CN113762862A - Cargo abnormity monitoring method and device, electronic equipment and storage medium - Google Patents
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Abstract
The application provides a method and a device for monitoring goods abnormity, computer equipment and a storage medium, wherein the method comprises the steps of obtaining video or image data of goods placement in a first warehouse; and generating a detection result according to the video or image data and the trained target detection model, wherein the trained target detection model is obtained by joint training in a federal learning mode according to the sample video or image data of goods placed in the first warehouse and the second warehouse. According to the method, the device, the computer equipment and the storage medium for monitoring the abnormal goods, sufficient effective sample video or image data can be obtained to train the target detection model, so that the performance of the target detection model is improved, and the effect of monitoring the abnormal goods is really achieved.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method and an apparatus for monitoring a cargo anomaly, an electronic device, and a storage medium.
Background
The warehousing operation relates to the processes of goods unloading, carrying, keeping and the like, and is directly related to the safety of goods, personnel and warehouse facilities. However, accidents such as collapse and falling of goods are easy to happen in warehousing operation, which causes problems such as personnel injury and goods damage, and the main reason is that the goods in the warehouse are not placed reasonably, for example, goods on a shelf are placed irregularly or the goods are placed anywhere. Therefore, how to timely detect and put unreasonable goods and realize abnormal monitoring of the goods is the most problem to be solved.
In the related art, when each enterprise warehouse realizes abnormal monitoring of goods, a goods detection model is usually trained based on respective accumulated data. However, in an actual storage scene, the abnormal goods placing state is less, and sufficient effective data is difficult to obtain to train the goods detection model, so that the performance of the goods detection model is poor, and the effect of monitoring the goods abnormity cannot be really achieved.
Disclosure of Invention
The application provides a cargo abnormity monitoring method and device, electronic equipment and a storage medium.
An embodiment of a first aspect of the present application provides a method for monitoring a cargo anomaly, including: acquiring video or image data of goods placement in a first warehouse; and generating a detection result according to the video or image data and the trained target detection model, wherein the trained target detection model is obtained by joint training in a federal learning mode according to the sample video or image data of goods placed in the first warehouse and the second warehouse.
According to the method for monitoring the abnormal goods, a trained target detection model is obtained through joint training in a federal learning mode according to sample video or image data of goods placed in a first warehouse and a second warehouse, the video or image data of the goods placed in the first warehouse are obtained, and a detection result is generated according to the video or image data and the trained target detection model. The trained target detection model is obtained by joint training according to the sample videos or image data of the goods placed in the first warehouse and the second warehouse, so that sufficient effective sample videos or image data can be obtained to train the target detection model, the performance of the target detection model is improved, and the effect of monitoring goods abnormity is really achieved.
The embodiment of the second aspect of the present application provides a device for monitoring abnormal goods, including: the system comprises a first acquisition module, a second acquisition module and a display module, wherein the first acquisition module is configured to acquire video or image data of goods placement in a first warehouse; and the first generation module is configured to generate a detection result according to the video or image data and a trained target detection model, and the trained target detection model is obtained by joint training in a federal learning mode according to the sample video or image data of goods placed in the first warehouse and the second warehouse.
The device for monitoring the abnormal goods obtains a trained target detection model through federated training according to sample video or image data of goods placement in a first warehouse and a second warehouse, obtains the video or image data of goods placement in the first warehouse, and generates a detection result according to the video or image data and the trained target detection model. The trained target detection model is obtained by joint training according to the sample videos or image data of the goods placed in the first warehouse and the second warehouse, so that sufficient effective sample videos or image data can be obtained to train the target detection model, the performance of the target detection model is improved, and the effect of monitoring goods abnormity is really achieved.
An embodiment of a third aspect of the present application provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of cargo anomaly monitoring as described in the embodiment of the first aspect above.
An embodiment of a fourth aspect of the present application provides a computer-readable storage medium storing computer instructions for causing the computer to perform the method for monitoring cargo anomaly as described in the embodiment of the first aspect.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flowchart of a cargo anomaly monitoring method according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a cargo anomaly monitoring method according to another embodiment of the present application;
fig. 3 is a schematic flow chart of a cargo anomaly monitoring method according to another embodiment of the present application;
FIG. 4 is a logic diagram of a method for anomaly monitoring of cargo according to an embodiment of the present application;
FIG. 5 is a logic diagram of an off-line training of a target detection model according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a device for monitoring cargo anomaly according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
In recent years, with the continuous acceleration of industrial process in China, the transportation requirements of industrial production raw materials, semi-finished products and even commodities are steadily improved, and the logistics industry in China is rapidly developed. The logistics total of the nationwide society is continuously improved, and investigation shows that the logistics total of the nationwide society reaches 215.9 trillion yuan three quarters before 2019. In the whole logistics industry, the warehousing industry is very important and is an indispensable link for commodity and goods circulation in a logistics system.
