A cross-platform data task scheduling system and method based on an authorization chain

Abstract

The application discloses a kind of cross-platform data task scheduling systems and methods based on authorization chain, it is related to data task scheduling technical field, when task processing platform receives task, pre-processes task data, constructs task database according to the task characteristics obtained by pre-processing, task processing platform starts self-checking, according to real-time running load, select preset task processing platform, consider task matching degree to judge the scheduling demand of task, real-time analysis platform between transmission condition, according to the transmission condition between platform, select the selected task processing platform, comprehensively consider the matching degree of selected task processing platform to task and transmission condition, analysis the predicted completion rate to task, analysis the real-time task execution state of preferred task processing platform, select preferred task processing platform to execute task, the present application can reasonably balance the task amount of each platform by considering the dynamic allocation mode of platform load and running state, avoid part of task allocation imbalance.

Description

Technical Field

[0001] This invention relates to the field of data task scheduling technology, specifically to a cross-platform data task scheduling system and method based on an authorization chain. Background Technology

[0002] In existing cross-platform data task scheduling technologies, the mining of task characteristics is often insufficient, failing to fully extract key task attributes and establish a system-wide task information reserve. Furthermore, there is a lack of integration with the past task processing experience and results of each processing platform. The scheduling process often relies on simple task type classification or matching based on basic platform function parameters, failing to accurately determine a platform's processing capability for a specific task. This results in tasks being frequently assigned to platforms less adept at handling such needs, leading to frequent processing deviations and result errors, severely impacting the overall quality and reliability of cross-platform task processing. Existing cross-platform data task scheduling methods lack dynamic perception and flexible allocation mechanisms for the real-time operating status of each processing platform. When allocating tasks, the current load of different platforms is often not fully considered; tasks are allocated solely according to fixed rules or priorities. This easily leads to some platforms becoming congested due to excessive task accumulation, while others remain idle, unable to effectively utilize their processing potential. This uneven resource allocation makes it difficult to improve the overall resource utilization efficiency of the cross-platform system, resulting in a significant waste of processing resources. Existing cross-platform task scheduling technologies have significant shortcomings in data transmission and task execution. On the one hand, the stability differences in data transmission between different processing platforms are ignored, and the transmission effect between platforms is not analyzed and screened, which can easily lead to data transmission failure due to unstable transmission links. On the other hand, after task allocation, there is a lack of real-time monitoring of the running status of the execution platform. If the execution platform suddenly experiences abnormal operation or a sudden increase in load, the task arrangement cannot be adjusted in time, which can lead to task delays or even interruptions, making it difficult to ensure the smooth progress of cross-platform data tasks from transmission to execution. Summary of the Invention

[0003] The purpose of this invention is to provide a cross-platform data task scheduling system and method based on an authorization chain to solve the problems mentioned in the background art.

[0004] To address the aforementioned technical problems, this invention provides the following technical solution: a cross-platform data task scheduling method based on an authorization chain, comprising the following steps: S1. When the task processing platform receives a task, it preprocesses the task data and builds a task database based on the task characteristics obtained from the preprocessing. S2. When the task processing platform receives a task, it starts a self-check and selects a preset task processing platform based on the real-time operating load. S3. Analyze the matching degree of the preset task processing platform with the task, and determine the task scheduling requirements based on the task matching degree. S4. Analyze the transmission status between platforms in real time and select the candidate task processing platform based on the transmission status between platforms; S5. Taking into account the matching degree and transmission status of the candidate task processing platforms, analyze and consider the predicted completion rate of the candidate task processing platforms for the tasks. S6. Analyze the real-time task execution status of the preferred task processing platform and select the preferred task processing platform to execute the task.

[0005] Furthermore, in step S1, after authorization, any task processing platform begins receiving tasks. When a task processing platform receives task α, it collects task data, preprocesses and normalizes the task data to obtain task features, which are {A1, A2, ..., A...}. m ,…,A M}, where A m Let M represent the m-th task feature. Task features include: the ratio of the task's data volume to the historical average data volume, and the ratio of the amount of non-repeating data in the task to the total task data volume. M represents the number of task features. After obtaining the task features, the task processing platform transmits them to a cloud repository to establish a task database based on these features. By preprocessing and normalizing the task data, redundant and messy information can be effectively removed, allowing the extracted task features to more accurately reflect the core attributes of the task and avoiding deviations in subsequent task analysis due to raw data quality issues. Simultaneously, transmitting task features to the cloud repository and establishing a dedicated task database systematically accumulates feature information for various tasks, providing comprehensive and reliable data support for task-platform matching and scheduling in subsequent steps. This breaks through the limitations of fragmented and unreusable task information in traditional scheduling, ensuring the quality of early-stage cross-platform task scheduling from the source and laying the foundation for accurate scheduling later.

