in the prior art that the mobile device fails to detect the size and weight of the cargo when transporting the cargo, resulting in the transportation of unqualified cargo and a high transportation error rate.

[0004] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a cargo detection method for a mobile device is provided, which is applied to a controller in a cargo detection system, and the method includes: acquiring size information and weight information of the cargo; determining whether the size of the cargo is qualified according to the size information of the cargo, and determining whether the weight of the cargo is qualified according to the weight information of the cargo; at least when the size of the cargo is qualified and the weight of the cargo is qualified, controlling the mobile device to transport the cargo, and when the size of the cargo is unqualified and / or the weight of the cargo is unqualified, controlling the mobile device to return the cargo.

[0005] Optionally, at least when the size of the goods is qualified and the weight of the goods is qualified, the mobile device is controlled to transport the goods, and when the size of the goods is unqualified and / or the weight of the goods is unqualified, the mobile device is controlled to return the goods, including: obtaining the cargo type of the goods and the cargo status of the goods, the cargo status representing whether the goods are damaged; when the size of the goods is qualified, the weight of the goods is qualified, the cargo type of the goods is a target type, and the cargo status of the goods is an undamaged state, the mobile device is controlled to transport the goods; when the goods meet a return condition, the mobile device is controlled to return the goods, and the return condition is that the goods meet at least one of the following: the size of the goods is unqualified, the weight of the goods is unqualified, the cargo type of the goods is a non-target type, and the cargo status of the goods is a damaged state.

[0006] Optionally, obtaining the cargo type and the cargo status of the cargo includes: obtaining initial image data of the cargo, the initial image data being image data of the cargo acquired by an image acquisition device, the image acquisition device being installed on the mobile device; preprocessing the initial image data to obtain processed image data, the preprocessing including at least one of the following: grayscale processing, denoising processing and contrast enhancement processing; extracting multiple cargo features from the processed image data, the cargo features including at least one of the following: cargo color, cargo shape, cargo size, cargo texture; inputting all of the cargo features into an image processing model to obtain the cargo type and the cargo status of the cargo.

[0007] Optionally, determining whether the size of the goods is qualified based on the size information of the goods, and determining whether the weight of the goods is qualified based on the weight information of the goods, includes: determining the size of the goods based on the size information of the goods, and determining that the size of the goods is qualified if the size of the goods is within a preset size range, and determining that the size of the goods is unqualified if the size of the goods is not within the preset size range; determining the weight of the goods based on the weight information of the goods, and determining that the weight of the goods is qualified if the weight of the goods is within a preset weight range, and determining that the weight of the goods is unqualified if the weight of the goods is not within the preset weight range.

[0008] Optionally, determining whether the size of the goods is qualified according to the size information of the goods, and determining whether the weight of the goods is qualified according to the weight information of the goods, includes: constructing a neural network model, wherein the neural network model is trained using multiple sets of training data, and each set of training data in the multiple sets of training data includes: parameter information of historical goods and historical tags of the historical goods corresponding to the parameter information of the historical goods acquired within a historical time period, the parameter information of the historical goods includes the size information of the historical goods and the weight information of the historical goods, and the historical tags of the historical goods represent whether the historical goods are qualified, wherein, when the historical goods are qualified, it represents that both the size and weight of the historical goods are qualified, and when the historical goods are unqualified, it represents that at least one of the size and weight of the historical goods is unqualified; determining whether the size and weight of the goods are qualified according to the size information of the goods, the weight information of the goods and the neural network model.

[0009] Optionally, when the size of the goods is qualified and the weight of the goods is qualified, the mobile device is controlled to transport the goods, and when the size of the goods is unqualified and / or the weight of the goods is unqualified, the mobile device is controlled to return the goods, including: when the size of the goods is qualified and the weight of the goods is qualified, the conveyor line of the mobile device is controlled to rotate forward to transport the goods; when the size of the goods is unqualified and / or the weight of the goods is unqualified, the conveyor line of the mobile device is controlled to reverse to return the goods.

[0010] Optionally, the method further includes: visualizing the size information of the goods, the weight information of the goods, the size judgment information of the goods and the weight judgment information of the goods, the size judgment information of the goods representing whether the size of the goods is qualified, and the weight judgment information of the goods representing whether the weight of the goods is qualified.

[0011] According to another aspect of the present application, a cargo detection system for a mobile device is provided, comprising: an ultrasonic sensor for collecting size information of cargo; a weight sensor for collecting weight information of the cargo; and a controller for executing any one of the cargo detection methods for a mobile device, the controller being electrically connected to the ultrasonic sensor and the weight sensor, respectively.

[0012] Optionally, the controller is an STM32 single-chip microcomputer.

[0013] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the methods for detecting goods on a mobile device.

[0014] Applying the technical solution of the present application, the cargo detection method of the mobile device is applied to the controller in the cargo detection system. The method first obtains the size information and weight information of the cargo; then determines whether the size of the cargo is qualified according to the size information of the cargo, and determines whether the weight of the cargo is qualified according to the weight information of the cargo; finally, at least when the size of the cargo is qualified and the weight of the cargo is qualified, the mobile device is controlled to transport the cargo, and when the size of the cargo is unqualified and / or the weight of the cargo is unqualified, the mobile device is controlled to return the cargo. The method first detects the cargo type before transporting the cargo, and then transports the cargo after the detection is qualified, thereby reducing the time spent on transporting unqualified cargo back and forth, and solves the problem in the prior art that the mobile device does not detect the size and weight of the cargo when transporting the cargo, resulting in the transport of unqualified cargo and a high transport error rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0016] Figure 1 A hardware structure block diagram of a mobile terminal for executing a cargo detection method of a mobile device provided in an embodiment of the present application is shown;

[0017] Figure 2 A schematic diagram of a flow chart of a cargo detection method for a mobile device provided according to an embodiment of the present application is shown;

[0018] Figure 3 A schematic diagram showing a flow chart of another method for detecting goods using a mobile device provided according to an embodiment of the present application is shown;

[0019] Figure 4 A schematic structural diagram of a cargo detection system for a mobile device provided according to an embodiment of the present application is shown;

[0020] Figure 5 A structural block diagram of a controller provided according to an embodiment of the present application is shown.

