Delivery robot scheduling method and system, electronic equipment and storage medium

Abstract

The invention discloses a dispatching method and system of a delivery robot, electronic equipment and a storage medium. The method comprises the following steps: acquiring working data of the delivery robot; the work data is data related to a work task of the delivery robot at the current moment; inputting the working data into a prediction model to obtain the delivery time of the delivery robot; and scheduling the delivery robot according to the delivery time, and determining a corresponding relationship between each target cargo and the delivery robot. The delivery time of the delivery robot is predicted through the prediction model, and the delivery robot is scheduled based on the delivery time, so that the delivery time is shortened, and the delivery efficiency is improved. The method can be widely applied to the technical field of Internet of Things.

Description

Technical Field

[0001] The present application relates to the technical field of Internet of Things, and in particular to a dispatching method, system, electronic device and storage medium for a delivery robot. Background Art

[0002] In recent years, delivery robots have gradually become the new favorite in the field of logistics and distribution due to their high efficiency and flexibility. Robots have been widely used in hotels, buildings and large public places. In related technologies, delivery robot systems are mostly dispatched based on simple rules, especially in large public places or factories where multiple robots complete delivery operations. Due to the lack of dynamic adaptability, it is impossible to accurately predict delivery time and optimize dispatch strategies, resulting in uneven resource allocation and low delivery efficiency. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose an efficient delivery robot scheduling method, system, electronic device and storage medium.

[0004] To achieve the above-mentioned purpose, one aspect of an embodiment of the present application proposes a method for scheduling a delivery robot, the method comprising: obtaining the working data of the delivery robot; the working data is data related to the working task of the delivery robot at the current moment; inputting the working data into a prediction model to obtain the delivery time of the delivery robot; scheduling the delivery robot according to the delivery time, and determining the corresponding relationship between each target cargo and the delivery robot. The present application predicts the delivery time of the delivery robot through a prediction model, and schedules the delivery robot based on the delivery time, which is conducive to shortening the delivery time and improving the delivery efficiency.

[0005] In some embodiments, the method provided by the embodiments of the present application further includes:

[0006] Extract key features from the work data; the key features are features that affect delivery time;

[0007] The key features are subjected to feature scaling, feature selection and feature cross processing to obtain feature vectors.

[0008] In some embodiments, in the method provided by the embodiments of the present application, the prediction model includes an LSTM model, and the step of inputting the work data into the prediction model to obtain the delivery time of the delivery robot includes:

[0009] Processing the feature vector and the historical data within the time step through the input layer to obtain a first feature;

[0010] Processing the first feature through the LSTM layer to obtain a second feature; the second feature includes time information;

[0011] The second feature is processed through the output layer to obtain the delivery time.

[0012] In some embodiments, in the method provided by the embodiments of the present application, the prediction model is trained by the following steps:

[0013] Get sample data;

[0014] Inputting the sample data into the prediction model to obtain a predicted sample time;

[0015] According to the predicted sample time and the actual sample time, a loss function is constructed using a mean square error;

[0016] The prediction model is trained based on the optimizer through the loss function to obtain a trained prediction model.

[0017] In some embodiments, the method provided by the embodiments of the present application, wherein the delivery robot is dispatched according to the delivery time, and the corresponding relationship between each target cargo and the delivery robot is determined, including:

[0018] According to the delivery time and work data, the delivery robot is dispatched based on an intelligent dispatching algorithm;

[0019] Through iterative learning, the corresponding relationship between each target cargo and the delivery robot is continuously optimized.

[0020] In some embodiments, the method provided by the embodiments of the present application further includes:

[0021] Determine a state space according to the state of the delivery robot and the order state;

[0022] Determining an action space according to the path planning and the task allocation of the delivery robot;

[0023] Determine the reward function based on the delivery time;

[0024] According to the state space, the action space and the reward function, the current optimal correspondence is determined, and the state space is determined according to the state of the delivery robot and the order status, and the correspondence is updated.

[0025] In some embodiments, the method provided by the embodiments of the present application, wherein the step of obtaining the working data of the delivery robot includes:

[0026] connecting the delivery robot to an Internet of Things platform;

[0027] The working data of the delivery robot is obtained through the Internet of Things platform.

[0028] To achieve the above object, another aspect of the embodiment of the present application provides a dispatching system for a delivery robot, the system comprising:

[0029] The first module is used to obtain the working data of the delivery robot; the working data is the data related to the working task of the delivery robot at the current moment;

[0030] The second module is used to input the working data into a prediction model to obtain the delivery time of the delivery robot;

[0031] The third module is used to schedule the delivery robot according to the delivery time and determine the corresponding relationship between each target cargo and the delivery robot.