[0006] Furthermore, in step S2, when any task processing platform receives a task, the task processing platform begins a self-check. If the real-time operating load of the task processing platform exceeds a preset self-check load threshold, it is determined that the task processing platform cannot process the task, and the task processing platform is classified as a non-preset task processing platform. The real-time operating load is set to the CPU real-time operating utilization rate. Otherwise, it is determined that the task processing platform can process the task, and the task processing platform is classified as a preset task processing platform, thus obtaining N preset task processing platforms, where the N preset task processing platforms are {B1, B2, ..., B...}. n ,…,B N}, where B nThe nth preset task processing platform; through a self-checking mechanism, preset task processing platforms that meet the load requirements are selected. This mechanism accurately identifies platforms that are currently overloaded and unable to process tasks normally, excluding them from the initial scheduling scope. This operation effectively avoids assigning tasks to platforms with excessive operating pressure, preventing task processing delays, crashes, or incorrect results due to platform overload. Simultaneously, it ensures that all platforms participating in subsequent scheduling have basic task processing capabilities, acting as the first line of defense for the rationality and security of subsequent task allocation, reducing invalid scheduling attempts, and improving overall scheduling efficiency.

[0007] Furthermore, in step S3, when the preset task processing platform B... n Upon receiving task α, analyze the preset task processing platform B. n Based on the matching degree of task α, the preset task processing platform B is invoked. n Historical task processing data, preset task processing platform B n The m-th feature in the historical task completion is A m The number of tasks is D n_m The number of tasks that were successfully processed is d. n_m This leads to the pre-defined task processing platform B. n The matching degree C of the m-th feature of task α n_m C n_m For d n_m With D n_m The ratio is used to obtain the preset task processing platform B. n Task matching degree E for task α n E n Preset task processing platform B n The average value of the matching degree of the M features for task α; Substituting each value into the input n=1,2,…,N, we obtain the matching degree {E1,E2,…,E} of the N task processing platforms for task α. n ,…,E N Set a matching degree self-check threshold. If the preset task processing platform B... n Task matching degree E for task α n The match degree is greater than the self-check threshold and the task processing platform B n Task matching degree E for task α n If the matching degree of task α is greater than the average of N task processing platforms, then task α will be assigned to task processing platform B. nIf a task is not successfully processed, it is processed; otherwise, task α is scheduled based on task matching degree. By analyzing the platform's historical task processing data and calculating the matching degree in combination with the successful processing of various task characteristics, the processing capability of each preset platform for the current task can be accurately evaluated, rather than being roughly matched based solely on the platform's surface functions or task type. This matching method based on historical success experience allows tasks to be preferentially assigned to platforms that are truly good at handling this type of task, significantly reducing processing errors caused by inappropriate platform-task compatibility. At the same time, through the dual judgment of matching degree threshold and average value, platforms with better overall adaptability are further screened out, which not only avoids low-compatibility platforms from participating in task processing, but also reduces the workload of subsequent complex scheduling, improving the accuracy and efficiency of the initial task allocation.

[0008] Furthermore, in step S4, the X task processing platforms with the highest matching degree to task α are selected as candidate task processing platforms, and the X candidate task processing platforms are {Z1, Z2, ..., Z...} x ,…,Z X}, where Z x Let x be the candidate task processing platform. Analyze the preset task processing platform B. n For the preset task processing platform Z x The transmission stability, if the preset task processing platform B n and the preset task processing platform Z x If they are on the same task processing platform, then determine the preset task processing platform B. n For the preset task processing platform Z x The predicted transmission success rate is 1; otherwise, the preset task processing platform B is used for data acquisition. n For the preset task processing platform Z x The data transmission characteristics are {F1, F2, ..., F...}. x ,…,F X}, where X represents the number of data transmission features, F x Indicates the default task processing platform B n For the preset task processing platform Z x The xth data transmission characteristic calls the historical database, where task processing platform B... n For the preset task processing platform Z x The xth data transmission feature is F x The number of times is H x The number of successful transmissions is h. x This leads to the task processing platform B. n For the preset task processing platform Z x The transmission feature adaptation rate J of the xth data transmission feature x J x =h x / H x Substitute x=1,2,…,X one by one to obtain the task processing platform B. n For the preset task processing platform Z x The adaptation rate of the transmission features of X data transmission features is used to obtain the task processing platform B. n For the preset task processing platform Z x Predicted transmission success rate L x Y pre-defined task processing platforms are selected as candidate platforms based on their predicted transmission success rates, ranked from highest to lowest. When selecting candidate platforms, not only is the platform's suitability for the task considered, but the stability of data transmission between platforms is also analyzed, overcoming the shortcomings of traditional scheduling that neglect the reliability of the transmission link. For the same platform, directly determining the high transmission success rate is both realistic and simplifies calculations. For different platforms, the adaptation rate and predicted transmission success rate are calculated using historical transmission data, accurately selecting platforms with stable transmission and low risk of data loss or failure. This operation ensures that the candidate platforms not only adapt to task processing but also guarantee smooth data flow between platforms, reducing the risk of task failure at the transmission level and providing stable data flow support for subsequent task progress.

