Employee education platform system integration method and system

Abstract

The invention relates to the technical field of Internet, cloud computing and big data service, and discloses a staff education platform system integration method and system, and the method comprises the steps: obtaining a real-time behavior event set of a platform user, extracting a cross-behavior association rule, and generating a dynamic feature vector group; collecting interface response time delay parameters and load state data of each subsystem, and constructing a resource state topological graph; superposing the dynamic feature vector group on a resource state topological graph in a time dimension, marking a space-time mismatching region of resource distribution and demand density, and generating a demand overflow coordinate set; if the resource gap density of the demand overflow coordinate set exceeds a preset system pressure-bearing threshold value, dynamically adjusting default service priorities among subsystems, and constructing a dynamic compensation channel of the staff education platform; and configuring the dynamic compensation channel to the platform integrated gateway, outputting a resource redistribution instruction set, and executing cross-system deployment verification. According to the invention, the integration efficiency of the staff education platform system can be improved.

Description

Technical Field

[0001] This invention relates to the fields of Internet and cloud computing and big data service technology, and in particular to a method and system for integrating an employee education platform. Background Technology

[0002] Existing employee education platform system integration technologies primarily rely on traditional server cluster architectures and static load balancing strategies based on hardware metrics. Traditional methods typically assume uniform or linear growth in system traffic, triggering scaling or traffic distribution solely by monitoring a single physical resource threshold. However, in real-world applications, employee learning behavior on the platform exhibits high randomness and concurrency. Existing integration solutions often overlook the deep correlation between user behavior logic and underlying system resources, resulting in systems that can only passively respond to traffic surges and are unable to proactively plan resources from a business logic perspective.

[0003] A significant shortcoming of existing technologies lies in the lack of joint analysis capabilities regarding the spatiotemporal relationship between dynamic user behavior and system resource topology. It is difficult to capture and extract correlation rules across user behaviors, and it is impossible to construct a global resource status topology map reflecting the latency and load status of each subsystem interface. Due to the lack of this global view, it is impossible to overlay and analyze the dynamic characteristics of user demands with the physical distribution of system resources over time, thus failing to accurately identify spatiotemporal mismatches between resource distribution and demand density. This means that when local resource shortages occur, it is impossible to dynamically adjust the service priority of subsystems or construct temporary dynamic compensation channels based on the shortage density; only crude rate limiting or global restart measures can be adopted. This approach not only results in ineffective redundancy of server computing power but also leads to delayed responses to critical business operations, lack of cross-system deployment verification feedback, and insufficient overall system load capacity, severely impacting the operational efficiency and user experience of the employee education platform. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a method and system for integrating an employee education platform system to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for integrating an employee education platform system, comprising: S1: Obtain the real-time behavior event set of platform users, extract the cross-behavior association rules of the real-time behavior event set, and generate the dynamic feature vector group of the employee education platform; S2: Collect the interface response latency parameters and load status data of each subsystem of the employee education platform, and construct the resource status topology diagram of the employee education platform; S3: Overlay the dynamic feature vector group onto the resource status topology map in the time dimension to mark the spatiotemporal mismatch area between resource distribution and demand density, and generate the demand overflow coordinate set of the employee education platform; S4: If the resource gap density of the demand overflow coordinate set exceeds the preset system pressure threshold, dynamically adjust the default service priority between subsystems and construct a dynamic compensation channel for the employee education platform. S5: Configure the dynamic compensation channel to the platform integration gateway, output the resource redistribution instruction set, and perform cross-system deployment verification.

[0006] Optionally, the step of obtaining the real-time behavioral event set of platform users, extracting cross-behavioral association rules from the real-time behavioral event set, and generating a dynamic feature vector set for the employee education platform includes: Collect real-time behavioral event sets of platform users, and perform abnormal data cleaning on the real-time behavioral event sets to obtain behavioral session sequences of the real-time behavioral event sets; Based on the behavioral session sequence, construct the behavioral co-occurrence matrix of the real-time behavioral event set, and filter the cross-behavioral association rules of the real-time behavioral event set based on the conditional probability density of the behavioral co-occurrence matrix; The user behavior feature data of the cross-behavior association rules are vectorized and encoded to obtain the dynamic feature vector group of the employee education platform.

[0007] Optionally, the step of performing abnormal data cleaning on the real-time behavioral event set to obtain the behavioral session sequence of the real-time behavioral event set includes: Traverse the timestamp sequence of the real-time behavioral event set to identify instantaneous noise data with time intervals below the valid limit; Remove the instantaneous noise data and retain the valid real-time behavioral events; The repeated operation instructions in the real-time behavior event are deduplicated; The processed real-time behavioral events are sorted according to the chronological order of the timestamp sequence to obtain the behavioral session sequence of the real-time behavioral event set.

[0008] Optionally, the step of collecting interface response latency parameters and load status data of each subsystem of the employee education platform, and constructing a resource status topology diagram of the employee education platform, includes: Collect interface response latency parameters and load status data of each subsystem of the employee education platform; Each subsystem is mapped to a logical entity node, and the load status data is encapsulated into the real-time processing capacity of the logical entity node. Based on the interface call dependencies of each subsystem and the interface response latency parameters, a communication transmission weighted edge is constructed for the logical entity node; The resource status topology diagram of the employee education platform is generated based on the spatial connection relationship between the logical entity nodes and the weighted edges of the communication transmission.

