Intelligent information service management system

Abstract

The invention belongs to the technical field of information management, and provides an intelligent information service management system, which comprises the following steps of: acquiring full-link data of information transmission in a historical period by adopting an Flink stream processing framework, and constructing a full-link information analysis library; based on the full-link information analysis library, content distortion and aging distortion analysis is carried out on the distortion degree of information transmission in work, and the distortion degree of full-link data in work is evaluated; if the working full-link data distortion is serious, performing quantitative deviation analysis on all works with serious full-link data distortion in a historical period, evaluating hierarchical distortion risks in the works, identifying serious distortion hierarchies, and requiring a processor of the serious distortion hierarchies to re-check and supplement original information; according to the method, the serious distortion level in the whole link is positioned, and the risk is quantified by fusing the double dimensions of the distortion occurrence frequency and the severity, so that targeted rectification and level process optimization are realized.

Description

Technical Field

[0001] This invention belongs to the field of information management technology, specifically a smart information service management system. Background Technology

[0002] As work progresses and digital transformation deepens, the importance of information service management is becoming increasingly prominent. Intelligence, as a key carrier of digital transformation, aims to improve work efficiency and quality, and ensure the accuracy and timeliness of information transmission through information technology. However, current information service management technologies still face many unresolved issues in practical applications, making it difficult to meet the needs of refined and systematic governance in the new era.

[0003] Information is scattered across different systems and offline ledgers, forming information silos. The "hierarchical reporting and layer-by-layer transmission" model for information dissemination easily leads to the filtering of raw information from the grassroots level and the embellishment of work data. This results in distortion and inaccuracy of actual information during transmission, creating a "whip effect" and causing excessive fluctuations in the information received by higher levels. Addressing information distortion relies heavily on traditional qualitative descriptions, lacking quantitative assessment methods for the degree of distortion across the entire process of a single task. This makes it difficult to accurately identify high-distortion stages, leading to problems such as unreasonable allocation of governance resources and indiscriminate governance resulting in efficiency losses. Furthermore, existing technologies fail to accurately pinpoint the levels of severe distortion throughout the entire information transmission chain and lack a risk quantification assessment mechanism that integrates both the frequency and severity of distortion. This results in a lack of targeted rectification measures and insufficient optimization of hierarchical processes, ultimately leading to low precision, implementation, and systematization in addressing information distortion, and failing to effectively guarantee the fidelity and efficiency of information transmission.

[0004] Therefore, the present invention provides an intelligent information service management system. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0006] The technical solution adopted by this invention to solve its technical problem is: Information Analysis Library Construction Module: Using the Flink stream processing framework, it acquires end-to-end data of information transmission within a historical period and constructs an end-to-end information analysis library; Distortion Analysis Module: Based on the full-link information analysis library, it analyzes the distortion of information transmission in the work by performing content distortion and timeliness distortion analysis to assess the degree of distortion of the full-link data in the work. Distortion Risk Assessment Module: If the end-to-end data distortion is severe, a quantitative deviation analysis is performed on all end-to-end data distortion-severe tasks within the historical period to assess the hierarchical distortion risk in the work, identify the severe distortion level, and require the handlers of the severe distortion level to re-verify and supplement the original information.

[0007] As a further aspect of the present invention: the process of constructing the full-link information analysis database is as follows: The end-to-end data includes hierarchy and information. A database combining MySQL and ClickHouse is used for hybrid storage. The end-to-end data of information transmission within the historical period is written into the database. Each piece of information is assigned a unique ID. Using the unique ID of the information as the association key, the hierarchy and information are associated through the Flink KeyBy operator to form end-to-end data. The end-to-end data of information transmission within the historical period is read in batches through the Flink Batch mode. The parallelism is set to 4, and the data is processed by monthly shards to build an end-to-end information analysis database.

[0008] As a further aspect of the present invention: the process of evaluating the distortion level of the end-to-end data in the evaluation work is as follows: By analyzing the distortion of information transmission in the work, including content distortion and timeliness distortion, the distortion assessment value of the work is determined; If the distortion assessment value is greater than or equal to the distortion assessment value threshold, it indicates that the end-to-end data distortion is severe; otherwise, it indicates that the end-to-end data distortion is slight.

