Method for Matching User and Data Acquisition Mode in Design Management System

Abstract

The present invention discloses a method for matching users with data acquisition modes in a design management system, specifically relating to the technical field of information matching, which includes the following steps: collecting access request data of users from multiple data sources, and conducting access anomaly analysis to determine whether a user triggers an adjustment mechanism, screening out users who trigger the adjustment mechanism, for users who trigger the adjustment mechanism, respectively evaluating the frequency of data acquisition and the effectiveness of data acquisition, then inferring the type of data acquisition mode adapted to the users, and optimizing the current data acquisition mode type of the users based on the inference result; the present invention not only solves the problems of data dispersion, untimely information submission, and repetitive labor in the existing design management system, but also realizes the precise matching of data acquisition and utilization through intelligent analysis and dynamic adjustment, providing a solid technical support and the possibility of continuous optimization for the enterprise informatization construction.

Description

Technical Field

[0001] The present invention relates to the technical field of information matching, and more specifically, to a method for matching users with data acquisition modes in a design management system. Background Art

[0002] In modern design management systems, enterprises need to process complex information from multiple data sources, including project management data, design drawings, CAD files, engineering documents, etc. However, the data of design institutes and related enterprises is distributed among different systems and departments, lacking a unified management mechanism, resulting in inconvenient data acquisition and untimely information updates. Due to the lack of an efficient data sharing method, downstream professional teams need to frequently obtain data manually and repeatedly verify the data validity, which affects the design efficiency and quality. Due to multiple iterations and version updates of design data, data asynchronization in the information submission process will lead to problems such as "errors, omissions, collisions, and deficiencies", affecting the implementation of subsequent projects. After design changes, downstream users are difficult to obtain the latest data in a timely manner, resulting in data lag during the construction process and increasing the project adjustment cost.

[0003] Existing systems mainly rely on manual queries and lack an intelligent analysis and reasoning mechanism for users' data requirements. The data access requirements of different users are dynamically changing. For example, high-frequency query users need real-time data support, such as project managers or core designers. Low-frequency access users may only need to synchronize data regularly, such as construction teams or archivists. However, the current system lacks an intelligent evaluation mechanism for users' data access behaviors, resulting in the inability of data push and query modes to effectively match users' needs. Therefore, the present invention proposes a method for matching users with data acquisition modes in a design management system to solve the above problems. Summary of the Invention

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A method for matching users with data acquisition modes in a design management system, comprising the following steps:

[0006] Step 1: Collect users' access request data from multiple data sources according to users' data access behaviors to form a user access log data set;

[0007] Step 2: Perform access anomaly analysis based on the user access log data set, determine whether users trigger a regulation mechanism, and screen out users who trigger the regulation mechanism;

[0008] Step 3: For users who trigger the regulation mechanism, respectively evaluate the frequency of data acquisition and the effectiveness of data acquisition to obtain an evaluation result combination;

[0009] Step 4. Infer the data acquisition mode type adapted to the user according to the evaluation result combination, and optimize the user's current data acquisition mode type based on the inference result.

[0010] In a preferred embodiment, performing access anomaly analysis means:

[0011] Divide the user access log dataset into windows of a fixed duration, and obtain the number of user data requests in the current time window , the number of valid data requests in the current time window , the historical average number of requests , the historical number of valid requests , the standard deviation of the historical number of user data requests , and the standard deviation of the historical number of valid data requests , and substitute them into the access anomaly calculation formula:

[0012] ; represents the data request anomaly index.

[0013] In a preferred embodiment, determining whether the user triggers the adjustment mechanism means:

[0014] Compare the data request anomaly index with a preset trigger threshold. If the data request anomaly index is greater than the preset trigger threshold, trigger the adjustment mechanism; if the data request anomaly index is less than or equal to the preset trigger threshold, do not trigger the adjustment mechanism.

[0015] In a preferred embodiment, the evaluation result combination means:

[0016] Evaluate the frequency of data acquisition to obtain a data call sensitivity index, evaluate the effectiveness of data acquisition to obtain a data effective application index, and the evaluation result combination consists of the data call sensitivity index and the data effective application index.

