Multi-product service opening resource configuration method and system

Abstract

The invention provides a multi-product service opening resource allocation method and system, and belongs to the technical field of resource allocation, and the method comprises the steps: constructing a service resource parameter library, forming a mapping relation between resources and multi-product service demands, collecting and processing real-time resource operation data required by multi-product service opening, and obtaining effective resource data; analyzing multi-product information input by a user, extracting a demand keyword, matching the demand keyword with the business resource parameter library, and generating a product-resource configuration file; predicting a resource demand and generating a pre-scheduling instruction based on the effective resource data and the historical service data; and executing resource allocation and protocol binding according to the pre-scheduling instruction, completing product service resource configuration, identifying and processing exceptions in a resource configuration monitoring process, and updating exception information to an exception knowledge base. According to the invention, the problems of inaccurate matching of resources and service requirements, low efficiency of resource allocation and protocol binding, difficulty in guaranteeing compatibility, and difficulty in timely identification and processing of configuration process abnormity in a multi-product service opening process are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of resource configuration, in particular to a multi-product service opening resource configuration method and system. BACKGROUND

[0002] With the diversified development of enterprise business, the demand for shared access resources and terminal resources is increasingly urgent when multiple products are opened simultaneously. The precise matching of "multi-product-resources" needs to be achieved through resource configuration to support stable landing of business, while taking into account resource utilization efficiency and configuration efficiency to avoid resource idling or business opening delay.

[0003] In the current resource configuration process of multi-product business opening, there are many technical defects: first, there is no unified business resource parameter library, and the mapping relationship between resources and multi-product business demand relies on manual establishment, which is not only inefficient, but also prone to resource and business demand mismatch due to human judgment bias; second, multi-product information analysis relies on manual demand extraction, without automatic keyword recognition and library matching mechanism, when the product type is multiple and the function is complex, the demand analysis takes a long time and is easy to miss key requirements; third, resource demand prediction is mostly based on historical experience value estimation, without combining real-time resource running data and scientific algorithms, making it difficult to accurately predict resource demand peaks, leading to problems of insufficient resources during peak periods and idle resources during idle times; fourth, in the resource allocation and protocol binding process, there is no priority division according to business importance, and there is no compatibility verification link between protocol and resource, which is prone to configuration failure caused by high-priority business resources being occupied and protocol and resource being incompatible; fifth, the exception monitoring and processing of the configuration process relies on manual troubleshooting, which cannot identify resource running deviations in real time, and the exception handling is lagging, further prolonging the business opening period.

[0004] Therefore, the above-mentioned existing technical defects directly lead to low efficiency, low resource utilization, and high configuration exception rate of multi-product business opening, especially in cross-industry multi-product scenarios, due to large differences in business demand and complex resource types, the above-mentioned problems are more prominent. Such problems not only increase the operating costs of enterprises, but also may affect customer experience due to business opening delay or configuration exception, which cannot meet the business demand of enterprises to open multiple products on a large scale and efficiently. SUMMARY

[0005] In order to overcome the deficiencies of the prior art, the purpose of the present application is to provide a multi-product service opening resource configuration method and system, which integrates complex logic such as multi-product resource sharing relationship and configuration sequence control into the system, without pre-drawing fixed configuration processes, returning to the essence of resource and business bearing, greatly reducing the number of processes, while improving the accuracy of resource demand prediction and the efficiency of exception handling, making multi-product business opening more simple and interactive.

[0006] To achieve the above object, the present application provides the following scheme: One of the objects of the present application is to provide a multi-product service opening resource configuration method, comprising the following steps: S1, constructing a service resource parameter library, forming a mapping relationship between resources and multi-product service demand, collecting and processing real-time resource operation data required for multi-product service opening, and obtaining effective resource data; S2, analyzing the multi-product information input by the user, extracting demand keywords and matching the service resource parameter library, and generating a product-resource configuration file; S3, based on the effective resource data and historical service data, predicting resource demand and generating a pre-scheduling instruction; S4, performing resource allocation and protocol binding according to the pre-scheduling instruction, completing product service resource configuration, monitoring the resource configuration process, identifying and processing exceptions, and updating exception information to an exception knowledge base.

[0007] Preferably, in S1, the construction of the service resource parameter library comprises: dividing the type sub-library according to resource functions; establishing a mapping relationship between the sub-library resources and the service demand, specifically: mapping the bandwidth parameters according to the service data transmission volume, mapping the encryption protocol parameters according to the service security level, and mapping the computing power parameters according to the service concurrency; The collection and processing of real-time resource operation data required for multi-product service opening comprises: collecting resource real-time state information, the resource real-time state information including port traffic, CPU occupancy, voltage stability, and memory usage; The collected resource real-time state information is denoised and standardized to obtain effective resource data, and the format of the effective resource data matches the storage format of the service resource parameter card.

