Multi-core deep management method and system based on enterprise cloud management platform

Abstract

The invention relates to the technical field of cloud platform management, in particular to a multi-core deep management method and system based on an enterprise cloud management platform. The method comprises the following steps: acquiring point task data in an enterprise cloud management platform; capturing a contact event in a task processing process of a cloud seat in the enterprise cloud management platform according to the point task data, and synchronously obtaining point task interaction data with the cloud seat; determining a seat efficiency entropy value of the corresponding cloud seat according to the point task interaction data; the agent real-time states of the cloud agents are judged based on the agent efficiency entropy values, tasks are intelligently matched for the cloud agents in the normal states in combination with a real-time task scheduling sequence in the enterprise cloud management platform, and directional task putting instructions are formed; and multi-core deep management of the enterprise cloud management platform is realized based on the directional task delivery instruction. According to the method, dynamic and accurate matching of the high-priority tasks in the cloud seat mode is realized, so that the problems of blindness of task allocation and resource mismatching are solved.

Description

Technical Field

[0001] This invention relates to the field of cloud platform management technology, and in particular to a multi-core deep management method and system based on an enterprise cloud management platform. Background Technology

[0002] Existing cloud operations systems often employ a "bidding system," simple random allocation, or a coarse matching strategy based on static skill tags when assigning tasks. This approach ignores the real-time priority of tasks, subtle differences in difficulty, and the recent work status and performance fluctuations of cloud agents. The direct consequence is that experienced, high-performing agents are assigned simple, low-value tasks, resulting in a waste of valuable productivity; while novice agents or those with poor recent performance are mistakenly assigned complex tasks beyond their capabilities, leading to excessively long processing times and a sharp increase in rework rates. This mismatch, starting from the source of task allocation, plunges the entire service into inefficiency and chaos from the initial startup phase. The management platform cannot automatically and intelligently guide the platform's best productivity (i.e., the most stable and efficient cloud agents) to these high-priority tasks. Traditional operations managers often have to rely on manual intervention methods such as issuing announcements and offline communication. This approach is not only inefficient and slow to respond, but also struggles to achieve accurate matching, failing to fundamentally guarantee the service quality and timeliness of critical tasks. Summary of the Invention

[0003] Based on this, the present invention provides a multi-core deep management method and system based on an enterprise cloud management platform to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a multi-core deep management method based on an enterprise cloud management platform includes the following steps: Step S1: Obtain point task data from the enterprise cloud management platform; Step S2: Capture the touchpoint events of the cloud agent's task processing process in the enterprise cloud management platform based on the point task data, and synchronously obtain the point task interaction data with the cloud agent. Step S3: Calculate the single interaction resistance value based on the point task interaction data, and then determine the corresponding cloud agent's agent efficiency entropy value; determine the real-time status of the cloud agent based on the agent efficiency entropy value, and combine it with the real-time task scheduling sequence in the enterprise cloud management platform to intelligently match tasks for cloud agents with normal status and form targeted task delivery instructions. Step S4: Implement multi-core deep management of the enterprise cloud management platform based on targeted task delivery instructions.

[0005] This invention also provides a multi-core deep management system based on an enterprise cloud management platform, which executes the multi-core deep management method based on the enterprise cloud management platform as described above. The multi-core deep management system based on the enterprise cloud management platform includes: The data acquisition module is used to acquire point task data from the enterprise cloud management platform; The task monitoring module is used to capture touchpoint events in the process of cloud agents processing tasks in the enterprise cloud management platform based on point task data, and to synchronously acquire point task interaction data with cloud agents. The intelligent task allocation module is used to calculate the single interaction resistance value based on the point task interaction data, and then determine the corresponding cloud agent's agent efficiency entropy value; based on the agent efficiency entropy value, it determines the real-time status of the cloud agent, and combines it with the real-time task scheduling sequence in the enterprise cloud management platform to intelligently match tasks for cloud agents with normal status and form targeted task delivery instructions. The multi-core linkage management module is used to achieve in-depth multi-core management of the enterprise cloud management platform based on targeted task delivery instructions.

[0006] The beneficial effects of this invention are as follows: On the one hand, by capturing touchpoint events during the task processing of cloud agents and calculating the resistance value of each interaction, this invention generates an agent performance entropy value that quantifies the real-time work stability of agents. This allows for real-time and accurate assessment of the current actual working status and performance fluctuations of each cloud agent. This fundamentally changes the traditional cloud operation system's reliance on a "bidding system," random allocation, or blind matching based on static skill tags. It avoids productivity mismatch caused by information lag, where experienced agents are wasted on low-value tasks, while novice agents struggle with complex tasks. This invention makes decisions based on dynamic, quantified process data, ensuring that the platform can dynamically identify high-performing agents in optimal condition, providing a precise and reliable data foundation for optimal resource allocation.

[0007] On the other hand, by filtering the candidate agent pool based on agent efficiency entropy and generating targeted task delivery instructions in conjunction with real-time task scheduling sequences, the platform's high-quality productivity is automatically and intelligently guided. This effectively solves the problem that existing technologies, when faced with high-priority or backlogged tasks, can only rely on inefficient methods such as manual announcements and offline communication for intervention. The targeted delivery mechanism of this invention ensures that the most critical and complex tasks are always automatically assigned to the most stable and efficient cloud agents, achieving dynamic load awareness and task management without manual intervention. This significantly enhances the platform's service quality and timeliness under conditions of drastic load fluctuations, improving the overall orderliness and stability of operations.