The warehousing operation relates to the processes of goods unloading, carrying, keeping and the like, and is directly related to the safety of goods, personnel and warehouse facilities. However, accidents such as collapse and falling of goods are easy to happen in warehousing operation, which causes problems such as personnel injury and goods damage, and the main reason is that the goods in the warehouse are not placed reasonably, for example, goods on a shelf are placed irregularly or the goods are placed anywhere. Therefore, how to timely detect and put unreasonable goods and realize abnormal monitoring of the goods is the most problem to be solved.
In the related art, when each enterprise warehouse realizes abnormal monitoring of goods, a goods detection model is usually trained based on respective accumulated data. However, in an actual storage scene, the abnormal goods placing state is less, and sufficient effective data is difficult to obtain to train the goods detection model, so that the performance of the goods detection model is poor, and the effect of monitoring the goods abnormity cannot be really achieved. Therefore, the method, the device, the electronic equipment and the storage medium for monitoring the abnormal goods can obtain sufficient effective sample video or image data to train the target detection model, so that the performance of the target detection model is improved, and the effect of monitoring the abnormal goods is really achieved.
A method, an apparatus, an electronic device, and a storage medium for cargo anomaly monitoring according to embodiments of the present application are described below with reference to the accompanying drawings.
Fig. 1 is a schematic flowchart of a cargo anomaly monitoring method according to an embodiment of the present application. The method for monitoring the abnormal goods can be executed by the device for monitoring the abnormal goods provided by the embodiment of the application. As shown in fig. 1, the method for monitoring abnormal goods in the embodiment of the present application may specifically include the following steps:
s101, video or image data of goods placement in the first warehouse are obtained.
Specifically, the first warehouse is a warehouse to be subjected to abnormal goods monitoring. When the goods placement state in the first warehouse is monitored abnormally, video or image data of goods placement in the first warehouse is obtained. The video or image data of the goods in the first warehouse can be acquired through the image acquisition device arranged in the first warehouse, such as a camera.
And S102, generating a detection result according to the video or image data and the trained target detection model, wherein the trained target detection model is obtained by joint training in a federal learning mode according to the sample video or image data of goods placed in the first warehouse and the second warehouse.
Specifically, the video or image data acquired in step S101 is input into a pre-trained target detection model, and the trained target detection model outputs a corresponding detection result. The detection result may specifically be the position of the cargo and the probability of placing abnormality.
The trained target detection model is obtained through pre-training, and is obtained through joint training in a federal learning mode according to sample video or image data of goods placement in a first warehouse and sample video or image data of goods placement in other warehouses except the first warehouse, namely a second warehouse. The training data used for model training comprises sample video or image data of goods placement in the first warehouse and sample video or image data of goods placement in other warehouses except the first warehouse, namely the second warehouse, so that sufficient effective data can be obtained to train the target detection model, the performance of the target detection model is improved, and the effect of monitoring goods abnormity is really achieved.
Federal learning is also known as Federal machine learning, Joint learning, and Union learning. Federal learning is a machine learning framework, and can effectively help a plurality of organizations to perform data use and machine learning modeling under the condition of meeting the requirements of user privacy protection, data safety and government regulations. In the embodiment of the application, the trained target detection model is obtained through joint training in a federal learning mode, so that model training can be performed by utilizing multi-party data under the condition that the original data of each warehouse is not exported, the performance of the target detection model is improved, and the effect of monitoring abnormal goods is really achieved.
It should be noted that, for a specific process of joint training in the federal learning manner, reference may be made to the following description in the embodiments, and details are not described herein again.
According to the method for monitoring the abnormal goods, a trained target detection model is obtained through combined training according to sample video or image data of goods placement in a first warehouse and a second warehouse, the video or image data of the goods placement in the first warehouse is obtained, and a detection result is generated according to the video or image data and the trained target detection model. The trained target detection model is obtained by joint training according to the sample videos or image data of the goods placed in the first warehouse and the second warehouse, so that sufficient effective sample videos or image data can be obtained to train the target detection model, the performance of the target detection model is improved, and the effect of monitoring goods abnormity is really achieved. The trained target detection model is generated in a federal learning mode, and model training can be performed by utilizing multi-party data under the condition that the original data of each warehouse is not taken out of the warehouse, so that the performance of the target detection model is improved, and the effect of monitoring abnormal goods is really achieved.