[0008] Preferably, in S2, the analysis of the multi-product information input by the user, the extraction of demand keywords and the matching of the service resource parameter library, and the generation of a product-resource configuration file comprise: using a natural language processing algorithm to analyze the multi-product information input by the user, extracting not less than two types of demand keywords, which are industry keywords representing the business domain and function demand keywords representing the core function of the business; matching the corresponding type sub-library in the service resource parameter library based on the industry keywords to obtain an adaptive resource type range; Within the resource type range, matching specific resource parameters based on the function demand keywords to determine the adaptive resource model, the corresponding protocol type and the resource demand; Integrating the resource model, protocol type and resource demand, a standardized product-resource configuration file is generated.

[0009] Preferably, in S3, the prediction of resource demand based on the effective resource data and historical business data includes: The effective resource data is aggregated with the historical business data of the same period for a preset number of historical periods, and the preset number is determined by the historical period rule of business opening; The resource demand peak value for a future prediction period is calculated by a preset algorithm, and the standby resource data to be activated is determined, and the formula is: ; Wherein, is the resource demand peak value at the future moment; is the fusion weight of the time sequence model, and the value range is 0~1, which is adaptively adjusted by the backward propagation of historical prediction error; is the prediction output of the long short-term memory network to the resource time sequence feature , and the resource time sequence feature is the time sequence sequence containing the resource running data of the past 24 hours; is the prediction output of the gated recurrent unit to the business feature , and the business feature is the business feature vector containing the same period business opening amount and product type proportion; is the business load influence coefficient, and the value range is 0.1~0.5, which is obtained by training the correlation between business type and resource consumption; is the current business peak period length, unit: hour; is the current business idle period length, unit: hour; is the prediction length, unit: hour.

[0010] Preferably, in S4, the resource allocation and protocol binding according to the pre-scheduling instruction are executed to complete the multi-product business resource configuration, which includes: Analyzing the resource allocation priority and protocol binding rule in the pre-scheduling instruction, the priority is sorted according to the importance level of the business, and the importance level is pre-stored in the business resource parameter library; According to the priority, the resource allocation is executed in turn: the core resource is reserved for high-priority business, and the edge resource is allocated for low-priority business, and the resource allocation result is recorded; Based on the protocol type in the product-resource configuration file, the allocated resource is bound with the corresponding business protocol, and the compatibility of the protocol and the resource is verified in the binding process. If it is incompatible, parameter library matching correction is triggered, and the adaptive protocol is re-determined; Integrating the resource allocation result and the protocol binding result, a resource configuration completion report for multi-product business opening is generated; The monitoring resource configuration process, identifying and processing the exception includes: Real-time comparison of resource real-time operation parameters with pre-stored resource standard operation parameters in the business resource parameter library; When the deviation exceeds the pre-stored exception determination threshold in the business resource parameter library, an exception processing instruction is generated, an operation of switching to a standby resource to a pre-device port or a fault resource power-off isolation is performed, and the exception type, processing instruction and adaptive resource information are updated to the exception knowledge base.

[0011] The second object of the application is to provide a multi-product business opening resource configuration system, comprising: A parameter library construction module for constructing a business resource parameter library and establishing a mapping relationship between resources and multi-product business requirements; A data processing module for collecting and processing real-time resource operation data required for multi-product business opening, and outputting valid resource data; An information analysis module for analyzing multi-product information input by a user, extracting demand keywords and matching the business resource parameter library, and generating product-resource configuration files; A demand prediction module for predicting resource demand and generating pre-scheduling instructions based on valid resource data and historical business data; A configuration execution module for performing resource allocation and protocol binding according to the pre-scheduling instructions to complete multi-product business resource configuration; An exception processing module for monitoring the resource configuration process, identifying and processing exceptions, and updating the exception knowledge base.

[0012] Preferably, the information analysis module comprises: A keyword extraction unit for analyzing multi-product information using natural language processing algorithms to extract industry keywords and functional demand keywords; A library matching unit for matching corresponding type sub-libraries based on industry keywords, and then matching specific resource parameters in the sub-libraries based on functional demand keywords; A file generation unit for integrating resource models, protocol types and resource demand quantities to generate standardized product-resource configuration files.