[0009] As a further aspect of the present invention: the process for evaluating the distortion value of the work is as follows: Obtain the content distortion and timeliness distortion of the level, and multiply the content distortion and timeliness distortion of the level to obtain the overall distortion of the level; The average of the combined distortion values ​​across all levels is summed to obtain the distortion assessment value for the work.

[0010] As a further aspect of the present invention: the process of obtaining the content distortion of the aforementioned level is as follows: Based on the full-link data of any given task, the number of organizational levels involved in the task is counted, and the information actually received by different levels of organizations is collected to form an information set corresponding to the historical tasks. Based on any level, Flink combined with NLP tools is used to segment and extract keywords from the information text at each level, generating a keyword set for the level. All basic original keywords are obtained and integrated into a basic original keyword set. Any keyword in the keyword set of the level is extracted. All basic original keywords in the basic original keyword set are traversed. If there is a basic original keyword in the basic original keyword set that is the same as the keyword of the level, the keyword of the corresponding level is recorded as a duplicate keyword. The number of overlapping keywords is counted and compared with the total number of original keywords at the grassroots level to obtain the content distortion degree of the hierarchy.

[0011] As a further aspect of the present invention: the process of obtaining the time-sensitivity distortion of the aforementioned level is as follows: The actual transmission time of information transmitted at each level is obtained. The absolute value of the difference between the actual transmission time and the standard transmission time is taken to obtain the time deviation of the level. The time deviation of the level is then compared with the standard transmission time to obtain the timeliness distortion of the level.

[0012] As a further aspect of the present invention: the process for determining the severe distortion level is as follows: Quantitative deviation analysis is performed on all work with severe data distortion across the entire link within the historical period to determine the high-risk distortion level. The high-risk distortion level is then analyzed and processed to determine the comprehensive distortion risk value of the high-risk distortion level. If the overall distortion risk value of a high-risk distortion level is greater than or equal to the overall distortion risk threshold, then the corresponding high-risk distortion level is recorded as a severe distortion level.

[0013] As a further aspect of the present invention: the process for determining the high-risk distortion level is as follows: Work with severe data distortion across the entire chain is marked as distorted work, and the comprehensive distortion degree of each level in the distorted work is extracted; If the overall distortion of a level is greater than or equal to the overall distortion limit, the corresponding level will be recorded as a high-risk distortion level.

[0014] As a further aspect of the present invention: the process for determining the comprehensive distortion risk value of the high-risk distortion level is as follows: Obtain the anomaly frequency and overall distortion degree value of the high-risk distortion level, and multiply the anomaly frequency and overall distortion degree value of the high-risk distortion level to obtain the overall distortion risk value of the high-risk distortion level.

[0015] As a further aspect of the present invention: the process of obtaining the anomaly frequency and overall distortion level value of high-risk distortion levels is as follows: Based on any high-risk distortion level, calculate the percentage of times the current high-risk distortion level appears in all work within the historical period, and record it as the abnormal frequency of the high-risk distortion level. The comprehensive distortion deviation of the high-risk distortion level is obtained by taking the absolute value of the difference between the comprehensive distortion degree of the high-risk distortion level and the comprehensive distortion degree limit. The comprehensive distortion deviations of the high-risk distortion level in all work are summed and averaged to obtain the mean comprehensive distortion deviation. The ratio of the mean comprehensive distortion deviation to the comprehensive distortion degree limit is then calculated to obtain the comprehensive distortion degree value of the high-risk distortion level.

[0016] The beneficial effects of this invention are as follows: By building a standardized, end-to-end data foundation through the information analysis database construction module, the pain points of scattered and non-standardized historical data are solved, providing high-quality data support for subsequent distortion analysis and risk assessment; relying on the distortion analysis module, the limitations of traditional qualitative descriptions are overcome, enabling quantitative assessment of the degree of distortion throughout the entire process of a single task, accurately identifying high-distortion tasks to focus governance resources and avoid the efficiency loss of indiscriminate governance; with the help of the distortion risk assessment module, the severe distortion levels in the entire process are further accurately located, and by integrating the dual dimensions of distortion frequency and severity to quantify risk, targeted rectification and hierarchical process optimization are achieved, effectively improving the accuracy, implementation, and systematization of information transmission distortion governance, and ensuring the fidelity and efficiency of information transmission. Attached Figure Description

[0017] The invention will now be further described with reference to the accompanying drawings.

