Target template determination method and device, storage medium and computer program product

Abstract

The invention discloses a target template determination method and device, a storage medium and a computer program product, and relates to the field of finance, and the target template determination method comprises the steps: for any first template in N task templates, under the condition that a first task sent by a target object is received, determining the first task; performance indexes of M first parameters in the first template and first scores of the M performance indexes are obtained, and N and M are positive integers; determining a comprehensive score of the first template according to the M first scores, and determining a task category of the first task; determining a plurality of second templates with the same task category from the N task templates; the third template with the highest score in the multiple second templates is determined as a target template according to the comprehensive score, and the target template is used for executing the first task.

Description

Technical Field

[0001] This application relates to the financial field, and more specifically, to a method and apparatus for determining a target template, a storage medium, and a computer program product. Background Technology

[0002] In the context of real-time data processing and integrated stream and batch processing, big data processing frameworks such as Flink are highly favored due to their powerful real-time data analysis capabilities. However, with the increasing prevalence and complexity of Flink applications, the construction, configuration, and optimization of Flink tasks face a series of challenges, especially in parameter configuration selection. The parameter template manager, as a core component for the automated generation and management of Flink tasks, aims to quickly configure and launch Flink tasks using predefined parameter templates to adapt to the needs of different business scenarios. However, existing parameter template managers lack a self-evaluation mechanism for template performance and cannot adjust template selection based on the real-time status and performance metrics of the task. This makes template selection rely heavily on preset rules or manual experience, hindering intelligent and dynamic implementation.

[0003] There is currently no effective solution to the problem that the parameter template manager does not select templates accurately when receiving tasks in related technologies.

[0004] Therefore, it is necessary to improve the relevant technology to overcome the aforementioned defects. Summary of the Invention

[0005] This application provides a method and apparatus for determining a target template, a storage medium, and a computer program product, to at least solve the problem in the related art that the traditional version file difference identification method is inefficient due to the disorder and heterogeneity of version files in data scheduling tasks.

[0006] According to one aspect of the embodiments of this application, a method for determining a target template is provided, comprising: for any first template among N task templates, upon receiving a first task issued by a target object, obtaining performance indicators of M first parameters and first scores of M performance indicators in the first template, wherein N and M are both positive integers; determining a comprehensive score of the first template based on the M first scores, and determining the task category of the first task; determining a plurality of second templates with the same task category from the N task templates; and determining a third template with the highest score among the plurality of second templates as the target template based on the comprehensive score, wherein the target template is used to execute the first task.

[0007] In an exemplary embodiment, before determining the comprehensive score of the first template based on the M first scores, the method further includes: determining first weights corresponding to the M performance indicators; and performing a weighted summation of the M first scores based on the M first weights to obtain a second score.

[0008] In an exemplary embodiment, before determining the comprehensive score of the first template based on the M first scores, the method further includes: performing fuzzy level determination on the M performance indicators based on the M first scores to obtain fuzzy levels corresponding to the M performance indicators; determining third scores and second weights corresponding to the M fuzzy levels according to preset fuzzy logic rules; and performing weighted summation on the M third scores based on the M second weights to obtain a fourth score.

[0009] In an exemplary embodiment, determining the comprehensive score of the first template based on M first scores includes: determining a target fusion function based on the first template; and determining the comprehensive score based on the target fusion function, the second score, and the fourth score.

[0010] In an exemplary embodiment, before determining the comprehensive score of the first template based on the M first scores, the method further includes: determining whether there is a fifth score among the M first scores that is lower than a first preset value; if it is determined that there is a fifth score among the M first scores that is lower than the first preset value, adjusting the second parameter corresponding to the fifth score through an iterative optimization algorithm, wherein the M first parameters include the second parameter.

[0011] In an exemplary embodiment, after adjusting the second parameter corresponding to the fifth score using an iterative optimization algorithm, the method further includes: determining whether the sixth score of the adjusted second parameter is higher than or equal to the first preset value; if the sixth score is higher than or equal to the first preset value, determining that the second parameter has been successfully optimized; if the sixth score is lower than the first preset value, sending a warning signal, wherein the warning signal is used to instruct the target object to analyze the cause of the abnormality of the second parameter.