[0009] Optionally, the step of superimposing the dynamic feature vector group onto the resource status topology map in the time dimension, marking the spatiotemporal mismatch regions of resource distribution and demand density, and generating the demand overflow coordinate set of the employee education platform includes: The feature values ​​of the dynamic feature vector group are superimposed onto the resource status topology map to calculate the supply-demand density difference between the real-time processing capacity and the dynamic feature vector group. Based on the distribution gradient of the supply-demand density difference, the node range where the resource supply is saturated is identified and marked as the spatiotemporal mismatch region between resource distribution and demand density. Data aggregation is performed on the spatial index data of the spatiotemporal mismatch region to obtain the demand overflow coordinate set of the employee education platform.

[0010] Optionally, the formula for calculating the supply-demand density difference is:

[0011] in, It is the supply and demand density difference mentioned above. It is the scalarized load strength of the dynamic feature vector group. This refers to the real-time processing capacity. It is the load fatigue index. It is the degree of discreteness of the dynamic feature vector group. It is the physical time decay factor of the logical entity node. It is an exponential function.

[0012] Optionally, if the resource gap density of the demand overflow coordinate set exceeds a preset system pressure threshold, dynamically adjusting the default service priority among subsystems and constructing a dynamic compensation channel for the employee education platform includes: Based on the comparison between the resource gap density of the demand overflow coordinate set and the system pressure threshold, the system pressure status of the resource status topology map is determined. Based on the system's stress state, the service response priority sequence of the logical entity nodes is adjusted to obtain the hierarchical resource scheduling strategy for the logical entity nodes; Retrieve logical entity nodes with remaining real-time processing capacity from the resource status topology map, and construct auxiliary transmission links for the demand overflow coordinate set; The hierarchical resource scheduling strategy and the auxiliary transmission link are logically encapsulated to obtain the dynamic compensation channel of the employee education platform.

[0013] Optionally, configuring the dynamic compensation channel to the platform integration gateway, outputting a resource redistribution instruction set, and performing cross-system deployment verification includes: The dynamic compensation channel is loaded into the platform integration gateway of the employee education platform, and the traffic scheduling rules of the platform integration gateway are updated. The hierarchical resource scheduling strategy is parsed to generate a resource redistribution instruction set for the logical entity nodes; The resource redistribution instruction set is issued through the platform integration gateway to drive each subsystem to perform cross-system resource reorganization. Collect real-time operational feedback data after resource reorganization, and deploy and verify the dynamic compensation channel based on the real-time operational feedback data.

[0014] Optionally, the real-time operational feedback data after resource reorganization is collected, and the deployment verification of the dynamic compensation channel is performed based on the real-time operational feedback data, including: Monitor the service response latency of the logical entity nodes after resource reorganization; Compare the service response latency with the interface response latency parameter. The convergence status of the dynamic compensation channel is determined based on the comparison results.

[0015] To address the above problems, the present invention also provides an integrated system for an employee education platform, the system comprising: The dynamic feature vector generation module is used to obtain the real-time behavior event set of platform users, extract the cross-behavior association rules of the real-time behavior event set, and generate the dynamic feature vector group of the employee education platform. The resource status topology construction module is used to collect interface response latency parameters and load status data of each subsystem of the employee education platform, and construct the resource status topology diagram of the employee education platform. The demand overflow coordinate positioning module is used to overlay the dynamic feature vector group onto the resource status topology map in the time dimension, mark the spatiotemporal mismatch area between resource distribution and demand density, and generate the demand overflow coordinate set of the employee education platform. The dynamic compensation channel configuration module is used to dynamically adjust the default service priority between subsystems and construct the dynamic compensation channel of the employee education platform if the resource gap density of the demand overflow coordinate set exceeds the preset system pressure threshold. The cross-system deployment verification module is used to configure the dynamic compensation channel to the platform integration gateway, output resource redistribution instruction set, and perform cross-system deployment verification.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention acquires real-time behavioral event sets from platform users and extracts cross-behavioral association rules to generate dynamic feature vector sets reflecting business logic pressure, effectively improving the accuracy of perceiving dynamic user needs. By collecting interface response latency and load status data from each subsystem to construct a resource status topology map, and superimposing the dynamic feature vector sets onto this topology map in the time dimension, it can accurately calculate the supply-demand density difference between real-time processing capacity and load intensity. This deep analysis mechanism, combining business behavior logic and physical resource status, can accurately mark spatiotemporal mismatch areas between resource distribution and demand density, thereby significantly improving the accuracy of identifying potential bottlenecks and resource overflow risks, and avoiding the lag of traditional methods that rely solely on static hardware indicators for monitoring.

[0017] This invention, by configuring a dynamic compensation channel to the platform integration gateway and parsing and generating a resource redistribution instruction set, can automatically drive various subsystems to perform cross-system resource reorganization. A deployment verification mechanism is introduced to determine the convergence status of the compensation channel by monitoring the service response latency after resource reorganization and comparing interface parameters. This mechanism ensures the effectiveness of the resource scheduling strategy and significantly improves the response efficiency, fault tolerance, and overall operational stability of the employee education platform when facing high concurrency or resource conflicts. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a method for integrating an employee education platform system according to an embodiment of the present invention. Figure 2 This is a functional module diagram of an integrated system for an employee education platform provided in one embodiment of the present invention. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0020] This application provides a method for integrating an employee education platform system. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for integrating an employee education platform system can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data and artificial intelligence platforms.

[0021] Reference Figure 1 The diagram shown is a flowchart illustrating a method for integrating an employee education platform system according to an embodiment of the present invention. In this embodiment, the method for integrating an employee education platform system includes: S1: Obtain the real-time behavioral event set of platform users, extract the cross-behavioral association rules of the real-time behavioral event set, and generate the dynamic feature vector group of the employee education platform.