[0021] The above drawings include the following reference numerals:

[0022] 01. Ultrasonic sensor; 02. Weight sensor; 03. Controller; 102. Processor; 104. Memory; 106. Transmission device; 108. Input and output device. DETAILED DESCRIPTION

[0023] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] As introduced in the background technology, the traditional cargo type detection method relies on manual measurement, which is inefficient, labor-intensive, and prone to errors. In order to solve the problem in the prior art that mobile equipment does not detect the size and weight of cargo when transporting cargo, resulting in the transportation of unqualified cargo and a high transportation error rate, the embodiments of the present application provide a cargo detection method for a mobile equipment and a cargo detection system for a mobile equipment.

[0027] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0028] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal of a method for detecting goods by a mobile device according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0029] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the cargo detection method of the mobile device in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0030] In this embodiment, a cargo detection method for a mobile device running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0031] Figure 2 1 is a flow chart of a method for detecting goods by a mobile device according to an embodiment of the present application. The method for detecting goods by a mobile device is applied to a controller in a cargo detection system, such as Figure 2 As shown, the method comprises the following steps:

[0032] Step S201, obtaining the size information and weight information of the goods;

[0033] Specifically, the mobile device may be an AGV (Automated Guided Vehicle).

[0034] The acquisition of size information is usually achieved through various types of sensors, such as ultrasonic sensors, laser sensors or visual sensors. Ultrasonic sensors measure the distance by emitting ultrasonic waves and receiving reflected signals to determine the size of the goods. Laser sensors use laser beams to scan the goods and calculate the size of the goods by receiving the reflected light signals. Visual sensors use cameras to capture images of goods and then use image processing algorithms to analyze the size of the goods.

[0035] Weight information is mainly obtained through weight sensors, such as pressure sensors or weighing sensors, which can convert the pressure of the goods on the sensor into electrical signals, and then into weight data. These sensors are usually installed on the carrying platform of the handling equipment. When the goods are placed on the platform, the sensor will immediately detect the weight of the goods and transmit the data to the control center of the system for analysis.

[0036] By integrating such a detection system on the AGV (Automated Guided Vehicle), the size and weight information of the goods can be quickly and accurately obtained before transportation, and whether the goods meet the transportation conditions can be determined, thereby avoiding ineffective transportation and improving logistics efficiency and safety.

[0037] In some instances, photoelectric sensor technology as well as depth detection and posture recognition technology can be used. Photoelectric sensors are widely used in cargo type detection. They can detect the height, width and length of cargo to determine whether the cargo exceeds the limit. For example, a cargo type detection device uses at least three pairs of photoelectric sensors to detect the size of the cargo, but this structure is complex and has a high failure rate. Another device improves the accuracy of detection by using photoelectric sensors and cylindrical swing arms. The intelligent cargo detection system uses depth detection equipment and posture recognition equipment to simultaneously detect the quantity of cargo and identify the placement posture of cargo. This system is based on the Kinect2.0 sensor, and uses the depth value and the actual height value of the cargo to reversely calculate the actual quantity of cargo, providing high-precision detection for intelligent production lines.

[0038] Step S202, determining whether the size of the goods is qualified according to the size information of the goods, and determining whether the weight of the goods is qualified according to the weight information of the goods;

[0039] Specifically, the size and weight information of the goods can be quickly and accurately obtained before transportation to determine whether the goods meet the transportation conditions, thereby avoiding invalid transportation and improving logistics efficiency and safety.

[0040] The method of determining whether the size of the goods is qualified according to the size information of the goods, and determining whether the weight of the goods is qualified according to the weight information of the goods, comprises the following steps:

[0041] Step S301, determining the size of the goods according to the size information of the goods, and determining that the size of the goods is qualified if the size of the goods is within a preset size range, and determining that the size of the goods is unqualified if the size of the goods is not within the preset size range;

[0042] Step S302, determining the weight of the goods according to the weight information of the goods, and determining that the weight of the goods is qualified if the weight is within a preset weight range, and determining that the weight of the goods is unqualified if the weight is not within the preset weight range.

[0043] Specifically, in this way, the goods can be inspected for type before being transported, and then transported if they pass the inspection. If they fail the inspection, they will be returned, thus reducing the time spent on transporting unqualified goods back and forth.

[0044] Determining whether the size of the goods is qualified according to the size information of the goods, and determining whether the weight of the goods is qualified according to the weight information of the goods, includes the following steps:

[0045] Step S401, constructing a neural network model, wherein the neural network model is obtained by training using multiple sets of training data, each set of training data in the multiple sets of training data includes: parameter information of historical goods and historical labels of the historical goods corresponding to the parameter information of the historical goods, obtained in a historical time period, wherein the parameter information of the historical goods includes size information of the historical goods and weight information of the historical goods, and the historical labels of the historical goods indicate whether the historical goods are qualified, wherein, if the historical goods are qualified, it indicates that both the size and weight of the historical goods are qualified, and if the historical goods are unqualified, it indicates that at least one of the size and weight of the historical goods is unqualified;

[0046] Step S402, determining whether the size and weight of the goods are qualified based on the size information of the goods, the weight information of the goods and the neural network model.

