An intelligent management system and method applied to an information technology service platform

Abstract

The application discloses an intelligent management system and method applied to an information technology service platform, relates to the technical field of big data analysis, and achieves standardized archiving and accurate searching management of the path by acquiring full-amount historical data transmission paths of the information technology service platform, constructing a data transmission path table based on unique identifiers of path nodes, avoiding information pheromone broadcast packet time sequence overlap and transmission conflict problems, and providing reliable data support for platform operation. The application solves the disadvantages of response lag and complicated operation by combining path table data analysis with broadcast packet length configuration to configure an independent first information pheromone broadcast packet spontaneous period, and guarantees information pheromone synchronization and order. The application dynamically adjusts and configures a second broadcast period by comparing the predicted information pheromone concentration with a threshold value, adapts to path increase and decrease and state change scenes, solves the state sensing distortion problem after path change, and realizes accurate management and stable operation of the platform data transmission path.

Description

Technical Field

[0001] This invention relates to the field of big data analytics, specifically to an intelligent management system and method applied to an information technology service platform. Background Technology

[0002] Information technology service platforms are the core carriers for data transmission services. The stable operation of the various data transmission paths they support is directly related to the orderly transmission of data on the platform. Standardized intelligent management of data transmission paths is a core prerequisite for the stable operation of information technology service platforms.

[0003] In high-frequency data transmission scenarios such as financial transaction data interaction and real-time industrial production monitoring on information technology service platforms, the requirements for real-time, orderly, and conflict-free data transmission are extremely high. Existing technologies typically use pheromone concentration for path status perception and scheduling management. When additional data transmission paths are added, such as for restoring historically faulty paths or adding new paths to the network, current solutions require pre-setting fixed pheromone broadcast packet initiation times for each data transmission path. This adjustment scheme suffers from response lag and cumbersome operation. If not adjusted in time, it can lead to overlapping pheromone broadcast packet initiation times and transmission conflicts, resulting in pheromone synchronization delays, distorted path status perception, and ultimately affecting the stability and reliability of platform data transmission. Therefore, there is an urgent need for an intelligent management system and method for information technology service platforms. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent management system and method for an information technology service platform, so as to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent management method applied to an information technology service platform, the intelligent management method comprising the following steps: Obtain all historical data transmission paths of the information technology service platform and construct a data transmission path table based on the unique identifiers of the path nodes; The data transmission path table includes all data transmission paths that the information technology service platform has ever used in its entirety. The unique identifiers of path nodes assigned to each historical data transmission path by the information technology service platform are used to manage each data transmission path and to find the corresponding data transmission path in service through the unique identifiers of path nodes. By standardizing and archiving all historical and current data transmission paths on the information technology service platform, precise retrieval and management of data transmission paths can be achieved based on the unique identifier of the path node. This ensures the orderliness of subsequent path management operations from the data foundation level and avoids issues such as overlapping pheromone broadcast packets, transmission conflicts, and pheromone synchronization delays.

[0006] Based on the data transmission path table and the current data transmission path analysis of the information technology service platform, the first pheromone broadcast packet spontaneous period of each currently in service data transmission path is set; the pheromone broadcast packet spontaneous period represents the time period during which each path node of each data transmission path in the information technology service platform independently initiates a broadcast data packet carrying path pheromone concentration data according to a set fixed time pattern. Using the unique identifier of the path node as the retrieval basis, the historical full path status records of each data transmission path in the information technology service platform are retrieved from the data transmission path table. The dynamic change information of the number of paths in the historical operation cycle of each data transmission path is extracted. The maximum number of data transmission paths newly added to the network in history is obtained by filtering. At the same time, the maximum number of data transmission paths that have recovered from an abnormal unavailable state to an available state in history is also obtained by filtering. The number of the two types of data transmission paths is merged to obtain the historical peak number of data transmission paths. The actual number of data transmission paths currently in service and available on the information technology service platform is retrieved, and the actual number of data transmission paths in service is added to the historical peak number of data transmission paths to obtain the base number of data transmission paths configured. Based on the unique identifier of the path node, the complete execution time of a single periodic broadcast packet independently initiated by the node of each data transmission path during the historical operation phase of the information technology service platform is extracted from the data transmission path table. The complete execution time is the independent time consumed from the corresponding node of a single data transmission path initiating a periodic broadcast packet to the completion of the full data interaction of the periodic broadcast packet. Based on the number of data transmission paths configured, and combined with the complete execution time of the broadcast packet corresponding to each in-service data transmission path, an independent first pheromone broadcast packet spontaneous period is configured for each currently in-service data transmission path. The first pheromone broadcast packet spontaneous period of each data transmission path is matched and adapted with the complete execution time of the broadcast packet of the corresponding path node, so that the nodes of each in-service data transmission path form an independent timing mismatch in the process of initiating the periodic broadcast packet. The periodic broadcast packet initiation actions of each data transmission path do not interfere with each other, and there is no transmission conflict in the periodic broadcast packets of each path during the data interaction phase. Based on the accurate analysis of historical and current path data in the data transmission path table, and combined with the configuration of an independent first pheromone broadcast packet spontaneous cycle according to the complete execution duration of the broadcast packet, each service data transmission path forms an independent timing mismatch, realizing that the periodic broadcast packet initiation actions do not interfere with each other and the data interaction has no transmission conflict, effectively avoiding the problem of pheromone broadcast packet timing overlap and ensuring the orderly synchronization of pheromones.