[0008] On the other hand, the invention's unique six-core deep management model, by using targeted task delivery instructions and the underlying agent performance entropy data as the core driver, achieves deep linkage and data integration across six core systems: cloud recruitment, cloud training, cloud operations, cloud quality inspection, cloud analytics, and cloud settlement. For example, the continuously changing agent performance entropy not only directly guides the cloud operations system in precise task scheduling but also dynamically triggers the cloud quality inspection system to adjust quality inspection strategies for agents with fluctuating performance, links with the cloud training system to push personalized refresher training courses, and serves as a precise basis for dynamic incentives or salary adjustments in the cloud settlement system. Simultaneously, the cloud analytics system uses this dynamic data to continuously optimize agent profiles and talent models, feeding back into the cloud recruitment system to improve recruitment accuracy. This constructs a closed-loop management system covering the entire lifecycle from talent selection, skills training, task execution, quality monitoring to value settlement, transforming the previously fragmented modular management into collaborative intelligent operations, thereby fundamentally ensuring the long-term, efficient, and stable operation of the cloud agent model. Attached Figure Description

[0009] Figure 1 This is a flowchart illustrating the steps of the multi-core deep management method based on an enterprise cloud management platform according to the present invention. Figure 2 This is a schematic diagram of the six-core deep management module based on an enterprise cloud management platform according to the present invention; Figure 3 This is an example diagram of the front-end interface of this invention; Figure 4 This is a schematic diagram of the task distribution and acceptance architecture based on an enterprise cloud management platform according to the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0010] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0011] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0012] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0013] To achieve the above objectives, please refer to Figures 1 to 4 This invention provides a multi-core deep management method based on an enterprise cloud management platform, comprising the following steps: Step S1: Obtain point task data from the enterprise cloud management platform; Step S2: Capture the touchpoint events of the cloud agent's task processing process in the enterprise cloud management platform based on the point task data, and synchronously obtain the point task interaction data with the cloud agent. Step S3: Calculate the single interaction resistance value based on the point task interaction data, and then determine the corresponding cloud agent's agent efficiency entropy value; determine the real-time status of the cloud agent based on the agent efficiency entropy value, and combine it with the real-time task scheduling sequence in the enterprise cloud management platform to intelligently match tasks for cloud agents with normal status and form targeted task delivery instructions. Step S4: Implement multi-core deep management of the enterprise cloud management platform based on targeted task delivery instructions.

[0014] In this embodiment of the invention, four types of core data streams, namely point task data, are predefined and structuredly stored in the backend database of the enterprise cloud management platform. These four types of data streams are: task stream data, which records the unique identifier, task type, initial difficulty coefficient (range 0.1 to 1.0), and creation timestamp of each task; user stream data, which records the unique identifier, current skill level (level 1 to 5), and historical task preference tags of each cloud agent; quality inspection stream data, which records the quality inspection results of completed tasks, including a Boolean value indicating whether rework is required and specific quality inspection opinions; and settlement stream data, which records the final settlement time (unit: seconds) and settlement amount of the task.

[0015] In this embodiment of the invention, a lightweight event listener is deployed at the application layer. This listener subscribes to four key state change events in the task lifecycle: "task claimed," "task first submitted," "task inspected," and "settlement completed." These four events are defined as touchpoint events. When any operation by any cloud agent triggers these events, the listener immediately captures the event and parses out the associated task identifier and cloud agent identifier. Using these two identifiers as a composite primary key, a parallel, atomic data query request is initiated to the four data streams described in step S1. The queried task difficulty coefficient, cloud agent level, current rework status, and task duration are aggregated in real time, ultimately generating a structured JSON object, i.e., the point task interaction data.

[0016] In one implementation of this invention, based on the task difficulty and cloud agent level in the point task interaction data, a preset two-dimensional matrix of "interaction benchmark parameter set" is queried to obtain the upper limit of dynamic benchmark time consumption and the number of dynamic rework benchmarks. Then, the actual time consumption and rework status are compared with the benchmarks to calculate the time deviation and quality deviation, and the single interaction resistance value is obtained by weighted summation (time weight 0.3, quality weight 0.7). A first-in-first-out queue with a capacity of 20 is maintained for each cloud agent to store its most recent 20 single interaction resistance values. Whenever a new value is generated, it is enqueued and the oldest value is squeezed out. Then, the standard deviation of all values ​​in the queue is immediately calculated and normalized to the interval [0, 1] to obtain the real-time agent performance entropy value of that agent. Next, the seat performance entropy value is compared with preset thresholds (stable threshold 0.2, warning threshold 0.6) to mark the real-time status of the seats as "stable", "fluctuating", or "warning". Combining the average entropy value of all seats (i.e. system load index) and the attributes of each task in the task pool, the scheduling weight of the task is dynamically calculated to generate a real-time task scheduling sequence. The task with the highest weight is selected and matched with the cloud seat in the candidate pool that is "stable" or "fluctuating" and has the lowest entropy value to generate a targeted task delivery instruction.

[0017] In this embodiment of the invention, the real-time status identifier of the target agent in the targeted task delivery instruction is checked. If the status is "stable" or "fluctuating," it is determined to be a normal delivery, and the instruction is directly sent to the cloud operations core. After receiving the instruction, the cloud operations core locks the specified task and pushes it to the target cloud agent, completing an efficient and accurate task allocation. If the status is "warning," it is determined to be an abnormal state, and the targeted task delivery instruction will be automatically suspended and not executed. Instead, an intervention calibration instruction will be generated. This instruction will link the cloud quality inspection core and the cloud training core to push a "calibration task package" containing standardized test questions or a micro-training course targeting the agent's recent weaknesses to the agent in the warning state. Only when the agent completes the calibration and passes the assessment, and their agent performance entropy value is corrected and returns to the normal range, will task allocation for them be restored.

[0018] Preferably, step S2 includes the following steps: Step S21: Define the task lifecycle in the task data as four independent touchpoint events: task being claimed, task first submission, task being inspected, and task completion settlement. Step S22: Capture touchpoint events occurring on any cloud agent in the enterprise cloud management platform using a preset event listener, and immediately lock the task identifier and cloud agent identifier associated with the event; Step S23: Using the task identifier as the first index and the cloud agent identifier as the second index, synchronously obtain the task difficulty coefficient, cloud agent level, current rework status, and task duration from the point task data, as point task interaction data.

[0019] In this embodiment of the invention, during the system architecture design phase, the entire process of interaction between the cloud agent and the task is decomposed into business processes. Four key nodes that are most decisive in evaluating the quality and efficiency of the interaction are identified: the moment the cloud agent clicks the "claim" button in the task pool, the moment the first click of the "submit" button uploads the processing result, the moment the quality inspector or AI quality inspection system gives a pass or rejection conclusion, and the moment the task is finally confirmed as completed and enters the financial settlement process. These four nodes are mapped in the system backend to four named events that can be programmatically subscribed to: the task claim event, the task first submission event, the task quality inspection event, and the task settlement event, which together constitute the touchpoint event set.