[0022] In this embodiment of the invention, the step of obtaining a real-time behavior event set of platform users, extracting cross-behavior association rules from the real-time behavior event set, and generating a dynamic feature vector set for the employee education platform includes: Collect real-time behavioral event sets of platform users, and perform abnormal data cleaning on the real-time behavioral event sets to obtain behavioral session sequences of the real-time behavioral event sets; Based on the behavioral session sequence, construct the behavioral co-occurrence matrix of the real-time behavioral event set, and filter the cross-behavioral association rules of the real-time behavioral event set based on the conditional probability density of the behavioral co-occurrence matrix; The user behavior feature data of the cross-behavior association rules are vectorized and encoded to obtain the dynamic feature vector group of the employee education platform.

[0023] Specifically, a real-time behavioral event set is the raw data stream received by the backend log server. At the physical level, it manifests as a series of unstructured data packets stored in a memory buffer or temporary log file. Each event contains the HTTP request header sent by the user terminal, the operation instruction code, and a timestamp recorded by the server's hardware clock.

[0024] In detail, the real-time behavioral event set in memory is read and linearly scanned according to the timestamp sequence. The processing unit calculates the time difference between the physical storage addresses of two adjacent events. The core of the process is comparison: if the calculated time difference is less than a preset valid limit, the processing unit marks the subsequent event as transient noise data.

[0025] Furthermore, once noise is identified, a removal operation is performed, releasing the memory space occupied by the invalid data and preventing it from being written to the downstream database. Next, duplicate operation commands are deduplicated, retaining only the first valid command reaching the network gateway.

[0026] Specifically, based on the physical chronological order of the timestamp sequence, the valid events residing in memory after cleaning are reordered. This action reassembles discrete data packets into a logically coherent sequence of behavioral sessions, providing a clean data stream for subsequent analysis of user intent.

[0027] In detail, the behavior co-occurrence matrix is ​​a two-dimensional array or sparse matrix structure allocated in the memory heap. The rows and columns of the matrix map to different functional subsystems of the employee education platform. The values ​​in the matrix represent the frequency with which a user continuously accesses these two physical nodes within a specific time window. It reflects the topology of internal traffic flow between different modules.

[0028] Furthermore, based on the generated behavior session sequence, a behavior co-occurrence matrix is ​​dynamically constructed in memory. The processing unit scans the sequence using a sliding window algorithm, incrementing the memory location of the corresponding coordinate in the matrix whenever two adjacent behaviors are detected. This action transforms the linear user operation flow into a planar module association graph.

[0029] Specifically, calculations are performed on the matrix data. Conditional probability densities between nodes are derived through division. The key step is filtering: the calculated probability values ​​are compared to a preset confidence threshold. Only when the probability density exceeds the threshold is a substantial business dependency determined between the two actions, thus extracting cross-action association rules. This effectively filters out random jumps and identifies high-frequency, high-concurrency cross-system call paths.

[0030] In detail, the user behavior feature data extracted from the cross-behavioral association rules is vectorized and encoded. This process maps abstract business logic to coordinate points in mathematical space. The resulting dynamic feature vector set will then serve as the physical input parameters for the load prediction model. Through this step, subjective intent is transformed into objective resource requirements, thereby achieving a mapping from the business layer to the physical resource layer.

[0031] In this embodiment of the invention, the step of performing abnormal data cleaning on the real-time behavior event set to obtain the behavior session sequence of the real-time behavior event set includes: Traverse the timestamp sequence of the real-time behavioral event set to identify instantaneous noise data with time intervals below the valid limit; Remove the instantaneous noise data and retain the valid real-time behavioral events; The repeated operation instructions in the real-time behavior event are deduplicated; The processed real-time behavioral events are sorted according to the chronological order of the timestamp sequence to obtain the behavioral session sequence of the real-time behavioral event set.

[0032] Specifically, a timestamp sequence is a set of precise time values ​​synchronized by a crystal oscillator on the server motherboard or a network time protocol. In physical storage, they are typically represented as a set of 64-bit long integers, recording the precise physical moment when each I / O request arrives at the gateway interface.

[0033] In detail, the effective limit is a latency threshold in a register or configuration file. It is the minimum logical interval set based on human neural reaction speed and physical device input latency. Transient noise data refers to excessively dense signal points on the physical timeline. At the electrical level, this could be due to repeated packet transmissions caused by network jitter, jitter of physical contacts in a mouse microswitch, or high-frequency attack requests initiated by malicious scripts.

[0034] Furthermore, the data addresses of the event set are first read sequentially from memory using pointers, and a traversal operation is performed. For two adjacent event data packets, the processing unit calls the arithmetic logic unit to calculate the difference in their timestamps.

[0035] Specifically, the metadata of the real-time behavioral event set is read, and the timestamp field is locked. The physical time difference between adjacent events is calculated by linearly scanning the entire sequence. Then, this difference is logically compared with a preset valid threshold. If the difference is less than the valid threshold, the current data packet is determined to be non-human-generated transient noise data, and its memory address is marked in preparation for subsequent cleanup.

[0036] In detail, valid real-time behavioral events refer to data packets that have passed timing verification. These represent genuine, business-meaning user interaction commands and are the effective payloads that can subsequently consume computing power and trigger database read / write operations. A cleaning operation is performed based on the identification results. Physically, invalid bitstreams identified as transient noise data are removed from the input stream to prevent them from consuming bandwidth and computing resources of downstream subsystems. Simultaneously, the retained real-time behavioral events that conform to human operational logic are temporarily stored in a cleaned data buffer to ensure the purity of the data source for subsequent analysis.

