Equipment scheduling method and system, electronic equipment and storage medium

Abstract

The invention discloses a device scheduling method and system, an electronic device and a storage medium, and relates to the technical field of device scheduling, the method comprises the following steps: using an artificial intelligence mode to analyze target data of each device to obtain a data analysis result of each device, the target data being data associated with a preset task; determining at least one target device according to all the data analysis results; and scheduling at least one target device to execute the preset task. The target data of each device can be automatically analyzed by using an artificial intelligence mode, then at least one target device is determined according to the data analysis result of each device, and task scheduling is performed, so that the scheduling efficiency can be greatly improved, the labor cost is saved, the device scheduling can be performed timely and comprehensively when an emergency occurs, and the user experience is improved. And the loss can be reduced to the greatest extent.

Description

Background Art

[0002] Currently, when performing equipment scheduling, it is necessary to manually analyze the data of each device one by one, which is time-consuming and laborious. Especially in case of an emergency, equipment scheduling cannot be carried out in a timely and comprehensive manner, which is likely to cause greater losses and pose a very big potential safety hazard. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method, a system, an electronic device and a storage medium for equipment scheduling in view of the deficiencies of the prior art, specifically as follows:

[0004] 1) In the first aspect, the present invention provides a method for equipment scheduling, and the specific technical solution is as follows:

[0005] Analyze the target data of each device by means of artificial intelligence to obtain the data analysis result of each device, wherein the target data refers to: data associated with a preset task;

[0006] Determine at least one target device according to all the data analysis results;

[0007] Schedule at least one target device to execute the preset task.

[0008] The beneficial effects of the method for equipment scheduling provided by the present invention are as follows:

[0009] By means of artificial intelligence, it is possible to automatically analyze the target data of each device, and then determine at least one target device according to the data analysis result of each device and perform task scheduling, which can greatly improve the scheduling efficiency, save labor costs, and in case of an emergency, can perform equipment scheduling in a timely and comprehensive manner, and can minimize losses to the greatest extent.

[0010] On the basis of the above solution, the method for equipment scheduling of the present invention can be further improved as follows.

[0011] Further, analyzing the target data of each device by means of artificial intelligence to obtain the data analysis result includes:

[0012] Analyze the target data of each device by using a trained preset deep learning model to obtain the data analysis result of each device.

[0013] Further, it further includes:

[0014] Preprocess the historical target data of multiple devices;

[0015] Add labels to each preprocessed historical target data to obtain a data set;

[0016] Based on a data set, a preset deep learning model is trained to obtain a trained preset deep learning model.

[0017] Furthermore, it further includes:

[0018] Optimize the trained deep learning model.

[0019] 2) In a second aspect, the present invention also provides a system for device scheduling, and the specific technical solution is as follows:

[0020] It includes a data analysis module, a target device determination module, and a scheduling module;

[0021] The data analysis module is used for: analyzing the target data of each device by means of artificial intelligence to obtain the data analysis result of each device, where the target data refers to: data associated with a preset task;

[0022] The target device determination module is used for: determining at least one target device according to all the data analysis results;

[0023] The scheduling module is used for: scheduling at least one target device to execute a preset task.

[0024] Based on the above solution, a system for device scheduling of the present invention can also be improved as follows.

[0025] Furthermore, the data analysis module is specifically used for:

[0026] Analyze the target data of each device by using the trained preset deep learning model to obtain the data analysis result of each device.

[0027] Furthermore, it further includes a model training module, and the model training module is used for:

[0028] Preprocess the historical target data of multiple devices;

[0029] Add labels to each preprocessed historical target data to obtain a data set;

[0030] Based on the data set, a preset deep learning model is trained to obtain a trained preset deep learning model.

[0031] Furthermore, it further includes a model optimization module, and the model optimization module is used for: optimizing the trained deep learning model.

[0032] 3) In a third aspect, the present invention also provides an electronic device. The electronic device includes a processor, the processor is coupled to a memory, and at least one computer program is stored in the memory. The at least one computer program is loaded and executed by the processor so that the electronic device implements any one of the above device scheduling methods.