[0009] Furthermore, in step S5, the matching degree {R1, R2, ..., R...} of the Y candidate task processing platforms for task α is determined. y ,…,R Y}, where R y This represents the matching degree of the y-th candidate task processing platform for task α, and the task processing platform B is called. n Predicted transmission success rates {S1,S2,…,S} for Y candidate task processing platforms y ,…,S Y}, where S y Indicates task processing platform B n For the predicted transmission success rate of the y-th candidate task processing platform, calculate the comprehensive task prediction completion rate T of task α on the y-th candidate task processing platform. y Comprehensive task prediction completion rate T y Let the matching degree between the y-th candidate task processing platform and task processing platform B be the degree of matching between task α and task processing platform B. n The product of the predicted transmission success rates of the y-th candidate task processing platform is used to obtain the comprehensive predicted task completion rate {T1,T2,…,T} of task α across the y candidate task processing platforms. y ,…,T YThe process involves ranking the Y candidate task processing platforms according to their overall task prediction completion rate from highest to lowest, and then selecting the Q candidate task processing platforms with the highest overall task prediction completion rates as the preferred task processing platforms. The overall task prediction completion rate is calculated by combining the task matching degree of the candidate platforms with the predicted transmission success rate between platforms, thus achieving a dual consideration of the platform's "processing capacity" and "transmission capacity." This comprehensive evaluation method avoids selection bias caused by relying solely on matching degree or transmission rate; it avoids selecting platforms that are good at processing but have difficulty receiving data stably, as well as platforms that are stable in transmission but are not good at task processing. The preferred platforms selected through ranking by overall completion rate have advantages in both task processing and data transmission, maximizing the overall reliability of the task from data reception to processing completion, and providing a scientific and accurate selection basis for final task authorization.

[0010] Furthermore, in step S6, the preferred task processing platform with the highest overall task prediction completion rate is monitored. If the preferred task processing platform with the highest overall task prediction completion rate is not executing a task, then the preferred task processing platform with the highest prediction completion rate is given the task α execution authorization tag; otherwise, the next preferred task processing platform is monitored until a preferred task processing platform that is not executing a task is detected. The preferred task processing platform that is not executing a task is selected and given the task α execution authorization tag. If Q preferred task processing platforms are all executing tasks, then the preferred task processing platform with the lowest real-time running load among the Q preferred task processing platforms is selected and given the task α execution authorization tag, so that task α is added to the list of tasks to be executed by the preferred task processing platform with the lowest real-time running load. By monitoring the running status of the preferred platforms, priority is given to granting task execution rights to idle platforms, which can enable tasks to quickly enter the execution stage, reduce task waiting time caused by platform busyness, and improve task execution efficiency. When all preferred platforms are busy, selecting the platform with the lowest load and adding the task to the pending list avoids task omissions and balances the operational pressure across platforms. This prevents some platforms from becoming overloaded with tasks while others are underloaded, thus avoiding resource waste. This flexible authorization and scheduling method balances the timeliness of task execution with the balanced utilization of platform resources, ensuring that tasks proceed in an orderly manner while maximizing the processing potential of the preferred platforms.

[0011] A cross-platform data task scheduling system based on an authorization chain, comprising: a task database establishment module, a task processing platform self-checking module, a platform matching degree judgment module, a platform transmission status analysis module, a predicted completion rate analysis module, and a task execution platform confirmation module; The task data database establishment module is used to preprocess task data when the task processing platform receives a task, and to build a task database based on the task characteristics obtained from the preprocessing. The task processing platform self-check module is used to start the task processing platform self-check when the task processing platform receives a task, and select a preset task processing platform according to the real-time operating load. The platform matching degree judgment module is used to analyze the matching degree of the preset task processing platform with the task, and to consider the scheduling requirements of the task matching degree judgment task. The platform transmission status analysis module is used to analyze the transmission status between platforms in real time and select the candidate task processing platform based on the transmission status between platforms. The prediction completion rate analysis module is used to comprehensively consider the matching degree and transmission status of the candidate task processing platform for the task, and analyze the prediction completion rate of the candidate task processing platform for the task. The task execution platform confirmation module is used to analyze the real-time task execution status of the preferred task processing platform and select the preferred task processing platform to execute the task.