[0032] To achieve the above objective, another aspect of an embodiment of the present application provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above method when executing the computer program.

[0033] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0034] The embodiments of the present application include at least the following beneficial effects: The method provided by the embodiments of the present application includes: obtaining the working data of the delivery robot; the working data is the data related to the working task of the delivery robot at the current moment; inputting the working data into the prediction model to obtain the delivery time of the delivery robot; scheduling the delivery robot according to the delivery time, and determining the corresponding relationship between each target cargo and the delivery robot. The present application predicts the delivery time of the delivery robot through the prediction model, and schedules the delivery robot based on the delivery time, which is conducive to shortening the delivery time and improving the delivery efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a flow chart of an embodiment of a method for dispatching a delivery robot provided by the present application;

[0036] Figure 2 is a flow chart of another embodiment of the dispatching method of the delivery robot provided by the present application;

[0037] Figure 3 It is a flow chart of an embodiment of the working data processing process provided by the present application;

[0038] Figure 4 is a flowchart of an embodiment of the prediction process of the prediction model provided by the present application;

[0039] Figure 5 is a flowchart of an embodiment of the training process of the prediction model provided by the present application;

[0040] Figure 6 is a flowchart of an embodiment of the scheduling process provided by the present application;

[0041] Figure 7 is a flowchart of another embodiment of the scheduling process provided by the present application;

[0042] Figure 8 It is a structural schematic diagram of an embodiment of the prediction model provided by the present application;

[0043] Fig. 9 It is a structural schematic diagram of a dispatching system for a delivery robot provided in an embodiment of the present application;

[0044] Fig.10 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0046] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0047] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0048] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0049] Before describing the embodiments of the present application in detail, some nouns and terms involved in the embodiments of the present application are first described. The nouns and terms involved in the embodiments of the present application are subject to the following explanations.

[0050] Long Short-Term Memory (LSTM) is a time recurrent neural network that is specially designed to solve the long-term dependency problem of general RNNs (recurrent neural networks). All RNNs have a chain form of repeated neural network modules.

[0051] AIOT: Artificial Intelligence Internet of Things platform.

[0052] In the related technologies, delivery robots have gradually become the new favorite in the field of logistics and distribution due to their high efficiency and flexibility. Robots have been widely used in hotels, buildings and large public places. However, most of the existing delivery robot systems are scheduled based on simple rules, especially in large public places or factories where multiple robots complete delivery operations. Due to the lack of dynamic adaptability, it is impossible to accurately predict the delivery time and optimize the dispatch strategy, resulting in uneven resource allocation and low delivery efficiency. Therefore, it is particularly important to develop a delivery robot system that can perform intelligent scheduling and dispatch based on real-time data and machine learning algorithms.

[0053] The dispatching method of the delivery robot provided in the embodiment of the present application relates to the field of Internet of Things technology. The dispatching method of the delivery robot provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server, and can also be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a car terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or it can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements the dispatching method of the delivery robot, etc., but is not limited to the above forms.

[0054] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0055] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0056] Figure 1 is an optional flow chart of the dispatching method of the delivery robot provided in the embodiment of the present application; Figure 1 The method may include but is not limited to steps S100 to S400.

[0057] Step S100, obtaining the working data of the delivery robot; the working data is the data related to the working task of the delivery robot at the current moment;

[0058] Step S200, inputting the work data into the prediction model to obtain the delivery time of the delivery robot;

[0059] Step S300, dispatching the delivery robot according to the delivery time, and determining the corresponding relationship between each target cargo and the delivery robot.

[0060] The working data of the delivery robot in the embodiment of the present application may be the current state of the delivery robot, information about the work to be performed, etc. It is understandable that the present application includes several delivery robots, and it is necessary to obtain the working data of all delivery robots, and for each delivery robot, the delivery time is predicted through the prediction model, and then all delivery robots are scheduled. In some embodiments, the prediction model in the present application may be any artificial intelligence model, and the present application does not make any specific limitation. Figure 2 As shown, the present invention aims to provide an intelligent scheduling and dispatching system for delivery robots based on machine learning algorithms. Through the Internet of Things platform, key data such as the robot's working status, work queue, travel distance and time are collected in real time, and advanced machine learning models are used to predict delivery time. Based on this, the robot is automatically scheduled for optimal dispatching, which can significantly improve delivery efficiency, reduce costs, and improve customer satisfaction.

[0061] In some embodiments, reference Figure 3 As shown, the method provided in the embodiment of the present application further includes:

[0062] Step S110, extracting key features from the work data; the key features are features that affect the delivery time;

[0063] Step S120, performing feature scaling, feature selection and feature cross processing on the key features to obtain feature vectors.