[0033] 4) Fourthly, the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for device scheduling as described in any one of the above is implemented.

[0034] It should be noted that for the beneficial effects obtained by the technical solutions of the second to fourth aspects of the present invention and the corresponding possible implementation manners, reference may be made to the technical effects of the first aspect and its corresponding possible implementation manners as described above, and details will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments of the present invention:

[0036] Figure 1 It is a schematic flowchart of a method for device scheduling according to an embodiment of the present invention;

[0037] Figure 2 It is a schematic structural diagram of a system for device scheduling according to an embodiment of the present invention;

[0038] Figure 3 It is a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] The principles and features of the present invention are described below, and the examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0040] The technical solutions of the present invention and how the technical solutions of the present invention solve the above technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present invention will be described below with reference to the drawings.

[0041] As Figure 1 shown, a method for device scheduling according to an embodiment of the present invention includes the following steps:

[0042] S1. Analyze the target data of each device by means of artificial intelligence to obtain the data analysis result of each device, where the target data refers to: data associated with a preset task;

[0043] Among them, the device can specifically be a router, a computer device, a drone, etc. Each device in S1 can specifically refer to: each device in a cluster. For example, when the device is a router, each device in S1 can specifically refer to: each router in a router cluster; when the device is a drone, each device in S1 can specifically refer to: each drone in a drone cluster.

[0044] Among them, due to different preset tasks and devices, the target data of the device will also be different. For example, when the preset task is: data forwarding task, and when the device is a router, the target data includes the memory occupancy rate, the average delay, jitter, and packet loss rate of the wireless network, etc.; when the preset task is: data forwarding task, and when the device is a computer device, the target data includes the memory occupancy rate, the average delay, jitter, and packet loss rate of the wireless network, etc.; when the preset task is: photo-taking task, and when the device is a drone, the target data includes the position of the drone, the remaining battery power of the drone, the average flight speed of the drone, the shooting parameters of the drone's camera, etc.

[0045] Among them, the data analysis result of the device is specifically: the probability that the device can complete the preset task and the duration of completing the preset task.

[0046] S2. Determine at least one target device according to all the data analysis results. Specifically:

[0047] 1) The first case: When there is a data analysis result with a probability of 100%, the device corresponding to the data analysis result with a probability of 100% can be determined as the target device.

[0048] 2) The second case: When the number N of devices for executing the preset task is preset, when the number of devices corresponding to the data analysis result with a probability of 100% is not less than the preset number N of devices for executing the preset task, sort the devices corresponding to the data analysis result with a probability of 100% in ascending order of the duration of the preset task, and take the first N devices as the target devices; when the number of devices corresponding to the data analysis result with a probability of 100% is less than the preset number N of devices for executing the preset task, a reminder is sent to enable the device scheduler to set new devices and analyze the data analysis results of the new devices until N target devices are determined.

[0049] 3) The third case: When there is no data analysis result with a probability of 100%, determine whether the preset task can be divided into multiple subtasks. If so, divide the preset task into multiple subtasks. For example, divide the forwarded data in the data forwarding task into multiple data packets, and take forwarding each data packet as a subtask respectively, and take each subtask as the preset task, and return to execute S1. At this time, the target data of each device refers to: the target data associated with the subtask, and the data analysis result of each device refers to: the probability that the device can complete the subtask and the duration of completing the subtask, and combine the above first case and the second case until the target devices corresponding to each subtask are determined, and then schedule the target devices corresponding to each subtask to execute the corresponding subtasks.

[0050] When the preset task cannot be divided into multiple subtasks, a reminder is sent to enable the device dispatcher to set up a new device and analyze the data analysis results of the new device until at least one target device is determined.

[0051] S3. Schedule at least one target device to execute the preset task.

[0052] Optionally, in S1, by using artificial intelligence, analyze the target data of each device to obtain data analysis results, including:

[0053] S10. Use the trained preset deep learning model to analyze the target data of each device to obtain the data analysis results of each device.