[0020] In one implementation of this invention, a commit count field is maintained for each task instance, with an initial value of 0. The first commit event is triggered only when a cloud agent submits a task and the commit count field for that task is 0. At the same time, the value of the field is updated to 1. Any subsequent commits, such as those after rework, will only trigger the general task commit event, thereby ensuring the uniqueness and accuracy of the first commit event.

[0021] In this embodiment of the invention, a set of preset event listeners is deployed in the underlying framework of the front-end working interface of all cloud agents. This listener runs automatically when the cloud agent interface loads and subscribes to the entire defined set of touchpoint events on the event bus. When any defined touchpoint event is triggered by the platform's front-end or back-end business logic, the event is published to the event bus carrying a data payload containing necessary context information. Because the event listener has already subscribed, it receives the event and its data payload with near-zero latency. Upon receiving it, the listener immediately parses the data payload and extracts the predefined task identifier and cloud agent identifier fields.

[0022] Specifically, suppose a cloud agent accepts a task. The frontend application sends a request to the backend interface. In the business logic processing this request, after successfully binding the task to the agent, the backend publishes a task acceptance event to the event bus. The data payload of this event includes the current task identifier and the cloud agent identifier. Upon receiving this published event, the event listener's internal processing function immediately reads and locks these two key identifiers, storing them in the memory of the currently processing thread.

[0023] In this embodiment of the invention, a locked task identifier is used to query the task table to obtain the task difficulty coefficient field; a locked cloud agent identifier is used to query the user table to obtain the cloud agent level field; the task identifier is then used to query the task instance table to obtain the rework count field, and a boolean value for the current rework status is generated accordingly; finally, the task identifier and event type (e.g., task settlement event) are used to query the task log table, and the task duration is obtained by calculating the difference between the timestamp of the task settlement event and the timestamp of the task claim event. All the retrieved data is integrated into a temporary in-memory data structure to form the final point-to-point task interaction data.

[0024] It should be noted that the calculation method for the task time is dynamic. The endpoint of the time calculation is determined by comparing the currently captured touch event types. For example, if the captured event is the first submission of the task, the time calculation is the time from receipt to the first submission; if the captured event is the task settlement, the calculation is the total time from receipt to final settlement. This design ensures that meaningful time consumption data can be obtained at different stages of the task lifecycle.

[0025] In one implementation of this invention, it is assumed that a task settlement event for a specific task and a specific cloud agent is captured. The data aggregation operation is performed as follows: 1. Query the task table based on the task identifier, and the task difficulty coefficient is returned as 0.7; 2. Query the user table based on the cloud agent identifier, and the cloud agent level is returned as level 3; 3. Query the task instance table based on the task identifier, and the number of rework attempts is returned as 0. Based on this, it is calculated whether the current rework status is positive or negative. The task log table is queried to obtain the task claim event timestamp as 1677612000 and the task settlement event timestamp as 1677612350. The time taken for this task is calculated as 1677612350 minus 1677612000, which is 350 seconds. These data are encapsulated into point task interaction data, the content of which is: task difficulty coefficient is 0.7, cloud agent level is level 3, current rework status is positive or negative, and the time taken for this task is 350 seconds.

[0026] Preferably, step S3, which calculates the single interaction resistance value based on the point task interaction data, includes: Using the task difficulty coefficient and cloud seat level in the point task interaction data as query conditions, the corresponding dynamic baseline time limit and dynamic rework baseline number of times can be retrieved. The time deviation is obtained by calculating the difference between the current task time in the point task interaction data and the upper limit of the dynamic benchmark time. The difference between the current rework status in the point task interaction data and the dynamic rework baseline number is calculated as the quality deviation. The single-cycle resistance value is generated by weighted summation based on time deviation and mass deviation.

[0027] In this embodiment of the invention, a pre-set set of interactive benchmark parameters is stored in the background, using a two-dimensional parameter matrix data structure. The row indices of this matrix correspond to the task difficulty coefficient, and the column indices correspond to the cloud agent level. Each cell in the matrix stores a pair of data containing two values: the first value is the upper limit of dynamic benchmark time (in seconds), and the second value is the dynamic rework benchmark number of times (in times). It should be noted that this parameter set is not static but is periodically (e.g., every 24 hours) updated by an offline analysis task in the background based on statistical analysis of massive historical task data from the platform, ensuring the timeliness and accuracy of the benchmark values.

[0028] In this embodiment of the invention, the time consumed in the current task is extracted from the point task interaction data and subtracted from the dynamic baseline time limit retrieved in the previous step. To ensure the non-negativity of the deviation value, this operation is designed as follows: if the actual time consumed exceeds the baseline limit, the time deviation is the difference between the two; if the actual time consumed does not exceed the baseline limit, the time deviation is recorded as 0. This processing method aims to only punish timeout behavior and not reward early completion, thereby ensuring the unidirectionality of the resistance value model.

[0029] In this embodiment of the invention, the current rework status (a Boolean value) in the point task interaction data is converted into a numerical value. Specifically, "Yes" (i.e., rework has occurred) is mapped to the numerical value 1, and "No" (i.e., no rework has occurred) is mapped to the numerical value 0. Subsequently, this mapped numerical value is subtracted from the retrieved dynamic rework baseline number, and the absolute value is taken. The result is the quality deviation. Taking the absolute value is to ensure that any deviation from the expected baseline, whether higher or lower, is considered a deviation.

[0030] In this embodiment of the invention, a weighted summation method is used to merge the time deviation and quality deviation calculated in the first two steps into a single, dimensionless index, namely the single-interaction resistance value. It should be noted that the calculation formula for the single-interaction resistance value is preset as follows: ; in, This represents the resistance value for a single interaction. This is due to time deviation; The upper limit of the dynamic benchmark time is used as the denominator to convert the absolute time deviation into a relative deviation for normalization. Time weight is a preset parameter that reflects the platform's emphasis on efficiency. This is a quality deviation; The number of rework references is used as the denominator to convert absolute quality deviations into relative deviations for normalization. Quality weight is a preset parameter that reflects the platform's emphasis on quality. and The sum of is 1.