In practical application, in order to further improve the performance of the trained target detection model, in an embodiment of the present application, the acquired video or image data may be processed to obtain training data. Fig. 2 is a schematic flowchart of a cargo anomaly monitoring method according to another embodiment of the present application. As shown in fig. 2, the method for monitoring abnormal goods in the embodiment of the present application may specifically include the following steps:
s201, video or image data of goods placement in the first warehouse are obtained.
Specifically, step S201 is similar to step S101 in the foregoing embodiment, and the detailed process is not described here again.
The step S102 in the above embodiment may specifically include the following steps S202 and S203.
S202, preprocessing the video or image data.
Specifically, to further improve the performance of the trained target detection model, the video or image data acquired in step S201 is preprocessed to obtain preprocessed video or image data. The preprocessing specifically includes, but is not limited to, data cleaning, feature engineering, and the like.
Data cleansing (Data cleansing), which is a process of reviewing and verifying Data, aims to delete duplicate information, correct existing errors, and provide Data consistency, and may be various existing Data cleansing methods, which are not limited in this application.
The feature engineering is a process of converting original data into training data of a model, and aims to obtain better training data features so that a machine learning model approaches the upper limit. The feature engineering generally comprises three parts of feature construction, feature extraction and feature selection. Feature construction refers to manually finding some features with physical significance from raw data. Feature extraction and feature selection are both to find the most efficient features from the original features. But the feature extraction emphasizes that a group of features with obvious physical or statistical significance is obtained by means of feature conversion, and the feature selection is to select a group of feature subsets with obvious physical or statistical significance from a feature set. Feature extraction and feature selection can help to reduce feature dimensionality and data redundancy, sometimes more meaningful feature attributes can be found by feature extraction, and the importance of each feature to model construction can often be represented by the feature selection process.
And S203, inputting the preprocessed video or image data into the trained target detection model to generate a detection result.
Specifically, the preprocessed video or image data obtained in step S202 is input into a trained target detection model, and the trained target detection model outputs a corresponding detection result. For a specific process, reference may be made to related descriptions in step S102 in the foregoing embodiments, and details are not described here again.
And S204, sending the detection result to a storage center of the first warehouse, so that the storage center sends the abnormal monitoring information to the first warehouse according to the detection result.
Specifically, the detection result obtained in step S203 is sent to the warehouse center of the first warehouse, the warehouse center of the first warehouse judges whether the goods in the first warehouse are placed abnormally according to the detection result, and if the goods in the first warehouse are placed abnormally, the abnormal monitoring information is sent to the first warehouse for timely remediation. Specifically, whether the placement of the goods in the first warehouse is abnormal is determined according to the detection result, and whether the placement abnormal probability in the detection result exceeds a preset probability threshold, for example, 70%, and if the placement abnormal probability exceeds the probability threshold, the placement of the goods in the first warehouse is determined to be abnormal.
According to the method for monitoring the abnormal goods, a trained target detection model is obtained through combined training according to sample video or image data of goods placement in a first warehouse and a second warehouse, the video or image data of the goods placement in the first warehouse is obtained, and a detection result is generated according to the video or image data and the trained target detection model. The trained target detection model is obtained by joint training according to the sample videos or image data of the goods placed in the first warehouse and the second warehouse, so that sufficient effective sample videos or image data can be obtained to train the target detection model, the performance of the target detection model is improved, and the effect of monitoring goods abnormity is really achieved. The trained target detection model is generated in a federal learning mode, and model training can be performed by utilizing multi-party data under the condition that the original data of each warehouse is not taken out of the warehouse, so that the performance of the target detection model is improved, and the effect of monitoring abnormal goods is really achieved.
In one embodiment of the present application, the method shown in fig. 3 may be used to train a trained target detection model. Fig. 3 is a schematic flowchart of a cargo anomaly monitoring method according to another embodiment of the present application. As shown in fig. 3, the training process of the trained target detection model in the above embodiment may specifically include the following steps:
s301, sample video or image data of goods placed in the first warehouse, manually marked goods positions and corresponding placing states are obtained.
Specifically, historical video or image data of goods placement in the first warehouse is obtained as sample video or image data. And labeling the sample video or image data by adopting a manual labeling mode, wherein the label specifically comprises a cargo position and a corresponding placing state. The placing state comprises placing abnormity or placing normality.