[0012] According to another aspect of the embodiments of this application, a target template determination apparatus is also provided, comprising: an acquisition module, configured to, for any first template among N task templates, upon receiving a first task issued by a target object, acquire performance indicators of M first parameters and first scores of M performance indicators in the first template, wherein N and M are both positive integers; a first determination module, configured to determine a comprehensive score of the first template based on the M first scores, and determine the task category of the first task; a second determination module, configured to determine a plurality of second templates with the same task category from the N task templates; and a third determination module, configured to determine the third template with the highest score among the plurality of second templates as the target template based on the comprehensive score, wherein the target template is used to perform the first task.

[0013] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer program, and the computer program is configured to execute the above-described method for determining the target template when it is run.

[0014] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method for determining the target template through the computer program.

[0015] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of the methods described in various embodiments of this application.

[0016] This application addresses the problem in related technologies where, for any first template among N task templates, upon receiving a first task from a target object, the system obtains the performance metrics of M first parameters and the first scores of these M performance metrics, where N and M are positive integers. Based on the M first scores, it determines the overall score of the first template and the task category of the first task. Multiple second templates with the same task category are selected from the N task templates. Finally, the third template with the highest score among the multiple second templates is identified as the target template, which is used to execute the first task. This solves the problem of low accuracy in template selection by the parameter template manager when receiving a task in related technologies. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a hardware structure block diagram of a computer terminal for a method of determining a target template according to an embodiment of this application.

[0020] Figure 2 This is a flowchart of a method for determining a target template according to an embodiment of this application;

[0021] Figure 3 This is a structural block diagram of a target template determination device according to an embodiment of this application. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] The methods and embodiments provided in this application can be executed on a computer terminal or similar computing device. Taking running on a computer terminal as an example, Figure 1 This is a hardware structure block diagram of a computer terminal for a method of determining a target template according to an embodiment of this application. For example... Figure 1 As shown, a computer terminal may include one or more ( Figure 1Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a central processing unit (CPU) or a field-programmable gate array (FPGA)) and a memory 104 for storing data are also shown. The computer terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the computer terminal described above. For example, the computer terminal may also include components that are more complex than those described above. Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0025] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the target template determination method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, thus implementing the above-described method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0026] The computer terminal uses a wireless network provided by a communications provider. In one example, transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, transmission device 106 may be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0027] This embodiment provides a method for determining a target template, which is applied to the aforementioned computer terminal. Figure 2 This is a flowchart of a method for determining a target template according to an embodiment of this application, such as... Figure 2 As shown, the process includes the following steps:

[0028] Step S202: For any first template among N task templates, when receiving the first task issued by the target object, obtain the performance indicators of M first parameters and the first scores of the M performance indicators in the first template, where N and M are both positive integers.

[0029] Step S204: Determine the comprehensive score of the first template based on the M first scores, and determine the task category of the first task;

[0030] Step S206: Determine multiple second templates that are the same as the task category from the N task templates;

[0031] Step S208: Based on the comprehensive score, the third template with the highest score among the plurality of second templates is determined as the target template, wherein the target template is used to perform the first task.

[0032] Through the above steps, for any first template among N task templates, upon receiving a first task from the target object, the performance indicators of M first parameters and the first scores of the M performance indicators in the first template are obtained, where N and M are both positive integers. Based on the M first scores, the comprehensive score of the first template is determined, as well as the task category of the first task. Multiple second templates with the same task category are selected from the N task templates. Based on the comprehensive score, the third template with the highest score among the multiple second templates is determined as the target template, which is used to execute the first task. This solves the problem in related technologies where the parameter template manager has low accuracy in template selection when receiving a task.