[0037] Furthermore, operation instructions refer to consecutive data packets with identical opcodes and operation object IDs at the application layer protocol. After eliminating temporal noise, the business logic of the operation instructions is examined more thoroughly within the data packets. If consecutive repetition of operation instructions for the same object is detected within the same session window, deduplication is performed. Physically, only the first instruction in the sequence is retained as a valid trigger signal, and subsequent redundant instructions are discarded, thereby preventing deadlocks or duplicate records in the backend database.

[0038] Specifically, to reconstruct the user's actual business operation path, the impact of network out-of-order events must be eliminated. Based on the chronological order of the timestamp sequence, the cleaned and deduplicated real-time behavioral events are sorted. This process reassembles discrete events into a sequence of behavioral sessions with causal logical relationships. The resulting sequence accurately maps the user's complete interaction trajectory on the employee education platform, providing a standardized data foundation for subsequent extraction of cross-behavioral association rules.

[0039] S2: Collect the interface response latency parameters and load status data of each subsystem of the employee education platform, and construct the resource status topology diagram of the employee education platform.

[0040] In this embodiment of the invention, the step of collecting interface response latency parameters and load status data of each subsystem of the employee education platform, and constructing a resource status topology diagram of the employee education platform, includes: Collect interface response latency parameters and load status data of each subsystem of the employee education platform; Each subsystem is mapped to a logical entity node, and the load status data is encapsulated into the real-time processing capacity of the logical entity node. Based on the interface call dependencies of each subsystem and the interface response latency parameters, a communication transmission weighted edge is constructed for the logical entity node; The resource status topology diagram of the employee education platform is generated based on the spatial connection relationship between the logical entity nodes and the weighted edges of the communication transmission.

[0041] Specifically, interface response latency refers to the total physical time required for data packets to be transmitted through the physical network medium and processed by the network interface card (NIC) and protocol stack. It typically includes network transmission latency and internal server processing latency. Load status data refers to real-time utilization indicators of server hardware resources. Specifically, it includes CPU instruction cycle utilization, random access memory page swapping frequency, disk I / O wait queue length, and network bandwidth throughput. These data directly reflect the current workload of the physical devices.

[0042] Furthermore, data collection is performed through monitoring probes deployed on various subsystem servers or through proactive polling via a simple network management protocol. The probe program reads the kernel's counters and status registers to obtain a snapshot of the current hardware usage. Simultaneously, the integrated gateway sends heartbeat packets to each subsystem interface, accurately determining response latency by calculating the difference between the sending timestamp and the receiving acknowledgment timestamp.

[0043] In detail, the first step is to initiate a global monitoring process. This process delves into the underlying hardware environment, collecting key performance indicators of each subsystem in real time. It not only reads CPU and memory load status data to assess the computing capacity of nodes, but also obtains interface response latency parameters through network probing technology, thereby providing basic physical data for subsequently building an accurate resource map.

[0044] Specifically, a logical entity node is an object-oriented representation of a physical server or virtual machine instance in system memory. At the data structure level, it is a class instance containing attributes such as IP address, service port, and hardware configuration information, and is the basic building block of the topology graph.

[0045] Furthermore, the hardware infrastructure of the physical world is abstracted, and distributed physical servers are transformed into manageable logical entity nodes in memory through mapping operations. Subsequently, encapsulation operations are performed to transform complex underlying hardware load status data into unified, measurable real-time processing capacity metrics. This enables the service capabilities of different architectural subsystems to be evaluated using a unified standard.

[0046] In detail, the configuration files of each subsystem or the routing table of the API gateway are parsed to identify which nodes interact with each other, thereby establishing connections between the corresponding logical nodes. The construction process involves more than just connecting the lines; it also involves assigning values: assigning physical latency values ​​to each connection as its weight.

[0047] Specifically, connectivity between nodes is determined based on interface call dependencies. Then, these connections are quantified using interface response latency parameters measured physically, constructing weighted edges for communication transmission connecting each logical entity node. In this way, the originally abstract software call relationships are transformed into a network graph with traffic information, and the edge weights intuitively represent the physical cost of data transmission.

[0048] Furthermore, the prepared set of node objects and weighted edge set are combined and serialized to generate a complete graph data structure. This process may involve graph connectivity checks to ensure there are no isolated nodes. The generated topology graph is loaded into the system's shared memory area for subsequent scheduling algorithms to perform pathfinding and stress analysis.

[0049] In detail, by synthesizing all the aforementioned data and based on the spatial connectivity between logical entity nodes and weighted edges of communication transmission, a complete resource status topology map was generated. This map can reflect the availability of every physical resource and the smoothness of every communication link within the employee education platform in real time, providing a digital foundation for subsequent supply and demand overlay analysis.

[0050] S3: Overlay the dynamic feature vector group onto the resource status topology map in the time dimension to mark the spatiotemporal mismatch areas between resource distribution and demand density, and generate the demand overflow coordinate set of the employee education platform.

[0051] In this embodiment of the invention, the step of superimposing the dynamic feature vector group onto the resource status topology map in the time dimension, marking the spatiotemporal mismatch regions of resource distribution and demand density, and generating the demand overflow coordinate set of the employee education platform includes: The feature values ​​of the dynamic feature vector group are superimposed onto the resource status topology map to calculate the supply-demand density difference between the real-time processing capacity and the dynamic feature vector group. Based on the distribution gradient of the supply-demand density difference, the node range where the resource supply is saturated is identified and marked as the spatiotemporal mismatch region between resource distribution and demand density. Data aggregation is performed on the spatial index data of the spatiotemporal mismatch region to obtain the demand overflow coordinate set of the employee education platform.