[0007] Based on the spontaneous periodic analysis of the pheromone concentration of each data transmission path according to the first pheromone broadcast packet, the pheromone concentration represents the real-time operating status characterization value of each data transmission path within the information technology service platform. It is the core path status data continuously sent by each path node based on the pheromone broadcast data packet and is used for the status perception and scheduling management of the data transmission path. The spontaneous periodicity of the pheromone broadcast packet represents the time period during which each path node of each data transmission path within the information technology service platform independently initiates broadcast data packets carrying path pheromone concentration data according to a set fixed time pattern. We select any data transmission path of the current information technology service platform as the research object, denoted as the target path; based on the historical pheromone concentration of the target path, we train the model to predict the pheromone concentration in the spontaneous cycle of the next first pheromone broadcast packet, denoted as the predicted pheromone concentration. Based on the unique identifier of the path node, the pheromone concentration data corresponding to the spontaneous cycle of the first pheromone broadcast packet in the historical operation phase of each currently serving data transmission path is retrieved from the data transmission path table. The pheromone concentration data is archived in chronological order according to the spontaneous cycle of the first pheromone broadcast packet to form a pheromone concentration time-series dataset for each serving data transmission path. Based on the unique identifier of the path node corresponding to the target path as the unique retrieval identifier, the full historical pheromone concentration data of the target path in the spontaneous period of all historical first pheromone broadcast packets is extracted from the pheromone concentration time series dataset and arranged in chronological order. The historical pheromone concentration data within the spontaneous cycle of the first pheromone broadcast packet is selected as input. The data is trained through a time series model to establish a temporal correlation mapping between the historical pheromone concentration and the spontaneous cycle of the first pheromone broadcast packet. The pheromone concentration data of the target path within the spontaneous cycle of the next first pheromone broadcast packet is then output. By using unique identifiers for path nodes, pheromone concentration data can be accurately retrieved and archived in a time series. Through model training, a time series correlation mapping can be established to achieve accurate prediction of pheromone concentration on the target path, ensuring the accuracy of pheromone concentration perception and adapting to the concentration analysis needs of spontaneous cycles of pheromone broadcast packets.

[0008] Simultaneously process other data transmission paths of the information technology service platform to predict the predicted pheromone concentration of each data transmission path; set up a sliding window to extract historical pheromone concentration data of each data transmission path, analyze the changes in pheromone concentration, and obtain the pheromone concentration threshold. The sliding step size of the sliding data window is set to the spontaneous period of the first pheromone broadcast packet; historical pheromone concentration data of each data transmission path is extracted, and the mean and standard deviation of the pheromone concentration are calculated based on the pheromone concentration data of each data transmission path. The pheromone concentration threshold of the corresponding data transmission path is obtained by calculating the mean pheromone concentration and three times the standard deviation. The pheromone concentration threshold is used to determine whether the corresponding predicted pheromone concentration needs to be adjusted according to the spontaneous period of the first pheromone broadcast packet. By batch predicting pheromone concentration across the entire path and calculating data using a sliding window, pheromone concentration thresholds for each data transmission path are formed, enabling accurate determination of pheromone concentration changes and adapting to the adjustment and determination requirements of the spontaneous cycle of pheromone broadcast packets.