[0054] Optionally, in the above technical solution, it further includes:

[0055] S010. Preprocess the historical target data of multiple devices. Specifically:

[0056] ① Sort the multiple devices, and according to the sorted sequence, sort the same item of data in each historical target data (the same item of data refers to: including memory occupancy rate, average delay of wireless network, jitter, and packet loss rate, etc.) to obtain a sequence corresponding to each item of data, and sequentially number each data (specific value of memory occupancy rate, specific value of average delay of wireless network, specific value of jitter, and specific value of packet loss rate) in each sequence. At this time, each sequence with numbers set becomes time series data.

[0057] ② Perform Fourier transform on each time series data to obtain the Fourier transform data corresponding to each time series data. From each Fourier transform data, select multiple peaks greater than the preset peak threshold, and perform normalization processing on all selected peaks to obtain the normalized value corresponding to each selected peak, and use the normalized value corresponding to any selected peak as the first initial weight of the data corresponding to the selected peak, thereby obtaining the first initial weight of the data corresponding to each selected peak.

[0058] In the same sequence, first, calculate the ratio between the data corresponding to any non - selected peak and the data corresponding to each selected peak respectively, then calculate the product of each ratio corresponding to the non - selected peak and the corresponding first initial weight, and take the average value of all products as the first initial weight of the data corresponding to the non - selected peak until the first initial weight of the data corresponding to each non - selected peak is calculated.

[0059] ③Divide multiple amplitude ranges for the sequences corresponding to each item of data respectively. Divide the corresponding sequences according to the amplitude ranges corresponding to each item of data to obtain multiple data sets. Among them, the amplitude ranges set for the sequences corresponding to each item of data can be set according to the actual situation. For example, taking "dividing the difference between the maximum value and the minimum value in the sequence corresponding to each item of data into five equal parts" as the standard, divide multiple amplitude ranges.

[0060] ④Adopt the principal component analysis method to analyze each data set corresponding to the same data, and determine the variance contribution rate of each data set corresponding to the same data relative to the same data. The variance contribution rate can characterize the importance degree.

[0061] ⑤Take the variance contribution rate corresponding to any data set as the second initial weight of each data in this data set until the second initial weight of each data is obtained.

[0062] ⑥Take the mean of the first initial weight and the second initial weight of each data in any historical target data as the initial weight of this data in this historical target data. Or, configure different coefficients for the first initial weight and the second initial weight of each data in any historical target data, and take the sum and / or mean of the product of the first initial weight of each data in this historical target data and the corresponding coefficient and the product of the second initial weight and the corresponding coefficient as the initial weight of this data in this historical target data. Take the sum and / or mean of the initial weights of each data in this historical target data as the initial weight of this historical target data until the initial weight of each historical target data is obtained.

[0063] Take the historical target data and the corresponding initial weights as the preprocessed historical target data. Or, take the mean of the first initial weight and the second initial weight of each data in any historical target data as the initial weight of this data in this historical target data until the initial weight of each data in each historical target data is obtained. Take the historical target data and the corresponding initial weights of each data as the preprocessed historical target data.

[0064] S011. Add labels to each preprocessed historical target data to obtain a data set;

[0065] Among them, the added labels can specifically be: whether the preset task can be completed and the duration of completing the preset task.

[0066] S012. Based on the data set, train the preset deep learning model to obtain the trained preset deep learning model.

[0067] Among them, the preset deep learning model can be a convolutional neural network or other deep learning network models, which can be set according to the actual situation.

[0068] When training a preset deep learning model, an initial weight is set for each historical target data, which can represent the importance of the historical target data, or the initial weights of each data in each historical target data are obtained, which can represent the importance degree of each data. During the training process, more attention can be paid to the data or historical target data with larger initial weights, thereby improving the overall performance of the preset deep learning model. Specifically, by assigning appropriate initial weights, the dependence of the preset deep learning model on these abnormal samples can be reduced, the bias can be lowered, and the preset deep learning model can be made to pay more attention to those more representative historical target data, thereby improving the performance of the preset deep learning model on unseen data and enhancing the generalization ability. Generally speaking, by setting the initial weights, the performance of the preset deep learning model can be optimized, the bias can be reduced, the generalization ability can be improved, and the class differences can be balanced and the data changes can be adapted.