[0047] Specifically, the neural network model can better detect the size of the goods and whether the weight of the above goods is qualified. Develop intelligent algorithms, such as cargo identification algorithms based on machine learning, to improve the accuracy and efficiency of detection. The system should be able to provide decision support based on the detection results, such as automatic sorting instructions, maintenance suggestions, etc.

[0048] Here are some neural network models that can be used for this scenario:

[0049] 1. Multilayer Perceptron (MLP): This is a basic feedforward neural network that can learn complex mappings from input (size and weight information of goods) to output (qualified or not) through multiple layers of neuron connections and weight adjustment. MLP is suitable for classification tasks and can be trained to determine whether the size and weight of goods meet the preset qualified standards.

[0050] 2. Convolutional Neural Network (CNN): Although CNN is mainly used for image processing, in scenarios where visual inspection and size information are combined, CNN can be used to identify the shape characteristics of goods and further assist in the determination of size information. For example, CNN can identify whether the goods have protruding parts that are too long or too wide, which may affect the accuracy of size detection.

[0051] 3. Recurrent Neural Network (RNN): In scenarios where time series information needs to be considered, such as when continuously inspecting multiple goods, RNN can remember the information of previous goods and help adjust the thresholds or standards for subsequent goods size and weight inspections to adapt to different logistics environments and needs.

[0052] 4. Long Short-Term Memory (LSTM): As a special form of RNN, LSTM is particularly suitable for processing sequence data and can effectively handle long-term dependency issues. In the scenario of dynamically adjusting the inspection standards, LSTM can remember the characteristics of goods with a longer history to optimize the inspection strategy.

[0053] 5. Deep Neural Network (DNN): DNN is a neural network with multiple hidden layers that can learn more complex feature representations. When processing large amounts of cargo data, DNN can automatically discover the potential correlation between size and weight, improving the accuracy and efficiency of detection.

[0054] 6. Generative Adversarial Network (GAN): Although GAN is usually used to generate data, in some cases, GAN can be trained to generate cargo data that meets specific size and weight distribution for testing and optimizing detection systems.

[0055] 7. Transfer Learning: By using a trained neural network model (such as a pre-trained model on ImageNet) as a basis and fine-tuning it with a small amount of cargo data, an efficient detection model can be quickly established.

[0056] In practical applications, the selection of neural network models needs to be comprehensively considered based on specific detection requirements, data types, and computing resources. For example, if the data contains a large amount of image information, CNN may be more appropriate; if sequence data needs to be processed, RNN or LSTM may be more effective; if the data volume is large and the features are complex, DNN can provide more powerful learning capabilities. In addition, the training and optimization of neural network models usually require a large amount of labeled training data and high-performance computing resources to ensure the accuracy and generalization ability of the model.

[0057] Step S203, at least when the size of the goods is qualified and the weight of the goods is qualified, control the mobile device to transport the goods, and when the size of the goods is unqualified and / or the weight of the goods is unqualified, control the mobile device to return the goods.

[0058] Specifically, in this way, the goods can be inspected for type before being transported, and then transported if they pass the inspection. If they fail the inspection, they will be returned, thus reducing the time spent on transporting unqualified goods back and forth.

[0059] Wherein, at least when the size of the goods is qualified and the weight of the goods is qualified, the mobile equipment is controlled to transport the goods, and when the size of the goods is unqualified and / or the weight of the goods is unqualified, the mobile equipment is controlled to return the goods, including the following steps:

[0060] Step S501, obtaining the cargo type and cargo status of the cargo, wherein the cargo status indicates whether the cargo is damaged;

[0061] Wherein, obtaining the cargo type and cargo status of the cargo includes the following steps:

[0062] Step S5011, obtaining initial image data of the goods, the initial image data being image data of the goods acquired by an image acquisition device, the image acquisition device being installed on the mobile device;

[0063] Step S5012, preprocessing the initial image data to obtain processed image data, wherein the preprocessing includes at least one of the following: grayscale processing, denoising processing and contrast enhancement processing;

[0064] Step S5013, extracting a plurality of cargo features from the processed image data, wherein the cargo features include at least one of the following: cargo color, cargo shape, cargo size, and cargo texture;

[0065] Step S5014: input all the above cargo features into the image processing model to obtain the cargo type and cargo status of the above cargo.

[0066] Specifically, through visual judgment technology, it is possible to determine whether a shelf or a rack is empty or has goods. This method reduces the cost of judging whether a shelf has goods and avoids the problem of wiring and installation of photoelectric sensors.