[0009] Based on the pheromone concentration threshold, the spontaneous period of the second pheromone broadcast packet for each data transmission path is determined, and the information technology service platform is intelligently managed based on the spontaneous period of the second pheromone broadcast. The predicted pheromone concentration for each data transmission path is compared with the corresponding pheromone concentration threshold for each data transmission path. When the predicted pheromone concentration does not exceed the pheromone concentration threshold of the corresponding data transmission path, the spontaneous period of the first pheromone broadcast packet of the corresponding data transmission path remains unchanged. When the predicted pheromone concentration exceeds the corresponding pheromone concentration threshold for the data transmission path, a second pheromone broadcast packet spontaneous cycle is set for each in-service data transmission path of the information technology service platform, based on the configuration base number of data transmission paths. The setting method for the second pheromone broadcast packet spontaneous cycle is the same as that for the first pheromone broadcast packet spontaneous cycle, specifically: The actual number of data transmission paths currently in service is retrieved and added to the historical peak number of transmission paths to obtain the base number of transmission paths. Then, using the unique identifier of each path node as a unique identifier, the complete execution time of each data transmission path node independently initiating a single periodic broadcast packet is extracted from the data transmission path table, along with the independent time taken for a single path node to initiate a broadcast packet and complete full data interaction. Finally, based on the base number of transmission paths and combined with the complete execution time of the broadcast packet corresponding to each data transmission path in service, an independent spontaneous period of second pheromone broadcast packets is configured for each data transmission path in service. This ensures that the period of each path matches and adapts to the complete execution time of the broadcast packet of the corresponding node, ensuring that each path node initiates a periodic broadcast packet with an independent timing mismatch, that broadcast actions do not interfere with each other, and that there are no transmission conflicts during the data interaction phase. By accurately comparing the predicted pheromone concentration with the threshold, the broadcast cycle is dynamically adjusted. The second pheromone broadcast packet spontaneous cycle is constructed according to the same configuration logic as the first broadcast cycle, ensuring that the broadcast actions of each path node are not mismatched in timing and have no interactive conflicts, thus ensuring the orderly synchronization of pheromones and realizing precise control of the data transmission path of the information technology service platform.

[0010] Furthermore, the intelligent management system includes a path data archiving module, a first broadcast cycle configuration module, a target path concentration prediction module, a full path concentration threshold module, and a second broadcast cycle control module; The path data archiving module is used to obtain all historical data transmission paths of the information technology service platform, construct a data transmission path table based on the unique identifier of the path node, and complete the retrieval and management of the corresponding data transmission paths; the first broadcast cycle configuration module is used to configure an independent first pheromone broadcast packet spontaneous cycle for each in-service data transmission path based on the data transmission path table and the analysis of the current data transmission path; the target path concentration prediction module is used to analyze the pheromone concentration based on the first pheromone broadcast packet spontaneous cycle and complete the extraction and output of the predicted pheromone concentration for the target path; the full path concentration threshold module is used to obtain the predicted pheromone concentration for each data transmission path, extract historical pheromone concentration data, and calculate the pheromone concentration threshold; the second broadcast cycle control module is used to configure the second pheromone broadcast packet spontaneous cycle according to the pheromone concentration threshold and carry out intelligent management of the information technology service platform based on the configuration results; The path data archiving module includes a historical path acquisition unit and a path table construction unit; the historical path acquisition unit is used to acquire all the data transmission paths that have been in service in the history of the information technology service platform; the path table construction unit is used to construct a data transmission path table based on the unique identifier of the path node, and to find the corresponding data transmission path in service through the unique identifier of the path node. The first broadcast cycle configuration module includes a configuration base calculation unit and an independent cycle allocation unit; the configuration base calculation unit is used to calculate the historical expansion peak number of data transmission paths and the number configuration base based on the unique identifier of the path node; the independent cycle allocation unit is used to extract the complete execution duration of the broadcast packet and allocate an independent first pheromone broadcast packet spontaneous cycle to each in-service data transmission path; The target path concentration prediction module includes a concentration time series construction unit and a predicted concentration output unit. The concentration time series construction unit is used to retrieve pheromone concentration data to construct a pheromone concentration time series dataset, extract all historical pheromone concentration data of the target path and sort them. The predicted concentration output unit is used to establish a time series correlation mapping through model training and output the pheromone concentration data of the target path in the next spontaneous period of the first pheromone broadcast packet. The full-path concentration threshold module includes a batch concentration prediction unit and a concentration threshold calculation unit. The batch concentration prediction unit is used to synchronously process the remaining data transmission paths and predict the predicted pheromone concentration for each data transmission path. The concentration threshold calculation unit is used to set a sliding data window to capture data and complete the calculation of the pheromone concentration threshold for each data transmission path. The second broadcast cycle control module includes a cycle adjustment decision unit and a platform intelligent control unit. The cycle adjustment decision unit is used to compare the predicted pheromone concentration with the pheromone concentration threshold and decide on the adjustment method of the spontaneous cycle of the first pheromone broadcast packet. The platform intelligent control unit is used to configure the spontaneous cycle of the second pheromone broadcast packet for each service data transmission path and realize platform intelligent management based on the spontaneous cycle of the second pheromone broadcast packet.