[0017] In a preferred embodiment, the acquisition logic of the data call sensitivity index is:

[0018] Within a preset time window, calculate the time distribution density of access behavior, and define the time distribution density coefficient as:

[0019] ; n is the number of requests in the current time window, is the time point of the i-th request, is the average request time in the current window, is a preset smoothing coefficient used to adjust the sensitivity of density calculation, is the time distribution density coefficient;

[0020] Define the data request fluctuation coefficient as the standard deviation of the number of access requests within a time window;

[0021] Define the data matching consistency coefficient as:

[0022] ; is the data matching degree of the i-th request, and the value range is [0, 1], is the average matching degree within the time window, is the data matching consistency coefficient;

[0023] Define the access success stability coefficient:

[0024] ; is the success rate of the i-th access, is the average success rate within the time window, is the access success stability coefficient;

[0025] The calculation formula for the data call sensitivity index is:

[0026] ; , , and are all preset non-zero weight coefficients, is the data request fluctuation coefficient, is the data call sensitivity index.

[0027] In a preferred embodiment, the acquisition logic of the data effective application index is:

[0028] Calculate the data survival time within a preset time window:

[0029] ; is the time point of the -th request, is the data version of the i-th access, is the -th data version of the access, is the data update decay coefficient, which is used to control the impact of data update on the survival time, is the data survival time;

[0030] Calculate the data feedback ratio reflecting the depth of data application:

[0031] ; is the number of feedback data submitted by the user after the i-th request, is the interval duration from the start of the user's i-th request to the next request. is the data feedback ratio;

[0032] Calculate the data decision correlation:

[0033] ; is the decision-making behavior intensity after the i-th request for data, and its value range is [0, 1]. is the average decision-making intensity after all data requests within the time window. is the data decision correlation;

[0034] Calculate the data stable utilization:

[0035] ; is the standard deviation of the data usage frequency within the time window. is the data stable utilization;

[0036] The formula for calculating the data effective application index is:

[0037] ; is the data effective application index.

[0038] In a preferred embodiment, inferring the type of data acquisition mode adapted to the user according to the evaluation result combination refers to:

[0039] Adopt fuzzy inference, take the data effective application index and the data call sensitivity index as input variables together, take the type of data acquisition mode adapted to the user as the output variable, perform fuzzy processing on the input variables, convert the values of the input variables into fuzzy sets, perform fuzzy processing on the output variable, convert the output variable into a fuzzy set, formulate fuzzy rules to describe the adaptation degree of the data acquisition mode type under different data type combinations, and infer the type of data acquisition mode adapted to the user through the fuzzy rules for the fuzzy input variables.

[0040] In a preferred embodiment, the data acquisition mode types include real-time query mode, intelligent push mode, batch acquisition mode, and on-demand pull mode.

[0041] The technical effects and advantages of the present invention:

[0042] In the design management system based on multi-source data fusion in the present invention, by integrating multi-source information such as project management data, design drawings, and engineering documents, the system can achieve cross-departmental and cross-professional data connection, effectively solve the problems of data dispersion and untimely information update in the existing system, make the data obtained in each link have consistency and real-time nature, thereby improving the design quality and overall collaboration efficiency. By using user access log data, abnormal analysis is carried out on the access behavior, the data request abnormal index is calculated, and comprehensive evaluation is carried out through the data call sensitivity index and data effective application index. Based on the fuzzy inference mechanism, the system can dynamically identify the current data access mode of the user and automatically optimize the data acquisition mode (such as real-time query, intelligent push, batch acquisition, or pull-on-demand), thereby realizing personalized services. This adaptive adjustment not only improves the accuracy of data acquisition but also can quickly respond to changes in user needs to ensure the efficiency and flexibility of data services.

[0043] In the present invention, by evaluating the frequency of data calls and the effectiveness of data utilization, managers can timely grasp the project progress, resource allocation, and design change situations, providing accurate and real-time basis for decision-making. The efficient data matching and feedback mechanism helps reduce engineering quality problems caused by data errors or delays, and improves the scientific nature of enterprise management and decision-making level. On the basis of distinguishing user access behaviors and needs, the system provides adapted access methods for different users, avoiding the overloading of the system caused by all users adopting a unified mode. By adopting multiple modes such as real-time query, intelligent push, regular synchronization, and pull-on-demand, the system can more reasonably allocate database query resources, reduce server pressure, and improve the overall operation efficiency.