[0031] Preferably, the task difficulty coefficient and cloud agent level in the point task interaction data are used as query conditions to retrieve the corresponding dynamic baseline time limit and dynamic rework baseline number of times, including: Based on the cloud agent identifier and task difficulty coefficient in the point task interaction data, all historical task records of the cloud agent within the task difficulty coefficient similarity range are retrieved from the preset historical work order database to obtain the agent's historical similar work orders; where the task similarity range is set to ±5% of the current task difficulty coefficient; Calculate the arithmetic mean and standard deviation of the time taken for all tasks in the same type of work order in the agent's history; The upper limit of the dynamic benchmark time is calculated by multiplying the standard deviation by a preset time fluctuation factor and then adding the standard deviation; the preset time fluctuation factor ranges from 1.2 to 2.0. The total number of reworks and the total number of tasks in the same type of work order in the history of the seats are counted, the historical average number of reworks is calculated, and the historical average number of reworks is summed with the preset rework tolerance index to obtain the dynamic rework baseline number of times. A two-dimensional parameter matrix is ​​constructed with the task difficulty coefficient as the rows and the cloud seat level as the columns. Then, the two-dimensional parameter matrix is ​​mapped with dynamic baseline time limit and dynamic rework baseline number of times.

[0032] In this embodiment of the invention, when a new interactive calculation benchmark is needed, the unique identifier of the cloud agent and the difficulty coefficient of the current task are extracted from the current point task interaction data. Based on this, a database query operation is performed, targeting the historical work order database of the cloud agent. The core condition for the query is the similarity of the task difficulty coefficients, and the similarity range of the task difficulty coefficients is set to ±5% of the current task difficulty coefficient.

[0033] In one implementation of this invention, assuming the cloud agent identifier corresponding to the current task interaction data is U-058, and the task difficulty coefficient is 0.7, the task similarity range is calculated as follows: the lower limit is 0.7 minus (0.7 multiplied by 5%), i.e., 0.665; the upper limit is 0.7 plus (0.7 multiplied by 5%), i.e., 0.735. From the historical work order database, all completed task records completed by cloud agent U-058 with a task difficulty coefficient between 0.665 and 0.735 are selected. Assuming that 50 records that meet the conditions are finally retrieved, this data set containing 50 records is defined as the historical similar work orders of the agent.

[0034] In this embodiment of the invention, after obtaining similar work orders from the agent's history, each record in the dataset is traversed, and the value of the "task time" field is extracted. Then, two basic statistical calculations are performed on all extracted time values: the arithmetic mean of these values ​​is calculated to reflect the average efficiency of the agent in handling this type of task; and the standard deviation of these values ​​is calculated to quantify the dispersion or instability of their performance.

[0035] In this embodiment of the invention, the calculation formula for the upper limit of dynamic benchmark time is defined as follows: the upper limit of dynamic benchmark time equals the arithmetic mean of task time plus (the standard deviation of task time multiplied by the time fluctuation coefficient). It should be noted that the time fluctuation coefficient is an engineering parameter used to adjust the benchmark tolerance, and its value is greater than 1. The purpose of setting this coefficient is to set the upper limit of the benchmark within a reasonable fluctuation range determined by historical performance volatility (i.e., standard deviation) above the average value, thereby avoiding misjudgments of the agent due to accidental, normal time fluctuations.

[0036] In this embodiment of the invention, the historical work orders of the same type are iterated again, and the total number of records with a "rework status" of "yes" (i.e., the total number of reworks) and the total number of records (i.e., the total number of tasks) are counted. The historical average number of reworks is obtained by dividing the two. It should be noted that, in order to take into account the inherent ambiguity or difficulty of certain task types, a preset rework tolerance index is introduced. This index is a small positive value, representing the basic probability of rework allowed by the platform and not due to agent capability issues. The final dynamic rework baseline number is equal to the sum of the historical average number of reworks and the rework tolerance index.

[0037] In this embodiment of the invention, after dynamically calculating a new pair of benchmark values ​​for a certain cloud agent and a specific task difficulty, the task difficulty coefficient and cloud agent level in the current task interaction data are used as coordinates, and the calculated dynamic benchmark time limit and dynamic rework benchmark number of times are written into the corresponding cell of the matrix for subsequent real-time query.

[0038] Preferably, determining the seat performance entropy value of the corresponding cloud agent in step S3 includes: Configure a queue with a fixed capacity for each cloud agent within the enterprise cloud management platform; the queue is set to first-in, first-out (FIFO). When the cloud agent generates a new single interaction resistance value, it pushes the single interaction resistance value to the tail of its corresponding queue. Determine if the length of the queue exceeds the fixed capacity; if so, remove one single-interaction resistance value from the head of the queue. Read all current values ​​in the queue, calculate their standard deviation, and use the standard deviation as the original entropy value; The original entropy value is processed by minimax normalization to obtain the seat efficiency entropy value.

[0039] In this embodiment of the invention, during the system initialization phase, an independent data structure, namely a fixed-capacity queue, is created in memory for each cloud agent registered on the platform. This queue is designed to operate in a First-In, First-Out (FIFO) mode, used to temporarily store the single-interaction resistance values ​​generated by the cloud agent's recent series of task interactions. It should be noted that the fixed capacity of the queue is a key engineering parameter, determining the size of the time window for calculating the performance entropy value. Setting the capacity too small will cause the results to be overly sensitive to single abnormal behaviors; setting it too large will make the results less responsive to recent state changes.

[0040] In one implementation of this invention, after analyzing and testing the platform's business characteristics, the fixed capacity is set to 20, and the stability of a cloud agent's current working status will always be evaluated based on the performance of the last 20 task interactions.

[0041] In this embodiment of the invention, whenever a cloud agent completes a task interaction and calculates a new single-interaction resistance value based on the point task interaction data, this value is immediately routed to the queue corresponding to the cloud agent's identifier and an enqueue operation is performed. Specifically, the new single-interaction resistance value is added to the end of the queue (tail end), becoming the latest data point in the queue.

[0042] In this embodiment of the invention, the current length of the queue is checked immediately after each enqueue operation. If the length exceeds a preset fixed capacity (e.g., 20), a dequeue operation is automatically performed. Specifically, the element at the very front of the queue (the head of the queue), which is the earliest element to enter the queue and represents the single interaction resistance value of the oldest interaction, is removed. This "one in, one out" mechanism ensures that the queue length always remains at a fixed capacity, and that the queue always stores the interaction data of the most recent N interactions.