[0011] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention acquires all historical data transmission paths of an information technology service platform and constructs a data transmission path table based on the unique identifiers of path nodes, achieving standardized archiving and precise retrieval management of all paths. This ensures the orderly operation of subsequent path management from a fundamental data perspective, effectively avoiding problems such as overlapping pheromone broadcast packet timings and transmission conflicts caused by chaotic path data management, thus providing stable and reliable data for the platform's stable operation.

[0012] 2. This invention utilizes precise analysis of historical and current path data from the path table, combined with the configuration of an independent spontaneous cycle for the first pheromone broadcast packet based on the complete execution duration of the broadcast packet, enabling each service path to form an independent timing mismatch. This resolves the drawbacks of delayed response and cumbersome operation, achieving non-interference in broadcast actions and conflict-free data interaction, ensuring the orderly synchronization of pheromones.

[0013] 3. This invention dynamically adjusts the broadcast cycle by comparing predicted pheromone concentration with a threshold, and configures the spontaneous cycle of the second pheromone broadcast packet according to a unified logic. The dynamic control mechanism can adapt to scenarios such as path additions and subtractions, and state changes, ensuring conflict-free broadcasting under various operating conditions. It effectively solves the problem of state perception distortion that easily occurs after path changes in existing technologies, and achieves precise control and stable operation of the platform's data transmission path. Attached Figure Description

[0014] Figure 1 This is a schematic diagram illustrating an application scenario of the intelligent management method of the present invention applied to an information technology service platform; Figure 2 This is a flowchart illustrating an intelligent management method for an information technology service platform according to the present invention. Figure 3 This is a schematic diagram of the spontaneous cycle process of pheromone broadcast packets in an intelligent management method applied to an information technology service platform according to the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Example 1: As Figure 1 As shown, the present invention provides a technical solution, an intelligent management method applied to an information technology service platform, the intelligent management method comprising the following steps: Obtain all historical data transmission paths of the information technology service platform and construct a data transmission path table based on the unique identifiers of the path nodes; The data transmission path table includes all data transmission paths that the information technology service platform has ever used in its entirety. The unique identifiers of path nodes assigned to each historical data transmission path by the information technology service platform are used to manage each data transmission path and to find the corresponding data transmission path in service through the unique identifiers of path nodes. In practical implementation, the core of the implementation is the historical data transmission path collection rules of the information technology service platform. The integration and sorting of all historical data transmission paths is completed by relying on the unique identifiers of the path nodes assigned by the platform to each data transmission path. In the process of constructing the data transmission path table, it is necessary to ensure that the information in the table completely covers all data transmission paths that have been used in the platform's history. At the same time, the unique characteristics of the path node identifiers are used to complete the accurate retrieval and association management of each data transmission path. The core principles of the integrity and uniqueness of the data transmission path must always be followed to ensure the accuracy of the basic data for all subsequent path-related analysis operations.

[0017] Based on the data transmission path table and the current data transmission path analysis of the information technology service platform, the first pheromone broadcast packet spontaneous period of each currently in service data transmission path is set. Using the unique identifier of the path node as the retrieval basis, the historical full path status records of each data transmission path in the information technology service platform are retrieved from the data transmission path table. The dynamic change information of the number of paths in the historical operation cycle of each data transmission path is extracted. The maximum number of data transmission paths newly added to the network in history is obtained by filtering. At the same time, the maximum number of data transmission paths that have recovered from an abnormal unavailable state to an available state in history is also obtained by filtering. The number of the two types of data transmission paths is merged to obtain the historical peak number of data transmission paths. The actual number of data transmission paths currently in service and available on the information technology service platform is retrieved, and the actual number of data transmission paths in service is added to the historical peak number of data transmission paths to obtain the base number of data transmission paths configured. Based on the unique identifier of the path node, the complete execution time of a single periodic broadcast packet independently initiated by the node of each data transmission path during the historical operation phase of the information technology service platform is extracted from the data transmission path table. The complete execution time is the independent time consumed from the corresponding node of a single data transmission path initiating a periodic broadcast packet to the completion of the full data interaction of the periodic broadcast packet. Based on the actual number of data transmission paths and the complete execution duration of the broadcast packets corresponding to each operational data transmission path, an independent spontaneous period for the first pheromone broadcast packets is configured for each operational data transmission path. The spontaneous period for the first pheromone broadcast packets of each data transmission path is matched and adapted with the complete execution duration of the broadcast packets of the corresponding path nodes. This ensures that the nodes of each operational data transmission path form independent timing mismatches during the initiation of periodic broadcast packets, and that the initiation actions of periodic broadcast packets of each data transmission path do not interfere with each other. There are no transmission conflicts in the periodic broadcast packets of each path during the data interaction phase. In practical implementation, a full-dimensional path analysis is carried out based on the core data of the data transmission path table. The unique identifier of the path node is used as the core for retrieval to accurately retrieve and parse the historical path status records. The calculation of the peak number of historical expansion paths is completed by focusing on the two types of path quantity changes: new network access and fault recovery. Combined with the actual number of currently serving paths, a configuration base for the number of paths that can adapt to dynamic changes in the path is formed. Then, the complete execution duration of the broadcast packet of each path node is accurately extracted to complete the independent spontaneous cycle configuration of the first pheromone broadcast packet. During implementation, it is necessary to ensure the accurate matching of the cycle of each path and the execution duration of the broadcast packet. The core implementation logic of independent timing mismatch is used to eliminate the initiation conflict and interactive interference of broadcast packets of each data transmission path from the root.

