Intelligent probe scheduling method and device based on load prediction, electronic equipment, storage medium and program product

Abstract

The invention provides an intelligent probe scheduling method based on load prediction, and the method comprises the steps: obtaining at least one probe detection task, and storing the probe detection task in a task set message queue; distributing the probe detection task in the task set message queue to at least one target probe for execution, wherein the target probe is obtained by evaluating an existing probe by a preset intelligent load scheduling module; load data of the probe detection task executed by at least one target probe is collected, evaluation parameters of an intelligent load scheduling module are adjusted based on the load data, and the evaluation parameters are used for evaluating existing probes to obtain the target probes; and adjusting the at least one target probe based on the intelligent load scheduling module after the evaluation parameters are adjusted, and improving the load capacity of the at least one target probe.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, and more specifically, to an intelligent probe scheduling method, device, electronic device, storage medium and program product based on load prediction. Background Art

[0002] With the rapid development of the Internet and information technology, the importance of network monitoring and management in various networks has become increasingly prominent. As an important scientific research and education infrastructure, the stability and reliability of the network are crucial to ensuring the smooth progress of scientific research and education. In order to ensure the efficient operation of the network, probe technology is widely used in network monitoring to collect network status information in real time and feed it back to the management system. However, with the continuous expansion of network scale and the diversification of network applications, traditional probe management methods face many challenges.

[0003] Existing probe management methods mainly rely on preset rules and manual intervention, lack intelligent and dynamic adjustment capabilities for probe load and task allocation, and are difficult to adapt to rapid changes in the network environment. The allocation of probe resources is often not reasonable, resulting in some probes working under high load while other probes are in a low load state. This not only affects the efficiency and accuracy of network monitoring, but may also lead to probe overload or resource waste. Summary of the invention

[0004] In view of this, the present disclosure provides an intelligent probe scheduling method, device, electronic device, storage medium and program product based on load prediction.

[0005] A first aspect of the present disclosure provides an intelligent probe scheduling method based on load prediction, comprising:

[0006] Acquire at least one probe detection task, and store the probe detection task in a task set message queue;

[0007] Allocate the probe detection tasks in the task set message queue to at least one target probe for execution, wherein the target probe is obtained by evaluating existing probes by a preset intelligent load scheduling module;

[0008] Collecting load data of the at least one target probe executing the probe detection task, and adjusting evaluation parameters of the intelligent load scheduling module based on the load data, wherein the evaluation parameters are used to evaluate existing probes to obtain the target probe;

[0009] The intelligent load scheduling module adjusts the at least one target probe based on the adjusted evaluation parameters to improve the load capacity of the at least one target probe.

[0010] According to an embodiment of the present disclosure, allocating the probe detection task in the task set message queue to at least one target probe for execution includes:

[0011] Based on the intelligent load scheduling module, the load capacity of existing probes is evaluated, and at least one existing probe having a load capacity greater than a preset threshold is selected as a target probe;

[0012] The probe detection tasks in the task set message queue are assigned to the at least one target probe for execution.

[0013] According to an embodiment of the present disclosure, the step of evaluating the load capacity of existing probes based on the intelligent load scheduling module and selecting at least one existing probe having a load capacity greater than a preset threshold as a target probe includes:

[0014] Acquire multiple historical data of each of the existing probes, perform weighted evaluation on the multiple historical data, and obtain evaluation results of each of the existing probes, wherein the evaluation results represent the load capacity;

[0015] The target probes having a load capacity greater than a preset threshold are screened according to the ranking of the evaluation results.

[0016] According to an embodiment of the present disclosure, collecting load data of the at least one target probe performing the probe detection task, and adjusting the evaluation parameters of the intelligent load scheduling module based on the load data includes:

[0017] regularly collecting actual load data of the at least one target probe, and obtaining predicted load data estimated by the load scheduling module for the at least one target probe;

[0018] The actual load data and the predicted load data are compared, and evaluation parameters of the intelligent load scheduling module are adjusted.