[0067] In the above steps, the goods are first photographed using a camera or other image acquisition device to generate image data of the goods. These images may contain information such as the appearance, packaging, logo, barcode, etc. of the goods. Then, the feature information of the goods is extracted from the captured images. This may include color, texture, shape, size, position, and any visible identifiers (such as barcodes, QR codes). Feature extraction is completed by image processing algorithms, such as edge detection, color segmentation, shape matching, etc. The extracted feature data is then preprocessed, including data cleaning, standardization, and normalization steps to ensure data quality and improve the training effect and prediction accuracy of the model. The preprocessed goods feature data is provided as input to a pre-trained image processing model. These models may be based on deep learning techniques, such as convolutional neural networks (CNN) or their variants, which can automatically learn and recognize complex patterns in images. The model calculates and predicts based on the input goods features and outputs the type and status of the goods. The type of goods may refer to the type of goods, such as electronic equipment, food, clothing, etc.; the status of the goods may include information such as whether the goods are damaged, whether the packaging is intact, and whether the label is clear. Finally, the cargo type and status information predicted by the model is output in digital or text form. This information can be used for subsequent logistics operations such as automatic sorting, inventory management, quality control, etc.

[0068] The whole process relies on high-quality image data and accurate image processing models. A large amount of labeled image data is required in the model training phase so that the model can learn the characteristics of goods of different types and states. In practical applications, in order to improve the accuracy and efficiency of recognition, other sensor data (such as size and weight sensors) and algorithms (such as pattern recognition and data fusion) may be combined to provide more comprehensive cargo information. This comprehensive detection method has broad application prospects in logistics automation, intelligent warehouse management and other fields.

[0069] In addition, the sensor components used in the cargo type detection mechanism are not limited to photoelectric sensors, but also include mirror reflective photoelectric switches, reflectors, alarms, etc. These sensor components can improve the accuracy and response speed of detection.

[0070] Step S502, when the size of the goods is qualified, the weight of the goods is qualified, the type of the goods is the target type, and the state of the goods is not damaged, control the mobile equipment to transport the goods;

[0071] Step S503, when the goods meet the return conditions, control the mobile device to return the goods, and the return conditions are that the goods meet at least one of the following: the size of the goods is unqualified, the weight of the goods is unqualified, the type of the goods is a non-target type, and the state of the goods is a damaged state.

[0072] Specifically, the goods are first inspected for type before being moved, and then moved after they pass the inspection, thus reducing the time spent on moving unqualified goods back and forth.

[0073] Wherein, at least when the size of the goods is qualified and the weight of the goods is qualified, the mobile equipment is controlled to transport the goods, and when the size of the goods is unqualified and / or the weight of the goods is unqualified, the mobile equipment is controlled to return the goods, including the following steps:

[0074] Step S601, when the size of the goods is qualified and the weight of the goods is qualified, controlling the conveyor line of the mobile equipment to rotate forward to transport the goods;

[0075] Step S602, when the size of the goods is unqualified and / or the weight of the goods is unqualified, control the conveying line of the mobile equipment to reverse to return the goods.

[0076] Specifically, the goods are first inspected for type before being moved, and then moved after they pass the inspection, thus reducing the time spent on moving unqualified goods back and forth.

[0077] Among them, the above method also includes the following steps: visualizing the size information of the above goods, the weight information of the above goods, the size judgment information of the above goods and the weight judgment information of the above goods, the size judgment information of the above goods represents whether the size of the above goods is qualified, and the weight judgment information of the above goods represents whether the weight of the above goods is qualified.

[0078] Specifically, an intuitive user interface is designed so that operators can easily view test results, system status and historical data. The user interface can be implemented through a touch screen, providing real-time data display, alarm prompts and system control functions.

[0079] The cargo detection method of the mobile device of the present application is applied to the controller in the cargo detection system. The method first obtains the size information and weight information of the cargo; then determines whether the size of the cargo is qualified according to the size information of the cargo, and determines whether the weight of the cargo is qualified according to the weight information of the cargo; finally, at least when the size of the cargo is qualified and the weight of the cargo is qualified, the mobile device is controlled to transport the cargo, and when the size of the cargo is unqualified and / or the weight of the cargo is unqualified, the mobile device is controlled to return the cargo. The method first detects the cargo type before transporting the cargo, and then transports the cargo after the detection is qualified, thereby reducing the time spent on transporting unqualified cargo back and forth, and solving the problem in the prior art that the mobile device does not detect the size and weight of the cargo when transporting the cargo, resulting in the transport of unqualified cargo and a high transport error rate.

[0080] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the cargo detection method of the mobile device of the present application will be described in detail below in combination with specific embodiments.

[0081] This embodiment relates to a specific method for detecting goods on a mobile device, such as Figure 3 As shown, the following steps are included:

[0082] Step S1: first obtain the value detected by the ultrasonic sensor on the AGV;

[0083] Step S2: Determine whether the goods are of qualified size according to the value detected by the ultrasonic sensor on the AGV. If the size is qualified, execute step S3. If the size is unqualified, control the conveyor line to reverse.

[0084] Step S3: Determine whether the goods are of qualified weight. If the weight is qualified, control the conveyor line to rotate forward. If the weight is unqualified, control the conveyor line to rotate reversely.

[0085] In this embodiment, a cargo detection system for a mobile device is provided. Figure 4 As shown, it includes: an ultrasonic sensor 01, used to collect the size information of the goods; a weight sensor 02, used to collect the weight information of the above-mentioned goods; a controller 03, used to execute any one of the above-mentioned cargo detection methods of the mobile device, and the above-mentioned controller is electrically connected to the above-mentioned ultrasonic sensor and the above-mentioned weight sensor respectively.

[0086] The above system first performs a cargo type inspection on the cargo before transporting the cargo, and then transports the cargo after the inspection is qualified, thereby reducing the time spent on transporting unqualified cargo back and forth.

[0087] Among them, the controller is STM32 microcontroller.