[0052] Specifically, the dynamic feature vector set is a set of high-dimensional floating-point vectors stored in the cache. Each vector represents the scalarized load intensity of a specific set of business requests on computing, storage, and communication resources.

[0053] In detail, the dynamic feature vector group is traversed, and the load vector is projected onto the corresponding logical entity node in the topology graph according to the business logic. The computation action is the process by which the CPU calls the floating-point arithmetic unit to execute a specific formula.

[0054] Furthermore, data fusion is performed along the time dimension. Physically, the numerical intensity of dynamic feature vector sets representing future demand is aligned with the resource state topology representing current capabilities. Subsequently, complex nonlinear operations are performed using arithmetic logic units, combining load fatigue index and dispersion, to accurately calculate the supply-demand density difference at each node. This step transforms the abstract supply-demand contradiction into a computer-processable digital metric.

[0055] Specifically, action identification is achieved through a threshold comparison algorithm. The values ​​and gradient changes of all nodes are scanned, and logical checks are performed to determine if conditions are met. Marking actions involves writing to the attributes of memory objects. A specific error code is written to the flag bit of the corresponding node in the topology graph, locking its state.

[0056] Furthermore, the distribution of the supply-demand density difference across the entire network topology is analyzed. By calculating the distribution gradient, the range of nodes in a saturated state where load is rapidly increasing and resources are about to be exhausted can be identified. Once these risk points are identified, they are immediately marked in the in-memory data structure as spatiotemporal mismatch regions of resource distribution and demand density, providing precise target location for subsequent isolation or compensation operations.

[0057] In detail, spatial index data can uniquely identify the addressing information of physical or logical nodes, such as IP addresses, MAC addresses, or containers.

[0058] Specifically, the aggregation action refers to data cleaning and reorganization. Since a physical server may run multiple logical services, or multiple alarms may point to the same subnet, it is necessary to deduplicate and categorize the scattered indexes to form a streamlined set. The final action is data encapsulation and I / O output, writing the processed coordinate set into shared memory or a message queue.

[0059] Furthermore, after identifying all risk areas, spatial index data for these spatiotemporal mismatch areas is extracted. To improve the efficiency of subsequent scheduling, data aggregation is performed to remove duplicate alarm sources and merge multiple logical alarms from the same physical machine. Ultimately, a clear and unique set of demand overflow coordinates is obtained. This coordinate set precisely indicates which locations on the employee education platform require immediate resource expansion or traffic offloading, serving as the direct trigger for activating the dynamic compensation channel.

[0060] In this embodiment of the invention, the formula for calculating the supply-demand density difference is:

[0061] in, It is the supply and demand density difference mentioned above. It is the scalarized load strength of the dynamic feature vector group. This refers to the real-time processing capacity. It is the load fatigue index. It is the degree of discreteness of the dynamic feature vector group. It is the physical time decay factor of the logical entity node. It is an exponential function.

[0062] Specifically, This refers to the supply-demand density difference, in physical memory. It is typically represented as a double-precision floating-point number. It is the core output variable of the resource scheduling algorithm, and is a quantified result obtained after a series of complex arithmetic and logical operations.

[0063] In detail, This is the scalarized load intensity of the dynamic feature vector group, representing the aggregate pressure of current business demands on physical resources. The dynamic feature vector group itself is multi-dimensional, including CPU instruction count, memory page write volume, network throughput bytes, etc. This projects the multidimensional vector as a scalar value.

[0064] Furthermore, This refers to the real-time processing capacity, which represents the upper limit of physical resources currently available to the logical entity node. It is not the nominal performance of the device, but rather the net remaining capacity after deducting system overhead.

[0065] In detail, This is the load fatigue index, a parameter used to simulate the physical characteristic of electronic equipment performance declining non-linearly with increasing load. Server performance is not always linear; when the load approaches its limit, performance can drop sharply due to context switching and lock contention.

[0066] Specifically, This refers to the degree of discreteness of the dynamic feature vector group, a parameter that measures the randomness and volatility of user request distribution. It reflects whether the load is stable.

[0067] Furthermore, It is the physical time decay factor of the logical entity node, which represents the physical inertia or time window sensitivity of the system resource state recovery.

[0068] In detail, It is an exponential function, used as a dynamic adjustment coefficient, to weight the base gap according to the load pattern. When the load fluctuates... Relative to capacity When it is very large, the exponent term This will increase dramatically. This means that even if the average load is not exceeded, if the traffic is extremely unstable, the actual pressure it causes will still be significant, because handling fluctuations requires additional scheduling overhead.

[0069] Specifically, The main calculation is the static resource gap. It directly compares supply and demand. When > When the ratio is greater than 1, it indicates that the physical resources are overloaded. use The exponent amplifies the degree of overload non-linearly. This means that if CPU utilization increases from 90% to 95%, the resulting... The increment is much larger than the increase from 40% to 45%. This is consistent with the nonlinear characteristics of physical circuits in the saturation region. Align the balance point to 0. A positive result indicates an overflow, and a negative result indicates a surplus.

[0070] S4: If the resource gap density of the demand overflow coordinate set exceeds the preset system pressure threshold, dynamically adjust the default service priority between subsystems and construct a dynamic compensation channel for the employee education platform.