[0019] According to an embodiment of the present disclosure, the method includes:

[0020] Before comparing the actual load data with the predicted load data, the actual load data and the predicted load data are subjected to standardization preprocessing.

[0021] According to an embodiment of the present disclosure, adjusting the at least one target probe based on the intelligent load scheduling module after adjusting the evaluation parameter includes:

[0022] Re-evaluating the existing probes based on the intelligent load scheduling module after adjusting the evaluation parameters to obtain the latest predicted load data of the existing probes, wherein the existing probes include the at least one target probe;

[0023] A new target probe is determined according to the predicted load data.

[0024] A second aspect of the present disclosure provides an intelligent probe scheduling device based on load prediction, comprising:

[0025] A task acquisition module, used to acquire at least one probe detection task and store the probe detection task in a task set message queue;

[0026] A task allocation module, used for allocating the probe detection tasks in the task set message queue to at least one target probe for execution, wherein the target probe is obtained by evaluating the existing probes by a preset intelligent load scheduling module;

[0027] A data acquisition module, used for acquiring load data of the at least one target probe executing the probe detection task, and adjusting evaluation parameters of the intelligent load scheduling module based on the load data, wherein the evaluation parameters are used for evaluating existing probes to obtain the target probe;

[0028] The task reallocation module is used to adjust the at least one target probe based on the intelligent load scheduling module after adjusting the evaluation parameters, so as to improve the load capacity of the at least one target probe.

[0029] A third aspect of the present disclosure provides an electronic device, including:

[0030] one or more processors;

[0031] a memory for storing one or more programs,

[0032] When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described above.

[0033] A fourth aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the method described above when executed.

[0034] A fifth aspect of the present disclosure provides a computer program product, which includes computer executable instructions, and the instructions are used to implement the method as described above when executed.

[0035] According to the embodiments of the present disclosure, an intelligent probe scheduling method based on load prediction is proposed. By introducing deep learning technology and processing the resource occupancy data of each monitoring item, the resource occupancy of the probe in the future can be effectively predicted. Based on the prediction results, intelligent resource allocation and planning can be performed for the tasks in the next stage, thereby achieving optimal configuration of probe resources and improving the overall efficiency and reliability of network monitoring. This method uses artificial intelligence methods to predict the load of the probe and dynamically allocates it according to the results. It does not require complex configuration and management and can flexibly respond to dynamic changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0037] Figure 1 A flowchart of an intelligent probe scheduling method based on load prediction according to an embodiment of the present disclosure is schematically shown;

[0038] Figure 2 The structure block diagram of the intelligent probe scheduling device based on load prediction according to an embodiment of the present disclosure is schematically shown;

[0039] Figure 3 A block diagram of an electronic device 300 suitable for implementing a robot according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0040] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0041] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0042] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0043] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0044] In the embodiments of the present disclosure, the collection, updating, analysis, processing, use, transmission, provision, disclosure, storage, etc. of the data involved (for example, including but not limited to user personal information) are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information, network security, and national security.

[0045] In the embodiments of the present disclosure, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0046] In view of the shortcomings of the existing probe scheduling method using load balancers, the present disclosure provides an intelligent probe scheduling method based on load prediction, which predicts the resource occupancy of a specific period of time by intelligently predicting the probe load and resource occupancy, and performs multi-weight analysis on the planned tasks to recommend available probes. After running for a period of time, the recommendation algorithm can be built into the load balancer to realize intelligent and automatic allocation of tasks.

[0047] The technical solution of the embodiment of the present disclosure is as follows:

[0048] 1. Intelligent prediction of probe load / resource occupancy: Through deep learning algorithms, the resource occupancy data (i.e. load data) of probes in different time periods are analyzed to establish a load prediction model. Based on historical data and real-time monitoring data, the resource occupancy of probes in a specific period of time in the future is predicted.

[0049] 2. Multi-weight analysis and recommendation: When planning a task, multiple weight factors are considered, such as the current load, geographical location, response time, etc. of the probe, and the available probes are analyzed and evaluated to recommend the best probe to perform the task.