[0088] Specifically, the system is based on the STM32 microcontroller, supplemented by necessary sensors (such as weight sensors, dimension measurement sensors, RFID readers, etc.) to collect basic information about the goods. At the same time, an image recognition module is integrated to identify the type and status of the goods using a camera and image processing algorithm. The hardware design of the system should ensure that each module can work together efficiently and perform data processing and decision-making through the powerful processing capabilities of the STM32.

[0089] The system needs to have real-time data processing capabilities. Through the high-speed data processing characteristics of STM32, the data collected by the sensor can be analyzed in real time to quickly identify the type of goods and detect the status of the goods, such as whether the goods are damaged and whether they meet the transportation standards.

[0090] Among them, the above-mentioned system also includes wireless communication and remote monitoring, using 4G or 5G communication modules to realize wireless transmission of data, so that managers can remotely monitor the status of the cargo detection system, obtain cargo detection information in a timely manner, and realize remote diagnosis and maintenance.

[0091] In addition, ensure the security and reliability of the system, including data security, stable system operation and fault self-diagnosis functions. The system should have anomaly detection and alarm mechanisms to promptly notify management personnel when abnormal conditions are detected. Consider the scalability of the system when designing, so that new functions or modules can be added as needed in the future. At the same time, the system should be easy to maintain and upgrade to reduce maintenance costs. The system should be able to work stably under various environmental conditions, including different temperature, humidity and light conditions.

[0092] The embodiment of the present application also provides a controller. It should be noted that the controller of the embodiment of the present application can be used to execute the cargo detection method for a mobile device provided in the embodiment of the present application. The controller is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the controller described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0093] The following is an introduction to the controller provided in the embodiments of the present application.

[0094] Figure 5 is a schematic diagram of a controller according to an embodiment of the present application. Figure 5As shown, the controller includes an acquisition unit 10, a determination unit 20 and a control unit 30, the acquisition unit 10 is used to acquire the size information of the goods and the weight information of the goods; the determination unit 20 is used to determine whether the size of the goods is qualified according to the size information of the goods, and to determine whether the weight of the goods is qualified according to the weight information of the goods; the control unit 30 is used to control the mobile device to transport the goods at least when the size of the goods is qualified and the weight of the goods is qualified, and to control the mobile device to return the goods when the size of the goods is unqualified and / or the weight of the goods is unqualified.

[0095] The controller of the present application includes an acquisition unit, a determination unit and a control unit. The acquisition unit is used to acquire the size information and weight information of the goods; the determination unit is used to determine whether the size of the goods is qualified according to the size information of the goods, and determine whether the weight of the goods is qualified according to the weight information of the goods; the control unit is used to control the mobile device to transport the goods at least when the size of the goods is qualified and the weight of the goods is qualified, and control the mobile device to return the goods when the size of the goods is unqualified and / or the weight of the goods is unqualified. The controller first detects the type of goods before transporting the goods, and then transports them after the detection is qualified, thereby reducing the time spent on transporting unqualified goods back and forth, and solving the problem in the prior art that the mobile device does not detect the size and weight of the goods when transporting the goods, resulting in the transport of unqualified goods and a high transport error rate.

[0096] In some instances, the control unit includes a first acquisition module, a first control module, and a second control module, the first acquisition module is used to acquire the cargo type of the above-mentioned cargo and the cargo status of the above-mentioned cargo, and the above-mentioned cargo status represents whether the above-mentioned cargo is damaged; the first control module is used to control the mobile device to transport the above-mentioned cargo when the size of the above-mentioned cargo is qualified, the weight of the above-mentioned cargo is qualified, the cargo type of the above-mentioned cargo is the target type, and the cargo status of the above-mentioned cargo is in an undamaged state; the second control module is used to control the mobile device to return the above-mentioned cargo when the above-mentioned cargo meets the return conditions, and the above-mentioned return conditions are that the above-mentioned cargo meets at least one of the following: the size of the above-mentioned cargo is unqualified, the weight of the above-mentioned cargo is unqualified, the cargo type of the above-mentioned cargo is a non-target type, and the cargo status of the above-mentioned cargo is a damaged state.

[0097] In some instances, the first acquisition module includes an acquisition submodule, a processing submodule, an extraction submodule and a processing submodule, the acquisition submodule is used to acquire initial image data of the above-mentioned goods, the above-mentioned initial image data is image data of the above-mentioned goods acquired by an image acquisition device, and the above-mentioned image acquisition device is installed on the above-mentioned mobile device; the processing submodule is used to preprocess the above-mentioned initial image data to obtain processed image data, and the above-mentioned preprocessing includes at least one of the following: grayscale processing, denoising processing and contrast enhancement processing; the extraction submodule is used to extract multiple cargo features from the above-mentioned processed image data, and the above-mentioned cargo features include at least one of the following: cargo color, cargo shape, cargo size, cargo texture; the processing submodule is used to input all of the above-mentioned cargo features into the image processing model to obtain the cargo type and cargo status of the above-mentioned cargo.

[0098] In some instances, the determination unit includes a first determination module and a second determination module, the first determination module is used to determine the size of the above-mentioned goods according to the size information of the above-mentioned goods, and determine that the size of the above-mentioned goods is qualified when the size of the above-mentioned goods is within a preset size range, and determine that the size of the above-mentioned goods is unqualified when the size of the above-mentioned goods is not within the preset size range; the second determination module is used to determine the weight of the above-mentioned goods according to the weight information of the above-mentioned goods, and determine that the weight of the above-mentioned goods is qualified when the weight of the above-mentioned goods is within the preset weight range, and determine that the weight of the above-mentioned goods is unqualified when the weight of the above-mentioned goods is not within the preset weight range.