S302, training the target detection model to be trained according to the sample video or image data, the goods position and the corresponding placement state to obtain a candidate target detection model.
Specifically, the sample video or image data obtained in step S301 is used as input data of the model, the cargo position and the corresponding placement state obtained in step S301 are used as input data of the model, and the target detection model to be trained is trained to obtain the candidate target detection model. The target detection model to be trained may be a target detection model with preset parameters, and the target detection model may be any of the existing various target detection models, which is not limited in the present application.
As a possible implementation manner, the step S302 may specifically include the following steps: preprocessing sample video or image data; and training the target detection model to be trained according to the preprocessed sample video or image data, the goods position and the corresponding placement state. The specific process of the preprocessing can refer to the related description in the above embodiments, and is not described herein again.
And S303, generating a trained target detection model according to the candidate target detection model.
Specifically, according to the candidate target detection model obtained in step S302, a well-trained target detection model may be generated in a federal learning manner.
As a possible implementation manner, the step S303 may specifically include the following steps: sending model parameters in the candidate target detection model to a server; receiving aggregated model parameters sent by a server, wherein the aggregated model parameters are obtained by the server according to the received model parameters of a plurality of warehouses in an aggregation manner; and updating the candidate target detection model according to the aggregated model parameters to obtain the trained target detection model.
According to the method for monitoring the abnormal goods, a trained target detection model is obtained through combined training according to sample video or image data of goods placement in a first warehouse and a second warehouse, the video or image data of the goods placement in the first warehouse is obtained, and a detection result is generated according to the video or image data and the trained target detection model. The trained target detection model is obtained by joint training according to the sample videos or image data of the goods placed in the first warehouse and the second warehouse, so that sufficient effective sample videos or image data can be obtained to train the target detection model, the performance of the target detection model is improved, and the effect of monitoring goods abnormity is really achieved. The trained target detection model is generated in a federal learning mode, and model training can be performed by utilizing multi-party data under the condition that the original data of each warehouse is not taken out of the warehouse, so that the performance of the target detection model is improved, and the effect of monitoring abnormal goods is really achieved.
For clarity, the method for monitoring the cargo anomaly according to the embodiment of the present application is described in detail below with reference to fig. 4. As shown in fig. 4, taking the warehouse of enterprise a and the warehouse of enterprise B as an example, the offline platform of enterprise a collects sample video or image data of the warehouse site of enterprise a as a data source, and performs data preprocessing on the data source to obtain effective training data. And the offline platform of the enterprise B collects sample video or image data of the warehouse site of the enterprise B as a data source, and performs data preprocessing on the data source to obtain effective training data. And performing cross-domain joint modeling, namely joint model training according to effective training data of the enterprises A and B, finishing training, wherein the enterprises A and B respectively acquire a trained offline model (namely a trained target detection model), and push the trained offline model to the respective corresponding online platform. The online platforms of the enterprises A and B collect real-time video or image data of respective warehouse sites through camera monitoring, and the real-time video or image data is input into respective pushed online models for anomaly detection to obtain detection results. And sending the detection result to the storage centers of the respective warehouses, and sending the danger early warning information (namely the abnormal monitoring information) to the respective warehouses by the storage centers according to the detection result to carry out timely remediation. After continuous iteration, real-time video or image data acquired by the online platform can be collected to an offline platform data source for data expansion, so that the performance of the trained target detection model is improved.
For clarity of illustrating the cargo anomaly monitoring method according to the embodiment of the present application, the following describes in detail the training process of the target detection model with reference to fig. 5. As shown in fig. 5, taking the warehouse of enterprise a and the warehouse of enterprise B as an example, sample video or image data of the respective warehouses of enterprise A, B is collected, and the sample video or image data is preprocessed to obtain valid training data. Model training is carried out according to effective training data of the enterprise A, B to obtain candidate target detection models, and model parameters in the candidate target detection models are sent to a main service center (namely a server). The main service center aggregates the received model parameters of the warehouses of the enterprise A, B to obtain aggregated model parameters, sends the aggregated model parameters to the enterprise A, B, the enterprise A, B updates the candidate target detection model obtained by training before according to the aggregated model parameters to obtain a trained target detection model, and performs anomaly monitoring on goods in the respective warehouses according to the trained target detection model.