[0044] The intelligent data management and automatic adjustment mechanism of the present invention helps enterprises realize the transformation from traditional manual query to efficient and intelligent data matching methods. By optimizing the design management process, improving the accuracy of information transmission and the speed of data sharing, enterprises can improve management efficiency and response speed in the fierce market competition, thereby enhancing the overall competitive advantage. The present invention not only solves the problems of data dispersion, untimely information submission, and repetitive labor in the existing design management system, but also realizes the accurate matching of data acquisition and utilization through intelligent analysis and dynamic adjustment, providing a solid technical support and the possibility of continuous optimization for enterprise informatization construction. Brief Description of the Drawings

[0045] For the convenience of those skilled in the art to understand, the present invention will be further described below in conjunction with the accompanying drawings;

[0046] Figure 1 It is the schematic diagram of the method for matching users with data acquisition modes in the design management system of the present invention. Detailed Embodiments

[0047] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0048] Referring to Figure 1 the following embodiments are obtained:

[0049] Embodiment 1: In a modern design management system, an enterprise needs to process complex information from multiple data sources, including project management data, design drawings, CAD files, engineering documents, etc. However, the data of design institutes and related enterprises are distributed among different systems and departments, lacking a unified management mechanism, resulting in inconvenient data acquisition and untimely information update. Due to the lack of an efficient data sharing method, downstream professional teams need to frequently manually acquire data and repeatedly verify the data validity, which affects the design efficiency and quality. Due to the multiple iterations and version updates of design data, data asynchronization in the information submission process will lead to problems such as "errors, omissions, clashes, and deficiencies", affecting the implementation of subsequent projects. After a design change, downstream users are difficult to obtain the latest data in a timely manner, resulting in data lag during the construction process and increasing the project adjustment cost.

[0050] The existing system mainly relies on manual queries and lacks an intelligent analysis and reasoning mechanism for user data requirements. The data acquisition frequencies and validities of different users are different, but the existing methods fail to provide personalized data acquisition methods, resulting in high-demand users facing slow data response problems, while low-demand users may obtain a large amount of invalid data, causing waste of system resources. The data access requirements of different users are dynamically changing. For example, high-frequency query users need real-time data support, such as project managers or core designers. Low-frequency access users may only need to synchronize data regularly, such as construction teams or file managers. However, the current system lacks an intelligent evaluation mechanism for user data access behaviors, resulting in the inability of data push and query modes to effectively match user requirements.

[0051] To address the above problems, the present invention proposes a method for matching users with data acquisition modes in a design management system. The core objectives are: dynamically analyzing data access patterns through user access log datasets to improve the accuracy and efficiency of data acquisition; constructing an evaluation model for data acquisition sensitivity and data application effectiveness to automatically match the optimal data acquisition method for different users; and using a fuzzy reasoning method to optimize the data acquisition mode to ensure that different users can access data in the optimal way, thereby reducing information islands and repetitive labor.

[0052] The specific methods include: Access anomaly analysis mechanism: By calculating the Data Request Anomaly Index (DARI), it determines whether the user's data access behavior deviates from the historical pattern and decides whether to optimize the data acquisition method.

[0053] Data acquisition evaluation; Calculate the Data Call Sensitivity Index (DRSI) to measure the frequency of the user's data acquisition. Calculate the Data Effective Application Index (DEAI) to measure whether the data obtained by the user is effectively utilized.

[0054] Dynamically adapt the data acquisition mode: Using fuzzy inference, according to the results of DRSI and DEAI, automatically adjust the user's data acquisition mode: real-time query mode (for high-demand users), intelligent push mode (for medium-demand users), batch acquisition mode (for low-frequency demand users), and on-demand pull mode (for low-demand or external collaboration users).

[0055] The implementation of the present invention will bring the following values:

[0056] Improve the accuracy and intelligence level of data acquisition: Through the analysis of data access behavior, reduce inefficient and repetitive data acquisition requests, and improve the intelligence of the system's data interaction. By automatically evaluating the user's data requirements and optimizing the data distribution strategy, high-frequency users can quickly access the required data, and low-frequency users will not be interfered by redundant data.

[0057] Optimize the design management process and improve data consistency: Solve the problems of untimely data matching and inconsistent information, ensure that the data after design changes can be accurately and quickly synchronized to downstream users, and reduce the errors caused by data delay. By optimizing the data matching and push methods, reduce manual intervention and improve the consistency of design and construction data.

[0058] Improve the utilization rate of system resources and reduce data redundancy: Adopt an intelligent adjustment mechanism to reduce the waste of system resources, avoid unnecessary database queries and repeated data pushes. Improve the query load efficiency of the server and reduce the system bottleneck caused by high-frequency inefficient queries.

[0059] Adapt to dynamic business requirements and improve enterprise competitiveness: The data access requirements in the design management system are dynamically changing. The present invention can automatically adapt to these changes and improve the enterprise's informatization management ability. Through data acquisition optimization, improve the overall management efficiency, enabling the enterprise to more efficiently respond to complex engineering design requirements.