[0033] In an exemplary embodiment, before determining the comprehensive score of the first template based on the M first scores, the method further includes: determining first weights corresponding to the M performance indicators; and performing a weighted summation of the M first scores based on the M first weights to obtain a second score.

[0034] In an exemplary embodiment, before determining the comprehensive score of the first template based on the M first scores, the method further includes: performing fuzzy level determination on the M performance indicators based on the M first scores to obtain fuzzy levels corresponding to the M performance indicators; determining third scores and second weights corresponding to the M fuzzy levels according to preset fuzzy logic rules; and performing weighted summation on the M third scores based on the M second weights to obtain a fourth score.

[0035] In an exemplary embodiment, determining the comprehensive score of the first template based on M first scores includes: determining a target fusion function based on the first template; and determining the comprehensive score based on the target fusion function, the second score, and the fourth score.

[0036] The above embodiment describes a multi-level scoring mechanism for evaluating and selecting parameter templates for Flink tasks. First, the importance of M performance metrics (such as data backpressure rate, data distribution uniformity, data processing latency, and memory usage efficiency) is determined, i.e., a first weight is assigned to each performance metric. Then, based on the actual performance of these metrics (first scores), a weighted sum is calculated using the first weights to obtain a second score reflecting the overall performance. Next, the scoring mechanism introduces fuzzy logic to evaluate performance metrics more finely through fuzzy level determination. Based on the first score of each performance metric, each first score is mapped to a preset fuzzy level (such as "low," "medium," and "high"), and a third score and second weight under fuzzy logic rules are assigned to each fuzzy level. The third score is weighted and summed using the second weight to obtain a fourth score. This step further refines the performance evaluation, enabling it to handle uncertainties and complex relationships between metrics. Finally, a target fusion function, customized for the first template, is used to combine the second and fourth scores, comprehensively considering both quantitative and fuzzy logic scoring, to obtain the final comprehensive score. The overall score directly reflects the template's overall impact on task performance and is an important basis for selecting the optimal template.

[0037] The core of this multi-level scoring mechanism lies in its consideration of not only direct measurement of performance metrics but also the handling of fuzzy logic to mitigate the fuzziness and uncertainty of metric evaluation, thereby enhancing the flexibility and adaptability of the scoring. Simultaneously, through the objective fusion function, the scoring results can be customized according to the specific needs and characteristics of the task, ensuring that the final selected template maximally meets the task's performance requirements. This mechanism provides a more accurate and intelligent template selection strategy when dealing with complex and variable task scenarios, thereby improving the execution efficiency and resource utilization of Flink tasks.

[0038] In an exemplary embodiment, before determining the comprehensive score of the first template based on the M first scores, the method further includes: determining whether there is a fifth score among the M first scores that is lower than a first preset value; if it is determined that there is a fifth score among the M first scores that is lower than the first preset value, adjusting the second parameter corresponding to the fifth score through an iterative optimization algorithm, wherein the M first parameters include the second parameter.

[0039] This embodiment describes a dynamic parameter optimization mechanism for improving the performance of Flink tasks. First, it checks if any of the first scores of M performance metrics (the fifth score) is lower than a preset performance threshold (the first preset value). This threshold typically reflects the minimum acceptable performance standard for the task, such as the upper limit of data processing latency or the lower limit of memory usage efficiency. If a metric score below the first preset value is detected, it means that the current parameter configuration has failed to achieve the expected performance level. At this point, the parameter template manager will initiate an iterative optimization algorithm to dynamically adjust the parameters (the second parameter) corresponding to the metric causing the anomaly. The iterative optimization algorithm may be based on mechanisms such as gradient descent, genetic algorithms, or particle swarm optimization, gradually fine-tuning parameter values ​​to find the optimal parameter combination that can improve the relevant performance metrics.

[0040] In an exemplary embodiment, after adjusting the second parameter corresponding to the fifth score using an iterative optimization algorithm, the method further includes: determining whether the sixth score of the adjusted second parameter is higher than or equal to the first preset value; if the sixth score is higher than or equal to the first preset value, determining that the second parameter has been successfully optimized; if the sixth score is lower than the first preset value, sending a warning signal, wherein the warning signal is used to instruct the target object to analyze the cause of the abnormality of the second parameter.