[0012] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: On the one hand, task data is preprocessed and normalized to extract key features and a dedicated task database is established. Simultaneously, historical task processing data from various task processing platforms is combined to analyze the matching degree of each platform with the current task. Subsequently, the matching degree of each platform with the task and the stability of data transmission between platforms will be considered to select the optimal platform for processing the current task. This multi-dimensional adaptation analysis allows each task to be accurately matched with a platform that excels at handling that type of task, effectively reducing processing deviations or errors caused by improper platform-task compatibility, and significantly improving the overall accuracy and reliability of task processing. On the one hand, in the task scheduling process, platforms with suitable load are first screened through load self-check to avoid assigning tasks to platforms that are already overloaded. In the preferred platform selection phase, platforms that are idle are given priority. If all preferred platforms are busy, the platform with the lowest load is selected, and the task is added to its pending list. This dynamic allocation method based on platform load and operating status can reasonably balance the workload of each platform, avoiding situations where some platform resources are idle for a long time while others are overly congested due to task accumulation. It fully taps the processing potential of each platform and maximizes the overall utilization efficiency of cross-platform resources. On the other hand, based on historical data transmission between different platforms, the success rate of data transmission between platforms is analyzed and predicted. Platforms with stable transmission are prioritized for data interaction, reducing the risk of data transmission failure from the source. During task execution, the operational status of the preferred platforms is monitored in real time to ensure that tasks are assigned to platforms with immediate execution capabilities. Even if all preferred platforms are busy, the platform with the lowest load will be selected for subsequent execution. This dual protection for both transmission and execution effectively reduces task delays caused by transmission failures or platform congestion, providing stable support for the smooth progress of cross-platform data tasks. Attached Figure Description

[0013] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a structural diagram of a cross-platform data task scheduling system based on an authorization chain according to the present invention; Figure 2 This is a flowchart of a cross-platform data task scheduling method based on an authorization chain according to the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] Please see Figure 1 and Figure 2 This invention provides a technical solution: a cross-platform data task scheduling method based on an authorization chain, comprising the following steps: S1. When the task processing platform receives a task, it preprocesses the task data and builds a task database based on the task characteristics obtained from the preprocessing. S2. When the task processing platform receives a task, it starts a self-check and selects a preset task processing platform based on the real-time operating load. S3. Analyze the matching degree of the preset task processing platform with the task, and determine the task scheduling requirements based on the task matching degree. S4. Analyze the transmission status between platforms in real time and select the candidate task processing platform based on the transmission status between platforms; S5. Taking into account the matching degree and transmission status of the candidate task processing platforms, analyze and consider the predicted completion rate of the candidate task processing platforms for the tasks. S6. Analyze the real-time task execution status of the preferred task processing platform and select the preferred task processing platform to execute the task.

[0016] In step S1, any task processing platform, after authorization, begins receiving tasks. When a task processing platform receives task α, it collects task data, preprocesses and normalizes the task data to obtain task features, which are {A1, A2, ..., A...}. m ,…,A M}, where A m Let M represent the m-th task feature. Task features include: the ratio of the task's data volume to the historical average data volume, and the ratio of the amount of non-repeating data in the task to the total task data volume. M represents the number of task features. After obtaining the task features, the task processing platform transmits them to a cloud repository to establish a task database based on these features. By preprocessing and normalizing the task data, redundant and messy information can be effectively removed, allowing the extracted task features to more accurately reflect the core attributes of the task and avoiding deviations in subsequent task analysis due to raw data quality issues. Simultaneously, transmitting task features to the cloud repository and establishing a dedicated task database systematically accumulates feature information for various tasks, providing comprehensive and reliable data support for task-platform matching and scheduling in subsequent steps. This breaks through the limitations of fragmented and unreusable task information in traditional scheduling, ensuring the quality of early-stage cross-platform task scheduling from the source and laying the foundation for accurate scheduling later.

[0017] In step S2, when any task processing platform receives a task, the task processing platform begins a self-check. If the real-time operating load of the task processing platform exceeds a preset self-check load threshold, it is determined that the task processing platform cannot process the task, and the task processing platform is classified as a non-preset task processing platform. The real-time operating load is set to the CPU real-time operating utilization rate. Otherwise, it is determined that the task processing platform can process the task, and the task processing platform is classified as a preset task processing platform, thus obtaining N preset task processing platforms, which are {B1, B2, ..., B...}. n ,…,B N}, where B n The nth preset task processing platform; through a self-checking mechanism, preset task processing platforms that meet the load requirements are selected. This mechanism accurately identifies platforms that are currently overloaded and unable to process tasks normally, excluding them from the initial scheduling scope. This operation effectively avoids assigning tasks to platforms with excessive operating pressure, preventing task processing delays, crashes, or incorrect results due to platform overload. Simultaneously, it ensures that all platforms participating in subsequent scheduling have basic task processing capabilities, acting as the first line of defense for the rationality and security of subsequent task allocation, reducing invalid scheduling attempts, and improving overall scheduling efficiency.