In order to realize the embodiment, the embodiment of the application further provides a device for monitoring the abnormal goods. Fig. 6 is a schematic structural diagram of a device for monitoring cargo anomaly according to an embodiment of the present application. As shown in fig. 6, the device 600 for monitoring abnormal goods in the embodiment of the present application may specifically include: a first acquisition module 601 and a first generation module 602.
The first obtaining module 601 is configured to obtain video or image data of the placement of the goods in the first warehouse.
And the first generation module 602 is configured to generate a detection result according to the video or image data and a trained target detection model, wherein the trained target detection model is obtained by joint training in a federal learning manner according to the video or image data of the samples of the goods in the first warehouse and the second warehouse.
In an embodiment of the present application, the first generating module 602 may specifically include: a first preprocessing unit configured to preprocess video or image data; and the generating unit is configured to input the preprocessed video or image data into the trained target detection model and generate a detection result.
In an embodiment of the present application, the cargo anomaly monitoring device 600 may further include: the second acquisition module is configured to acquire sample video or image data of goods placed in the first warehouse, manually marked goods positions and corresponding placing states; the training module is configured to train a target detection model to be trained according to the sample video or image data, the goods position and the corresponding placement state to obtain a candidate target detection model; a second generation module configured to generate a trained target detection model from the candidate target detection models.
In an embodiment of the present application, the second generating module may specifically include: a transmitting unit configured to transmit the model parameters in the candidate target detection model to a server; the receiving unit is configured to receive the aggregated model parameters sent by the server, and the aggregated model parameters are obtained by the server according to the received model parameters of the plurality of warehouses in an aggregation manner; and the updating unit is configured to update the candidate target detection model according to the aggregated model parameters to obtain a trained target detection model.
In an embodiment of the present application, the training module may specifically include: a second preprocessing unit configured to preprocess the sample video or image data; and the training unit is configured to train the target detection model to be trained according to the preprocessed sample video or image data, the goods position and the corresponding placing state.
In an embodiment of the present application, the cargo anomaly monitoring device 600 may further include: and the sending module is configured to send the detection result to a storage center of the first warehouse so that the storage center can send the abnormal monitoring information to the first warehouse according to the detection result.
In one embodiment of the present application, the preprocessing includes data cleansing and/or feature engineering.
It should be noted that the above explanation of the embodiment of the method for monitoring abnormal goods is also applicable to the device for monitoring abnormal goods in this embodiment, and the detailed process is not repeated here.
The device for monitoring the abnormal goods obtains a trained target detection model according to sample video or image data joint training of goods placement in the first warehouse and the second warehouse, obtains video or image data of goods placement in the first warehouse, and generates a detection result according to the video or image data and the trained target detection model. The trained target detection model is obtained by joint training according to the sample videos or image data of the goods placed in the first warehouse and the second warehouse, so that sufficient effective sample videos or image data can be obtained to train the target detection model, the performance of the target detection model is improved, and the effect of monitoring goods abnormity is really achieved. The trained target detection model is generated in a federal learning mode, and model training can be performed by utilizing multi-party data under the condition that the original data of each warehouse is not taken out of the warehouse, so that the performance of the target detection model is improved, and the effect of monitoring abnormal goods is really achieved.
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
Fig. 7 is a block diagram of an electronic device according to an embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as smart voice interaction devices, personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 7, the electronic apparatus includes: one or more processors 701, a memory 702, and interfaces for connecting the various components, including a high-speed interface and a low-speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor 701 may process instructions for execution within the electronic device, including instructions stored in or on a memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to an interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). In fig. 7, one processor 701 is taken as an example.
The memory 702 is a non-transitory computer readable storage medium as provided herein. The memory stores instructions executable by the at least one processor to cause the at least one processor to perform the cargo anomaly monitoring method provided herein. The non-transitory computer readable storage medium of the present application stores computer instructions for causing a computer to perform the method of cargo anomaly monitoring provided by the present application.
The memory 702, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the method for monitoring cargo anomaly in the embodiment of the present application (for example, the first acquiring module 601 and the first generating module 602 shown in fig. 6). The processor 701 executes various functional applications of the server and data processing by running non-transitory software programs, instructions and modules stored in the memory 702, so as to implement the method for monitoring the cargo anomaly in the above method embodiment.
The memory 702 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the electronic device of the cargo abnormality monitoring method, and the like. Further, the memory 702 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 702 may optionally include memory located remotely from the processor 701, and such remote memory may be connected to the electronics of the method for cargo anomaly monitoring over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the cargo anomaly monitoring method may further include: an input device 703 and an output device 704. The processor 701, the memory 702, the input device 703 and the output device 704 may be connected by a bus or other means, and fig. 7 illustrates an example of a connection by a bus.