[0071] In this embodiment of the invention, the step of dynamically adjusting the default service priority among subsystems and constructing a dynamic compensation channel for the employee education platform if the resource gap density of the demand overflow coordinate set exceeds a preset system pressure threshold includes: Based on the comparison between the resource gap density of the demand overflow coordinate set and the system pressure threshold, the system pressure status of the resource status topology map is determined. Based on the system's stress state, the service response priority sequence of the logical entity nodes is adjusted to obtain the hierarchical resource scheduling strategy for the logical entity nodes; Retrieve logical entity nodes with remaining real-time processing capacity from the resource status topology map, and construct auxiliary transmission links for the demand overflow coordinate set; The hierarchical resource scheduling strategy and the auxiliary transmission link are logically encapsulated to obtain the dynamic compensation channel of the employee education platform.

[0072] Specifically, resource gap density is a floating-point value stored in a register that quantifies the degree of overload currently faced by a specific memory region or CPU core. Physically, it corresponds to the ratio of request queue length to processing rate.

[0073] In detail, the real-time resource gap density value is read and logically subtracted or compared with the pressure threshold. The action is determined by writing the corresponding status code to the status register based on the ALU's output flag bits.

[0074] Furthermore, in addition to reading the generated overflow coordinate data, this data is also quantified into specific resource gap densities. Subsequently, the CPU executes comparison instructions to determine whether the current gap has breached the hardware-defined system stress threshold. Based on the comparison results, the current system stress status of the entire topology network is determined, and this status code will serve as the fundamental basis for subsequently activating the emergency response mechanism.

[0075] Specifically, the interrupt priority of specific service processes is modified, moving them forward or backward in the hardware scheduling queue. The resulting action is to solidify these modified rules into executable policy objects. Once the system is confirmed to be under high pressure, the underlying task scheduling is immediately intervened. By adjusting the process service response priority sequence corresponding to each logical entity node, this combined hardware and software approach yields a hierarchical resource scheduling strategy for operation in resource-constrained environments. This ensures that critical services still receive computing power guarantees even when physical resources are insufficient.

[0076] In detail, the retrieval action is a graph traversal algorithm, such as breadth-first search, executed on the memory topology graph. The system scans node attributes to find nodes with remaining real-time processing capacity greater than the system's load threshold. The construction action involves network I / O operations, performing handshake protocols, and establishing communication tunnels from overflowing nodes to idle nodes.

[0077] Furthermore, to alleviate traffic pressure, a global search is performed on the resource state topology graph in memory to find logical entity nodes that are idle and have remaining real-time processing capacity. Once found, an auxiliary transmission link connecting the overflow node and the idle node is immediately constructed at the network layer. This link physically opens a second data channel, allowing overflowing traffic to be bypassed and transmitted to a capable server for processing.

[0078] Specifically, priorities are bound to link IP ports. The resulting action is to generate the final output, ready to be sent to the gateway for execution. The calculated hierarchical resource scheduling strategy, software-level control, physically established auxiliary transmission links, and hardware-level traffic offloading are logically encapsulated. This action packages the dispersed emergency measures into a unified execution entity, namely the dynamic compensation channel. The resulting channel contains both logical judgments and physical paths, providing the gateway with a set of directly executable instructions.

[0079] S5: Configure the dynamic compensation channel to the platform integration gateway, output the resource redistribution instruction set, and perform cross-system deployment verification.

[0080] In this embodiment of the invention, configuring the dynamic compensation channel to the platform integration gateway, outputting a resource redistribution instruction set, and performing cross-system deployment verification includes: The dynamic compensation channel is loaded into the platform integration gateway of the employee education platform, and the traffic scheduling rules of the platform integration gateway are updated. The hierarchical resource scheduling strategy is parsed to generate a resource redistribution instruction set for the logical entity nodes; The resource redistribution instruction set is issued through the platform integration gateway to drive each subsystem to perform cross-system resource reorganization. Collect real-time operational feedback data after resource reorganization, and deploy and verify the dynamic compensation channel based on the real-time operational feedback data.

[0081] Specifically, data packets for the dynamic compensation channel are sent to the gateway via the management interface. The gateway unpacks these packets and writes them to a temporary configuration buffer. Update actions typically involve a hot reload mechanism. The gateway process atomically switches the memory pointer to the new rule set without interrupting existing connections.

[0082] In detail, the dynamic compensation channel is loaded as a patch package into the configuration store of the platform integration gateway via the internal management network. Subsequently, the gateway executes an update command, refreshing the traffic scheduling rules in its memory. Physically, this means that the gateway's forwarding logic changes; subsequently arriving specific data packets will be redirected to the new secondary link, instead of following the congested default path.

[0083] Further, the processing unit reads the policy file and identifies the conditional statements and action keywords within it. Action generation is a compilation or translation process. Based on the parsing results, control messages for specific subsystem interfaces are assembled.

[0084] Specifically, the gateway's control plane parses the abstract hierarchical resource scheduling strategy and translates it into an operating language that the underlying physical devices can understand. It generates a set of resource redistribution instructions containing specific operational parameters, such as CPU core limits, memory allocation sizes, and thread pool thresholds. This step achieves the translation from business intent to hardware control signals.

[0085] In detail, the action sent down is a network I / O operation. The gateway encapsulates the instruction set into RPC request packets through the TCP / IP protocol stack and sends them to the agents of each subsystem. The driving action is remote execution. After receiving the instruction, the subsystem calls the operating system kernel interface to modify the process resource quota.

[0086] Furthermore, the platform integration gateway acts as a command center, distributing resource redistribution instructions to each physical node via a high-bandwidth internal network. These instructions drive each subsystem to immediately execute cross-system resource reorganization. Physically, video servers may start new encoding containers, while file servers may release some cache memory, thereby physically altering the overall computing power distribution pattern of the platform.