[0099] In some instances, the determination unit includes a construction module and a third determination module, the construction module is used to construct a neural network model, wherein the neural network model is trained using multiple sets of training data, each set of training data in the multiple sets of training data includes: parameter information of historical goods and historical labels of the historical goods corresponding to the parameter information of the historical goods, the parameter information of the historical goods includes size information of the historical goods and weight information of the historical goods, the historical labels of the historical goods represent whether the historical goods are qualified, wherein, when the historical goods are qualified, it represents that both the size and weight of the historical goods are qualified, and when the historical goods are unqualified, it represents that at least one of the size and weight of the historical goods is unqualified; the third determination module is used to determine whether the size and weight of the goods are qualified based on the size information of the goods, the weight information of the goods and the neural network model.

[0100] In some instances, the control unit includes a third control module and a fourth control module, the third control module being used to control the conveyor line of the mobile equipment to rotate forward to transport the goods when the size of the goods is qualified and the weight of the goods is qualified; the fourth control module being used to control the conveyor line of the mobile equipment to rotate reversely to return the goods when the size of the goods is unqualified and / or the weight of the goods is unqualified.

[0101] In some instances, the device further includes a visualization module for visualizing the size information of the goods, the weight information of the goods, the size judgment information of the goods, and the weight judgment information of the goods, wherein the size judgment information of the goods indicates whether the size of the goods is qualified, and the weight judgment information of the goods indicates whether the weight of the goods is qualified.

[0102] The cargo detection device of the mobile device includes a processor and a memory. The acquisition unit and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions. The modules are all located in the same processor; or, the modules are located in different processors in any combination.

[0103] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the kernel parameters can be adjusted to solve the problem in the prior art that when a mobile device transports goods, the size and weight of the goods are not detected, resulting in the transport of unqualified goods and a high transport error rate.

[0104] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0105] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the cargo detection method for the mobile device.

[0106] An embodiment of the present invention provides a processor, and the processor is used to run a program, wherein the cargo detection method of the mobile device is executed when the program is run.

[0107] An embodiment of the present invention provides a device, the device including a processor, a memory, and a program stored in the memory and executable on the processor, and when the processor executes the program, at least the following steps are implemented:

[0108] Step S201, obtaining the size information and weight information of the goods;

[0109] Step S202, determining whether the size of the goods is qualified according to the size information of the goods, and determining whether the weight of the goods is qualified according to the weight information of the goods;

[0110] Step S203, at least when the size of the goods is qualified and the weight of the goods is qualified, control the mobile device to transport the goods, and when the size of the goods is unqualified and / or the weight of the goods is unqualified, control the mobile device to return the goods.

[0111] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0112] Optionally, at least when the size of the goods is qualified and the weight of the goods is qualified, the mobile device is controlled to transport the goods, and when the size of the goods is unqualified and / or the weight of the goods is unqualified, the mobile device is controlled to return the goods, including: obtaining the cargo type and the cargo status of the goods, the cargo status indicating whether the goods are damaged; when the size of the goods is qualified, the weight of the goods is qualified, the cargo type of the goods is a target type, and the cargo status of the goods is an undamaged state, the mobile device is controlled to transport the goods; when the goods meet the return conditions, the mobile device is controlled to return the goods, the return conditions being that the goods meet at least one of the following: the size of the goods is unqualified, the weight of the goods is unqualified, the cargo type of the goods is a non-target type, and the cargo status of the goods is a damaged state.

[0113] Optionally, obtaining the cargo type and the cargo status of the cargo includes: obtaining initial image data of the cargo, the initial image data being image data of the cargo acquired by an image acquisition device, and the image acquisition device being installed on the mobile device; preprocessing the initial image data to obtain processed image data, the preprocessing including at least one of the following: grayscale processing, denoising processing and contrast enhancement processing; extracting multiple cargo features from the processed image data, the cargo features including at least one of the following: cargo color, cargo shape, cargo size, cargo texture; inputting all of the cargo features into an image processing model to obtain the cargo type and the cargo status of the cargo.

[0114] Optionally, determining whether the size of the goods is qualified based on the size information of the goods, and determining whether the weight of the goods is qualified based on the weight information of the goods, includes: determining the size of the goods based on the size information of the goods, and determining that the size of the goods is qualified if the size of the goods is within a preset size range, and determining that the size of the goods is unqualified if the size of the goods is not within the preset size range; determining the weight of the goods based on the weight information of the goods, and determining that the weight of the goods is qualified if the weight of the goods is within a preset weight range, and determining that the weight of the goods is unqualified if the weight of the goods is not within the preset weight range.

[0115] Optionally, determining whether the size of the goods is qualified based on the size information of the goods, and determining whether the weight of the goods is qualified based on the weight information of the goods, includes: constructing a neural network model, wherein the neural network model is trained using multiple sets of training data, and each set of training data in the multiple sets of training data includes: parameter information of historical goods and historical labels of the historical goods corresponding to the parameter information of the historical goods acquired within a historical time period, the parameter information of the historical goods includes the size information of the historical goods and the weight information of the historical goods, and the historical labels of the historical goods represent whether the historical goods are qualified, wherein, when the historical goods are qualified, it represents that both the size and weight of the historical goods are qualified, and when the historical goods are unqualified, it represents that at least one of the size and weight of the historical goods is unqualified; determining whether the size and weight of the goods are qualified based on the size information of the goods, the weight information of the goods and the neural network model.