[0018] In step S3, when the preset task processing platform B... n Upon receiving task α, analyze the preset task processing platform B. n Based on the matching degree of task α, the preset task processing platform B is invoked. n Historical task processing data, preset task processing platform B n The m-th feature in the historical task completion is A m The number of tasks is D n_m The number of tasks that were successfully processed is d. n_m This leads to the pre-defined task processing platform B. n The matching degree C of the m-th feature of task α n_m C n_m For d n_m With D n_m The ratio is used to obtain the preset task processing platform B. n Task matching degree E for task α n E n Preset task processing platform B n The average value of the matching degree of the M features for task α; Substituting each value into the input n=1,2,…,N, we obtain the matching degree {E1,E2,…,E} of the N task processing platforms for task α. n ,…,E N Set a matching degree self-check threshold. If the preset task processing platform B... n Task matching degree E for task α n The match degree is greater than the self-check threshold and the task processing platform B n Task matching degree E for task α n If the matching degree of task α is greater than the average of N task processing platforms, then task α will be assigned to task processing platform B. n If a task is not successfully processed, it is processed; otherwise, task α is scheduled based on task matching degree. By analyzing the platform's historical task processing data and calculating the matching degree in combination with the successful processing of various task characteristics, the processing capability of each preset platform for the current task can be accurately evaluated, rather than being roughly matched based solely on the platform's surface functions or task type. This matching method based on historical success experience allows tasks to be preferentially assigned to platforms that are truly good at handling this type of task, significantly reducing processing errors caused by inappropriate platform-task compatibility. At the same time, through the dual judgment of matching degree threshold and average value, platforms with better overall adaptability are further screened out, which not only avoids low-compatibility platforms from participating in task processing, but also reduces the workload of subsequent complex scheduling, improving the accuracy and efficiency of the initial task allocation.

[0019] In step S4, the X task processing platforms with the highest matching degree to task α are selected as candidate task processing platforms. The X candidate task processing platforms are {Z1, Z2, ..., Z...} x ,…,Z X}, where Z x Let x be the candidate task processing platform. Analyze the preset task processing platform B. n For the preset task processing platform Z x The transmission stability, if the preset task processing platform B n and the preset task processing platform Z x If they are on the same task processing platform, then determine the preset task processing platform B. n For the preset task processing platform Z x The predicted transmission success rate is 1; otherwise, the preset task processing platform B is used for data acquisition. n For the preset task processing platform Z x The data transmission characteristics are {F1, F2, ..., F...}. x ,…,F X}, where X represents the number of data transmission features, F x Indicates the default task processing platform B n For the preset task processing platform Z x The xth data transmission characteristic calls the historical database, where task processing platform B... n For the preset task processing platform Z x The xth data transmission feature is F x The number of times is H x The number of successful transmissions is h. x This leads to the task processing platform B. n For the preset task processing platform Z x The transmission feature adaptation rate J of the xth data transmission feature x J x =h x / H x Substitute x=1,2,…,X one by one to obtain the task processing platform B. n For the preset task processing platform Z x The adaptation rate of the transmission features of X data transmission features is used to obtain the task processing platform B. n For the preset task processing platform Z x Predicted transmission success rate L xY pre-defined task processing platforms are selected as candidate platforms based on their predicted transmission success rates, ranked from highest to lowest. When selecting candidate platforms, not only is the platform's suitability for the task considered, but the stability of data transmission between platforms is also analyzed, overcoming the shortcomings of traditional scheduling that neglect the reliability of the transmission link. For the same platform, directly determining the high transmission success rate is both realistic and simplifies calculations. For different platforms, the adaptation rate and predicted transmission success rate are calculated using historical transmission data, accurately selecting platforms with stable transmission and low risk of data loss or failure. This operation ensures that the candidate platforms not only adapt to task processing but also guarantee smooth data flow between platforms, reducing the risk of task failure at the transmission level and providing stable data flow support for subsequent task progress.