[0041] This embodiment describes how to verify the effectiveness of parameter optimization and make corresponding decisions or warnings during dynamic parameter adjustment. After adjusting the second parameter through an iterative optimization algorithm, the score of the relevant performance index, i.e., the sixth score, is recalculated. Next, the sixth score is compared with the previously set performance threshold (first preset value) to determine whether the optimization is effective. If the sixth score is higher than or equal to the first preset value, it means that the adjustment of the second parameter has restored the performance index to or exceeded the expected minimum standard, indicating that the parameter optimization has been successful. Continue to monitor the task execution to ensure performance stability. However, if the sixth score is still lower than the first preset value, it means that the optimization process has not solved the problem, or the new parameter configuration has produced unexpected negative effects. In this case, an early warning signal is sent to the target object. The early warning signal contains key information indicating that the second parameter may be abnormal and that further analysis of the underlying cause is needed. The target object receiving the early warning signal can then use logs, statistical data, and anomaly detection reports to analyze the cause of the second parameter anomaly in depth, such as whether it is due to changes in data characteristics, hardware failure, improper cluster resource allocation, etc. Based on the results of the anomaly analysis, the target object can manually adjust the parameters or set a new optimization strategy to solve the problem and restore the normal performance of the task.

[0042] In another exemplary embodiment, to eliminate (or prevent) false triggering of metrics due to network jitter or garbage collection, when calculating the M performance metrics, the original metrics are first smoothed using the Exponential Moving Average (EMA) algorithm to obtain the EMA values ​​of the M performance metrics. These performance metrics include: backpressure index V1, consumption lag rate (backlog growth rate) V2, data skewness V3, memory pressure value (resource utilization) V4, and end-to-end latency V5. A state feature vector Vt=[V1,V2,V3,V4,V5] is constructed based on the smoothed M performance metrics. This state feature vector characterizes the health of the current task. Furthermore, based on the aforementioned M performance metrics, a loss function L=W1(V1) is applied. 2 The loss value of the current template is calculated using the formula: +W2V2 + W3V3 + W4(1 / resource utilization) + W5V5. The total weights are 1. The weights W1 and W2 corresponding to the backpressure index V1 and consumption lag rate (backlog growth rate) V2 are the highest, because data timeliness is paramount in financial scenarios. Resource utilization is also introduced as a penalty to avoid blindly increasing resources. When L exceeds a certain threshold, an optimization strategy is triggered, initiating an automatic optimization process. Based on rule adjustments or gradient descent algorithms, key parameters are fine-tuned (such as increasing source parallelism or adjusting buffer size). For example, if the backpressure index is detected to be too high, parallelism is automatically increased; conversely, if the backpressure index is too low, parallelism is appropriately reduced. Different thresholds are set for different Flink tasks based on the importance of the business scenario.

[0043] When optimization is triggered, task parameters are not directly modified in the production environment. Instead, a shadow job is created for the current task (by copying the current task). The shadow job executes in a completely isolated resource environment to ensure that its operation has no direct impact on the production environment. The shadow job uses the parameter template to be tested (the optimized template) to process backtracking data. During this process, the performance of the shadow job is continuously monitored, and the loss function score is recalculated based on its performance. If the loss function score of the shadow job in the parameter template to be tested improves by more than 15% compared to the original task, the parameter optimization is considered effective. Conversely, if the loss function score decreases compared to the original task, a rollback is performed, and the parameters are fine-tuned in reverse. After the template completes parameter optimization, the shadow job is closed and restarted during the next template optimization. Once a better parameter configuration is confirmed, Flink's Savepoint mechanism is used to smoothly restart the production task without losing any state, applying the new parameters to the production environment. For core financial processes, a manual approval process can be set up, where the system only pushes optimization suggestions, and manual confirmation of the update is required.