[0018] Based on the spontaneous cycle of the first pheromone broadcast packet, the pheromone concentration of each data transmission path is analyzed. Any data transmission path of the current information technology service platform is selected as the research object and denoted as the target path. Based on the historical pheromone concentration of the target path, the pheromone concentration in the next spontaneous cycle of the first pheromone broadcast packet is predicted through model training and denoted as the predicted pheromone concentration. Based on the unique identifier of the path node, the pheromone concentration data corresponding to the spontaneous cycle of the first pheromone broadcast packet in the historical operation phase of each currently serving data transmission path is retrieved from the data transmission path table. The pheromone concentration data is archived in chronological order according to the spontaneous cycle of the first pheromone broadcast packet to form a pheromone concentration time-series dataset for each serving data transmission path. Based on the unique identifier of the path node corresponding to the target path as the unique retrieval identifier, the full historical pheromone concentration data of the target path in the spontaneous period of all historical first pheromone broadcast packets is extracted from the pheromone concentration time series dataset and arranged in chronological order. The historical pheromone concentration data within the spontaneous cycle of the first pheromone broadcast packet is selected as input. The data is trained through a time series model to establish a temporal correlation mapping between the historical pheromone concentration and the spontaneous cycle of the first pheromone broadcast packet. The pheromone concentration data of the target path within the spontaneous cycle of the next first pheromone broadcast packet is then output. In practical implementation, the spontaneous cycle of the first pheromone broadcast packet is used as the core time dimension. Relying on the retrieval characteristics of the unique identifier of the path node, the pheromone concentration data of each data transmission path is accurately retrieved and time-series rectified. A complete pheromone concentration time-series dataset is formed according to the logic of time-series archiving. There are no special path restrictions when selecting target paths for concentration prediction. The core is to conduct deep training and fitting on historical pheromone concentration data through a time series model. Based on the inherent time-series correlation between pheromone concentration and broadcast cycle, the concentration trend is predicted, ensuring the accurate binding of pheromone concentration data with the corresponding broadcast cycle. This ensures that the prediction results can accurately adapt to the needs of subsequent periodic analysis.

[0019] Simultaneously process other data transmission paths of the information technology service platform to predict the predicted pheromone concentration of each data transmission path; set up a sliding window to extract historical pheromone concentration data of each data transmission path, analyze the changes in pheromone concentration, and obtain the pheromone concentration threshold. The sliding step size of the sliding data window is set to the spontaneous period of the first pheromone broadcast packet; historical pheromone concentration data of each data transmission path is extracted, and the mean and standard deviation of the pheromone concentration are calculated based on the pheromone concentration data of each data transmission path. The pheromone concentration threshold of the corresponding data transmission path is obtained by calculating the mean pheromone concentration and three times the standard deviation. The pheromone concentration threshold is used to determine whether the corresponding predicted pheromone concentration needs to be adjusted according to the spontaneous period of the first pheromone broadcast packet. In practical implementation, the pheromone concentration prediction logic of the target path is used to complete the batch prediction of the concentration of the data transmission path of the entire platform in service. The historical pheromone concentration data of each path is continuously extracted and processed by a sliding data window. The sliding step size is strictly matched with the spontaneous cycle of the first pheromone broadcast packet to fit the temporal change pattern of the concentration data. The pheromone concentration threshold is formed by standardized mean and standard deviation calculation of the extracted complete concentration data. By unifying the continuity of data extraction and calculation logic, the obtained concentration threshold will serve as the core reference standard for determining whether the broadcast cycle needs to be adjusted. The objectivity and accuracy of the threshold determination are guaranteed by no additional data correction operation throughout the process.