[0060] The method for matching users with data acquisition modes in the design management system includes the following steps:

[0061] Step 1: Collect the user's access request data from multiple data sources according to the user's data access behavior to form a user access log dataset. This step is the foundation of the entire method, ensuring that the system can record the user's data access behavior in real time and provide reliable data support for subsequent analysis. By collecting the user's access request data from multiple data sources (such as project management data, CAD files, design drawings, engineering documents, etc.), a user access log dataset is formed to establish a complete data access record. The collected data includes key information such as the user's query time, query content, access frequency, and request success rate, which helps to analyze and optimize the user's access pattern in the future. This step can help the system understand which users access which data at what time and in what way, and provide basic data for the next anomaly detection.

[0062] Step 2: Based on the user access log dataset, conduct access anomaly analysis to determine whether the user triggers the adjustment mechanism and filter out the users who trigger the adjustment mechanism. The core goal of this step is to identify the abnormal situations in the user's data access behavior and determine whether it is necessary to adjust the data acquisition strategy. By calculating the Data Request Anomaly Index (DARI), analyze the user's data request situation within the current time window and compare it with the historical access behavior. This step can find out: whether there are abnormally high-frequency queries (such as a large number of data requests in a short period of time); whether there are abnormally inefficient queries (such as the user frequently requests but fails to successfully match the data); whether the user's access behavior deviates significantly from the past behavior pattern; if the data request anomaly index exceeds the preset threshold, the system will trigger the adjustment mechanism to ensure that the user can obtain the required data more efficiently and reduce the waste of system resources. The core role of this step is to reduce unnecessary data queries, optimize the system performance, and improve the user data access experience.

[0063] Step 3: For the users who trigger the adjustment mechanism, conduct an assessment of the frequency of data acquisition and the effectiveness of data acquisition respectively to obtain an evaluation result combination. This step is used to quantify the user's data access pattern, evaluate the frequency and effectiveness of their data acquisition, and thus provide a scientific basis for optimizing the data acquisition pattern. The main evaluation indicators: Data Call Sensitivity Index (DRSI): Measure factors such as the frequency, stability, and success rate of the user's data acquisition. Data Effective Application Index (DEAI): Measure factors such as the utilization degree, decision-making relevance, and feedback situation of the data obtained by the user in the business process. The significance of this step is: through DRSI, evaluate whether the user's query needs are frequent and stable, and determine the user's real-time needs; through DEAI, evaluate the application situation of the data obtained by the user, and identify which users obtain a large amount of data but fail to use it effectively; comprehensively evaluate the result combination, avoid the ineffective occupation of system resources, and ensure the efficient use of data acquisition pattern optimization.

[0064] Step 4: Infer the data acquisition mode type adapted to the user based on the evaluation result combination, and optimize the user's current data acquisition mode type based on the inference result. This step is based on a fuzzy inference method, taking the data call sensitivity index (DRSI) and the data effective application index (DEAI) as inputs, inferring the most suitable data acquisition mode for the user, and performing optimization. The system matches the optimal data acquisition method for the user according to the user's access mode, including: real-time query mode (suitable for users with high demands who need to access data frequently in real time); intelligent push mode (suitable for users with relatively stable demands but low requirements for data timeliness); batch acquisition mode (suitable for users who access data periodically); on-demand pull mode (suitable for users with low access frequencies who only obtain data under specific circumstances). The core goal of this step is: to improve data acquisition efficiency, reduce redundant queries, and reduce the database pressure; to ensure that high-priority users can access data quickly, while optimizing the acquisition method for low-frequency users to avoid unnecessary data push or storage consumption; to intelligently adapt to the needs of different users, improve the matching degree and effectiveness of data, and ensure that the application of data in the business process is more accurate.

[0065] Performing access anomaly analysis means:

[0066] Dividing the user access log dataset into windows of a fixed duration, and obtaining the number of user data requests in the current time window , the number of valid data requests in the current time window , the historical average request number , the historical valid request number , the standard deviation of the historical user data request number , and the standard deviation of the historical valid data request number , substituting them into the access anomaly calculation formula:

[0067] ; represents the data request anomaly index.

[0068] Judging whether the user triggers the adjustment mechanism means:

[0069] Comparing the data request anomaly index with a preset trigger threshold. If the data request anomaly index is greater than the preset trigger threshold, the adjustment mechanism is triggered; if the data request anomaly index is less than or equal to the preset trigger threshold, the adjustment mechanism is not triggered.