[0044] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0045] This embodiment also provides a target template determination device, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0046] Figure 3 This is a structural block diagram of a target template determining device according to an embodiment of this application. The device includes:

[0047] The acquisition module 32 is used to acquire, for any first template among N task templates, the performance indicators of M first parameters and the first scores of M performance indicators in the first template when a first task is received from the target object, where N and M are both positive integers.

[0048] The first determining module 34 is used to determine the comprehensive score of the first template based on the M first scores, and to determine the task category of the first task;

[0049] The second determining module 36 is used to determine a plurality of second templates that are the same as the task category from the N task templates;

[0050] The third determining module 38 is used to determine the third template with the highest score among the plurality of second templates as the target template based on the comprehensive score, wherein the target template is used to perform the first task.

[0051] Using the aforementioned device, for any first template among N task templates, upon receiving a first task from a target object, the device obtains the performance indicators of M first parameters and the first scores of the M performance indicators, where N and M are both positive integers. Based on the M first scores, it determines the overall score of the first template and the task category of the first task. Multiple second templates with the same task category are selected from the N task templates. Based on the overall score, the third template with the highest score among the multiple second templates is selected as the target template, which is used to execute the first task. This solves the problem in related technologies where the parameter template manager has low accuracy in template selection when receiving a task.

[0052] In an exemplary embodiment, the first determining module 34 is further configured to determine the first weights corresponding to the M performance indicators; and to perform a weighted summation of the M first scores based on the M first weights to obtain a second score.

[0053] In an exemplary embodiment, the first determining module 34 is further configured to: determine the fuzzy level of the M performance indicators based on the M first scores to obtain the fuzzy levels corresponding to the M performance indicators; determine the third scores and second weights corresponding to the M fuzzy levels according to preset fuzzy logic rules; and perform a weighted summation of the M third scores based on the M second weights to obtain a fourth score.

[0054] In an exemplary embodiment, the first determining module 34 is further configured to determine a target fusion function based on the first template; and to determine the comprehensive score based on the target fusion function, the second score, and the fourth score.

[0055] In an exemplary embodiment, the first determining module 34 is further configured to determine whether there is a fifth score lower than a first preset value among the M first scores; if it is determined that there is a fifth score lower than the first preset value among the M first scores, the second parameter corresponding to the fifth score is adjusted by an iterative optimization algorithm, wherein the M first parameters include the second parameter.

[0056] In an exemplary embodiment, the first determining module 34 is further configured to determine whether the sixth score of the adjusted second parameter is higher than or equal to the first preset value; if the sixth score is determined to be higher than or equal to the first preset value, the second parameter is determined to be successfully optimized; if the sixth score is determined to be lower than the first preset value, an early warning signal is sent, wherein the early warning signal is used to instruct the target object to analyze the abnormal cause of the second parameter.

[0057] Embodiments of this application also provide a computer-readable storage medium storing a computer program, wherein the computer program is configured to execute the steps in any of the above method embodiments when run.

[0058] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0059] S1, for any first template among N task templates, when receiving the first task issued by the target object, obtain the performance indicators of M first parameters and the first scores of the M performance indicators in the first template, where N and M are both positive integers;

[0060] S2, determine the comprehensive score of the first template based on the M first scores, and determine the task category of the first task;

[0061] S3, determine a plurality of second templates that are the same as the task category from the N task templates;

[0062] S4, based on the comprehensive score, the third template with the highest score among the plurality of second templates is determined as the target template, wherein the target template is used to perform the first task.

[0063] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0064] Specific examples in this embodiment can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0065] Embodiments of this application also provide an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0066] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0067] S1, for any first template among N task templates, when receiving the first task issued by the target object, obtain the performance indicators of M first parameters and the first scores of the M performance indicators in the first template, where N and M are both positive integers;

[0068] S2, determine the comprehensive score of the first template based on the M first scores, and determine the task category of the first task;

[0069] S3, determine a plurality of second templates that are the same as the task category from the N task templates;

[0070] S4, based on the comprehensive score, the third template with the highest score among the plurality of second templates is determined as the target template, wherein the target template is used to perform the first task.