[0069] Optionally, in the above technical solution, it further includes: optimizing the trained deep learning model. Specifically, it is optimized by using the stochastic gradient descent method, the gradient descent method, the mini-batch gradient descent method, etc. Moreover, when the historical targets in the data set increase, the trained deep learning model can also be trained based on the expanded data set to achieve the optimization of the trained deep learning model.

[0070] In another embodiment, it includes: receiving data, cleaning and organizing the data into structured data, manually adding category labels of normal and needing adjustment to the structured data, selecting a suitable model from various statistical classification algorithms, and training the model with training data. The training data is data with already existing category labels, which is used to let the model learn classification rules, using test data to evaluate the performance and generalization ability of the model to verify and improve the classification effect of the model, and putting the tested model online to automatically / implement the discrimination of whether the device needs adjustment or scheduling.

[0071] In the above embodiments, although the steps are numbered S1, S2, etc., these are only specific embodiments given by the present invention. Those skilled in the art can adjust the execution order of S1, S2, etc. according to the actual situation, and this is also within the protection scope of the present invention. It can be understood that in some embodiments, it may include some or all of the above embodiments.

[0072] As Figure 2 shown, a device scheduling system 200 according to an embodiment of the present invention includes a data analysis module 201, a target device determination module 202, and a scheduling module 203;

[0073] The data analysis module 201 is used to: analyze the target data of each device by means of artificial intelligence to obtain the data analysis results of each device, where the target data refers to: the data associated with the preset task;

[0074] The target device determination module 202 is used to: determine at least one target device according to all the data analysis results;

[0075] The scheduling module 203 is used to: schedule at least one target device to execute the preset task. Optionally, in the above technical solution, the data analysis module 201 is specifically used to:

[0076] Analyze the target data of each device by using the trained preset deep learning model to obtain the data analysis results of each device.

[0077] Optionally, in the above technical solution, it further includes a model training module, and the model training module is used to:

[0078] Preprocess the historical target data of multiple devices;

[0079] Add labels to each preprocessed historical target data to obtain a data set;

[0080] Based on the data set, train the preset deep learning model to obtain the trained preset deep learning model.

[0081] Optionally, in the above technical solution, it further includes a model optimization module, and the model optimization module is used to: optimize the trained deep learning model.

[0082] It should be noted that the beneficial effects of the device scheduling system 200 provided in the above embodiments are the same as those of the above device scheduling method, and will not be elaborated here. In addition, when the system provided in the above embodiments realizes its functions, only the above division of each functional module is used for illustration. In actual applications, the above functions can be allocated to different functional modules according to needs, that is, the system can be divided into different functional modules according to the actual situation to complete all or part of the functions described above. In addition, the system provided in the above embodiments and the method embodiments belong to the same concept, and the specific implementation process can be seen in the method embodiments, and will not be elaborated here.

[0083] Among them, the device scheduling system of the present invention can be a computer program (including program code) running in a computer device. For example, the device scheduling system of the present invention is an application software and can be used to execute the corresponding steps in the device scheduling method of the present invention.

[0084] In some embodiments, the device scheduling system of the present invention can be implemented in a combination of software and hardware. As an example, the device scheduling system of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the device scheduling method of the present invention. For example, a processor in the form of a hardware decoding processor can employ one or more application specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0085] Among them, the modules involved in the embodiments of the present invention can be implemented in software or in hardware. The name of the module does not, in some cases, constitute a limitation on the module itself.

[0086] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned device scheduling method is implemented. That is to say, an electronic device according to an embodiment of the present invention may include, but is not limited to, a processor and a memory; the memory is used to store the computer program; the processor is used to execute the device scheduling method shown in any embodiment of the present invention by calling the computer program.