[0013] Preferably, the demand prediction module comprises: A data aggregation unit for integrating valid resource data and a preset number of same period historical business data in the past, the preset number being determined by historical period regularity of business opening; A peak calculation unit for calculating resource demand peaks in a future prediction period by a preset algorithm to determine the number of standby resources to be activated.

[0014] Preferably, the configuration execution module comprises: An instruction analysis unit is configured to analyze the resource allocation priority and the protocol binding rule in the pre-scheduling instruction, and the priority is sorted according to the importance level of the service; A resource allocation unit is configured to perform resource allocation according to the priority, reserve core resources for high-priority services, and allocate edge resources for low-priority services. A protocol binding unit is configured to bind the allocated resources to the corresponding service protocol, verify the compatibility during the binding process, trigger parameter library matching correction when the compatibility is not met, and generate a resource configuration completion report. A result generation unit is configured to integrate the resource allocation and protocol binding results, and generate a resource configuration completion report.

[0015] Preferably, the abnormality processing module comprises: A deviation monitoring unit is configured to compare the real-time running parameters of the resources with the pre-stored resource standard running parameters in the service resource parameter library in real time. An instruction generation unit is configured to generate a processing instruction for switching the standby resources to the pre-device port or isolating the fault resources by power-off when the deviation exceeds the pre-stored abnormality judgment threshold in the service resource parameter library, and trigger the abnormality knowledge base update.

[0016] According to the embodiments of the present application, the following technical effects are achieved: (1) The present application improves the accuracy of multi-product and resource matching, solves the problem of manual matching deviation, constructs a service resource parameter library containing resource-service mapping relationship, extracts industry and function demand keywords by combining natural language processing algorithm, and matches the parameter library to generate a standardized product-resource configuration file, thereby replacing the manual mapping and demand analysis method, avoiding the problem of resource and service demand mismatch caused by manual judgment deviation, and converting the multi-product information analysis from manual operation to automatic processing, thereby greatly improving the demand analysis and resource matching efficiency.

[0017] (2) The present application improves the resource demand prediction accuracy and configuration execution efficiency, optimizes the resource utilization, predicts the resource demand peak value based on effective resource data and historical business data by fusing LSTM, GRU model and preset algorithm of business load influencing factor, can accurately predict the resource demand in a certain time in the future, avoids resource shortage in peak period and resource idling in idle time, verifies the compatibility and triggers the correction when binding the protocol, guarantees the resource supply for high-priority services, reduces the configuration failure caused by protocol incompatibility, and improves the resource configuration execution efficiency and resource utilization.

[0018] (3) The application ensures the stability of the multi-product resource allocation process, reduces the business opening risk, automatically performs standby resource switching or fault isolation when the deviation of the resource running parameter from the standard parameter is compared in real time and exceeds the threshold, and updates the abnormal knowledge base, thereby realizing real-time identification and rapid processing of abnormalities, reducing the business opening interruption caused by abnormalities; and the system modules work cooperatively, integrating complex resource sharing relationships, configuration control, etc. in the system, without presetting a fixed process, returning to the essence of resource and business bearing, greatly reducing the number of configuration processes, and reducing the complexity of system operation and maintenance and business opening. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0020] Figure 1 A multi-product business opening resource allocation method principle flowchart is provided for the embodiments of the present application. Figure 2 A multi-product business opening resource allocation system structure schematic diagram is provided for the embodiments of the present application. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0022] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail with reference to the drawings and specific embodiments.

[0023] An embodiment of the present application, as shown in Figure 1 The present embodiment provides a multi-product business opening resource allocation method, comprising the following steps: S1, constructing a business resource parameter library to form a mapping relationship between resources and multi-product business requirements, collecting and processing real-time resource running data required for multi-product business opening to obtain effective resource data; S2, analyzing the multi-product information input by the user, extracting the demand keywords and matching the business resource parameter library to generate a product-resource configuration file; S3. Based on the effective resource data and historical business data, predict resource demand and generate pre-scheduling instructions; S4. Perform resource allocation and protocol binding according to the pre-scheduling instructions to complete the product business resource configuration. During the resource configuration monitoring process, identify and handle anomalies and update the anomaly information to the anomaly knowledge base.

[0024] In step S1 above, constructing the business resource parameter library includes dividing the resource into sub-libraries according to resource functions and establishing a mapping relationship between sub-library resources and business requirements. Specifically, this involves establishing corresponding associations with resource bandwidth parameters, encryption protocol parameters, and computing power parameters through three dimensions: business data transmission volume, business security level, and business concurrency. The collection and processing of real-time resource operation data required for the activation of multiple product services includes collecting key information reflecting the resource operation status, denoising the collected information to eliminate interference data, and standardizing the data to unify the data format. The final valid resource data must be consistent with the storage format of the business resource parameter library to ensure the smoothness of subsequent data calls and matching.