[0050] 3. Intelligent allocation of built-in load balancer: After the system has been running for a period of time, the recommended algorithm is built into the load balancer to achieve intelligent and automatic allocation of tasks. The load balancer dynamically adjusts the task allocation of the probe based on the predicted load situation and real-time monitoring data to ensure efficient use of probe resources and balanced load.

[0051] Through the above technical solutions, the method provided by the embodiment of the present disclosure can effectively improve the utilization efficiency of probe resources, reduce resource waste, enhance reliability, and realize intelligent and automated probe management.

[0052] Figure 1 The flowchart of the intelligent probe scheduling method based on load prediction according to an embodiment of the present disclosure is schematically shown.

[0053] like Figure 1 As shown, the method includes operations S110 to S140.

[0054] In operation S110, at least one probe detection task is acquired, and the probe detection task is stored in a task set message queue.

[0055] In the disclosed embodiment, a probe detection task is sent to a registered probe group, a message is sent to a task set message queue, and is transmitted to an intelligent load scheduling module.

[0056] In operation S120, the probe detection task in the task set message queue is assigned to at least one target probe for execution. The target probe is obtained by evaluating existing probes by a preset intelligent load scheduling module.

[0057] Operation S120 includes S121-S122.

[0058] In operation S121 , the load capacity of existing probes is evaluated based on the intelligent load scheduling module, and at least one existing probe having a load capacity greater than a preset threshold is selected as a target probe.

[0059] The intelligent load scheduling module uses a deep learning algorithm to analyze the resource usage data of existing probes in different time periods, establish a load prediction model, and configure it in the intelligent load scheduling module. Based on historical data and real-time monitoring data, it predicts the resource usage of probes in a specific period of time in the future (reflecting load capacity).

[0060] The intelligent load scheduling module evaluates the load capacity of existing probes including S1211~S1212.

[0061] In operation S1211, multiple historical data of each existing probe are obtained, and weighted evaluation is performed on the multiple historical data to obtain evaluation results of each existing probe, where the evaluation results represent the load capacity.

[0062] Collect historical data related to probe load, such as historical probe load data, device configuration information, working environment parameters, etc. Ensure the accuracy and completeness of the data. Historical probe load data includes resource usage data of each probe in the network over a period of time, including CPU usage, memory usage, network bandwidth utilization, etc.

[0063] Clean, preprocess and feature engineer the collected data to ensure data quality. This includes data cleaning, denoising, normalization and other operations, as well as removing outliers, filling missing values, normalizing or standardizing data, and extracting relevant features.

[0064] According to the data characteristics and problem requirements, select the appropriate artificial intelligence algorithm for load forecasting. Common algorithms include neural networks, support vector machines, decision trees, random forests, etc. Based on the preprocessed data, select the artificial intelligence model (such as LSTM, GRU, etc.) to train the load forecasting model. Divide the data into training sets, validation sets, and test sets to evaluate the performance of the model and adjust hyperparameters. The model input is historical resource occupancy data, and the output is the predicted resource occupancy value for a specific period in the future.

[0065] Apply the trained model to real-time data to predict the probe load. You can use real-time monitoring data as input and output load prediction results in real time. In actual applications, the above methods can be further optimized and customized based on specific scenarios and needs. In addition, you can also consider using ensemble learning methods, multimodal data fusion and other technologies to improve the accuracy and reliability of predictions. At the same time, working with domain experts and combining their knowledge and experience can better understand and interpret the prediction results.

[0066] In the disclosed embodiment, multiple weight factors are determined according to the probe resource management requirements, including but not limited to the current load, geographic location, response time, task type, task execution frequency, etc. of the probe. The weight factor of each probe is calculated and normalized to ensure that the weights of different factors can be compared. The entropy method can be used here to calculate the weights and calculate the corresponding weight coefficients for different weight factors. Based on the weight factors and their weight coefficients, the comprehensive score of the load capacity of each probe is calculated, and the scoring formula is as follows:

[0067] in, is the comprehensive score of probe i, is the weight corresponding to the jth weight factor, is the score of probe i on the jth weight factor.