[0018] Figure 1 This is a system block diagram of a smart information service management system according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the steps of a smart information service management method according to an embodiment of the present invention. Detailed Implementation

[0019] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments. Example

[0020] Please see Figure 1 As shown in the figure, the intelligent information service management system of this invention includes the following modules: Information Analysis Library Construction Module: Using the Flink stream processing framework, it acquires end-to-end data of information transmission within a historical period and constructs an end-to-end information analysis library; The end-to-end data includes hierarchy and information; It should be noted that the historical period includes, but is not limited to, 6 months, 1 year, and 3 years; Multi-dimensional data governance logic is constructed using Flink's Filter, FlatMap, Aggregate, and Distinct operators to standardize the entire chain of historical periodic information transmission data, including invalid data filtering, duplicate data deduplication, missing field completion, and outlier correction. The standardized data after Flink operator governance is used in a database that combines MySQL and ClickHouse for hybrid storage. The entire link data of information transmission within the historical period is written into the database. Each piece of information is assigned a unique ID. Using the unique ID of the information as the association key, the hierarchy and information are associated through the Flink KeyBy operator to form the entire link data. The entire link data of information transmission within the historical period is read in batches through the FlinkBatch mode. The parallelism is set to 4, and the data is processed by monthly sharding to build a full link information analysis library. For example, for the "Learning Needs Collection" information with information ID "DJ2025001", the KeyBy operator generates the full-link data of the information after grouping according to the information ID: "Branch original needs (keywords: insufficient learning resources, complex platform operation, low training frequency, complete OCR evidence) → General branch forwarding needs (keywords: insufficient learning resources, low training frequency, transmission time 4 hours) → Reported needs (5 items, keywords: insufficient learning resources, transmission time 3 hours)"; Distortion Analysis Module: Based on the full-link information analysis library, it analyzes the distortion of information transmission in the work by performing content distortion and timeliness distortion analysis to assess the degree of distortion of the full-link data in the work. Based on the full-link data of any given task, the number of organizational levels involved in the task is counted, and the information actually received by different levels of organizations is collected to form an information set corresponding to the historical tasks. Based on any level, Flink combined with NLP tools is used to segment and extract keywords from the information text at each level, generating a keyword set for the level. All basic original keywords are obtained and integrated into a basic original keyword set. Any keyword in the keyword set of the level is extracted. All basic original keywords in the basic original keyword set are traversed. If there is a basic original keyword in the basic original keyword set that is the same as the keyword of the level, the keyword of the corresponding level is recorded as a duplicate keyword. If there is no identical original keyword in the set of original keywords at the grassroots level, then the keyword at the corresponding level will be recorded as a non-overlapping keyword. The number of overlapping keywords is counted and compared with the total number of original keywords at the grassroots level to obtain the content distortion degree of the hierarchy; The actual transmission time of information transmitted at each level is obtained. The absolute value of the difference between the actual transmission time and the standard transmission time is taken to obtain the time deviation of the level. The time deviation of the level is then compared with the standard transmission time to obtain the timeliness distortion of the level. It should be noted that standard transmission time refers to the baseline time taken for a single message to be transmitted between a certain level of organization (e.g., branch → general branch) from the time the message is officially sent to the next level for receipt, confirmation, and archiving, under the absence of special abnormal scenarios (holidays, major organizational events, system failures, or sudden emergencies). This is achieved by obtaining historical transmission times for each level to define the range, and then splitting the transmission chain by level: different levels of the chain have separate standards, and standards are not used across chains; and by information type: different information types have different processing complexities, so standards are set accordingly. The average of the historical transmission times from each category is then calculated to obtain the standard transmission time for information transmitted at each level. The overall distortion of the hierarchy is obtained by multiplying the content distortion and the timeliness distortion. The average of the combined distortion at all levels is summed to obtain the distortion assessment value of the work. It should be noted that the distortion assessment value quantifies the overall distortion degree of the entire information transmission chain of a single task. In essence, it is an average representation of the combined impact of content distortion and timeliness distortion during the transmission of information from the grassroots level to the superior level. By integrating the comprehensive distortion degree of each level of a single task and taking the average, it reflects the fidelity level of information transmission throughout the entire chain, providing a clear basis for subsequent governance resources to focus on high-distortion tasks and avoiding inefficiency caused by indiscriminate governance. If the distortion assessment value is greater than or equal to the distortion assessment value threshold, it indicates that the entire data chain is severely distorted. If the distortion assessment value is less than the distortion assessment value threshold, it indicates that the end-to-end data distortion is slight. The beneficial effect of this module lies in breaking through the limitation of only being able to qualitatively describe information transmission distortion. It achieves a quantitative assessment of the degree of distortion throughout the entire process of a single task from both content and timeliness dimensions. By combining Flink with NLP tools, unstructured information content is transformed into calculable content distortion. At the same time, timeliness distortion is quantified based on standards and actual transmission time. The integrated comprehensive distortion and averaged distortion assessment values ​​can objectively and accurately reflect the fidelity level of information transmission throughout the entire