[0070] Access anomaly analysis calculates the Data Request Anomaly Index (DARI) to evaluate whether a user's access behavior exceeds the historical pattern, and then determines whether it is necessary to adjust the data acquisition method. Example: Normal situation: A certain user accesses 10 - 20 times a day, and the data acquisition success rate is over 80%, with stable changes, no adjustment needed. Abnormal situation 1 (sudden surge): A certain user suddenly accesses 500 times one day, far exceeding the historical average, which may indicate problems with data submission or user misoperation, and the data submission process needs to be optimized. Abnormal situation 2 (data inefficiency): A certain user frequently requests data, but the effective usage rate is extremely low (data is acquired successfully but not utilized), and the push strategy or data structure may need to be improved.

[0071] The result of access anomaly analysis is used to determine whether to trigger the adjustment mechanism, that is: If the Data Request Anomaly Index (DARI) exceeds the preset threshold → trigger the adjustment mechanism and adjust the user's data acquisition method;

[0072] If the Data Request Anomaly Index (DARI) is within a reasonable range → do not trigger the adjustment mechanism and maintain the existing method.

[0073] Through the adjustment mechanism, it is ensured that data is pushed to users who really need it, rather than being passively distributed based on fixed rules. If it is found that a user's access pattern has changed, the data acquisition method can be actively optimized to improve the data hit rate. If a certain user's access requests surge but the effectiveness is low, it may indicate improper query methods or the need to optimize the data structure. Through the adjustment mechanism, invalid requests can be reduced, query efficiency can be improved, and the database load can be reduced. Different users have different data requirements at different times, and the adjustment mechanism can optimize the data access pattern according to real-time needs. For example: Design stage → provide real-time query mode (quick response); Construction stage → adopt intelligent push mode (reduce repeated queries); Archiving stage → pull on demand (reduce resource occupancy). Prevent data access anomalies from affecting the business process. If a certain user accesses data for a long time but the effective utilization rate is extremely low, it may indicate problems with data submission, affecting the downstream work progress. After triggering the adjustment mechanism, the system can actively optimize the data matching method, reduce invalid interactions, and improve data consistency.

[0074] The evaluation result combination refers to:

[0075] Evaluate the frequency of data acquisition to obtain the data call sensitivity index, evaluate the effectiveness of data acquisition to obtain the data effective application index, and the evaluation result combination consists of the data call sensitivity index and the data effective application index.

[0076] The acquisition logic of the data call sensitivity index is:

[0077] Within a preset time window, calculate the time distribution density of access behaviors. The time density of data requests affects the stability of users' data requirements. Define the time distribution density coefficient as:

[0078] ; n is the number of requests within the current time window, is the time point of the i-th request, is the average request time within the current window, is a preset smoothing coefficient used to adjust the sensitivity of density calculation, is the time distribution density coefficient; when the request time points are concentrated, that is, most queries occur within a short time, TDD increases, indicating that the user is more sensitive to data calls at a specific moment. If the requests are evenly distributed, TDD decreases, indicating that the user's access pattern is more stable.

[0079] The volatility of user access requests reflects the stability or suddenness of their data acquisition. Define the data request volatility coefficient DRV as the standard deviation of the number of access requests within the time window; DRV is high: the user's access behavior fluctuates greatly, the data requirements are unstable, and there may be sudden access requirements. DRV is low: the user's access pattern is more stable, and the data requests are periodic.

[0080] The data matching degree reflects the consistency between the user's requested data and the actually obtained data. Define the data matching consistency coefficient as:

[0081] ; is the data matching degree of the i-th request, and the value range is [0,1], is the average matching degree within the time window, is the data matching consistency coefficient; DMC is close to 1: the user's access data matching degree is high, and the data call is accurate. DMC is low: there is a large deviation between the user's query and the obtained data, which may be due to incomplete data submission or inaccurate user query methods. is the maximum value of the data matching degree, used for normalizing the data.

[0082] "Data matching degree" refers to the degree of consistency between the data content actually requested by the user and the target data content stored in the system. Its value range is [0,1], taking 1 indicates complete consistency, and 0 indicates complete mismatch. This parameter is calculated by comparing the key fields of the requested data (such as data type, timestamp, version number, etc.) with the similarity of the system-matched data. The higher the matching degree, the higher the degree of understanding and satisfaction of the system for the user's data requirements.

[0083] The stability of the request success rate reflects whether the user's access behavior is reliable. Define the access success stability coefficient:

[0084] ; is the success rate of the i-th access, is the average success rate within the time window, is the access success stability coefficient; SSR close to 1 indicates that the user's access success rate is relatively stable. A lower SSR indicates that there are significant fluctuations during the user's access, and there may be data quality issues.