[0025] Furthermore, in practical implementation, the construction of the business resource parameter library aims to achieve a precise quantitative correlation between business requirements and resource parameters: sub-libraries divided by resource function must cover access, computing power, and terminal resources required for business activation. Resource parameters within each sub-library must form clear correspondence rules with business requirement dimensions. For example, based on different ranges of business data transmission volume, access resources with corresponding bandwidth capabilities are matched; based on differences in business security levels, resources with corresponding encryption capabilities are matched; based on the scale of business concurrency, resources meeting computing power requirements are matched. This correlation must be determined based on the compatibility analysis of business characteristics and resource capabilities to avoid resource mismatch with requirements. In the real-time resource operation data processing stage, noise reduction requires the adoption of adaptation strategies for different types of resource status information to eliminate the impact of instantaneous fluctuations or abnormal interference on data validity; standardization processing requires unifying the data volume and format to ensure that valid resource data can be directly compared and correlated with resource parameters in the parameter library. If there are format differences, automatic adjustment and correction are required to ensure data compatibility.

[0026] In step S2 above, parsing the multi-product information input by the user, extracting requirement keywords and matching them with the business resource parameter library to generate a product-resource configuration file includes using natural language processing technology to parse the multi-product information input by the user and extracting two types of key information: one type is industry keywords representing the business's domain, and the other type is functional requirement keywords representing the core functions of the business; based on the extracted industry keywords, matching the corresponding type sub-library in the business resource parameter library narrows the resource selection range; within the determined resource type range, further matching specific resource parameters in combination with functional requirement keywords to clarify the suitable resource model, corresponding protocol type, and required resource quantity; finally, integrating the above information to form a standardized product-resource configuration file with a unified format and complete content.

[0027] Specifically, the key to this step lies in achieving precise matching of multi-product information and resources through automated parsing and hierarchical matching: the application of natural language processing technology needs to focus on targeted keyword identification to ensure accurate differentiation between industry attributes and functional requirements, and the two types of keywords need to be associated to avoid interpreting functional requirements in isolation from the industry context; the matching of industry keywords and type sub-libraries needs to be based on preset domain-resource correspondence rules to ensure that the selected sub-libraries can cover all resource types required by the industry's business; the matching of functional requirement keywords and specific resource parameters needs to delve into the details of resource capabilities, and determine the resource model, protocol type and quantity in combination with the actual needs of business functions to avoid insufficient or redundant resource capabilities; the generated product-resource configuration file needs to contain the core information of the business and resource association, with complete fields and standardized format, providing a clear basis for subsequent resource configuration.

[0028] In step S3 above, predicting resource demand based on the effective resource data and historical business data includes: Summarize valid resource data with historical business data of the same period in the past, with the preset quantity determined by the historical periodic pattern of business activation. The peak resource demand for the predicted duration is calculated using a pre-set algorithm to determine the backup resource data that needs to be activated. The formula is as follows: ; in, For the future Peak resource demand at any given moment; The weights used for time series model fusion have a value range of 0 to 1 and are adaptively adjusted by backpropagation of historical prediction errors. For Long Short-Term Memory networks to assess the temporal characteristics of resources Predicted output, resource time series characteristics It is a time-series sequence containing resource operation data from the past 24 hours; For gated loop units to access business characteristics Predicted output, business characteristics This is a business feature vector that includes the number of services launched during the same period and the proportion of product types. The business load impact coefficient has a value range of 0.1 to 0.5 and is obtained by training the correlation between business type and resource consumption. This represents the duration of the current peak business period, in hours. This represents the current idle period in hours. The predicted duration is in hours.

[0029] Specifically, this step involves achieving scientific prediction of resource demand through data fusion and algorithmic calculation: the selection of historical data should focus on the cyclical patterns of the same period, avoiding the use of data that differs significantly from the characteristics of the current business cycle, ensuring that historical data can reflect the resource demand characteristics of similar businesses within similar cycles; the design of the preset algorithm should integrate the real-time characteristics of resource operation with the historical demand characteristics of the business, while considering the impact of changes in business load on resource demand, and the key parameters in the algorithm should be able to adaptively adjust according to historical prediction errors to improve prediction accuracy; the calculation of peak resource demand should be combined with the duration and scale of future business operations to ensure that the peak can cover the maximum resource demand of the business during that period; the pre-scheduling instructions should clearly specify the resource type, the number of standby resources to be activated, the effective time, and the applicable businesses to ensure that the instructions are executable.