[0020] In step S5, the matching degree {R1, R2, ..., R...} of task α among the Y candidate task processing platforms is called. y ,…,R Y}, where R y This represents the matching degree of the y-th candidate task processing platform for task α, and the task processing platform B is called. n Predicted transmission success rates {S1,S2,…,S} for Y candidate task processing platforms y ,…,S Y}, where S y Indicates task processing platform B n For the predicted transmission success rate of the y-th candidate task processing platform, calculate the comprehensive task prediction completion rate T of task α on the y-th candidate task processing platform. y Comprehensive task prediction completion rate T y Let the matching degree between the y-th candidate task processing platform and task processing platform B be the degree of matching between task α and task processing platform B. n The product of the predicted transmission success rates of the y-th candidate task processing platform is used to obtain the comprehensive predicted task completion rate {T1,T2,…,T} of task α across the y candidate task processing platforms. y ,…,T YThe process involves ranking the Y candidate task processing platforms according to their overall task prediction completion rate from highest to lowest, and then selecting the Q candidate task processing platforms with the highest overall task prediction completion rates as the preferred task processing platforms. The overall task prediction completion rate is calculated by combining the task matching degree of the candidate platforms with the predicted transmission success rate between platforms, thus achieving a dual consideration of the platform's "processing capacity" and "transmission capacity." This comprehensive evaluation method avoids selection bias caused by relying solely on matching degree or transmission rate; it avoids selecting platforms that are good at processing but have difficulty receiving data stably, as well as platforms that are stable in transmission but are not good at task processing. The preferred platforms selected through ranking by overall completion rate have advantages in both task processing and data transmission, maximizing the overall reliability of the task from data reception to processing completion, and providing a scientific and accurate selection basis for final task authorization.

[0021] In step S6, the preferred task processing platform with the highest overall task prediction completion rate is monitored. If the preferred task processing platform with the highest overall task prediction completion rate is not executing a task, the preferred task processing platform with the highest prediction completion rate is given the task α execution authorization tag; otherwise, the next preferred task processing platform is monitored until a preferred task processing platform that is not executing a task is detected. The preferred task processing platform that is not executing a task is selected and given the task α execution authorization tag. If Q preferred task processing platforms are all executing tasks, the preferred task processing platform with the lowest real-time running load among the Q preferred task processing platforms is selected and given the task α execution authorization tag. Task α is then added to the pending task list of the preferred task processing platform with the lowest real-time running load. By monitoring the running status of the preferred platforms, priority is given to granting task execution rights to idle platforms, which can enable tasks to quickly enter the execution stage, reduce task waiting time caused by platform busyness, and improve task execution efficiency. When all preferred platforms are busy, selecting the platform with the lowest load and adding the task to the pending list avoids task omissions and balances the operational pressure across platforms. This prevents some platforms from becoming overloaded with tasks while others are underloaded, thus avoiding resource waste. This flexible authorization and scheduling method balances the timeliness of task execution with the balanced utilization of platform resources, ensuring that tasks proceed in an orderly manner while maximizing the processing potential of the preferred platforms.

[0022] A cross-platform data task scheduling system based on an authorization chain, the system comprising: a task database establishment module, a task processing platform self-checking module, a platform matching degree judgment module, a platform transmission status analysis module, a predicted completion rate analysis module, and a task execution platform confirmation module; The task data database establishment module is used by the task processing platform to preprocess task data when receiving tasks and build a task database based on the preprocessed task characteristics. The task processing platform self-check module is used when the task processing platform receives a task. The task processing platform starts a self-check and selects a preset task processing platform based on the real-time operating load. The platform matching degree judgment module is used to analyze the matching degree of the preset task processing platform with the task, and to consider the scheduling requirements of the task matching degree judgment task. The platform transmission status analysis module is used to analyze the transmission status between platforms in real time and select the candidate task processing platform based on the transmission status between platforms. The prediction completion rate analysis module is used to comprehensively consider the matching degree and transmission status of the candidate task processing platforms to the task, and analyze the prediction completion rate of the candidate task processing platforms for the task. The task execution platform confirmation module is used to analyze the real-time task execution status of the preferred task processing platform and select the preferred task processing platform to execute the task.

[0023] Example 1: Entering the Task Feature Acquisition and Database Establishment Phase. After being authorized, any task processing platform initiates its task receiving function. Upon receiving a target task, the platform first comprehensively collects all task-related data, then performs data preprocessing, including removing invalid data, supplementing missing information, and then normalizing the data format and magnitude. Finally, it extracts the key features of the task. The platform transmits all features to a cloud repository, gradually accumulating them to form a task database that can be used for subsequent querying and analysis.

[0024] Next, the task processing platform performs self-checks and classifications. Upon receiving a task, the platform automatically initiates a real-time load self-check. The platform has a pre-set load threshold, determined based on hardware performance and operational stability requirements. If the platform's current real-time load exceeds this threshold, it is deemed temporarily unable to handle new tasks and will be classified as a platform without processing capacity. If the load does not exceed the threshold, it is considered capable of processing and is included in the pool of candidate platforms for subsequent scheduling.