[0020] Based on the pheromone concentration threshold, the spontaneous period of the second pheromone broadcast packet for each data transmission path is determined, and the information technology service platform is intelligently managed based on the spontaneous period of the second pheromone broadcast. The predicted pheromone concentration of each data transmission path is compared with the corresponding pheromone concentration threshold for each data transmission path. When the predicted pheromone concentration does not exceed the pheromone concentration threshold of the corresponding data transmission path, the spontaneous period of the first pheromone broadcast packet of the corresponding data transmission path remains unchanged. When the predicted pheromone concentration exceeds the corresponding data transmission path pheromone concentration threshold, based on the number of data transmission paths configured as a baseline, a second pheromone broadcast packet spontaneous period is set for each in-service data transmission path of the information technology service platform. The setting method of the second pheromone broadcast packet spontaneous period is the same as that of the first pheromone broadcast packet spontaneous period. In practical implementation, the comparison between the predicted pheromone concentration of each data transmission path and the corresponding pheromone concentration threshold is used as the core decision-making basis to perform the maintenance or reconfiguration of the broadcast cycle. When it is determined that a new cycle needs to be configured, the complete configuration logic and calculation standard of the spontaneous cycle of the first pheromone broadcast packet are strictly followed to complete the construction of the spontaneous cycle of the second pheromone broadcast packet. The consistency of the configuration logic and the accuracy of path adaptation are maintained throughout the process, without any adjustment or change of the configuration logic. Relying on this dynamic adjustment mode, it adapts to various operating scenarios such as the increase or decrease in the number of data transmission paths and changes in status within the platform, and finally realizes the standardized and intelligent management and control of all data transmission paths within the information technology service platform.

[0021] Example 2, as Figure 2 As shown, the present invention provides an intelligent management system for an information technology service platform, which includes a path data archiving module, a first broadcast cycle configuration module, a target path concentration prediction module, a full path concentration threshold module, and a second broadcast cycle control module. The path data archiving module is used to obtain all historical data transmission paths of the information technology service platform, construct a data transmission path table based on the unique identifier of the path node, and complete the retrieval and management of the corresponding data transmission paths; the first broadcast cycle configuration module is used to configure an independent first pheromone broadcast packet spontaneous cycle for each in-service data transmission path based on the data transmission path table and the analysis of the current data transmission path; the target path concentration prediction module is used to analyze the pheromone concentration based on the first pheromone broadcast packet spontaneous cycle and complete the extraction and output of the predicted pheromone concentration for the target path; the full path concentration threshold module is used to obtain the predicted pheromone concentration for each data transmission path, extract historical pheromone concentration data, and calculate the pheromone concentration threshold; the second broadcast cycle control module is used to configure the second pheromone broadcast packet spontaneous cycle according to the pheromone concentration threshold and carry out intelligent management of the information technology service platform based on the configuration results; The path data archiving module includes a historical path acquisition unit and a path table construction unit; the historical path acquisition unit is used to acquire all the data transmission paths that have been in service in the history of the information technology service platform; the path table construction unit is used to construct a data transmission path table based on the unique identifier of the path node, and to find the corresponding data transmission path in service through the unique identifier of the path node. The first broadcast cycle configuration module includes a configuration base calculation unit and an independent cycle allocation unit; the configuration base calculation unit is used to calculate the historical expansion peak number of data transmission paths and the number configuration base based on the unique identifier of the path node; the independent cycle allocation unit is used to extract the complete execution duration of the broadcast packet and allocate an independent first pheromone broadcast packet spontaneous cycle to each in-service data transmission path; The target path concentration prediction module includes a concentration time series construction unit and a predicted concentration output unit. The concentration time series construction unit is used to retrieve pheromone concentration data to construct a pheromone concentration time series dataset, extract all historical pheromone concentration data of the target path and sort them. The predicted concentration output unit is used to establish a time series correlation mapping through model training and output the pheromone concentration data of the target path in the next spontaneous period of the first pheromone broadcast packet. The full-path concentration threshold module includes a batch concentration prediction unit and a concentration threshold calculation unit. The batch concentration prediction unit is used to synchronously process the remaining data transmission paths and predict the predicted pheromone concentration for each data transmission path. The concentration threshold calculation unit is used to set a sliding data window to capture data and complete the calculation of the pheromone concentration threshold for each data transmission path. The second broadcast cycle control module includes a cycle adjustment decision unit and a platform intelligent control unit. The cycle adjustment decision unit is used to compare the predicted pheromone concentration with the pheromone concentration threshold and decide on the adjustment method of the spontaneous cycle of the first pheromone broadcast packet. The platform intelligent control unit is used to configure the spontaneous cycle of the second pheromone broadcast packet for each service data transmission path and realize platform intelligent management based on the spontaneous cycle of the second pheromone broadcast packet.

[0022] 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 invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of equivalents of the claims be included within the present invention.