[0030] In step S4 above, the step of performing resource allocation and protocol binding according to the pre-scheduling instruction to complete the multi-product service resource configuration includes parsing the resource allocation priority and protocol binding rules contained in the pre-scheduling instruction. The resource allocation priority needs to be determined according to the importance level of the business, and the importance level of the business needs to be pre-stored in the business resource parameter library. The resource allocation is performed according to the determined priority order, reserving core resources with better performance and stronger stability for high-priority businesses, and allocating edge resources for low-priority businesses. At the same time, the detailed results of resource allocation are recorded. Based on the protocol type determined in the product-resource configuration file, the allocated resources are bound to the corresponding business protocol. During the binding process, the compatibility between the protocol and the resource needs to be verified. If there is an incompatibility problem, the matching correction mechanism of the business resource parameter library is triggered to re-determine the appropriate protocol. The resource allocation results and protocol binding results are integrated to generate a resource configuration completion report for the activation of multi-product services.

[0031] The process of monitoring resource configuration, identifying and handling anomalies, includes real-time collection of resource operation parameters during resource configuration, comparison with pre-stored standard resource operation parameters in the business resource parameter library, and calculation of the deviation between the two. When the deviation exceeds the anomaly judgment threshold pre-stored in the business resource parameter library, an anomaly handling instruction is automatically generated. Based on the anomaly type, operations such as switching the backup resource to a preset backup port or power-off isolation of the faulty resource are performed. At the same time, the anomaly type, handling instruction, and adapted resource information are updated to the anomaly knowledge base to provide a reference for subsequent handling of similar anomalies.

[0032] This step requires priority scheduling and real-time anomaly control to ensure the efficiency and stability of multi-product resource allocation: the priority mechanism for resource allocation must clearly define the criteria for distinguishing between core and edge resources, ensuring that high-priority services receive high-quality resource support while preventing low-priority services from consuming too many core resources; the compatibility verification of protocol binding must delve into the adaptation details of resources and protocols, and if incompatibility is found, a correction mechanism must be initiated in a timely manner to avoid configuration failure due to protocol issues; the resource configuration completion report must comprehensively reflect the configuration results, providing a basis for service activation and subsequent operation and maintenance; anomaly monitoring must be highly real-time, deviation calculation must accurately reflect the differences between resource operating status and standards, and the anomaly judgment threshold must be determined according to the service's requirements for resource stability; anomaly handling must respond quickly, the handling measures must effectively solve the anomaly problem, and the archiving of anomaly information must be standardized to ensure that the anomaly knowledge base can continuously accumulate handling experience.

[0033] Another embodiment of the present invention, such as Figure 2 As shown in the figure, this embodiment provides a multi-product service activation resource configuration system, including a parameter library construction module, a data processing module, an information parsing module, a demand prediction module, a configuration execution module, and an exception handling module. The parameter library construction module is used to build a business resource parameter library and establish a mapping relationship between resources and multi-product service requirements. The data processing module is used to collect and process real-time resource operation data required for multi-product service activation and output valid resource data. The information parsing module is used to parse multi-product information input by the user, extract demand keywords, match them with the business resource parameter library, and generate product-resource configuration files. The demand prediction module is used to predict resource requirements and generate pre-scheduling instructions based on valid resource data and historical business data. The configuration execution module is used to execute resource allocation and protocol binding according to the pre-scheduling instructions to complete the multi-product service resource configuration. The exception handling module is used to monitor the resource configuration process, identify and handle exceptions, and update the exception knowledge base.

[0034] The information parsing module includes a keyword extraction unit, a library matching unit, and a file generation unit. The keyword extraction unit uses natural language processing algorithms to parse multi-product information and extract industry keywords and functional requirement keywords. The library matching unit matches the corresponding type sub-library based on industry keywords and then matches specific resource parameters in the sub-library based on functional requirement keywords. The file generation unit integrates resource models, protocol types, and resource requirements to generate standardized product-resource configuration files.

[0035] The information parsing module works collaboratively with three units to transform multi-product information into resource configuration files: the keyword extraction unit needs to accurately identify two types of keywords to ensure a clear distinction and close correlation between industry attributes and functional requirements; the library matching unit needs to first narrow down the resource range using industry keywords, and then lock in specific resources using functional requirement keywords to avoid inefficiency or insufficient accuracy caused by a matching range that is too large or too small; the file generation unit needs to integrate the matching results to ensure that the file information is complete, formatted correctly, and can directly support subsequent configuration operations. At the same time, the data flow between the three units needs to be smooth to avoid data stagnation or loss.