[0043] In this embodiment of the invention, after the queue data is updated (i.e., the enqueue and dequeue operations are completed), all currently stored values ​​in the queue are read. Then, the standard deviation calculation formula in statistics is applied to these values, and the result is defined as the original entropy value. It should be noted that the physical meaning of the standard deviation here is to measure the dispersion of the recent performance of the cloud agent (i.e., a series of single-interaction resistance values). The larger the standard deviation, the more volatile and inconsistent its performance; the smaller the standard deviation, the more stable and consistent its performance.

[0044] In this embodiment of the invention, in order to eliminate the dimensions of the original entropy value (i.e., standard deviation) and make it easier to compare among different agents and set a uniform threshold, the original entropy value is subjected to a minimum-maximum normalization process. This process linearly maps the original entropy value to a fixed, closed interval, typically between 0 and 1. Specifically, the formula for calculating the agent performance entropy value is as follows: ; in, This represents the seat efficiency entropy value. This is the original entropy value; The maximum value found from all original entropy records in the history of this seat; The minimum value found in all original entropy records of this seat's history.

[0045] Preferably, before determining the real-time status of cloud agents based on agent performance entropy values ​​in step S3, and combining this with the real-time task scheduling sequence in the enterprise cloud management platform to intelligently match tasks for cloud agents with normal status and generate targeted task delivery instructions, the process further includes: Extract the seat performance entropy values ​​of all active cloud agents within the enterprise cloud management platform, and calculate the average value as the system load index; Extract the basic priority, timeliness factor, and difficulty coefficient of each task from the task pool to be assigned in the enterprise cloud management platform; Determine if the system load index is greater than the preset system health threshold: if so, calculate the scheduling weight of each task using the preset first weighting formula; otherwise, calculate the scheduling weight of each task using the preset second weighting formula. All tasks in the task pool to be assigned are sorted in descending order according to their scheduling weights to generate a real-time task scheduling sequence.

[0046] In this embodiment of the invention, a global status scan is performed periodically (e.g., every minute). This scan first retrieves a list of all cloud agents currently in an "online" or "working" state from the user management module, i.e., active cloud agents. Then, it iterates through this list, extracting the latest agent performance entropy value from the real-time status data of each cloud agent. Finally, all extracted agent performance entropy values ​​are summed and divided by the total number of active cloud agents to calculate the arithmetic mean. This average is defined as the system load index, which macroscopically reflects the overall working stability of the entire cloud agent group.

[0047] In one implementation of this invention, assuming there are 500 active cloud agents on the platform during a scan, the efficiency entropy values ​​of these 500 agents are extracted, for example, {0.15, 0.22, 0.31, ..., 0.18}. After calculation, the sum of these 500 values ​​is 125. The system load index is calculated as follows: System load index = 125 ÷ 500 = 0.25. This value indicates that the overall operating status of the platform is relatively stable.

[0048] In this embodiment of the invention, the system accesses the task pool to be assigned on the enterprise cloud management platform. This task pool stores all tasks that have been created but have not yet been assigned to any cloud agent. The system traverses each task record in the task pool and extracts three key scheduling attributes: basic priority, an integer value preset by business requirements to represent the importance of the task, such as levels 1 to 5; timeliness factor, a value dynamically calculated based on the proximity of the task's deadline to the current time, with a larger factor for more urgent tasks; and task difficulty coefficient, a floating-point number assessed by the system when the task is created to represent the complexity of the task.

[0049] Specifically, the timeliness factor is calculated as follows: the timeliness factor equals 1 divided by (task deadline timestamp minus current timestamp). This reciprocal relationship ensures that the timeliness factor of a task will increase rapidly and non-linearly over time, thus ensuring that tasks nearing their deadline receive higher scheduling weights.

[0050] In this embodiment of the invention, a preset system health threshold is built-in to distinguish whether the platform is in a "stable operation" state or a "load fluctuation" state. It should be noted that this threshold is an empirical value derived from long-term operational data statistical analysis. It is used to trigger different task scheduling strategies, comparing the system load index calculated in the previous step with this threshold, and selecting different weighting formulas to calculate the final scheduling weight of each task based on the comparison results.

[0051] In one implementation of this invention, a preset system health threshold is assumed to be 0.4. The currently calculated system load index is 0.25, which is less than 0.4; therefore, the system is determined to be in a "stable operation" state, and the second weighting formula is selected. Conversely, if the system load index rises to 0.5 at a certain moment, the system will switch to the first weighting formula.

[0052] In this embodiment of the invention, after calculating the scheduling weight for each task in the task pool to be assigned, a global sorting operation is performed. Specifically, all tasks and their corresponding scheduling weights are treated as a whole and sorted in descending order of scheduling weight. This dynamically sorted task list is defined as the real-time task scheduling sequence. The top of this sequence always represents the task that should be prioritized for processing by the entire platform.

[0053] Most importantly, the first weighting formula and the second weighting formula are as follows: The mathematical expression for the first weighted formula is: ; The second weighting formula is: The first weighting formula is: ; in, The scheduling weight of the task; For the timeliness factor of the task; As the basic priority of the task; This represents the difficulty level of the task.

[0054] In one implementation of this invention, when the system load index exceeds a preset system health threshold of 0.4, it indicates that the platform is in a state of load fluctuation or instability, and the first weighted formula will be activated. It should be noted that this formula is designed to quickly stabilize the system. Specifically, the timeliness factor is given the highest positive weight (1.8) to ensure that tasks nearing their expiration date receive the highest priority, avoiding service interruptions. The weight of the basic priority is moderately reduced (0.8), indicating that when the system is unstable, the importance of business value gives way to system stability. Most importantly, the difficulty coefficient is given a significant negative weight (-1.2), meaning that the system will intentionally reduce the priority of complex, time-consuming tasks, instead prioritizing simple, quickly completed tasks to rapidly clear the task queue and reduce the overall system load.

[0055] In another implementation of this invention, when the system load index is less than or equal to the system health threshold of 0.4, it indicates that the platform is operating smoothly, and the system will switch to the second weighted formula. It should be noted that the design goal of this formula is to maximize the platform's output value while ensuring service timeliness. Specifically, the basic priority is given the highest weight (1.5), ensuring that high-value tasks are processed first. The weight of the timeliness factor returns to the standard level (1.0), maintaining adherence to service commitments. In contrast to the first formula, the difficulty coefficient is given a positive weight (0.5), meaning that when the system has surplus capacity, it will moderately encourage the processing of more challenging tasks, as these tasks are usually accompanied by higher business value or contribute to improving agent skills.