[0087] In an alternative embodiment, an electronic device is provided, as Figure 3 shown Figure 3 The electronic device 4000 shown includes a processor 4001 and a memory 4003. Among them, the processor 4001 and the memory 4003 are connected, such as by a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, and the transceiver 4004 can be used for data interaction between the electronic device and other electronic devices, such as data sending and / or data receiving, etc. It should be noted that in practical applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0088] The processor 4001 can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logical blocks, modules, and circuits described in connection with the disclosure of the present invention. The processor 4001 can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0089] The bus 4002 can include a path for transmitting information between the above components. The bus 4002 can be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus 4002 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 only a thick line is used in the figure to represent the bus 4002, but it does not mean that there is only one bus or one type of bus.

[0090] The memory 4003 can be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, or it can also be an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0091] The memory 4003 is used to store the application program code (computer program) for executing the solution of the present invention, and is controlled by the processor 4001 for execution. The processor 4001 is used to execute the application program code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.

[0092] Among them, the electronic device may also be a terminal device, and the terminal device may be any device that can install an application, including at least one of a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a smart TV, and a smart vehicle device.

[0093] It should be noted that Figure 3 The electronic device shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0094] A computer-readable storage medium according to an embodiment of the present invention has a computer program stored thereon, and when the computer program is executed by a processor, the method for device scheduling described above is implemented.

[0095] Optionally, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0096] In an exemplary embodiment, a computer program product or a computer program is further provided. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device executes the method for device scheduling described above.

[0097] Computer program code for performing the operations of the present invention may be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0098] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0099] The computer-readable storage medium provided by the embodiments of the present invention may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EEPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0100] The above computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to execute the method shown in the above embodiments.

[0101] The above description is only a preferred embodiment of the present invention and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of disclosure involved in the present invention is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosure concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions disclosed in the present invention.

[0102] It should be noted that the terms "first", "second", etc. in the specification and claims of this application are used to distinguish similar objects, and represent a limitation on a specific order or sequence. In appropriate cases, the order of use of similar objects can be interchanged so that the embodiments of this application described here can be implemented in an order other than the illustrated or described order.

[0103] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present invention can be specifically implemented in the following forms, that is: it can be completely hardware, can also be completely software (including firmware, resident software, microcode, etc.), and can also be in the form of a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" in this article. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, which contains computer-readable program code.

[0104] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for device scheduling, characterized in that, including: Analyze the target data of each device by means of artificial intelligence to obtain the data analysis result of each device, where the target data refers to: data associated with a preset task; Determine at least one target device according to all the data analysis results; Schedule the at least one target device to execute the preset task.

2. The method for device scheduling according to claim 1, wherein, Analyze the target data of each device by means of artificial intelligence to obtain the data analysis result, including: Analyze the target data of each device by using a trained preset deep learning model to obtain the data analysis result of each device.

3. A method for device scheduling according to claim 2, characterized in that, It also includes: Preprocess the historical target data of multiple devices; Add labels to each preprocessed historical target data to obtain a data set; Train a preset deep learning model based on the data set to obtain the trained preset deep learning model.

4. A method for device scheduling according to claim 2 or 3, characterized in that, It also includes: Optimize the trained deep learning model.

5. A system for device scheduling, characterized in that, including a data analysis module, a target device determination module and a scheduling module; The data analysis module is used for: analyzing the target data of each device by means of artificial intelligence to obtain the data analysis result of each device, where the target data refers to: data associated with a preset task; The target device determination module is used for: determining at least one target device according to all the data analysis results; The scheduling module is used for: scheduling the at least one target device to execute the preset task.

6. The system for device scheduling according to claim 5, characterized in that, The data analysis module is specifically used for: Analyze the target data of each device by using a trained preset deep learning model to obtain the data analysis result of each device.

7. A system for device scheduling according to claim 6, characterized in that, It also includes a model training module, and the model training module is used for: Preprocess the historical target data of multiple devices; Add labels to each preprocessed historical target data to obtain a data set; Train a preset deep learning model based on the data set to obtain the trained preset deep learning model.

8. A system for device scheduling according to claim 6 or 7, characterized in that, It also includes a model optimization module, and the model optimization module is used for: optimizing the trained deep learning model.

9. An electronic device, characterized in that, including a memory, a processor and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, it implements the method for device scheduling according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by the processor, it implements the method for device scheduling according to any one of claims 1 to 4.

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