[0036] Furthermore, the demand forecasting module includes a data aggregation unit and a peak calculation unit. The data aggregation unit integrates valid resource data with historical business data from the same period of the past, with the preset quantity determined by the historical cyclical patterns of business activation. The peak calculation unit calculates the peak resource demand for the predicted duration using a preset algorithm, determining the number of standby resources to be activated. The demand forecasting module requires the collaboration of these two units to achieve scientific prediction of resource demand: the data aggregation unit must select comparable historical data and integrate it with real-time valid resource data to ensure that the data foundation reflects the actual characteristics of business and resources; the peak calculation unit must accurately calculate the peak demand using an algorithm, combine it with resource inventory to determine the number of standby resources to be activated, and the algorithm parameters must have adaptive adjustment capabilities to improve prediction accuracy. Simultaneously, both units must maintain real-time data transmission to avoid data lag affecting the prediction results.

[0037] The configuration execution module includes an instruction parsing unit, a resource allocation unit, a protocol binding unit, and a result generation unit. The instruction parsing unit parses the resource allocation priority and protocol binding rules in the pre-scheduled instructions, with priorities ordered according to business importance. The resource allocation unit performs resource allocation according to priority, reserving core resources for high-priority services and allocating edge resources for low-priority services. The protocol binding unit binds the allocated resources with the corresponding business protocols, verifying compatibility during the binding process and triggering parameter library matching and correction if incompatible. The result generation unit integrates the resource allocation and protocol binding results to generate a resource configuration completion report.

[0038] The configuration execution module requires the efficient implementation of resource configuration through the collaborative processes of four units: the instruction parsing unit must accurately interpret the key information in the pre-scheduled instructions to provide a clear basis for subsequent configuration; the resource allocation unit must strictly follow priority rules, reasonably divide core and edge resources, and ensure the rationality of resource allocation; the protocol binding unit must pay attention to compatibility verification, promptly handle incompatibility issues, and avoid configuration failures; and the result generation unit must comprehensively summarize the configuration results to provide a reference for business activation and operation and maintenance. At the same time, the four units must be closely connected to form a complete configuration process to avoid process interruption or repeated operations.

[0039] The anomaly handling module comprises a deviation monitoring unit and an instruction generation unit. The deviation monitoring unit compares the real-time operating parameters of resources with the pre-stored standard operating parameters in the business resource parameter library. The instruction generation unit generates instructions to switch backup resources to preset backup ports or to power-off isolate faulty resources when the deviation exceeds a pre-stored anomaly threshold in the business resource parameter library, and triggers an update to the anomaly knowledge base. The anomaly handling module relies on the collaboration of these two units to achieve real-time anomaly control and experience accumulation: the deviation monitoring unit collects and compares resource operating parameters in real-time, accurately calculates deviations, and ensures timely anomaly detection; the instruction generation unit generates effective processing instructions based on the deviation, quickly resolves anomalies, and updates the anomaly information to the knowledge base for future reference. The two units form a closed loop to ensure a complete process from anomaly detection to processing and archiving, improving system stability.