[0056] Preferably, in step S3, the real-time status of cloud agents is determined based on the agent performance entropy value, and combined with the real-time task scheduling sequence in the enterprise cloud management platform, tasks are intelligently matched for cloud agents with normal status to form targeted task delivery instructions, including: Obtain the task with the highest scheduling weight from the real-time task scheduling sequence; Select cloud agents in the enterprise cloud management platform who are idle and whose skills match the task to be assigned, and form a candidate agent pool. Iterate through each cloud agent in the candidate agent pool and obtain their real-time agent status identifier. Based on the real-time status identifier of the agent, all cloud agents marked as warnings are excluded, and the cloud agent with the lowest agent efficiency entropy value is searched and determined as the target agent from the remaining cloud agents. The ID of the task to be assigned is combined with the ID of the target agent to generate a targeted task delivery instruction.

[0057] In this embodiment of the invention, after the dynamic sorting of the task pool is completed, the generated real-time task scheduling sequence is directly accessed. This sequence is essentially a task queue arranged in descending order of scheduling weight. A "head-of-queue value retrieval" operation is performed, that is, the task at the top of the queue is read and locked as the only task to be assigned in the current allocation cycle. At the same time, the skill requirements necessary for its execution are extracted from the task's metadata.

[0058] In one implementation of this invention, it is assumed that the content of the real-time task scheduling sequence is: [Task TB, Task TA, Task TC, ...]. The task with the task identifier TB is obtained from the top of the sequence, and its associated skill requirements are extracted, such as: requiring a "financial product knowledge" level of no less than 3 and a "advanced communication skills" level of no less than 2.

[0059] In this embodiment of the invention, after locking down the task to be assigned, all cloud agents within the platform undergo a rapid screening to construct a pool of candidate agents qualified to undertake the task. This screening process includes two parallel hard conditions: first, the current working status of the cloud agent must be "idle" or "on standby" to ensure that it can immediately receive new tasks; second, the skill profile of the cloud agent must meet all the skill requirements of the task to be assigned. Specifically, the skill matching judgment logic is as follows: the skill tag set of the cloud agent must completely contain the skill tag set required by the task, and for each matched skill, the agent's proficiency level must not be lower than the minimum level required by the task.

[0060] In one implementation of this invention, it is assumed that the candidate agent pool is traversed and the following information is obtained: the real-time status of cloud agent U-101 is "stable". The real-time status of cloud agent U-205 is "fluctuating". The real-time status of cloud agent U-310 is "warning".

[0061] In this embodiment of the invention, a two-stage screening and selection logic is adopted. The first stage performs a hard filter, directly excluding all cloud agents in the candidate agent pool whose real-time status is marked as "warning," because these agents are considered to be in an unstable state and unsuitable for handling new, especially high-priority, tasks. In the second stage, among the cloud agents remaining after the first stage of filtering, their respective agent performance entropy values ​​are read and compared, and the cloud agent with the lowest performance entropy value is selected as the final target agent.

[0062] In this embodiment of the invention, after determining the tasks to be assigned and the target agents, a structured data instruction, namely a targeted task delivery instruction, is generated. This instruction clearly specifies which task should be assigned to which agent and is ultimately sent to the cloud operations core for processing, triggering the actual task allocation action.

[0063] Preferably, traversing each cloud agent in the candidate agent pool to obtain its real-time agent status identifier includes: Compare the seat performance entropy value of the cloud seat with the preset stability threshold and the preset warning threshold. If the seat efficiency entropy value is less than or equal to the preset stability threshold, the state is marked as stable. If the seat efficiency entropy value is greater than the preset stability threshold and less than the preset warning threshold, the state will be marked as fluctuating. If the seat efficiency entropy value is greater than or equal to the warning threshold, the status will be marked as a warning. Based on the marked status, generate a real-time status identifier for the cloud agent.

[0064] In one implementation of this invention, assuming that the preset stability threshold is set to 0.2 and the preset warning threshold is set to 0.6 based on the latest statistical analysis, when determining the status of a cloud agent, the latest agent performance entropy value will be extracted and compared with the two thresholds.

[0065] In this embodiment of the invention, the first-level judgment logic is executed. If the seat performance entropy value of a cloud agent is less than or equal to a preset stability threshold, it indicates that the agent's recent work performance is very stable, and the fluctuation of the interaction resistance value is minimal. Therefore, a temporary "stable" label is attached to the current state of the agent.

[0066] In this embodiment of the invention, if the seat performance entropy value does not meet the first-level judgment condition, the second-level judgment will be executed. If the performance entropy value falls between the stability threshold and the warning threshold, it indicates that the seat's performance has some fluctuations, but has not yet reached the severity level that requires immediate intervention. A temporary "fluctuation" label is then added to the seat's current state.

[0067] In this embodiment of the invention, for the seat performance entropy value that does not meet the first two judgment conditions, a third level of judgment is performed. If the value is greater than or equal to the preset warning threshold, it indicates that the seat's recent performance has shown significant and abnormal fluctuations, indicating a potential problem. A temporary "warning" mark is then added to the seat's current state.

[0068] Preferably, before generating the targeted task delivery instruction, the method further includes a step of intervening with cloud agents whose status is marked as "warning": Listen for and capture task requests initiated by cloud agents whose status is marked as alert; The task request is rejected, its normal task matching process is interrupted, and then a calibration task is randomly selected from the pre-set calibration task pool in the enterprise cloud management platform. The ID of the calibration task is bound to the ID of the cloud agent, an intervention calibration command is generated and sent to the corresponding cloud agent.

[0069] In this embodiment of the invention, a front-end request interceptor is set at the main entry point for task allocation. This interceptor listens for all "request new task" actions initiated by cloud agents. Whenever a task request is captured, the interceptor does not immediately hand it over to the task matching module. Instead, it first extracts the unique identifier of the cloud agent who initiated the request and queries its latest real-time agent status identifier. It should be noted that this interception and checking action is performed at the very beginning of the task matching process, ensuring that requests from agents with abnormal statuses can be identified and processed immediately.