[0025] Next, a platform-task matching assessment is conducted. For each candidate platform, historical task processing data for that platform is retrieved from the task database. The platform's past handling of tasks containing various characteristics of the current task is analyzed, calculating the success rate for each characteristic, and then averaging these results to obtain the platform's overall matching degree for the current task. Subsequently, the matching degree of each individual platform is compared with a preset matching degree self-checking threshold, and also with the average matching degree of all candidate platforms, to initially screen out platforms with higher suitability.

[0026] Next, candidate platforms are screened and transmission stability is analyzed. First, from the initial screening, a group with the highest matching degree is selected as candidate platforms. Then, the data transmission stability between the original platform and each candidate platform is analyzed: if they are the same platform, the transmission success rate is directly determined to be the highest; if they are different platforms, various characteristics of their past data transmissions are collected, and the predicted transmission success rate is calculated by combining the proportion of successful transmissions for each characteristic to the total number of transmissions in historical transmissions. Then, a new round of candidate platforms is selected based on the success rate from high to low.

[0027] Next, the overall task prediction completion rate is calculated and the preferred platforms are determined. For each of the new candidate platforms, its matching degree to the current task and the prediction transmission success rate between the original platform and these platforms are obtained. The two are multiplied to obtain the overall task prediction completion rate for each candidate platform. The platforms are then sorted from highest to lowest overall completion rate, and the batch with the highest overall completion rate is selected as the preferred platforms.

[0028] Finally, authorization tags are issued and tasks are assigned. Each preferred platform is monitored individually. If a preferred platform is not currently executing a task, it is given an authorization tag for that task. If the platform is executing a task, the monitoring continues to the next preferred platform. If all preferred platforms are executing tasks, the platform with the lowest real-time load is selected, given an authorization tag, and its current task is added to that platform's pending task list for further processing.

[0029] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary sensing device embodiments described above, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A cross-platform data task scheduling method based on authorization chains, characterized in that: The method includes the following steps: S1. When the task processing platform receives a task, it preprocesses the task data and builds a task database based on the task characteristics obtained from the preprocessing. S2. When the task processing platform receives a task, it begins a self-check and selects N preset task processing platforms based on the real-time operating load: {B1, B2, ..., B...} n ,…,B N }, where B n This is the nth preset task processing platform; S3. Analyze the matching degree of the preset task processing platform with the task, and determine the task scheduling requirements based on the task matching degree. S4. Select the X task processing platforms with the highest matching degree to task α as candidate task processing platforms. The X candidate task processing platforms are {Z1, Z2, ..., Z...} x ,…,Z X }, where Z x Let x be the candidate task processing platform. Analyze the preset task processing platform B. n For the preset task processing platform Z x The transmission stability, if the preset task processing platform B n and the preset task processing platform Z x If they are on the same task processing platform, then determine the preset task processing platform B. n For the preset task processing platform Z x The predicted transmission success rate is 1; otherwise, the preset task processing platform B is used for data acquisition. n For the preset task processing platform Z x The data transmission characteristics are {F1, F2, ..., F...}. x ,…,F X }, where X represents the number of data transmission features, F x Indicates the default task processing platform B n For the preset task processing platform Z x The xth data transmission characteristic calls the historical database, where the preset task processing platform B... n For the preset task processing platform Z x The xth data transmission feature is F x The number of times is H x The number of successful transmissions is h. x This leads to the pre-defined task processing platform B. n For the preset task processing platform Z x The transmission feature adaptation rate J of the xth data transmission feature x J x =h x / H x Substitute x=1,2,…,X one by one to obtain the preset task processing platform B. n For the preset task processing platform Z x The adaptation rate of the transmission features of X data transmission features is used to obtain the preset task processing platform B. n For the preset task processing platform Z x Predicted transmission success rate L x Y preset task processing platforms are selected as candidate task processing platforms based on the predicted transmission success rate from high to low. S5. Invoke the matching degree {R1, R2, ..., R} of the Y candidate task processing platforms for task α. y ,…,R Y }, where R y This indicates the matching degree of the y-th candidate task processing platform to task α, and calls the preset task processing platform B. n Predicted transmission success rates {S1,S2,…,S} for Y candidate task processing platforms y ,…,S Y }, where S y Indicates the default task processing platform B n For the predicted transmission success rate of the y-th candidate task processing platform, calculate the comprehensive task prediction completion rate T of task α on the y-th candidate task processing platform. y :T y =R y *S y This leads to the comprehensive task prediction completion rate {T1,T2,…,T} of task α on Y candidate task processing platforms. y ,…,T Y Then, the Y candidate task processing platforms are sorted from high to low according to the comprehensive task prediction completion rate, and the Q candidate task processing platforms with the highest comprehensive task prediction completion rate are selected as the preferred task processing platforms. S6. Analyze the real-time task execution status of the preferred task processing platform and select the preferred task processing platform to execute the task.