Claims

1. An intelligent management method applied to an information technology service platform, characterized in that: The intelligent management method includes: Obtain all historical data transmission paths of the information technology service platform and construct a data transmission path table based on the unique identifiers of the path nodes; Based on the data transmission path table and the current data transmission path analysis of the information technology service platform, the first pheromone broadcast packet spontaneous period of each currently in service data transmission path is set. Based on the spontaneous cycle of the first pheromone broadcast packet, the pheromone concentration of each data transmission path is analyzed. Any data transmission path of the current information technology service platform is selected as the research object and denoted as the target path. Based on the historical pheromone concentration of the target path, the pheromone concentration in the next spontaneous cycle of the first pheromone broadcast packet is predicted through model training and denoted as the predicted pheromone concentration. Simultaneously process other data transmission paths of the information technology service platform to predict the predicted pheromone concentration of each data transmission path; set up a sliding window to extract historical pheromone concentration data of each data transmission path, analyze the changes in pheromone concentration, and obtain the pheromone concentration threshold. Based on the pheromone concentration threshold, the spontaneous period of the second pheromone broadcast packet for each data transmission path is determined, and the information technology service platform is intelligently managed based on the spontaneous period of the second pheromone broadcast. The automatic period for the first pheromone broadcast packet of each currently active data transmission path is set, including: Using the unique identifier of the path node as the retrieval basis, the historical full path status records of each data transmission path in the information technology service platform are retrieved from the data transmission path table. The dynamic change information of the number of paths in the historical operation cycle of each data transmission path is extracted. The maximum number of data transmission paths newly added to the network in history is obtained by filtering. At the same time, the maximum number of data transmission paths that have recovered from an abnormal unavailable state to an available state in history is also obtained by filtering. The number of the two types of data transmission paths is merged to obtain the historical peak number of data transmission paths. The actual number of data transmission paths currently in service and available on the information technology service platform is retrieved, and the actual number of paths is added to the historical peak number of paths during expansion to obtain the base number of data transmission paths configured. The setting for obtaining the spontaneous period of the first pheromone broadcast packet for each currently active data transmission path also includes: Based on the unique identifier of the path node, the complete execution time of a single periodic broadcast packet independently initiated by the node of each data transmission path during the historical operation phase of the information technology service platform is extracted from the data transmission path table. The complete execution time is the independent time consumed from the corresponding node of a single data transmission path initiating a periodic broadcast packet to the completion of the full data interaction of the periodic broadcast packet. Based on the actual number of data transmission paths and the complete execution duration of the broadcast packets corresponding to each operational data transmission path, an independent spontaneous period for the first pheromone broadcast packets is configured for each operational data transmission path. The spontaneous period for the first pheromone broadcast packets of each data transmission path is matched and adapted with the complete execution duration of the broadcast packets of the corresponding path nodes. This ensures that the nodes of each operational data transmission path form independent timing mismatches during the initiation of periodic broadcast packets, and that the initiation actions of periodic broadcast packets of each data transmission path do not interfere with each other. There are no transmission conflicts in the periodic broadcast packets of each path during the data interaction phase.

2. The intelligent management method applied to an information technology service platform according to claim 1, characterized in that: The predicted pheromone concentration for the target path includes: Based on the unique identifier of the path node, the pheromone concentration data corresponding to the spontaneous cycle of the first pheromone broadcast packet in the historical operation phase of each currently serving data transmission path is retrieved from the data transmission path table. The pheromone concentration data is archived in chronological order according to the spontaneous cycle of the first pheromone broadcast packet to form a pheromone concentration time-series dataset for each serving data transmission path. Based on the unique identifier of the path node corresponding to the target path as the unique retrieval identifier, the full historical pheromone concentration data of the target path in the spontaneous period of all historical first pheromone broadcast packets is extracted from the pheromone concentration time series dataset and arranged in chronological order. The historical pheromone concentration data within the spontaneous cycle of the first pheromone broadcast packet is selected as input. The data is trained through a time series model to establish a temporal correlation mapping between the historical pheromone concentration and the spontaneous cycle of the first pheromone broadcast packet. The pheromone concentration data of the target path within the spontaneous cycle of the next first pheromone broadcast packet is then output.

3. The intelligent management method applied to an information technology service platform according to claim 1, characterized in that: The analysis yielded changes in pheromone concentration, including: The sliding step size of the sliding data window is set to the spontaneous period of the first pheromone broadcast packet; historical pheromone concentration data of each data transmission path is extracted, and the mean and standard deviation of the pheromone concentration are calculated based on the pheromone concentration data of each data transmission path. The pheromone concentration threshold of the corresponding data transmission path is obtained by calculating the mean pheromone concentration and three times the standard deviation. The pheromone concentration threshold is used to determine whether the corresponding predicted pheromone concentration needs to be adjusted according to the spontaneous period of the first pheromone broadcast packet.