process. It identifies tasks with high distortion, enabling governance resources to focus on core issues and fundamentally avoids the inefficiency caused by indiscriminate governance. It provides a quantifiable and implementable core judgment basis for the governance of information transmission distortion. Distortion Risk Assessment Module: If the end-to-end data of a task is severely distorted, a quantitative deviation analysis is performed on all tasks with severe end-to-end data distortion in the historical period to assess the hierarchical distortion risk in the task, identify the level of severe distortion, and require the person handling the severe distortion level to re-verify and supplement the original information. Work with severe data distortion across the entire chain is marked as distorted work, and the comprehensive distortion degree of each level in the distorted work is extracted; If the overall distortion of a level is greater than or equal to the overall distortion limit, the corresponding level will be recorded as a high-risk distortion level. If the overall distortion of a level is less than the overall distortion limit, the corresponding level will be recorded as a normal level. It should be noted that the overall distortion limit was set by those skilled in the art based on historical experience; Based on any high-risk distortion level, the number of times the current high-risk distortion level appears within the historical period is counted, and the percentage of the number of times the current high-risk distortion level appears in all work within the historical period is calculated, which is recorded as the abnormal frequency of the high-risk distortion level. The comprehensive distortion deviation of the high-risk distortion level is obtained by taking the absolute value of the difference between the comprehensive distortion degree of the high-risk distortion level and the comprehensive distortion degree limit. The comprehensive distortion deviation of the high-risk distortion level in all work is summed and the average value is taken to obtain the average comprehensive distortion deviation. The average comprehensive distortion deviation is then compared with the comprehensive distortion degree limit to obtain the comprehensive distortion degree value of the high-risk distortion level. The comprehensive distortion risk value of the high-risk distortion level is obtained by multiplying the anomaly frequency of the high-risk distortion level with the comprehensive distortion degree value. It should be noted that the comprehensive distortion risk value represents the overall risk level of high-risk distortion levels in historical distortion work. By multiplying the frequency of anomalies by the comprehensive distortion degree value, it is a dual-dimensional representation of the frequency of distortion occurrence and the severity of individual distortions at this level. This avoids the one-sidedness of only looking at the frequency and ignoring the severity of distortion, and also makes up for the limitation of only looking at the severity of individual distortions and ignoring the probability of occurrence. It can screen out the severe distortion levels from the suspected anomaly levels, identify the core weak links in the entire information transmission chain, and provide precise governance targets for subsequent targeted rectification and optimization of the hierarchical transmission process. If the overall distortion risk value of a high-risk distortion level is greater than or equal to the overall distortion risk threshold, it indicates that the distortion risk of the level is high in the work, and the corresponding high-risk distortion level is recorded as a severe distortion level. If the overall distortion risk value of a high-risk distortion level is less than the overall distortion risk threshold, it indicates that the distortion risk of the level is low in the work, and the corresponding high-risk distortion level is recorded as a non-serious distortion level. Based on the level of severe distortion, personnel handling severe distortion are required to re-verify and supplement the original information; The beneficial effect of this module lies in its focus on tasks with severe distortion, enabling precise identification and quantification of distortion risks at different levels. By defining high-risk distortion levels, it integrates anomaly frequency (probability of distortion occurrence) and comprehensive distortion severity (severity of single distortion) to generate a comprehensive distortion risk value. This avoids the one-sidedness of only considering frequency while ignoring the severity of distortion, or only considering the severity of single distortion while ignoring the probability of occurrence. Furthermore, it can screen out high-risk distortion levels through risk thresholds, accurately locating the core weak links in the entire information transmission chain. Based on this, the personnel handling the distortion levels can be specifically required to re-verify and supplement the original information, achieving targeted rectification of information transmission distortion problems. This effectively improves the accuracy and implementation effect of information transmission governance, and promotes the optimization and improvement of the information transmission process from a hierarchical perspective. The technical solution of this invention is as follows: Using the Flink stream processing framework, the entire information transmission chain data within a historical period is acquired, and a full-chain information analysis library is constructed. Based on this library, the degree of distortion of the entire chain data is assessed by analyzing content distortion and timeliness distortion in the information transmission process. If the distortion of the entire chain data is severe, a quantitative deviation analysis is performed on all processes with severe distortion within the historical period to assess the risk of hierarchical distortion, identify the level of severe distortion, and require the handlers at the level of severe distortion to re-verify and supplement the original information. This invention builds a standardized, full-chain data foundation through an information analysis library construction module. This system addresses the pain points of scattered and non-standardized historical data, providing high-quality data support for subsequent distortion analysis and risk assessment. Leveraging the distortion analysis module, it overcomes the limitations of traditional qualitative descriptions, enabling quantitative assessment of the distortion level across the entire workflow for a single task. This allows for precise identification of high-distortion tasks, focusing governance resources and avoiding the efficiency losses of indiscriminate governance. Furthermore, the distortion risk assessment module further pinpoints the severity of distortion levels throughout the entire workflow. By integrating both the frequency and severity of distortion to quantify risk, it achieves targeted rectification and hierarchical process optimization, effectively improving the accuracy, implementation, and systematization of information transmission distortion governance, and ensuring the fidelity and efficiency of information transmission. Example