[0064] In some possible implementations, the working data is preprocessed, including but not limited to the method shown in steps S110 to S120.

[0065] In some embodiments, reference Figure 4 As shown, the method provided in the embodiment of the present application, the prediction model includes an LSTM model, the work data is input into the prediction model, and the delivery time of the delivery robot is obtained, including:

[0066] Step S210, processing the feature vector and the historical data within the time step through the input layer to obtain the first feature;

[0067] Step S220, the first feature is processed by the LSTM layer to obtain a second feature; the second feature includes time information;

[0068] Step S230, the second feature is processed through the output layer to obtain the delivery time.

[0069] In some possible implementations, the historical data includes time information, and the LSTM layer is also used to extract time series in the working data.

[0070] In some embodiments, reference Figure 5As shown, in the method provided in the embodiment of the present application, the prediction model is trained by the following steps:

[0071] Step S240, obtaining sample data;

[0072] Step S250, inputting sample data into the prediction model to obtain a predicted sample time;

[0073] Step S260, constructing a loss function using mean square error according to the predicted sample time and the actual sample time;

[0074] Step S270, training the prediction model based on the optimizer through the loss function to obtain a trained prediction model.

[0075] In some possible implementations, the present application may also train the prediction model through other types of loss functions, which are not specifically limited in the present application.

[0076] In some embodiments, reference Figure 6 As shown, the method provided in the embodiment of the present application schedules the delivery robot according to the delivery time and determines the corresponding relationship between each target cargo and the delivery robot, including:

[0077] Step S310, dispatching the delivery robot based on the intelligent dispatching algorithm according to the delivery time and work data;

[0078] Step S320, continuously optimizing the correspondence between each target cargo and the delivery robot through iterative learning.

[0079] In some possible implementations, the intelligent scheduling algorithm can be set according to actual needs, and can be scheduled through an artificial intelligence model; it can also be scheduled through a related algorithm, which is not specifically limited in this application.

[0080] In some embodiments, reference Figure 7 As shown, the method provided in the embodiment of the present application further includes:

[0081] Step S311, determining the state space according to the state of the delivery robot and the order state;

[0082] Step S312, determining the action space according to the path planning and the task allocation of the delivery robot;

[0083] Step S313, determining a reward function according to the delivery time;

[0084] Step S314, determine the current optimal correspondence based on the state space, action space and reward function, and return to determine the state space and update the correspondence based on the state of the delivery robot and the order status.

[0085] In some possible implementations, the settings of the state space, action space, and reward function can be adjusted according to actual needs.

[0086] In some embodiments, the method provided in the embodiments of the present application, for obtaining working data of a delivery robot, includes:

[0087] Connecting delivery robots to IoT platforms;

[0088] Obtain the delivery robot’s working data through the IoT platform.

[0089] The following is a detailed description and explanation of the solution of the embodiment of the present invention in conjunction with a specific application example:

[0090] The present invention proposes a delivery robot system based on deep learning algorithm and intelligent scheduling strategy, which realizes efficient distribution of delivery tasks and path planning through real-time data collection, preprocessing, feature extraction, deep learning model prediction and intelligent scheduling algorithm optimization. The following are specific embodiments of the present invention:

[0091] (1) IoT Integration:

[0092] Using Internet of Things technology, the delivery robot is seamlessly connected to the AIoT platform to collect robot status data (i.e., work data in this application) in real time, including power, speed, location, task queue (order ID, priority, target address), historical travel distance and time of completed tasks, etc.

[0093] (2) Data preprocessing:

[0094] The raw data is cleaned to remove noise and outliers, such as invalid data caused by sensor failure. The data is processed using a standardized method to ensure that the feature values ​​are on a uniform scale to facilitate model training.

[0095] (3) Feature extraction:

[0096] Extract key features that affect delivery time from historical data, such as order quantity, delivery distance, robot power, robot speed, delivery time period, whether to take the elevator, floors crossed during delivery, robot location, and other feature information.

[0097] (4) Feature Engineering:

[0098] The features are further processed, such as feature scaling, feature selection, feature crossover, etc., to improve the model prediction ability. The feature scaling uses the Min-Max standardization method to ensure that the feature values ​​are within a reasonable range.

[0099] (5) Deep learning algorithm used:

[0100] The long short-term memory network (LSTM) is selected as the delivery time prediction model.

[0101] A multi-layer LSTM network is constructed. The input layer receives the feature vector, the hidden layer captures the time series features through LSTM units, and the output layer outputs the predicted delivery time.