[0070] In this embodiment of the invention, once the interceptor confirms that a task request comes from a cloud agent in a "warning" state, it performs two key actions: First, it rejects the task request, meaning that the request will not enter the subsequent candidate agent pool screening and task matching stages, thus interrupting the normal task allocation process. Second, it automatically redirects to a special destination—a pre-set calibration task pool. Specifically, this calibration task pool is an independent database table that stores a series of standardized, low-difficulty micro-tasks with clearly defined correct answers, used to quickly test and calibrate the basic skills and focus of cloud agents. A calibration task is randomly selected from this pool.

[0071] In this embodiment of the invention, after a calibration task is extracted, a structured data instruction, namely an intervention calibration instruction, is generated. The core of this instruction is to strongly bind the unique identifier of the calibration task with the unique identifier of the cloud agent in a warning state. This instruction differs from conventional targeted task delivery instructions; it carries a special "intervention" or "calibration" action identifier to inform downstream systems to execute non-standard task processing procedures. Finally, this instruction is issued, triggering the corresponding module to push the calibration task to the target cloud agent.

[0072] In another implementation of this invention, the extracted calibration task identifier C-008 is bound to the cloud agent identifier U-310. The final generated intervention calibration instruction data content is: {Instruction type: Intervention calibration, Task identifier: C-008, Target agent identifier: U-310}. This instruction is sent to the cloud operations core, which parses the instruction type as "intervention calibration" and then calls the relevant interface of the cloud training or cloud quality inspection module to present the content of task C-008 on the operation interface of the cloud agent U-310.

[0073] Most importantly, after the intervention calibration command is issued to the corresponding cloud agent, it also includes: After receiving the results submitted by the cloud agent whose status is marked as "warning" after completing the calibration task, the results are immediately compared with the preset standard answer to calculate the accuracy of this calibration. Calculate the entropy correction factor based on the accuracy rate; The current seat performance entropy value of the cloud seat is summed with the entropy correction factor, and this sum is used as the seat capability correction value to overwrite its original seat performance entropy value.

[0074] In this embodiment of the invention, after a cloud agent in an alert state completes a calibration task and submits their answer, the result is sent to a dedicated calibration result processing service. This service first extracts the corresponding preset standard answer from the calibration task pool based on the calibration task identifier contained in the submitted result. Specifically, the standard answer and the agent's submitted result are usually structured data, such as a sequence of options for multiple-choice questions or a sequence of true / false questions. The processing service compares the agent's answer with the standard answer item by item, counting the number of items that match completely, i.e., the number of questions answered correctly. Finally, the accuracy rate of this calibration is calculated by dividing the number of correctly answered questions by the total number of questions in the calibration task.

[0075] In this embodiment of the invention, a preset nonlinear mapping function is used to convert the calibration accuracy calculated in the previous step into an entropy correction factor. This correction factor is a positive or negative value used to adjust the current performance entropy value of the agent. It should be noted that the design of this mapping function aims to achieve a differentiated reward and punishment effect: a high accuracy rate (good performance) will receive a large negative correction factor, which can significantly reduce the entropy value; a medium accuracy rate will receive a small negative correction factor or be close to zero; while a low accuracy rate (poor performance) will receive a positive correction factor, further increasing its entropy value, indicating that its state has not improved or has even deteriorated.

[0076] It should be noted that the entropy correction factor is calculated using a piecewise linear function to achieve a non-linear reward / penalty effect, and its mathematical expression is as follows: ; in, This is the entropy correction factor; To calibrate the accuracy of the task; The preset calibration pass threshold is set to 85% in this embodiment; This is the reward coefficient, a negative number used to reduce the entropy value when the accuracy is higher than the baseline; the default value is -2.0. This is the penalty coefficient, a positive number used to further increase the entropy value when the accuracy falls below the baseline; the default value is 1.0.

[0077] This invention also provides a multi-core deep management system based on an enterprise cloud management platform, which executes the multi-core deep management method based on the enterprise cloud management platform as described above. The multi-core deep management system based on the enterprise cloud management platform includes: The data acquisition module is used to acquire point task data from the enterprise cloud management platform; The task monitoring module is used to capture touchpoint events in the process of cloud agents processing tasks in the enterprise cloud management platform based on point task data, and to synchronously acquire point task interaction data with cloud agents. The intelligent task allocation module is used to calculate the single interaction resistance value based on the point task interaction data, and then determine the corresponding cloud agent's agent efficiency entropy value; based on the agent efficiency entropy value, it determines the real-time status of the cloud agent, and combines it with the real-time task scheduling sequence in the enterprise cloud management platform to intelligently match tasks for cloud agents with normal status and form targeted task delivery instructions. The multi-core linkage management module is used to achieve in-depth multi-core management of the enterprise cloud management platform based on targeted task delivery instructions.

[0078] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, 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 the equivalents of the application are intended to be included within the invention.

[0079] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A multi-core deep management method based on an enterprise cloud management platform, characterized in that, Includes the following steps: Step S1: Obtain point task data from the enterprise cloud management platform; Step S2: Capture the touchpoint events of the cloud agent's task processing process in the enterprise cloud management platform based on the point task data, and synchronously obtain the point task interaction data with the cloud agent. Step S3: Calculate the single interaction resistance value based on the point task interaction data, and then determine the corresponding cloud agent's agent efficiency entropy value; determine the real-time status of the cloud agent based on the agent efficiency entropy value, and combine it with the real-time task scheduling sequence in the enterprise cloud management platform to intelligently match tasks for cloud agents with normal status and form targeted task delivery instructions. Step S4: Implement multi-core deep management of the enterprise cloud management platform based on targeted task delivery instructions.

2. The multi-core deep management method based on an enterprise cloud management platform according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Define the task lifecycle in the task data as four independent touchpoint events: task being claimed, task first submission, task being inspected, and task completion settlement. Step S22: Capture touchpoint events occurring on any cloud agent in the enterprise cloud management platform using a preset event listener, and immediately lock the task identifier and cloud agent identifier associated with the event; Step S23: Using the task identifier as the first index and the cloud agent identifier as the second index, synchronously obtain the task difficulty coefficient, cloud agent level, current rework status, and task duration from the point task data, as point task interaction data.

3. The multi-core deep management method based on an enterprise cloud management platform according to claim 1, characterized in that, Step S3, which calculates the single-interaction resistance value based on the point task interaction data, includes: Using the task difficulty coefficient and cloud seat level in the point task interaction data as query conditions, the corresponding dynamic baseline time limit and dynamic rework baseline number of times can be retrieved. The time deviation is obtained by calculating the difference between the current task time in the point task interaction data and the upper limit of the dynamic benchmark time. The difference between the current rework status in the point task interaction data and the dynamic rework baseline number is calculated as the quality deviation. The single-cycle resistance value is generated by weighted summation based on time deviation and mass deviation.