[0021] Based on the same inventive concept as the intelligent information service management system in the foregoing embodiments, such as Figure 2 As shown, this application provides a smart information service management method, which specifically includes the following steps: Step 1: Using the Flink stream processing framework, acquire the end-to-end data of information transmission within the historical period and build an end-to-end information analysis library; Step 2: Based on the end-to-end information analysis database, analyze the distortion of information transmission in the work by analyzing content distortion and timeliness distortion, and evaluate the degree of distortion of the end-to-end data in the work. Step 3: If the end-to-end data distortion is severe, perform quantitative deviation analysis on all end-to-end data distortion in the historical period, assess the risk of hierarchical distortion in the work, identify the severe distortion level, and require the person in charge of the severe distortion level to re-verify and supplement the original information.

[0022] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A smart information service management system, characterized in that: include: Information Analysis Library Construction Module: Using the Flink stream processing framework, it acquires end-to-end data of information transmission within a historical period and constructs an end-to-end information analysis library; Distortion Analysis Module: Based on the full-link information analysis library, it analyzes the distortion of information transmission in the work by performing content distortion and timeliness distortion analysis to assess the degree of distortion of the full-link data in the work. Distortion Risk Assessment Module: If the end-to-end data distortion is severe, a quantitative deviation analysis is performed on all end-to-end data distortion-severe tasks within the historical period to assess the hierarchical distortion risk in the work, identify the severe distortion level, and require the handlers of the severe distortion level to re-verify and supplement the original information.

2. The intelligent information service management system according to claim 1, characterized in that: The process of constructing the full-link information analysis database is as follows: The end-to-end data includes hierarchy and information. A database combining MySQL and ClickHouse is used for hybrid storage. The end-to-end data of information transmission within the historical period is written into the database. Each piece of information is assigned a unique ID. Using the unique ID of the information as the association key, the hierarchy and information are associated through the Flink KeyBy operator to form end-to-end data. The end-to-end data of information transmission within the historical period is read in batches through the Flink Batch mode. The parallelism is set to 4, and the data is processed by monthly shards to build an end-to-end information analysis database.