[0068] In operation S1212, target probes having a load capacity greater than a preset threshold are screened according to the ranking of the evaluation results.

[0069] In the disclosed embodiment, all existing probes are sorted according to the comprehensive scores, and one or more target probes with the highest comprehensive scores are recommended to perform the task.

[0070] In operation S122 , the probe detection task in the task set message queue is assigned to at least one target probe for execution.

[0071] In operation S130, load data of at least one target probe executing a probe detection task is collected, and evaluation parameters of an intelligent load scheduling module are adjusted based on the load data. The evaluation parameters are used to evaluate existing probes to obtain target probes.

[0072] S130 includes S131~S132.

[0073] In operation S131 , actual load data of at least one target probe is collected periodically, and predicted load data estimated by a load scheduling module for at least one target probe is obtained.

[0074] Before comparing the actual load data with the predicted load data, the actual load data and the predicted load data are subjected to standardized preprocessing, including data cleaning, denoising, normalization and other operations, as well as removing outliers, filling missing values, performing data normalization or standardization, and extracting relevant features.

[0075] In operation S132, the actual load data and the predicted load data are compared, and evaluation parameters of the intelligent load scheduling module are adjusted.

[0076] In the process of adjusting the intelligent load scheduling module, the prediction accuracy of the model is improved by cross-validation and adjustment of model parameters. Use a partial data set for verification, adjust the model structure and parameters, and optimize the prediction performance. Use the validation set or test set to evaluate the trained model, compare the prediction results with the actual load data, and calculate evaluation indicators such as root mean square error (RMSE), mean absolute error (MAE), etc. to evaluate the accuracy of the model. Model optimization: Based on the evaluation results, optimize and adjust the model. This can include adjusting hyperparameters, increasing the amount of data, trying different algorithms or model structures, etc. Monitoring and updating: Continuously monitor the performance of the model. Over time, the data distribution may change, so the model needs to be updated regularly to maintain accuracy.

[0077] In operation S140, the intelligent load scheduling module adjusts at least one target probe based on the adjusted evaluation parameters to improve the load capacity of the at least one target probe.

[0078] During system operation, the load balancer monitors the resource usage of the probes in real time and dynamically adjusts task allocation based on the predicted load. Specifically, it obtains the latest resource usage prediction values ​​regularly; adjusts the task allocation strategy based on the prediction results to ensure efficient use of probe resources; dynamically updates the comprehensive score of the probes and re-recommends the optimal probe.

[0079] S140 includes S141~S142.

[0080] In operation S141 , the intelligent load scheduling module re-evaluates the existing probes based on the adjusted evaluation parameters to obtain the latest predicted load data of the existing probes, where the existing probes include at least one target probe.

[0081] In operation S142 , a new target probe is determined according to the predicted load data.

[0082] The above method can realize intelligent and automated management of probe resources through the load balancer, reduce manual intervention, improve the system's response speed and management efficiency, realize intelligent prediction and scheduling of probe resources, improve the utilization efficiency of probe resources, enhance the accuracy and reliability of network monitoring, and is suitable for probe management in various complex network environments.

[0083] Figure 2 A block diagram of an intelligent probe scheduling device based on load prediction according to an embodiment of the present disclosure is schematically shown.

[0084] like Figure 2 As shown, the intelligent probe scheduling device 200 based on load prediction includes a task acquisition module 210 , a task allocation module 220 , a data collection module 230 and a task reallocation module 240 .