[0040] Based on the above, the system works as follows: First, the parameter library construction module builds a business resource parameter library, forming a mapping relationship between resources and the business requirements of multiple products. Then, the data processing module collects real-time resource operation data required for the activation of multiple product services, performs noise reduction and standardization on the collected data, and outputs valid resource data. Subsequently, the information parsing module uses a keyword extraction unit to parse the multi-product information input by the user using natural language processing algorithms, extracting industry keywords representing the business's domain and functional requirement keywords representing the core functions of the business. The library matching unit matches the corresponding type sub-library in the business resource parameter library based on the industry keywords, and then matches specific resource parameters in the sub-library based on the functional requirement keywords. The file generation unit integrates resource models, protocol types, and resource requirements to generate standardized product-resource configuration files. The demand prediction module integrates valid resource data with historical business data of the same period in the past (the preset quantity is determined by the historical cycle pattern of business activation) through the data aggregation unit. The peak calculation unit calculates the peak resource demand for the predicted duration using a preset algorithm, determines the number of standby resources to be activated, and generates pre-scheduling instructions. The configuration execution module parses the pre-scheduling instructions through the instruction parsing unit. The resource allocation priority and protocol binding rules in the instructions (priority is sorted according to the business importance level, which is pre-stored in the business resource parameter library) are implemented. The resource allocation unit executes resource allocation in order of priority, reserving core resources for high-priority businesses and allocating edge resources for low-priority businesses, and records the resource allocation results. The protocol binding unit binds the allocated resources with the corresponding business protocols based on the protocol types in the product-resource configuration file. During the binding process, the compatibility between the protocol and the resource is verified. If incompatibility occurs, parameter library matching and correction are triggered. The result generation unit integrates the resource allocation results and protocol binding results to generate a resource configuration completion report for the activation of multiple product services. Finally, the exception handling module compares the deviation of the real-time operating parameters of the resources with the standard operating parameters of the resources pre-stored in the business resource parameter library in real time through the deviation monitoring unit. When the deviation exceeds the exception judgment threshold pre-stored in the business resource parameter library, the instruction generation unit generates a processing instruction to switch the backup resource to the preset backup port or to disconnect and isolate the faulty resource. After executing the instruction, the exception type, processing instruction, and adapted resource information are updated to the exception knowledge base, forming a complete work closed loop of construction, processing, parsing, prediction, execution, and control, realizing accurate and efficient configuration of multi-product business resources.

[0041] Therefore, by adopting the above-mentioned multi-product business activation resource configuration method and system, the complex logic such as multi-product resource sharing relationship and configuration order control is integrated into the system. There is no need to predefine the fixed configuration process, which returns to the essence of resource and business carrying, greatly reduces the number of processes, and improves the accuracy of resource demand prediction and the efficiency of anomaly handling, making multi-product business activation simpler and easier to interact with.

[0042] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for allocating resources for activating multiple product services, characterized in that, Includes the following steps: S1. Construct a business resource parameter library to form a mapping relationship between resources and the business requirements of multiple products, collect and process real-time resource operation data required for the activation of multiple product services, and obtain effective resource data. S2. Parse the multi-product information input by the user, extract the required keywords and match them with the business resource parameter library to generate a product-resource configuration file; S3. Based on the effective resource data and historical business data, predict resource demand and generate pre-scheduling instructions; S4. Perform resource allocation and protocol binding according to the pre-scheduling instructions to complete the product business resource configuration. During the resource configuration monitoring process, identify and handle anomalies and update the anomaly information to the anomaly knowledge base.

2. The method for allocating resources for multi-product service activation according to claim 1, characterized in that, In S1, the construction of the business resource parameter library includes: Sub-libraries are divided according to resource function; Establish a mapping relationship between sub-library resources and business requirements, specifically: map bandwidth parameters according to business data transmission volume, map encryption protocol parameters according to business security level, and map computing power parameters according to business concurrency. The collection and processing of real-time resource operation data required for the activation of multiple product services includes: Collect real-time resource status information, including port traffic, CPU utilization, voltage stability, and memory usage. The collected real-time status information of the resources is denoised and standardized to obtain valid resource data. The format of the valid resource data matches the storage format of the business resource parameter card.

3. The method for allocating resources for multi-product service activation according to claim 1, characterized in that, In S2, parsing the multi-product information input by the user, extracting requirement keywords and matching them with the business resource parameter library to generate a product-resource configuration file includes: Natural language processing algorithms are used to parse multi-product information input by users and extract no less than two types of requirement keywords: industry keywords representing the business field and functional requirement keywords representing the core functions of the business. Based on the industry keywords, the corresponding type sub-library in the business resource parameter library is matched to obtain the range of suitable resource types; Within the scope of the resource types, specific resource parameters are matched based on the functional requirement keywords to determine the appropriate resource model, corresponding protocol type, and resource requirement. By integrating the resource models, protocol types, and resource requirements, a standardized product-resource configuration file is generated.

4. The method for allocating resources for multi-product service activation according to claim 1, characterized in that, In S3, predicting resource requirements based on the effective resource data and historical business data includes: Summarize valid resource data with historical business data of the same period in the past, with the preset quantity determined by the historical periodic pattern of business activation. The peak resource demand for the predicted duration is calculated using a pre-set algorithm to determine the backup resource data that needs to be activated. The formula is as follows: ； in, For the future Peak resource demand at any given moment; The weights used for time series model fusion have a value range of 0 to 1 and are adaptively adjusted by backpropagation of historical prediction errors. For Long Short-Term Memory networks to assess the temporal characteristics of resources Predicted output, resource time series characteristics It is a time-series sequence containing resource operation data from the past 24 hours; For gated loop units to access business characteristics Predicted output, business characteristics This is a business feature vector that includes the number of services launched during the same period and the proportion of product types. The business load impact coefficient has a value range of 0.1 to 0.5 and is obtained by training the correlation between business type and resource consumption. This represents the duration of the current peak business period, in hours. This represents the current idle period in hours. The predicted duration is in hours.