2. The cross-platform data task scheduling method based on authorization chain according to claim 1, characterized in that: In step S1, any task processing platform, after authorization, begins receiving tasks. When a task processing platform receives task α, it collects task data, preprocesses and normalizes the task data to obtain task features, which are {A1, A2, ..., A...}. m ,…,A M }, where A m This represents the m-th task feature, where M represents the number of task features. After obtaining the task features, the task processing platform transmits them to the cloud repository to establish a task database based on the task features.

3. The cross-platform data task scheduling method based on authorization chain according to claim 2, characterized in that: In step S2, when any task processing platform receives a task, the task processing platform starts a self-check. When the real-time operating load of the task processing platform is greater than the preset self-check load threshold, it is determined that the task processing platform cannot process the task and the task processing platform is classified as a non-preset task processing platform. Otherwise, if the task processing platform is deemed capable of processing the task, it is classified as a preset task processing platform, thus resulting in N preset task processing platforms.

4. The cross-platform data task scheduling method based on authorization chain according to claim 3, characterized in that: In step S3, when the preset task processing platform B... n Upon receiving task α, analyze the preset task processing platform B. n Based on the matching degree of task α, the preset task processing platform B is invoked. n Historical task processing data, preset task processing platform B n The m-th feature in the historical task completion is A m The number of tasks is D n_m The number of tasks that were successfully processed is d. n_m This leads to the pre-defined task processing platform B. n The matching degree C of the m-th feature of task α n_m C n_m For d n_m With D n_m The ratio is used to obtain the preset task processing platform B. n Task matching degree E for task α n E n Preset task processing platform B n The average value of the matching degree of the M features for task α.

5. The cross-platform data task scheduling method based on authorization chain according to claim 4, characterized in that: Substituting each value into the input n=1,2,…,N, we obtain the matching degree {E1,E2,…,E} of the N task processing platforms for task α. n ,…,E N Set a matching degree self-check threshold. If the preset task processing platform B... n Task matching degree E for task α n The match degree is greater than the self-check threshold and the preset task processing platform B n Task matching degree E for task α n If the matching degree of task α is greater than the average of N task processing platforms, then task α will be assigned to the preset task processing platform B. n Process it; Otherwise, schedule task α based on task matching degree.

6. The cross-platform data task scheduling method based on authorization chain according to claim 5, characterized in that: In step S6, the preferred task processing platform with the highest overall task prediction completion rate is monitored. If the preferred task processing platform with the highest overall task prediction completion rate is not executing a task, the preferred task processing platform with the highest prediction completion rate is given the task α execution authorization tag; otherwise, the next preferred task processing platform is monitored until a preferred task processing platform that is not executing a task is detected. The preferred task processing platform that is not executing a task is selected and the task α execution authorization tag is given. If Q preferred task processing platforms are all executing tasks, the preferred task processing platform with the lowest real-time running load among the Q preferred task processing platforms is selected and the task α execution authorization tag is given, and task α is added to the list of tasks to be executed by the preferred task processing platform with the lowest real-time running load.

7. A cross-platform data task scheduling system based on an authorization chain, wherein the system is applied to the cross-platform data task scheduling method based on an authorization chain as described in any one of claims 1-6, characterized in that: The system includes: a task database establishment module, a task processing platform self-check module, a platform matching degree judgment module, a platform transmission status analysis module, a predicted completion rate analysis module, and a task execution platform confirmation module; The task database establishment module is used to preprocess task data when the task processing platform receives a task, and to build a task database based on the task characteristics obtained from the preprocessing. The task processing platform self-check module is used to start the task processing platform self-check when the task processing platform receives a task, and select a preset task processing platform according to the real-time operating load. The platform matching degree judgment module is used to analyze the matching degree of the preset task processing platform with the task, and to consider the scheduling requirements of the task matching degree judgment task. The platform transmission status analysis module is used to analyze the transmission status between platforms in real time and select the candidate task processing platform based on the transmission status between platforms. The prediction completion rate analysis module is used to comprehensively consider the matching degree and transmission status of the candidate task processing platform for the task, and analyze the prediction completion rate of the candidate task processing platform for the task. The task execution platform confirmation module is used to analyze the real-time task execution status of the preferred task processing platform and select the preferred task processing platform to execute the task.

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