[0085] The task acquisition module 210 is used to acquire at least one probe detection task and store the probe detection task in a task set message queue;

[0086] The task allocation module 220 is used to allocate the probe detection task in the task set message queue to at least one target probe for execution, and the target probe is obtained by evaluating the existing probes by the preset intelligent load scheduling module;

[0087] The data collection module 230 is used to collect load data of at least one target probe performing a probe detection task, and adjust evaluation parameters of the intelligent load scheduling module based on the load data, where the evaluation parameters are used to evaluate existing probes to obtain target probes;

[0088] The task reallocation module 240 is used to adjust at least one target probe based on the intelligent load scheduling module after adjusting the evaluation parameters, so as to improve the load capacity of the at least one target probe.

[0089] According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits, or at least part of the functions of any one of them can be implemented in one module. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be split into multiple modules for implementation. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be at least partially implemented as hardware circuits, such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), systems on chips, systems on substrates, systems on packages, application specific integrated circuits (ASICs), or can be implemented by hardware or firmware in any other reasonable way of integrating or packaging circuits, or can be implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, according to the embodiments of the present invention, one or more of the modules, submodules, units, and subunits can be at least partially implemented as computer program modules, and when the computer program modules are run, the corresponding functions can be performed.

[0090] For example, any multiple of the task acquisition module 210, the task allocation module 220, the data acquisition module 230, and the task reallocation module 240 can be combined in one module / unit / subunit for implementation, or any one of the modules / units / subunits can be split into multiple modules / units / subunits. Alternatively, at least part of the functions of one or more of these modules / units / subunits can be combined with at least part of the functions of other modules / units / subunits and implemented in one module / unit / subunit. According to an embodiment of the present disclosure, at least one of the task acquisition module 210, the task allocation module 220, the data acquisition module 230, and the task reallocation module 240 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, at least one of the task acquisition module 210 , the task allocation module 220 , the data collection module 230 and the task reallocation module 240 may be at least partially implemented as a computer program module, and when the computer program module is executed, the corresponding function may be executed.

[0091] It should be noted that the part of the intelligent probe scheduling device based on load prediction in the embodiments of the present disclosure corresponds to the part of the intelligent probe scheduling method based on load prediction in the embodiments of the present disclosure. The description of the part of the intelligent probe scheduling device based on load prediction specifically refers to the part of the intelligent probe scheduling method based on load prediction, which will not be repeated here.

[0092] Figure 3 A block diagram of an electronic device suitable for implementing the method described above according to an embodiment of the present disclosure is schematically shown. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0093] like Figure 3 As shown, the electronic device 300 according to an embodiment of the present disclosure includes a processor 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage part 308 to a random access memory (RAM) 303. The processor 301 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 301 may also include an onboard memory for caching purposes. The processor 301 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0094] In RAM 303, various programs and data required for the operation of electronic device 300 are stored. Processor 301, ROM 302 and RAM 303 are connected to each other through bus 304. Processor 301 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 302 and / or RAM 303. It should be noted that the program can also be stored in one or more memories other than ROM 302 and RAM 303. Processor 301 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0095] According to an embodiment of the present disclosure, the electronic device 300 may further include an input / output (I / O) interface 305, which is also connected to the bus 304. The electronic device 300 may further include one or more of the following components connected to the input / output (I / O) interface 305: an input portion 306 including a keyboard, a mouse, etc.; an output portion 307 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 308 including a hard disk, etc.; and a communication portion 309 including a network interface card such as a LAN card, a modem, etc. The communication portion 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the input / output (I / O) interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed, so that a computer program read therefrom is installed into the storage portion 308 as needed.

[0096] According to an embodiment of the present disclosure, the method flow according to an embodiment of the present disclosure can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 309, and / or installed from the removable medium 311. When the computer program is executed by the processor 301, the above-mentioned functions defined in the system of the embodiment of the present disclosure are executed. According to an embodiment of the present disclosure, the system, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0097] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0098] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include, but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0099] For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 302 and / or the RAM 303 described above and / or one or more memories other than the ROM 302 and the RAM 303 .

[0100] An embodiment of the present disclosure also includes a computer program product, which includes a computer program, which contains program code for executing the method provided by the embodiment of the present disclosure. When the computer program product runs on an electronic device, the program code is used to enable the electronic device to implement the intelligent probe scheduling method based on load prediction provided by the embodiment of the present disclosure.