[0116] Optionally, when the size of the goods is qualified and the weight of the goods is qualified, the mobile equipment is controlled to transport the goods, and when the size of the goods is unqualified and / or the weight of the goods is unqualified, the mobile equipment is controlled to return the goods, including: when the size of the goods is qualified and the weight of the goods is qualified, controlling the conveying line of the mobile equipment to rotate forward to transport the goods; when the size of the goods is unqualified and / or the weight of the goods is unqualified, controlling the conveying line of the mobile equipment to rotate reversely to return the goods.

[0117] Optionally, the method further includes: visualizing the size information of the goods, the weight information of the goods, the size judgment information of the goods and the weight judgment information of the goods, the size judgment information of the goods representing whether the size of the goods is qualified, and the weight judgment information of the goods representing whether the weight of the goods is qualified.

[0118] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program for initializing at least the following method steps:

[0119] Step S201, obtaining the size information and weight information of the goods;

[0120] Step S202, determining whether the size of the goods is qualified according to the size information of the goods, and determining whether the weight of the goods is qualified according to the weight information of the goods;

[0121] Step S203, at least when the size of the goods is qualified and the weight of the goods is qualified, control the mobile device to transport the goods, and when the size of the goods is unqualified and / or the weight of the goods is unqualified, control the mobile device to return the goods.

[0122] Optionally, at least when the size of the goods is qualified and the weight of the goods is qualified, the mobile device is controlled to transport the goods, and when the size of the goods is unqualified and / or the weight of the goods is unqualified, the mobile device is controlled to return the goods, including: obtaining the cargo type and the cargo status of the goods, the cargo status indicating whether the goods are damaged; when the size of the goods is qualified, the weight of the goods is qualified, the cargo type of the goods is a target type, and the cargo status of the goods is an undamaged state, the mobile device is controlled to transport the goods; when the goods meet the return conditions, the mobile device is controlled to return the goods, the return conditions being that the goods meet at least one of the following: the size of the goods is unqualified, the weight of the goods is unqualified, the cargo type of the goods is a non-target type, and the cargo status of the goods is a damaged state.

[0123] Optionally, obtaining the cargo type and the cargo status of the cargo includes: obtaining initial image data of the cargo, the initial image data being image data of the cargo acquired by an image acquisition device, and the image acquisition device being installed on the mobile device; preprocessing the initial image data to obtain processed image data, the preprocessing including at least one of the following: grayscale processing, denoising processing and contrast enhancement processing; extracting multiple cargo features from the processed image data, the cargo features including at least one of the following: cargo color, cargo shape, cargo size, cargo texture; inputting all of the cargo features into an image processing model to obtain the cargo type and the cargo status of the cargo.

[0124] Optionally, determining whether the size of the goods is qualified based on the size information of the goods, and determining whether the weight of the goods is qualified based on the weight information of the goods, includes: determining the size of the goods based on the size information of the goods, and determining that the size of the goods is qualified if the size of the goods is within a preset size range, and determining that the size of the goods is unqualified if the size of the goods is not within the preset size range; determining the weight of the goods based on the weight information of the goods, and determining that the weight of the goods is qualified if the weight of the goods is within a preset weight range, and determining that the weight of the goods is unqualified if the weight of the goods is not within the preset weight range.

[0125] Optionally, determining whether the size of the goods is qualified based on the size information of the goods, and determining whether the weight of the goods is qualified based on the weight information of the goods, includes: constructing a neural network model, wherein the neural network model is trained using multiple sets of training data, and each set of training data in the multiple sets of training data includes: parameter information of historical goods and historical labels of the historical goods corresponding to the parameter information of the historical goods acquired within a historical time period, the parameter information of the historical goods includes the size information of the historical goods and the weight information of the historical goods, and the historical labels of the historical goods represent whether the historical goods are qualified, wherein, when the historical goods are qualified, it represents that both the size and weight of the historical goods are qualified, and when the historical goods are unqualified, it represents that at least one of the size and weight of the historical goods is unqualified; determining whether the size and weight of the goods are qualified based on the size information of the goods, the weight information of the goods and the neural network model.

[0126] Optionally, when the size of the goods is qualified and the weight of the goods is qualified, the mobile equipment is controlled to transport the goods, and when the size of the goods is unqualified and / or the weight of the goods is unqualified, the mobile equipment is controlled to return the goods, including: when the size of the goods is qualified and the weight of the goods is qualified, controlling the conveying line of the mobile equipment to rotate forward to transport the goods; when the size of the goods is unqualified and / or the weight of the goods is unqualified, controlling the conveying line of the mobile equipment to rotate reversely to return the goods.

[0127] Optionally, the method further includes: visualizing the size information of the goods, the weight information of the goods, the size judgment information of the goods and the weight judgment information of the goods, the size judgment information of the goods representing whether the size of the goods is qualified, and the weight judgment information of the goods representing whether the weight of the goods is qualified.