[0071] In one exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0072] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium storing the computer program product, wherein the computer program, when executed by a processor, implements the steps of the methods described in various embodiments of this application.

[0073] Optionally, in this embodiment, the computer program described above can be configured to perform the following steps when executed by the processor:

[0074] S1, for any first template among N task templates, when receiving the first task issued by the target object, obtain the performance indicators of M first parameters and the first scores of the M performance indicators in the first template, where N and M are both positive integers;

[0075] S2, determine the comprehensive score of the first template based on the M first scores, and determine the task category of the first task;

[0076] S3, determine a plurality of second templates that are the same as the task category from the N task templates;

[0077] S4, based on the comprehensive score, the third template with the highest score among the plurality of second templates is determined as the target template, wherein the target template is used to perform the first task.

[0078] Specific examples in this embodiment can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0079] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0080] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for determining a target template, characterized in that, include: For any first template among N task templates, upon receiving the first task issued by the target object, obtain the performance indicators of M first parameters and the first scores of the M performance indicators in the first template, where N and M are both positive integers; The comprehensive score of the first template is determined based on the M first scores, and the task category of the first task is determined; From the N task templates, determine a plurality of second templates that are the same as the task category; Based on the overall score, the third template with the highest score among the plurality of second templates is determined as the target template, wherein the target template is used to perform the first task.

2. The method for determining the target template according to claim 1, characterized in that, Before determining the comprehensive score of the first template based on the M first scores, the method further includes: Determine the first weights corresponding to the M performance indicators; The second score is obtained by weighting and summing the M first scores according to the M first weights.

3. The method for determining the target template according to claim 2, characterized in that, Before determining the comprehensive score of the first template based on the M first scores, the method further includes: Based on the M first scores, the M performance indicators are fuzzy level determined to obtain the fuzzy levels corresponding to the M performance indicators; The third score and second weight corresponding to the M fuzzy levels are determined according to the preset fuzzy logic rules; The fourth score is obtained by weighting and summing the M third scores according to the M second weights.

4. The method for determining the target template according to claim 3, characterized in that, The comprehensive score of the first template is determined based on the M scores of the first template, including: The target fusion function is determined based on the first template; The comprehensive score is determined based on the target fusion function, the second score, and the fourth score.

5. The method for determining the target template according to claim 1, characterized in that, Before determining the comprehensive score of the first template based on the M first scores, the method further includes: Determine whether there exists a fifth score among the M first scores that is lower than a first preset value; If it is determined that there is a fifth score among the M first scores that is lower than the first preset value, the second parameter corresponding to the fifth score is adjusted by an iterative optimization algorithm, wherein the M first parameters include the second parameter.

6. The method for determining the target template according to claim 5, characterized in that, After adjusting the second parameter corresponding to the fifth score using an iterative optimization algorithm, the method further includes: Determine whether the sixth score of the adjusted second parameter is higher than or equal to the first preset value; If the sixth score is determined to be higher than or equal to the first preset value, the second parameter optimization is determined to be successful. If the sixth score is determined to be lower than the first preset value, an early warning signal is sent, wherein the early warning signal is used to instruct the target object to analyze the cause of the abnormality of the second parameter.

7. A device for determining a target template, characterized in that, include: The acquisition module is used to acquire, for any first template among N task templates, the performance indicators of M first parameters and the first scores of M performance indicators in the first template when a first task is received from the target object, where N and M are both positive integers. The first determining module is used to determine the comprehensive score of the first template based on the M first scores, and to determine the task category of the first task; The second determining module is used to determine multiple second templates that are the same as the task category from the N task templates; The third determining module is used to determine the third template with the highest score among the plurality of second templates as the target template based on the comprehensive score, wherein the target template is used to perform the first task.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program, when executed, performs the method of any one of claims 1 to 6.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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