[0102] Input layer: Receives feature vectors processed by feature engineering as input to the model. The feature vector includes the key features extracted above, as well as historical data within the time step (such as order volume, delivery distance, robot status, time period, number of floors crossed, and other feature information in the past few hours) to capture time series characteristics.

[0103] LSTM layer: Build a multi-layer LSTM network, each layer contains multiple LSTM units. LSTM units capture long-term dependencies in time series data through mechanisms such as forget gates, input gates, output gates, and cell states. Figure 8 As shown, specifically including:

[0104] Forget gate: determines the previous moment h t-1 How much information in the cell state needs to be retained. The calculation formula is:

[0105] f t =δ(W f ·[h t-1 ,x t ]+b f )

[0106] Among them, W f is the weight matrix of the forget gate, h t-1 is the hidden state at the previous moment, x t is the input feature at the current moment, b f is the bias term, and σ is the sigmoid activation function.

[0107] Input gate: determines how much new information needs to be added to the cell state at the current moment. The calculation formula is:

[0108] i t =δ(W i ·[h t-1 ,x t ]+b i )

[0109] Among them, W i is the weight matrix of the input gate, b i is the bias term.

[0110] Cell state update: calculate new cell states, including candidate cell states and the updated cell state C tThe calculation formula is:

[0111] tildeC t =tanh(W c ·[h t-1 ,x t ]+b c )

[0112]

[0113] Among them, W c is the weight matrix of the cell state, b c is the bias term, and tanh is the hyperbolic tangent activation function.

[0114] Output gate: determines the hidden state ht at the current moment. The calculation formula is:

[0115] O t =δ(W o ·[h t-1 ,x t ]+b o )

[0116] h t =o t tanh(C t )

[0117] Among them, W o is the weight matrix of the output gate, b o is the bias term.

[0118] Output layer: receives the output of the LSTM layer, and outputs the predicted delivery time through a fully connected layer (Dense layer) and an activation function (such as a linear activation function).

[0119] (6) Model training and verification:

[0120] Loss function: The mean square error (MSE) is used as the loss function to measure the difference between the model prediction result and the actual delivery time. The calculation formula is:

[0121]

[0122] Where N is the number of samples, y i is the actual delivery time, and yi is the model prediction result.

[0123] Optimizer: Select Adam optimizer as the optimization algorithm for model training to minimize the loss function by calculating gradients and updating model parameters.

[0124] Training process: The preprocessed data set is divided into a training set and a validation set, and the LSTM model is trained using the training set data. During the training process, cross-validation techniques are used to avoid overfitting, and hyperparameters such as learning rate, batch size, and number of LSTM units are adjusted to optimize model performance. The validation set is used to evaluate model performance and ensure that the model has good generalization capabilities.

[0125] Model evaluation: The prediction performance of the model is evaluated through the validation set, including calculating evaluation indicators such as MSE and MAE (mean absolute error), and drawing a comparison chart between the prediction results and the true values ​​to intuitively display the prediction effect of the model.

[0126] (7) Intelligent scheduling algorithm design:

[0127] After the LSTM model predicts the delivery time in the future, the intelligent scheduling algorithm constructs the state space, action space and reward function based on these prediction results. The state space includes the robot state, order queue state, etc., the action space includes robot task allocation, path planning, etc., and the reward function is designed based on indicators such as total delivery time and robot resource utilization. The algorithm continuously optimizes the scheduling strategy through iterative learning to maximize the cumulative reward.

[0128] In summary, the embodiments of the present application provide a real-time data collection and processing mechanism based on the Internet of Things; a delivery time prediction model integrating the deep learning algorithm LSTM; and an intelligent scheduling algorithm that comprehensively considers multiple factors. The present application is conducive to significantly improving delivery efficiency, shortening delivery time, optimizing resource allocation, reducing robot idle rate, reducing operating costs, and realizing intelligent management.

[0129] Possible application scenarios of the embodiments of the present application in the future: mainly used in the dispatching and dispatching of service robots when they are operating in large public places or factories.

[0130] This application uses a unified cloud platform to collect data reported by robots and shield data differences; uses an LSTM neural network to predict delivery order time and intelligently schedules orders based on the predicted data.

[0131] See also Fig. 9 The embodiment of the present application further provides a dispatching system for a delivery robot, which can implement the dispatching method for the delivery robot, and the system includes:

[0132] The first module 810 is used to obtain the working data of the delivery robot; the working data is the data related to the working task of the delivery robot at the current moment;

[0133] The second module 820 is used to input the work data into the prediction model to obtain the delivery time of the delivery robot;

[0134] The third module 830 is used to schedule the delivery robot according to the delivery time and determine the corresponding relationship between each target cargo and the delivery robot.