[0087] Specifically, the data acquisition process involves continuously monitoring data retrieval. A physical time window is allowed for the system to stabilize before the latest performance metrics are read. The verification process involves threshold regression testing to determine if the supply-demand density difference has returned to a safe range.

[0088] In detail, command execution is not the end goal; the effectiveness must be verified. Real-time operational feedback data from each node after resource reorganization is collected. Based on these physical measurements, the effectiveness of the dynamic compensation channel is deployed and verified. If verification passes, the current configuration is locked; if verification fails, a rollback or a new round of parameter fine-tuning may be triggered, thereby ensuring the stability and reliability of the system integration method.

[0089] In this embodiment of the invention, the real-time operational feedback data after resource reorganization is collected, and the deployment verification of the dynamic compensation channel is performed based on the real-time operational feedback data, including: Monitor the service response latency of the logical entity nodes after resource reorganization; Compare the service response latency with the interface response latency parameter. The convergence status of the dynamic compensation channel is determined based on the comparison results.

[0090] Specifically, a high-frequency sampling thread is initiated. By extending the Berkeley packet filter, TCP / IP packets flowing through logical entity nodes are captured in real time. The packet header fields are read, and the physical time difference between request and response packets is calculated.

[0091] In detail, to verify the effectiveness of the previous scheduling, first-hand on-site data must be obtained. Real-time monitoring of each subsystem after resource reorganization is essential. Physically, monitoring probes are mounted on the network stack of logical entity nodes to continuously track current service response latency. This is not merely recording a number, but capturing the true speed at which the physical circuitry and logic code process business requests under the new load balancing strategy.

[0092] Furthermore, the CPU reads two values ​​from different address segments of memory: one is the real-time latency that was just monitored, and the other is the original parameter stored historically. Then, it executes a floating-point comparison instruction to calculate the difference or rate of change between the two.

[0093] Specifically, the latest monitored service response latency is compared with the interface response latency parameters collected during system initialization or before a failure occurs. Physically, this is a backtesting process designed to quantify whether data throughput efficiency has been physically improved after the intervention of the dynamic compensation channel.

[0094] In detail, a conditional jump instruction is executed based on the comparison results. If the real-time delay is significantly better than the original parameters and the fluctuation variance is within the allowable range, a convergence flag is written to the status register, and the temporarily occupied computing resources are released; otherwise, a new round of PID control adjustment may be triggered.

[0095] like Figure 2The diagram shown is a functional module diagram of an employee education platform system integration system provided in an embodiment of the present invention.

[0096] The employee education platform system integration system 100 described in this invention can be installed in an electronic device. Depending on the functions implemented, the employee education platform system integration system 100 may include a dynamic feature vector generation module 101, a resource status topology construction module 102, a demand overflow coordinate positioning module 103, a dynamic compensation channel configuration module 104, and a cross-system deployment verification module 105. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0097] In this embodiment, the functions of each module / unit are as follows: The dynamic feature vector generation module 101 is used to obtain the real-time behavior event set of platform users, extract the cross-behavior association rules of the real-time behavior event set, and generate the dynamic feature vector group of the employee education platform. The resource status topology construction module 102 is used to collect the interface response latency parameters and load status data of each subsystem of the employee education platform, and construct the resource status topology diagram of the employee education platform. The demand overflow coordinate positioning module 103 is used to superimpose the dynamic feature vector group onto the resource status topology map in the time dimension, mark the spatiotemporal mismatch area between resource distribution and demand density, and generate the demand overflow coordinate set of the employee education platform. The dynamic compensation channel configuration module 104 is used to dynamically adjust the default service priority between subsystems and construct the dynamic compensation channel of the employee education platform if the resource gap density of the demand overflow coordinate set exceeds the preset system pressure threshold. The cross-system deployment verification module 105 is used to configure the dynamic compensation channel to the platform integration gateway, output the resource redistribution instruction set, and perform cross-system deployment verification.

[0098] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0099] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0100] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0101] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0102] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for integrating an employee education platform system, characterized in that, The method includes: S1: Obtain the real-time behavior event set of platform users, extract the cross-behavior association rules of the real-time behavior event set, and generate the dynamic feature vector group of the employee education platform; S2: Collect the interface response latency parameters and load status data of each subsystem of the employee education platform, and construct the resource status topology diagram of the employee education platform; S3: Overlay the dynamic feature vector group onto the resource status topology map in the time dimension to mark the spatiotemporal mismatch area between resource distribution and demand density, and generate the demand overflow coordinate set of the employee education platform; S4: If the resource gap density of the demand overflow coordinate set exceeds the preset system pressure threshold, dynamically adjust the default service priority between subsystems and construct a dynamic compensation channel for the employee education platform. S5: Configure the dynamic compensation channel to the platform integration gateway, output the resource redistribution instruction set, and perform cross-system deployment verification.

2. The employee education platform system integration method as described in claim 1, characterized in that, The process of acquiring a real-time behavioral event set of platform users, extracting cross-behavioral association rules from the real-time behavioral event set, and generating a dynamic feature vector set for the employee education platform includes: Collect real-time behavioral event sets of platform users, and perform abnormal data cleaning on the real-time behavioral event sets to obtain behavioral session sequences of the real-time behavioral event sets; Based on the behavioral session sequence, construct the behavioral co-occurrence matrix of the real-time behavioral event set, and filter the cross-behavioral association rules of the real-time behavioral event set based on the conditional probability density of the behavioral co-occurrence matrix; The user behavior feature data of the cross-behavior association rules are vectorized and encoded to obtain the dynamic feature vector group of the employee education platform.