[0101] When the computer program is executed by the processor 301, the above functions defined in the system / device of the embodiment of the present disclosure are executed. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0102] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 309, and / or installed from the removable medium 311. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0103] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0104] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code 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 box may also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions. It can be understood by those skilled in the art that the features recorded in the various embodiments of the present disclosure can be combined and / or combined in a variety of ways, even if such a combination or combination is not explicitly recorded in the present disclosure. In particular, without departing from the spirit and teaching of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0105] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. An intelligent probe scheduling method based on load prediction, comprising: Acquire at least one probe detection task, and store the probe detection task in a task set message queue; Allocate the probe detection tasks in the task set message queue to at least one target probe for execution, wherein the target probe is obtained by evaluating existing probes by a preset intelligent load scheduling module; Collecting load data of the at least one target probe executing the probe detection task, and adjusting evaluation parameters of the intelligent load scheduling module based on the load data, wherein the evaluation parameters are used to evaluate existing probes to obtain the target probe; The intelligent load scheduling module adjusts the at least one target probe based on the adjusted evaluation parameters to improve the load capacity of the at least one target probe.

2. The method according to claim 1, wherein: The allocating the probe detection task in the task set message queue to at least one target probe for execution comprises: Based on the intelligent load scheduling module, the load capacity of existing probes is evaluated, and at least one existing probe having a load capacity greater than a preset threshold is selected as a target probe; The probe detection tasks in the task set message queue are assigned to the at least one target probe for execution.

3. The method according to claim 2, wherein: The step of evaluating the load capacity of existing probes based on the intelligent load scheduling module and selecting at least one existing probe having a load capacity greater than a preset threshold as a target probe includes: Acquire multiple historical data of each of the existing probes, perform weighted evaluation on the multiple historical data, and obtain evaluation results of each of the existing probes, wherein the evaluation results represent the load capacity; The target probes having a load capacity greater than a preset threshold are screened according to the ranking of the evaluation results.

4. The method according to claim 1, wherein: The collecting load data of the at least one target probe executing the probe detection task, and adjusting the evaluation parameters of the intelligent load scheduling module based on the load data includes: regularly collecting actual load data of the at least one target probe, and obtaining predicted load data estimated by the load scheduling module for the at least one target probe; The actual load data and the predicted load data are compared, and evaluation parameters of the intelligent load scheduling module are adjusted.

5. The method according to claim 4, wherein: The method comprises: Before comparing the actual load data with the predicted load data, the actual load data and the predicted load data are subjected to standardization preprocessing.

6. The method according to claim 1, wherein: The adjusting of the at least one target probe based on the intelligent load scheduling module after adjusting the evaluation parameter comprises: Re-evaluating the existing probes based on the intelligent load scheduling module after adjusting the evaluation parameters to obtain the latest predicted load data of the existing probes, wherein the existing probes include the at least one target probe; A new target probe is determined according to the predicted load data.

7. An intelligent probe scheduling device based on load prediction, comprising: A task acquisition module, used to acquire at least one probe detection task and store the probe detection task in a task set message queue; A task allocation module, used for allocating the probe detection tasks in the task set message queue to at least one target probe for execution, wherein the target probe is obtained by evaluating the existing probes by a preset intelligent load scheduling module; A data acquisition module, used for acquiring load data of the at least one target probe executing the probe detection task, and adjusting evaluation parameters of the intelligent load scheduling module based on the load data, wherein the evaluation parameters are used for evaluating existing probes to obtain the target probe; The task reallocation module is used to adjust the at least one target probe based on the intelligent load scheduling module after adjusting the evaluation parameters, so as to improve the load capacity of the at least one target probe.

8. An electronic device comprising: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the method according to any one of claims 1 to 6.

10. A computer program product, comprising computer executable instructions, wherein the instructions are used to implement the method according to any one of claims 1 to 6 when executed.

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