[0128] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0129] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0130] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0131] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0132] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0133] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0134] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0135] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0136] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0137] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0138] 1) The cargo detection method of the mobile device of the present application is applied to the controller in the cargo detection system. The method first obtains the size information and weight information of the cargo; then determines whether the size of the cargo is qualified according to the size information of the cargo, and determines whether the weight of the cargo is qualified according to the weight information of the cargo; finally, at least when the size of the cargo is qualified and the weight of the cargo is qualified, the mobile device is controlled to transport the cargo, and when the size of the cargo is unqualified and / or the weight of the cargo is unqualified, the mobile device is controlled to return the cargo. The method first detects the cargo type before transporting the cargo, and then transports the cargo after the detection is qualified, thereby reducing the time spent on transporting unqualified cargo back and forth, and solving the problem in the prior art that the mobile device does not detect the size and weight of the cargo when transporting the cargo, resulting in the transport of unqualified cargo and a high transport error rate.

[0139] 2) The cargo detection system of the mobile device of the present application first detects the cargo type before transporting the cargo, and then transports the cargo after it passes the inspection, thereby reducing the time spent on transporting unqualified cargo back and forth.

[0140] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A cargo detection method for a mobile device, characterized in that: A controller applied to a cargo detection system, the method comprising: Obtaining dimension information of goods and weight information of said goods; Determining whether the size of the goods is qualified according to the size information of the goods, and determining whether the weight of the goods is qualified according to the weight information of the goods; At least when the size of the goods is qualified and the weight of the goods is qualified, the mobile device is controlled to transport the goods; when the size of the goods is unqualified and / or the weight of the goods is unqualified, the mobile device is controlled to return the goods.

2. The method according to claim 1, characterized in that At least when the size of the goods is qualified and the weight of the goods is qualified, the mobile device is controlled to transport the goods, and when the size of the goods is unqualified and / or the weight of the goods is unqualified, the mobile device is controlled to return the goods, including: Acquire the cargo type and cargo status of the cargo, wherein the cargo status indicates whether the cargo is damaged; When the size of the cargo is qualified, the weight of the cargo is qualified, the cargo type of the cargo is the target type, and the cargo state of the cargo is in an undamaged state, controlling the mobile equipment to transport the cargo; When the goods meet the return conditions, the mobile device is controlled to return the goods, and the return conditions are that the goods meet at least one of the following: the size of the goods is unqualified, the weight of the goods is unqualified, the goods type of the goods is a non-target type, and the goods state of the goods is a damaged state.

3. The method according to claim 2, characterized in that Obtain the cargo type of the cargo and the cargo status of the cargo, including: Acquire initial image data of the goods, where the initial image data is image data of the goods acquired by an image acquisition device, and the image acquisition device is installed on the mobile device; Preprocessing the initial image data to obtain processed image data, wherein the preprocessing includes at least one of the following: grayscale processing, denoising processing, and contrast enhancement processing; Extracting a plurality of cargo features from the processed image data, the cargo features including at least one of the following: cargo color, cargo shape, cargo size, and cargo texture; All of the cargo features are input into an image processing model to obtain the cargo type and cargo status of the cargo.

4. The method according to claim 1, characterized in that: Determining whether the size of the goods is qualified according to the size information of the goods, and determining whether the weight of the goods is qualified according to the weight information of the goods, including: determining the size of the goods according to the size information of the goods, and determining that the size of the goods is qualified if the size of the goods is within a preset size range, and determining that the size of the goods is unqualified if the size of the goods is not within the preset size range; The weight of the cargo is determined according to the weight information of the cargo, and if the weight of the cargo is within a preset weight range, the weight of the cargo is determined to be qualified, and if the weight of the cargo is not within the preset weight range, the weight of the cargo is determined to be unqualified.

5. The method according to claim 1, characterized in that Determining whether the size of the goods is qualified according to the size information of the goods, and determining whether the weight of the goods is qualified according to the weight information of the goods, including: Constructing a neural network model, wherein the neural network model is obtained by training using multiple sets of training data, each set of training data in the multiple sets of training data includes: parameter information of historical goods and historical labels of the historical goods corresponding to the parameter information of the historical goods, obtained in a historical time period, wherein the parameter information of the historical goods includes size information of the historical goods and weight information of the historical goods, and the historical labels of the historical goods represent whether the historical goods are qualified, wherein, if the historical goods are qualified, it represents that both the size and weight of the historical goods are qualified, and if the historical goods are unqualified, it represents that at least one of the size and weight of the historical goods is unqualified; According to the size information of the goods, the weight information of the goods and the neural network model, it is determined whether the size of the goods and the weight of the goods are qualified.

6. The method according to claim 1, characterized in that At least when the size of the goods is qualified and the weight of the goods is qualified, the mobile device is controlled to transport the goods, and when the size of the goods is unqualified and / or the weight of the goods is unqualified, the mobile device is controlled to return the goods, including: When the size of the goods is qualified and the weight of the goods is qualified, controlling the conveying line of the mobile equipment to rotate forward to transport the goods; In the case that the size of the goods is unqualified and / or the weight of the goods is unqualified, the mobile equipment conveyor line is controlled to reverse to return the goods.

7. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: The size information of the goods, the weight information of the goods, the size judgment information of the goods and the weight judgment information of the goods are visualized, the size judgment information of the goods represents whether the size of the goods is qualified, and the weight judgment information of the goods represents whether the weight of the goods is qualified.

8. A cargo detection system for a mobile device, characterized in that: include: Ultrasonic sensors are used to collect the size information of goods; A weight sensor, used to collect weight information of the goods; A controller, used to execute the cargo detection method for a mobile device according to any one of claims 1 to 7, wherein the controller is electrically connected to the ultrasonic sensor and the weight sensor respectively.

9. The system according to claim 8, characterized in that The controller is an STM32 single chip microcomputer.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the cargo detection method for a mobile device according to any one of claims 1 to 7.

Images