3. The employee education platform system integration method as described in claim 2, characterized in that, The step of performing abnormal data cleaning on the real-time behavior event set to obtain the behavior session sequence of the real-time behavior event set includes: Traverse the timestamp sequence of the real-time behavioral event set to identify instantaneous noise data with time intervals below the valid limit; Remove the instantaneous noise data and retain the valid real-time behavioral events; The repeated operation instructions in the real-time behavior event are deduplicated; The processed real-time behavioral events are sorted according to the chronological order of the timestamp sequence to obtain the behavioral session sequence of the real-time behavioral event set.

4. The employee education platform system integration method as described in claim 1, characterized in that, The process of collecting interface response latency parameters and load status data of each subsystem of the employee education platform, and constructing a resource status topology diagram of the employee education platform, includes: Collect interface response latency parameters and load status data of each subsystem of the employee education platform; Each subsystem is mapped to a logical entity node, and the load status data is encapsulated into the real-time processing capacity of the logical entity node. Based on the interface call dependencies of each subsystem and the interface response latency parameters, a communication transmission weighted edge is constructed for the logical entity node; The resource status topology diagram of the employee education platform is generated based on the spatial connection relationship between the logical entity nodes and the weighted edges of the communication transmission.

5. The employee education platform system integration method as described in claim 4, characterized in that, The step of superimposing the dynamic feature vector group onto the resource status topology map in the time dimension, marking the spatiotemporal mismatch regions between resource distribution and demand density, and generating the demand overflow coordinate set of the employee education platform includes: The feature values ​​of the dynamic feature vector group are superimposed onto the resource status topology map to calculate the supply-demand density difference between the real-time processing capacity and the dynamic feature vector group. Based on the distribution gradient of the supply-demand density difference, the node range where the resource supply is saturated is identified and marked as the spatiotemporal mismatch region between resource distribution and demand density. Data aggregation is performed on the spatial index data of the spatiotemporal mismatch region to obtain the demand overflow coordinate set of the employee education platform.

6. The employee education platform system integration method as described in claim 5, characterized in that, The formula for calculating the supply-demand density difference is: ， in, This refers to the supply-demand density difference. It is the scalarized load strength of the dynamic feature vector group. This refers to the real-time processing capacity. It is the load fatigue index. It is the degree of discreteness of the dynamic feature vector group. It is the physical time decay factor of the logical entity node. It is an exponential function.

7. The employee education platform system integration method as described in claim 4, characterized in that, If the resource gap density of the demand overflow coordinate set exceeds a preset system pressure threshold, the default service priority between subsystems is dynamically adjusted, and a dynamic compensation channel for the employee education platform is constructed, including: Based on the comparison between the resource gap density of the demand overflow coordinate set and the system pressure threshold, the system pressure status of the resource status topology map is determined. Based on the system's stress state, the service response priority sequence of the logical entity nodes is adjusted to obtain the hierarchical resource scheduling strategy for the logical entity nodes; Retrieve logical entity nodes with remaining real-time processing capacity from the resource status topology map, and construct auxiliary transmission links for the demand overflow coordinate set; The hierarchical resource scheduling strategy and the auxiliary transmission link are logically encapsulated to obtain the dynamic compensation channel of the employee education platform.

8. The employee education platform system integration method as described in claim 7, characterized in that, The step of configuring the dynamic compensation channel to the platform integration gateway, outputting a resource redistribution instruction set, and performing cross-system deployment verification includes: The dynamic compensation channel is loaded into the platform integration gateway of the employee education platform, and the traffic scheduling rules of the platform integration gateway are updated. The hierarchical resource scheduling strategy is parsed to generate a resource redistribution instruction set for the logical entity nodes; The resource redistribution instruction set is issued through the platform integration gateway to drive each subsystem to perform cross-system resource reorganization. Collect real-time operational feedback data after resource reorganization, and deploy and verify the dynamic compensation channel based on the real-time operational feedback data.

9. The employee education platform system integration method as described in claim 8, characterized in that, The real-time operational feedback data after resource reorganization is collected, and the deployment and verification of the dynamic compensation channel are performed based on the real-time operational feedback data, including: Monitor the service response latency of the logical entity nodes after resource reorganization; Compare the service response latency with the interface response latency parameter. The convergence status of the dynamic compensation channel is determined based on the comparison results.

10. A system for integrating an employee education platform, used to implement the system integration method for integrating an employee education platform as described in any one of claims 1-9, characterized in that, The system includes: The dynamic feature vector generation module is used to obtain the real-time behavior event set of platform users, extract the cross-behavior association rules of the real-time behavior event set, and generate the dynamic feature vector group of the employee education platform. The resource status topology construction module is used to collect interface response latency parameters and load status data of each subsystem of the employee education platform, and construct the resource status topology diagram of the employee education platform. The demand overflow coordinate positioning module is used to overlay the dynamic feature vector group onto the resource status topology map in the time dimension, mark the spatiotemporal mismatch area between resource distribution and demand density, and generate the demand overflow coordinate set of the employee education platform. The dynamic compensation channel configuration module is used to dynamically adjust the default service priority between subsystems and construct the dynamic compensation channel of the employee education platform if the resource gap density of the demand overflow coordinate set exceeds the preset system pressure threshold. The cross-system deployment verification module is used to configure the dynamic compensation channel to the platform integration gateway, output resource redistribution instruction set, and perform cross-system deployment verification.

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