4. The multi-core deep management method based on an enterprise cloud management platform according to claim 3, characterized in that, Using the task difficulty coefficient and cloud agent level from the task interaction data as query conditions, the corresponding dynamic baseline time limit and dynamic rework baseline number of times are retrieved, including: Based on the cloud agent identifier and task difficulty coefficient in the point task interaction data, all historical task records of the cloud agent within the task difficulty coefficient similarity range are retrieved from the preset historical work order database to obtain the agent's historical similar work orders; where the task similarity range is set to ±5% of the current task difficulty coefficient; Calculate the arithmetic mean and standard deviation of the time taken for all tasks in the same type of work order in the agent's history; The upper limit of the dynamic benchmark time is calculated by multiplying the standard deviation by a preset time fluctuation factor and then adding the standard deviation; the preset time fluctuation factor ranges from 1.2 to 2.

0. The total number of reworks and the total number of tasks in the same type of work order in the history of the seats are counted, the historical average number of reworks is calculated, and the historical average number of reworks is summed with the preset rework tolerance index to obtain the dynamic rework baseline number of times. A two-dimensional parameter matrix is ​​constructed with the task difficulty coefficient as the rows and the cloud seat level as the columns. Then, the two-dimensional parameter matrix is ​​mapped with dynamic baseline time limit and dynamic rework baseline number of times.

5. The multi-core deep management method based on an enterprise cloud management platform according to claim 1, characterized in that, Step S3, which determines the seat performance entropy value of the corresponding cloud agent, includes: Configure a queue with a fixed capacity for each cloud agent within the enterprise cloud management platform; the queue is set to first-in, first-out (FIFO). When the cloud agent generates a new single interaction resistance value, it pushes the single interaction resistance value to the tail of its corresponding queue. Determine if the length of the queue exceeds the fixed capacity; if so, remove one single-interaction resistance value from the head of the queue. Read all current values ​​in the queue, calculate their standard deviation, and use the standard deviation as the original entropy value; The original entropy value is processed by minimax normalization to obtain the seat efficiency entropy value.

6. The multi-core deep management method based on an enterprise cloud management platform according to claim 1, characterized in that, Step S3, which determines the real-time status of cloud agents based on their agent performance entropy value and combines this with the real-time task scheduling sequence in the enterprise cloud management platform to intelligently match tasks for cloud agents in normal status and generate targeted task delivery instructions, also includes: Extract the seat performance entropy values ​​of all active cloud agents within the enterprise cloud management platform, and calculate the average value as the system load index; Extract the basic priority, timeliness factor, and difficulty coefficient of each task from the task pool to be assigned in the enterprise cloud management platform; Determine if the system load index is greater than the preset system health threshold: if so, calculate the scheduling weight of each task using the preset first weighting formula; otherwise, calculate the scheduling weight of each task using the preset second weighting formula. All tasks in the task pool to be assigned are sorted in descending order according to their scheduling weights to generate a real-time task scheduling sequence.

7. The multi-core deep management method based on an enterprise cloud management platform according to claim 1, characterized in that, In step S3, the real-time status of cloud agents is determined based on the agent performance entropy value. Combined with the real-time task scheduling sequence in the enterprise cloud management platform, tasks are intelligently matched to cloud agents in normal status, generating targeted task delivery instructions, including: Obtain the task with the highest scheduling weight from the real-time task scheduling sequence; Select cloud agents in the enterprise cloud management platform who are idle and whose skills match the task to be assigned, and form a candidate agent pool. Iterate through each cloud agent in the candidate agent pool and obtain their real-time agent status identifier. Based on the real-time status identifier of the agent, all cloud agents marked as warnings are excluded, and the cloud agent with the lowest agent efficiency entropy value is searched and determined as the target agent from the remaining cloud agents. The ID of the task to be assigned is combined with the ID of the target agent to generate a targeted task delivery instruction.

8. The multi-core deep management method based on an enterprise cloud management platform according to claim 7, characterized in that, Iterate through each cloud agent in the candidate agent pool and obtain their real-time agent status identifier, including: Compare the seat performance entropy value of the cloud seat with the preset stability threshold and the preset warning threshold. If the seat efficiency entropy value is less than or equal to the preset stability threshold, the state is marked as stable. If the seat efficiency entropy value is greater than the preset stability threshold and less than the preset warning threshold, the state will be marked as fluctuating. If the seat efficiency entropy value is greater than or equal to the warning threshold, the status will be marked as a warning. Based on the marked status, generate a real-time status identifier for the cloud agent.

9. The multi-core deep management method based on an enterprise cloud management platform according to claim 1, characterized in that, Before generating targeted task delivery instructions, the process also includes steps to intervene in cloud agents whose status is marked as "warning": Listen for and capture task requests initiated by cloud agents whose status is marked as alert; The task request is rejected, its normal task matching process is interrupted, and then a calibration task is randomly selected from the pre-set calibration task pool in the enterprise cloud management platform. The ID of the calibration task is bound to the ID of the cloud agent, an intervention calibration command is generated and sent to the corresponding cloud agent.

10. A multi-core deep management system based on an enterprise cloud management platform, characterized in that, For executing the multi-core deep management method based on an enterprise cloud management platform as described in claim 1, the multi-core deep management system based on the enterprise cloud management platform includes: The data acquisition module is used to acquire point task data from the enterprise cloud management platform; The task monitoring module is used to capture touchpoint events in the process of cloud agents processing tasks in the enterprise cloud management platform based on point task data, and to synchronously acquire point task interaction data with cloud agents. The intelligent task allocation module is used to calculate the single interaction resistance value based on the point task interaction data, and then determine the corresponding cloud agent's agent efficiency entropy value; based on the agent efficiency entropy value, it determines the real-time status of the cloud agent, and combines it with the real-time task scheduling sequence in the enterprise cloud management platform to intelligently match tasks for cloud agents with normal status and form targeted task delivery instructions. The multi-core linkage management module is used to achieve in-depth multi-core management of the enterprise cloud management platform based on targeted task delivery instructions.

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