[0085] The calculation formula for the data call sensitivity index is:

[0086] ; , , and are all preset non-zero weight coefficients, adjusted according to different application scenarios, is the data request fluctuation coefficient, TDD reflects the user's access density, and a high density indicates that the data demand is more concentrated; DRV uses the Sigmoid transformation to make it have a greater impact during extreme value changes and reduce the interference of extreme values; DMC evaluates the matching degree of the user's accessed data, and the higher the consistency, the higher the sensitivity; SSR reflects the stability of the user's access success rate and improves the reliability of data calls, is the data call sensitivity index. The data call sensitivity index (DRSI) combines the time density (TDD), access volatility (DRV), matching consistency (DMC), and success rate stability (SSR), and through exponential smoothing, normalization processing, and multi-dimensional weight calculation, realizes the accurate evaluation of the user's data access behavior.

[0087] The data effective application index (DEAI) is used to measure the availability, utilization depth, decision-making value, and data demand stability of the data obtained by the user within a period of time. Different from the data call sensitivity index (DRSI), it does not focus on the user's access frequency, but pays attention to how the user applies, transforms, and feedbacks after obtaining the data to ensure the maximization of the actual value of the data. The acquisition logic of the data effective application index is:

[0088] The data survival time (DST) represents how long the user requests similar data again after obtaining the data. A short cycle indicates that the data is outdated or of low value, and a long cycle indicates that the data has lasting application value. Within the preset time window, calculate the data survival time:

[0089] ; is the time point of the th request, is the data version of the i-th access, is the th access data version, is the data update attenuation coefficient, which is used to control the impact of data update on the survival time. is the data survival time. If users rarely access data repeatedly, it indicates a long data survival time and high value. If users frequently access the same data but there are significant version differences, it indicates a short data survival time and may require optimizing data quality.

[0090] The Data Feedback Ratio (DFR) measures whether users have made modifications, supplements, or submitted feedback after obtaining data, reflecting the depth of data application. Calculate the Data Feedback Ratio for reflecting the depth of data application:

[0091] ; is the number of feedback data submitted by the user after the i-th request. is the time interval from the start of the i-th request by the user to the next request. is the Data Feedback Ratio. If DFR is high, it indicates that users have modified or improved the data and the depth of data application is high. If DFR is low, it indicates that the data is used passively and cannot be effectively converted into new information.

[0092] The Data Decision Correlation (DDC) indicates the correlation between the data obtained by users and their operation or decision-making behaviors. It can be measured by whether a specific business behavior (such as approval, file generation, production scheduling, etc.) is triggered after a data request, and the assignment method can be used to determine the intensity of the decision-making behavior after the data request. Calculate the Data Decision Correlation:

[0093] ; is the intensity of the decision-making behavior after the i-th data request, and the value range is [0,1]. is the average decision-making intensity after all data requests within the time window. is the Data Decision Correlation. When DDC is close to 1, it indicates that the user's data request highly affects the decision. When DDC is low, it indicates that the data acquisition has little impact on business decisions and there may be redundant queries.

[0094] The "intensity of decision-making behavior" refers to the frequency and impact degree of the business behavior triggered by users based on a certain type of data after obtaining the data, and its value range is also [0,1]. This parameter is obtained by scoring and counting whether users perform decision-making operations (such as approval, scheduling, modification, etc.) after obtaining data. The higher the intensity, the stronger the driving effect of the data on the user's subsequent business operations.

[0095] The Data Stable Utilization Index (DSU) measures whether users continuously and stably utilize data or there are fluctuations in data application. Calculate the data stable utilization degree:

[0096] ; is the standard deviation of the data usage frequency within the time window, and is the data stable utilization degree; if DSU is high (close to 1), it indicates that the data application is stable without large fluctuations; if DSU is low (close to 0), it indicates that the data application is unstable and the user requirements may be relatively random.

[0097] The calculation formula for the data effective application index is:

[0098] ; is the data effective application index. The arctangent function is used to avoid excessive exponents and maintain a reasonable range; the linear product of DST and DFR emphasizes the comprehensive influence of data survival time and feedback ratio; the square root operation is performed on DDC and DSU to reduce the influence of low values and improve the discrimination. The data effective application index (DEAI) evaluates the effective application of data from four aspects: data survival time (DST) - evaluating the length of the data life cycle; data feedback ratio (DFR) - measuring whether users modify or supplement the data; data decision correlation degree (DDC) - evaluating the impact of data on decision-making behavior; data stable utilization degree (DSU) - analyzing whether users continuously utilize the data. Finally, DEAI is calculated through a non-linear mapping formula to ensure that the index can reasonably reflect the application value of the data and provide an accurate basis for data optimization and user behavior analysis.