4. The intelligent management method applied to an information technology service platform according to claim 3, characterized in that: Based on the pheromone concentration threshold, the spontaneous period of the second pheromone broadcast packet for each data transmission path is determined. Intelligent management of the information technology service platform is then performed based on the spontaneous period of the second pheromone broadcast packet, including: The predicted pheromone concentration of each data transmission path is compared with the corresponding pheromone concentration threshold for each data transmission path. When the predicted pheromone concentration does not exceed the pheromone concentration threshold of the corresponding data transmission path, the spontaneous period of the first pheromone broadcast packet of the corresponding data transmission path remains unchanged. When the predicted pheromone concentration exceeds the corresponding data transmission path pheromone concentration threshold, a second pheromone broadcast packet spontaneous period is set for each in-service data transmission path of the information technology service platform, based on the configuration base number of data transmission paths. The setting method of the second pheromone broadcast packet spontaneous period is the same as that of the first pheromone broadcast packet spontaneous period.

5. The intelligent management method applied to an information technology service platform according to claim 1, characterized in that: Obtain all historical data transmission paths of the information technology service platform, and construct a data transmission path table based on the unique identifiers of the path nodes, including: The data transmission path table includes all data transmission paths that the information technology service platform has ever used in its entirety. The information technology service platform manages each data transmission path by acquiring the unique identifier of the path node assigned to each historical data transmission path and finding the corresponding data transmission path in service through the unique identifier of the path node.

6. An intelligent management system applied to an information technology service platform, wherein the intelligent management method for an information technology service platform as described in any one of claims 1-5 is characterized in that: The intelligent management system includes a path data archiving module, a first broadcast cycle configuration module, a target path concentration prediction module, a full path concentration threshold module, and a second broadcast cycle control module. The path data archiving module is used to obtain all historical data transmission paths of the information technology service platform, construct a data transmission path table based on the unique identifier of the path node, and complete the retrieval and management of the corresponding data transmission paths; the first broadcast cycle configuration module is used to configure an independent first pheromone broadcast packet spontaneous cycle for each in-service data transmission path based on the data transmission path table and the analysis of the current data transmission path; the target path concentration prediction module is used to analyze the pheromone concentration based on the first pheromone broadcast packet spontaneous cycle and complete the extraction and output of the predicted pheromone concentration for the target path; the full path concentration threshold module is used to obtain the predicted pheromone concentration for each data transmission path, extract historical pheromone concentration data, and calculate the pheromone concentration threshold; the second broadcast cycle control module is used to configure the second pheromone broadcast packet spontaneous cycle according to the pheromone concentration threshold and carry out intelligent management of the information technology service platform based on the configuration results.

7. The intelligent management system applied to an information technology service platform according to claim 6, characterized in that: The path data archiving module includes a historical path acquisition unit and a path table construction unit; the historical path acquisition unit is used to acquire all the data transmission paths that have been in service in the history of the information technology service platform; the path table construction unit is used to construct a data transmission path table based on the unique identifier of the path node, and to find the corresponding data transmission path in service through the unique identifier of the path node. The first broadcast cycle configuration module includes a configuration base calculation unit and an independent cycle allocation unit; the configuration base calculation unit is used to calculate the historical expansion peak number of data transmission paths and the number configuration base based on the unique identifier of the path node; the independent cycle allocation unit is used to extract the complete execution duration of the broadcast packet and allocate an independent first pheromone broadcast packet spontaneous cycle to each in-service data transmission path; The target path concentration prediction module includes a concentration time series construction unit and a predicted concentration output unit. The concentration time series construction unit is used to retrieve pheromone concentration data to construct a pheromone concentration time series dataset, extract all historical pheromone concentration data of the target path and sort them. The predicted concentration output unit is used to establish a time series correlation mapping through model training and output the pheromone concentration data of the target path in the next spontaneous period of the first pheromone broadcast packet.

8. The intelligent management system applied to an information technology service platform according to claim 6, characterized in that: The full-path concentration threshold module includes a batch concentration prediction unit and a concentration threshold calculation unit. The batch concentration prediction unit is used to synchronously process the remaining data transmission paths and predict the predicted pheromone concentration for each data transmission path. The concentration threshold calculation unit is used to set a sliding data window to capture data and complete the calculation of the pheromone concentration threshold for each data transmission path. The second broadcast cycle control module includes a cycle adjustment decision unit and a platform intelligent control unit; The cycle adjustment decision unit is used to compare the predicted pheromone concentration with the pheromone concentration threshold and decide on the adjustment method of the spontaneous cycle of the first pheromone broadcast packet; the platform intelligent management and control unit is used to configure the spontaneous cycle of the second pheromone broadcast packet for each service data transmission path and realize platform intelligent management based on the spontaneous cycle of the second pheromone broadcast packet.

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