3. The intelligent information service management system according to claim 2, characterized in that: The process of assessing the distortion of the end-to-end data in the aforementioned assessment work is as follows: By analyzing the distortion of information transmission in the work, including content distortion and timeliness distortion, the distortion assessment value of the work is determined; If the distortion assessment value is greater than or equal to the distortion assessment value threshold, it indicates that the end-to-end data distortion is severe; otherwise, it indicates that the end-to-end data distortion is slight.

4. The intelligent information service management system according to claim 3, characterized in that: The process for evaluating the distortion value of the work is as follows: Obtain the content distortion and timeliness distortion of the level, and multiply the content distortion and timeliness distortion of the level to obtain the overall distortion of the level; The average of the combined distortion values ​​across all levels is summed to obtain the distortion assessment value for the work.

5. The intelligent information service management system according to claim 4, characterized in that: The process of obtaining the content distortion of the aforementioned level is as follows: Based on the full-link data of any given task, the number of organizational levels involved in the task is counted, and the information actually received by different levels of organizations is collected to form an information set corresponding to the historical tasks. Based on any level, Flink combined with NLP tools is used to segment and extract keywords from the information text at each level, generating a keyword set for the level. All basic original keywords are obtained and integrated into a basic original keyword set. Any keyword in the keyword set of the level is extracted. All basic original keywords in the basic original keyword set are traversed. If there is a basic original keyword in the basic original keyword set that is the same as the keyword of the level, the keyword of the corresponding level is recorded as a duplicate keyword. The number of overlapping keywords is counted and compared with the total number of original keywords at the grassroots level to obtain the content distortion degree of the hierarchy.

6. The intelligent information service management system according to claim 4, characterized in that: The process of obtaining the timeliness distortion of the aforementioned level is as follows: The actual transmission time of information transmitted at each level is obtained. The absolute value of the difference between the actual transmission time and the standard transmission time is taken to obtain the time deviation of the level. The time deviation of the level is then compared with the standard transmission time to obtain the timeliness distortion of the level.

7. The intelligent information service management system according to claim 4, characterized in that: The process for determining the severe distortion level is as follows: Quantitative deviation analysis is performed on all work with severe data distortion across the entire link within the historical period to determine the high-risk distortion level. The high-risk distortion level is then analyzed and processed to determine the comprehensive distortion risk value of the high-risk distortion level. If the overall distortion risk value of a high-risk distortion level is greater than or equal to the overall distortion risk threshold, then the corresponding high-risk distortion level is recorded as a severe distortion level.

8. The intelligent information service management system according to claim 7, characterized in that: The process for determining the high-risk distortion level is as follows: Work with severe data distortion across the entire chain is marked as distorted work, and the comprehensive distortion degree of each level in the distorted work is extracted; If the overall distortion of a level is greater than or equal to the overall distortion limit, the corresponding level will be recorded as a high-risk distortion level.

9. The intelligent information service management system according to claim 8, characterized in that: The process for determining the comprehensive distortion risk value of the high-risk distortion level is as follows: Obtain the anomaly frequency and overall distortion degree value of the high-risk distortion level, and multiply the anomaly frequency and overall distortion degree value of the high-risk distortion level to obtain the overall distortion risk value of the high-risk distortion level.

10. The intelligent information service management system according to claim 9, characterized in that: The process of obtaining the anomaly frequency and overall distortion level value for high-risk distortion levels is as follows: Based on any high-risk distortion level, calculate the percentage of times the current high-risk distortion level appears in all work within the historical period, and record it as the abnormal frequency of the high-risk distortion level. The comprehensive distortion deviation of the high-risk distortion level is obtained by taking the absolute value of the difference between the comprehensive distortion degree of the high-risk distortion level and the comprehensive distortion degree limit. The comprehensive distortion deviations of the high-risk distortion level in all work are summed and averaged to obtain the mean comprehensive distortion deviation. The ratio of the mean comprehensive distortion deviation to the comprehensive distortion degree limit is then calculated to obtain the comprehensive distortion degree value of the high-risk distortion level.

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