[0099] Reasoning about the data acquisition mode type adapted to the user according to the evaluation result combination refers to:

[0100] Using fuzzy reasoning, taking the data effective application index and the data call sensitivity index as input variables together, and taking the data acquisition mode type adapted to the user as the output variable. Fuzzify the input variables, convert the values of the input variables into fuzzy sets, fuzzify the output variable, convert the output variable into a fuzzy set, formulate fuzzy rules to describe the adaptation degree of the data acquisition mode type under different data type combinations, and infer the data acquisition mode type adapted to the user through the fuzzy rules with the fuzzified input variables. The data acquisition mode types include direct database query (real-time query mode); intelligent push + recommendation (intelligent push mode); regular synchronization + cache (batch acquisition mode); pull-on-demand / obtain after approval (pull-on-demand mode).

[0101] The principle of the reasoning process lies in using fuzzy logic to linguistically express and fuzzily transform the user data using two evaluation indicators: data application depth and data call sensitivity, so as to enable intelligent judgment in an uncertain environment. First, based on the user access logs, the system converts the original numerical evaluations into descriptive levels such as "low", "medium", "high", etc., so that the data application depth and data call sensitivity are no longer limited to precise numbers, but are transformed into fuzzy sets that can reflect the user behavior trends. Next, the system also performs similar fuzzification on various data acquisition modes, including real-time query, intelligent push, batch acquisition, and on-demand pull, so that each mode is no longer a fixed single option, but has a certain degree of adaptability range. Subsequently, through a series of pre-established fuzzy rules that describe the adaptability degree of each mode under different input combinations, these rules reflect the empirical knowledge and actual needs. For example, when both the data application depth and data call sensitivity are high, the system may tend to recommend the real-time query mode; when one of them is at a high level and the other is low, intelligent push is more suitable; and if both are at a low level, batch acquisition or on-demand pull may be more appropriate. Finally, through the intersection and union operations of the fuzzy sets, the system comprehensively considers all the rules and obtains an optimal data acquisition mode that conforms to the current access behavior characteristics of the user. This process enables the system to dynamically respond to changes in user needs, can finely distinguish different situations, and avoids the rigidity of traditional fixed threshold methods, thus achieving more flexible and intelligent data service optimization.

[0102] More specifically: The data acquisition approach refers to how the system provides data to users. Different approaches are suitable for different priority requirements, mainly including the following ways:

[0103] Direct database query (real-time): Applicable users: High DRFI, high DUEI (high priority). Method: Users can access the database in real-time to query the latest data, and the results are returned immediately after the query.

[0104] Intelligent push + recommendation: Applicable users: High DRFI, low DUEI (medium priority). Method: Combining the user's historical query habits, using intelligent push to prepare the data that the user may need in advance. Matching the access data of similar users through the recommendation algorithm to improve the accuracy of acquisition.

[0105] Regular synchronization + caching: Applicable users: Low DRFI, high DUEI (low priority). Method: Users do not actively query. The system regularly synchronizes the latest data to the buffer area in batches, and users can quickly read it.

[0106] On-demand pull / obtain after approval: Applicable users: Low DRFI, low DUEI (lowest priority). Method: When users need data, they submit a request, and the system provides the data after permission approval.

[0107] Direct database query and intelligent push can reduce the query costs of high DRFI users, improve work efficiency, and avoid affecting the overall system performance due to frequent manual queries. For low-frequency users, it can reduce resource waste and improve system availability. By adopting methods such as regular synchronization and on-demand pulling, low-frequency users can still obtain valid data without occupying database query resources. For low DUEI users, optimize the data recommendation mechanism. Through intelligent push and data matching, improve the availability of data, reduce invalid queries by users, enhance the overall efficiency of the system, reduce the database load, improve system stability, and reduce high-concurrency queries through caching, regular synchronization, etc., and optimize the utilization of system resources. Based on DRFI and DUEI, user priorities can be adjusted, and different data acquisition methods can be adopted, which can effectively optimize data query efficiency, reduce invalid queries, improve the user experience, while reducing the database load and improving the overall performance of the system. In this way, the design management system can allocate resources more reasonably, ensure real-time access for high-demand users, while ensuring efficient utilization for low-demand users, making data matching more accurate and efficient.

[0108] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0109] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0110] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0111] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0112] The above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims described above.