The input device 703 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic equipment of the method of cargo anomaly monitoring, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointer stick, one or more mouse buttons, a track ball, a joystick, and the like. The output devices 704 may include a display device, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server can be a cloud Server, also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service ("Virtual Private Server", or simply "VPS").
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.
In the description of the present specification, the terms "first", "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (16)
1. A method of anomaly monitoring of cargo, comprising:
acquiring video or image data of goods placement in a first warehouse;
and generating a detection result according to the video or image data and the trained target detection model, wherein the trained target detection model is obtained by joint training in a federal learning mode according to the sample video or image data of goods placed in the first warehouse and the second warehouse.
2. The method of claim 1, wherein generating a detection result from the video or image data and the trained object detection model comprises:
preprocessing the video or image data;
and inputting the preprocessed video or image data into the trained target detection model to generate the detection result.
3. The method of claim 1, further comprising:
acquiring sample video or image data of goods placed in the first warehouse, manually marked goods positions and corresponding placing states;
training a target detection model to be trained according to the sample video or image data, the goods position and the corresponding placement state to obtain a candidate target detection model;
and generating the trained target detection model according to the candidate target detection model.
4. The method of claim 3, wherein the generating the trained object detection model from the candidate object detection models comprises:
sending model parameters in the candidate target detection model to a server;
receiving aggregated model parameters sent by the server, wherein the aggregated model parameters are obtained by the server according to the received model parameters of a plurality of warehouses in an aggregation manner;
and updating the candidate target detection model according to the aggregated model parameters to obtain the trained target detection model.
5. The method of claim 3, wherein training a target detection model to be trained based on the sample video or image data and the cargo positions and corresponding pose states comprises:
preprocessing the sample video or image data;
and training the target detection model to be trained according to the preprocessed sample video or image data, the goods position and the corresponding placing state.
6. The method of claim 1, further comprising:
and sending the detection result to a storage center of the first warehouse, so that the storage center sends abnormal monitoring information to the first warehouse according to the detection result.
7. The method according to claim 2 or 5, characterized in that the pre-processing comprises data washing and/or feature engineering.
8. A cargo anomaly monitoring device, comprising:
the system comprises a first acquisition module, a second acquisition module and a display module, wherein the first acquisition module is configured to acquire video or image data of goods placement in a first warehouse;
and the first generation module is configured to generate a detection result according to the video or image data and a trained target detection model, and the trained target detection model is obtained by joint training in a federal learning mode according to the sample video or image data of goods placed in the first warehouse and the second warehouse.
9. The apparatus of claim 8, wherein the first generating module comprises:
a first preprocessing unit configured to preprocess the video or image data;
and the generating unit is configured to input the preprocessed video or image data into the trained target detection model and generate the detection result.
10. The apparatus of claim 8, further comprising:
the second acquisition module is configured to acquire sample video or image data of goods placed in the first warehouse, manually marked goods positions and corresponding placing states;
the training module is configured to train a target detection model to be trained according to the sample video or image data, the goods position and the corresponding placing state to obtain a candidate target detection model;
a second generation module configured to generate the trained target detection model from the candidate target detection model.
11. The apparatus of claim 10, wherein the second generating module comprises:
a sending unit configured to send model parameters in the candidate target detection model to a server;
a receiving unit, configured to receive aggregated model parameters sent by the server, where the aggregated model parameters are obtained by aggregating the server according to received model parameters of multiple warehouses;
and the updating unit is configured to update the candidate target detection model according to the aggregated model parameters to obtain the trained target detection model.
12. The apparatus of claim 10, wherein the training module comprises:
a second preprocessing unit configured to preprocess the sample video or image data;
and the training unit is configured to train the target detection model to be trained according to the preprocessed sample video or image data, the goods position and the corresponding placing state.
13. The apparatus of claim 8, further comprising:
the sending module is configured to send the detection result to a storage center of the first warehouse, so that the storage center sends the abnormal monitoring information to the first warehouse according to the detection result.
14. The apparatus of claim 9 or 12, wherein the pre-processing comprises data washing and/or feature engineering.
15. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of cargo anomaly monitoring according to any one of claims 1-7.
16. A computer-readable storage medium having computer instructions stored thereon for causing a computer to perform the method for cargo anomaly monitoring according to any one of claims 1-7.
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