[0135] In some embodiments, the system provided by the embodiments of the present application further includes a fourth module for:

[0136] Determine the state space based on the delivery robot's state and order status;

[0137] Determine the action space based on path planning and task allocation of the delivery robot;

[0138] Determine the reward function based on the delivery time;

[0139] According to the state space, action space and reward function, the current optimal correspondence is determined, and the state space is determined based on the state of the delivery robot and the order status, and the correspondence is updated.

[0140] In some embodiments, the system provided by the embodiments of the present application further includes a fifth module for:

[0141] Get sample data;

[0142] Input the sample data into the prediction model to obtain the predicted sample time;

[0143] According to the predicted sample time and the actual sample time, the loss function is constructed using the mean square error;

[0144] The prediction model is trained based on the optimizer through the loss function to obtain a trained prediction model.

[0145] In some embodiments, the system provided by the embodiments of the present application further includes a sixth module for:

[0146] Extract key features from the work data; key features are features that affect delivery time;

[0147] Feature scaling, feature selection and feature cross processing are performed on the key features to obtain the feature vector.

[0148] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0149] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above-mentioned dispatching method of the delivery robot when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0150] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0151] See also Fig.10 , Fig.10 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0152] The processor 901 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0153] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 902, and the processor 901 calls and executes the dispatching method of the delivery robot in the embodiment of this application;

[0154] Input / output interface 903, used to implement information input and output;

[0155] Communication interface 904, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0156] A bus 905 that transmits information between various components of the device (e.g., the processor 901, the memory 902, the input / output interface 903, and the communication interface 904);

[0157] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0158] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the above-mentioned dispatching method of the delivery robot.

[0159] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0160] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor 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 combinations thereof.

[0161] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0162] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0163] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0164] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0165] The terms "first", "second", "third", "fourth", etc. (if any) in the specification 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 sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. 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.

[0166] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0167] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0168] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0169] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0170] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0171] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for dispatching a delivery robot, characterized in that: The method comprises: Acquire working data of the delivery robot; the working data is data related to the working task of the delivery robot at the current moment; Inputting the working data into a prediction model to obtain a delivery time of the delivery robot; The delivery robot is scheduled according to the delivery time, and the corresponding relationship between each target cargo and the delivery robot is determined.

2. The method according to claim 1, characterized in that The method further comprises: Extract key features from the work data; the key features are features that affect delivery time; The key features are subjected to feature scaling, feature selection and feature cross processing to obtain feature vectors.

3. The method according to claim 2, characterized in that The prediction model includes an LSTM model, and the step of inputting the work data into the prediction model to obtain the delivery time of the delivery robot includes: Processing the feature vector and the historical data within the time step through the input layer to obtain a first feature; Processing the first feature through the LSTM layer to obtain a second feature; the second feature includes time information; The second feature is processed through the output layer to obtain the delivery time.

4. The method according to claim 1, characterized in that: The prediction model is trained by the following steps: Get sample data; Inputting the sample data into the prediction model to obtain a predicted sample time; According to the predicted sample time and the actual sample time, a loss function is constructed using a mean square error; The prediction model is trained based on the optimizer through the loss function to obtain a trained prediction model.

5. The method according to claim 1, characterized in that The step of scheduling the delivery robot according to the delivery time and determining the corresponding relationship between each target cargo and the delivery robot includes: According to the delivery time and work data, the delivery robot is dispatched based on an intelligent dispatching algorithm; Through iterative learning, the corresponding relationship between each target cargo and the delivery robot is continuously optimized.

6. The method according to claim 5, characterized in that The method further comprises: Determine a state space according to the state of the delivery robot and the order state; Determining an action space according to the path planning and the task allocation of the delivery robot; Determine the reward function based on the delivery time; According to the state space, the action space and the reward function, the current optimal correspondence is determined, and the state space is determined according to the state of the delivery robot and the order status, and the correspondence is updated.

7. The method according to claim 1, characterized in that The obtaining of the working data of the delivery robot includes: connecting the delivery robot to an Internet of Things platform; The working data of the delivery robot is obtained through the Internet of Things platform.

8. A dispatching system for a delivery robot, characterized in that: The system comprises: The first module is used to obtain the working data of the delivery robot; the working data is the data related to the working task of the delivery robot at the current moment; The second module is used to input the working data into a prediction model to obtain the delivery time of the delivery robot; The third module is used to schedule the delivery robot according to the delivery time and determine the corresponding relationship between each target cargo and the delivery robot.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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