Claims

1. A method for matching user and data acquisition patterns in a design management system, characterized in that: The following steps are involved: Step 1: Collect user access request data from multiple data sources to form a user access log data set; Step 2: Perform access anomaly analysis based on the access log data set to obtain the data request anomaly index and determine whether the user triggers the adjustment mechanism, and select the users who trigger the adjustment mechanism; Step 3: For users who trigger the adjustment mechanism, the frequency and effectiveness of data acquisition are evaluated. The evaluation specifically includes obtaining a combination of evaluation results by calculating the data call sensitivity index and the data effective application index. The data call sensitivity index is calculated by combining the time distribution density, access volatility, matching consistency and access success stability. The data effective application index is calculated by combining the data survival time, feedback ratio, decision-making behavior intensity and stable utilization. Step 4: Infer the data acquisition mode type suitable for the user based on the evaluation result combination, and optimize the user's current data acquisition mode type based on the inference result; The logic for obtaining the data call sensitivity index is: In the preset time window, the time distribution density of access behavior is calculated, and the time distribution density coefficient is defined as: ; n is the number of requests in the current time window, is the time point of the i-th request, is the average request time in the current window, is the preset smoothing coefficient used to adjust the sensitivity of density calculation. is the time distribution density coefficient; The data request fluctuation coefficient is defined as the standard deviation of the number of accesses requested within the time window; The data matching consistency coefficient is defined as: ; is the data matching degree of the i-th request, and its value range is [0,1]. is the average matching degree in the time window, is the maximum value of data matching degree, is the data matching consistency coefficient; Define the access success stability coefficient: ; is the success rate of the i-th visit, is the average success rate within the time window, is the access success stability coefficient; The calculation formula of data call sensitivity index is: ; , , and are all preset non-zero weight coefficients, Request volatility coefficient for data, Call the sensitivity index for the data.

2. The method for matching user and data acquisition patterns in a design management system according to claim 1, characterized in that: Access anomaly analysis refers to: Divide the user access log data set into windows of fixed length and obtain the number of user data requests in the current time window , the number of valid data requests in the current time window , Historical average number of requests , Historical valid request times , Standard deviation of historical user data request times , and the standard deviation of the number of historical valid data requests , substitute the access exception calculation formula: ; Indicates the data request anomaly index.

3. The method for matching user and data acquisition patterns in a design management system according to claim 2, characterized in that: Determining whether a user triggers the adjustment mechanism refers to: The data request anomaly index is compared with the preset trigger threshold. If the data request anomaly index is greater than the preset trigger threshold, the adjustment mechanism is triggered. If the data request anomaly index is less than or equal to the preset trigger threshold, the adjustment mechanism is not triggered.

4. The method for matching user and data acquisition patterns in a design management system according to claim 3, characterized in that: The assessment result combination refers to: The frequency of data acquisition is evaluated to obtain the data call sensitivity index, and the effectiveness of data acquisition is evaluated to obtain the data effective application index. The evaluation result combination consists of the data call sensitivity index and the data effective application index.

5. The method for matching user and data acquisition patterns in a design management system according to claim 4, characterized in that: The logic for obtaining the data effective application index is: Calculate the data survival time within the preset time window: ; For the The time point of the request. is the data version accessed for the ith time, For the The data version accessed. is the data update attenuation coefficient, which is used to control the impact of data updates on survival time. is the data survival time; Calculate the data feedback ratio to reflect the depth of data application: ; is the number of feedback data submitted by the user after the i-th request, is the interval from the i-th request to the next request made by the user, is the data feedback ratio; Calculate the data decision correlation: ; is the decision-making behavior intensity after the i-th data request, with a value range of [0,1], is the average decision strength after all data requests in the time window, Determine the relevance of data decisions; Calculate the stability of data utilization: ; is the standard deviation of the frequency of data usage within the time window, To stabilize the utilization of data; The calculation formula of data effective application index is: ; Effectively apply an index to the data.

6. The method for matching user and data acquisition patterns in a design management system according to claim 5, characterized in that: Reasoning about the type of data acquisition mode suitable for the user based on the combination of evaluation results means: Fuzzy reasoning is adopted, and the data effective application index and data call sensitivity index are taken as input variables together, and the user-adapted data acquisition mode type is taken as the output variable. The input variables are fuzzified and the values ​​of the input variables are converted into fuzzy sets. The output variables are fuzzified and the output variables are converted into fuzzy sets. Fuzzy rules are formulated to describe the adaptation degree of the data acquisition mode type under different data type combinations. The fuzzified input variables are inferred through fuzzy rules to obtain the user-adapted data acquisition mode type.

7. The method for matching user and data acquisition patterns in a design management system according to claim 6, characterized in that: Data acquisition mode types include real-time query mode, intelligent push mode, batch acquisition mode, and on-demand pull mode.

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