5. The method for allocating resources for multi-product service activation according to claim 1, characterized in that, In S4, the step of performing resource allocation and protocol binding according to the pre-scheduled instructions to complete the multi-product service resource configuration includes: The resource allocation priority and protocol binding rules in the pre-scheduling instruction are parsed. The priorities are sorted according to the importance level of the business, and the importance level is pre-stored in the business resource parameter library. Resource allocation is performed sequentially according to the aforementioned priority: core resources are reserved for high-priority services, and edge resources are allocated for low-priority services. The resource allocation results are then recorded. Based on the protocol type in the product-resource configuration file, the allocated resources are bound to the corresponding business protocol. During the binding process, the compatibility between the protocol and the resource is verified. If they are incompatible, the parameter library matching and correction is triggered, and the compatible protocol is re-determined. Integrate resource allocation results with protocol binding results to generate a resource configuration completion report for the activation of multiple product services; The process of identifying and handling anomalies during resource configuration monitoring includes: Real-time comparison of the deviation between the real-time operating parameters of resources and the standard operating parameters of resources pre-stored in the business resource parameter library; When the deviation exceeds the anomaly judgment threshold pre-stored in the business resource parameter library, an anomaly handling instruction is generated, and the operation of switching the backup resource to the preset backup port or power-off isolation of the faulty resource is performed. The anomaly type, handling instruction and adapted resource information are updated to the anomaly knowledge base.

6. A resource allocation system for multi-product service activation, characterized in that, include: The parameter library construction module is used to build a business resource parameter library and establish a mapping relationship between resources and business requirements of multiple products. The data processing module is used to collect and process real-time resource operation data required for the activation of multiple product services, and output valid resource data. The information parsing module is used to parse multi-product information input by the user, extract requirement keywords and match them with the business resource parameter library to generate product-resource configuration files; The demand forecasting module is used to predict resource demand and generate pre-scheduling instructions based on available resource data and historical business data. The configuration execution module is used to perform resource allocation and protocol binding according to pre-scheduled instructions, and complete the configuration of multi-product business resources. The exception handling module is used to monitor the resource configuration process, identify and handle exceptions, and update the exception knowledge base.

7. A multi-product service activation resource allocation system according to claim 6, characterized in that, The information parsing module includes: The keyword extraction unit is used to parse multi-product information using natural language processing algorithms and extract industry keywords and functional requirement keywords. The library matching unit is used to match the corresponding type sub-library based on industry keywords, and then match specific resource parameters in the sub-library based on functional requirement keywords; The file generation unit is used to integrate resource models, protocol types, and resource requirements to generate standardized product-resource configuration files.

8. A multi-product service activation resource allocation system according to claim 6, characterized in that, The demand forecasting module includes: The data aggregation unit is used to integrate valid resource data with a preset quantity of historical business data from the same period in the past, the preset quantity being determined by the historical periodic patterns of business activation. The peak calculation unit is used to calculate the peak resource demand for the predicted future duration using a preset algorithm, and to determine the number of standby resources that need to be activated.

9. A multi-product service activation resource allocation system according to claim 6, characterized in that, The configuration execution module includes: The instruction parsing unit is used to parse the resource allocation priority and protocol binding rules in the pre-scheduling instruction, wherein the priority is sorted according to the level of business importance; The resource allocation unit is used to perform resource allocation according to priority, reserving core resources for high-priority services and allocating peripheral resources for low-priority services; The protocol binding unit is used to bind the allocated resources with the corresponding business protocol. During the binding process, compatibility is verified, and parameter library matching and correction are triggered when incompatibility occurs. The results generation unit is used to integrate the resource allocation and protocol binding results to generate a resource configuration completion report.

10. A multi-product service activation resource allocation system according to claim 6, characterized in that, The exception handling module includes: The deviation monitoring unit is used to compare the deviation between the real-time operating parameters of resources and the pre-stored standard operating parameters of resources in the business resource parameter library. The instruction generation unit is used to generate a processing instruction for switching the backup resource to a preset backup port or for power-off isolation of the faulty resource when the deviation exceeds the anomaly judgment threshold pre-stored in the service resource parameter library, and to trigger an update of the anomaly knowledge base.

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