A multi-time dimension data screening and dilution system, method and storage medium

Abstract

The present invention discloses a multi-time-dimensional data screening and dilution system, method and storage medium, and relates to the technical field of data processing and real-time control. The system comprises an acquisition module, which is used to acquire time point events and their triggering conditions of different business logics, and extract relevant time lag data of key operations, a priority calculation module, which is used to determine the priority weights of events at each time point according to the time lag data and preset control targets, a sorting module, which is used to sort events using priority weights and generate an optimized event sequence, and a control module, which is used to dynamically adjust the time point event triggering conditions based on the optimized event sequence to achieve precise control; the multi-time-dimensional data screening and dilution system, method and storage medium regulate the time point event triggering conditions driven by different business logics to solve the problem of excessive time lag or early triggering of key operations in specific situations.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and real-time control, and in particular to a multi-time dimension data screening and dilution system, method and storage medium. Background Art

[0002] With the rapid growth of data volume and the increasing complexity of business scenarios, how to effectively manage and regulate event triggering in multiple time dimensions has become a difficult problem in current technology. In traditional data screening methods, event triggering is usually based on fixed time rules or simple time window settings. Although this method is effective in some cases, it is easy to cause key operations to lag too long or be triggered prematurely when dealing with complex business logic. For example, in a financial trading system, if the transaction triggering conditions fail to respond to market changes in a timely manner, it may lead to missing the best trading opportunity; in an advertising delivery system, if the conditions are too sensitive or triggered too early, it may lead to a waste of advertising resources or unsatisfactory results.

[0003] In traditional data screening methods, time point events usually rely on fixed time rules or simple time window settings, which will lead to the following problems: Too long time lag. When the event triggering conditions fail to respond to key business changes in real time, it may cause the business processing process to lag. For example, in a financial trading system, if the time conditions for triggering certain transactions are too long, it may cause market price changes to exceed expectations, thereby causing greater risks. Early triggering. On the other hand, if the triggering conditions are too sensitive or too dependent on a certain time dimension, it may cause the event to be triggered too early, miss the best operating window, and even lead to wrong decisions due to incomplete information. For example, if the timing of dynamic adjustment in an online advertising delivery system is too early, it may lead to inefficient advertising effects or waste of resources. Multi-dimensional time problems. In practical applications, event triggering is not only based on a single time dimension, but often involves multiple intersecting time points and time periods. For example, the linkage triggering of multiple business modules or systems may cause time points to intersect, increasing the complexity of time regulation. Most existing technologies are limited to single-dimensional time control, and it is difficult to effectively deal with multi-dimensional time data screening problems. Therefore, when dynamically adjusting the trigger conditions of multiple business logic-driven time point events, the existing technology fails to fully consider the interaction of multiple time dimensions and business needs at different times, resulting in inaccuracy in the trigger timing and affecting the performance and reliability of the overall system. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-time dimension data screening and dilution system, method and storage medium, which regulate the time point event triggering conditions driven by different business logics to solve the problem of long time lag or premature triggering of key operations in specific situations.

[0005] To achieve the above object, the present invention provides the following technical solution: a multi-time dimension data screening dilution system, the system comprising: The acquisition module is used to obtain the time point events and their triggering conditions of different business logics, and extract the relevant time lag data of key operations; The priority calculation module connected to the acquisition module is used to determine the priority weight of events at each time point according to the time-lag data and the preset control target, including defining the initial priority weight of each event based on the time-lag data and the control target, calculating the decreasing law of the event priority, simulating its priority changing over time, and calculating the priority weight of each event. The specific formula is: ; in, Indicates an event at time The priority weight when Indicates the priority weight when the event starts. represents the base of natural logarithm, Indicates the speed at which the priority weight decreases, Indicates time; A sorting module connected to the priority calculation module is used to sort the events using the priority weights and generate an optimized event sequence; The control module connected to the sorting module is used to dynamically adjust the trigger conditions of time point events based on the optimized event sequence to achieve precise control, including setting the initial trigger conditions and sensitivity coefficients according to the optimized event sequence, and dynamically adjusting the trigger conditions according to the priority and influence of the event. The specific formula is: ; in, Indicates the final adjusted event trigger condition quantization value, Indicates the quantitative value of the trigger condition initially set for the event. represents the sensitivity coefficient, Indicates external change value; Output the adjusted trigger conditions and execute event control operations.

[0006] Preferably, the acquisition module acquires the time point events of different business logics and their triggering conditions, and extracts the relevant time lag data of key operations, including acquiring the time point events of different business logics and their triggering conditions, calculating the relationship between the time lag data and the event triggering conditions, and capturing the change of the time lag data. The specific formula is: ; in, Indicates the external change value that the event triggering condition is subject to. represents the impact factor of the event, Indicates the rate of change of the event triggering condition.

[0007] Preferably, the sorting module sorts the events using the priority weights and generates an optimized event sequence, including using the output of the priority calculation module to determine the priority of each event, calculate the execution rate of each event, and sort the events based on the execution rate. The specific formula is: ; in, Indicates the event execution rate, represents the rate constant, Indicates the priority of the corresponding event. Indicates the influence factor of the change of event triggering conditions on the event execution rate; The event sequence is optimized according to the reaction rate and the optimized event sequence is passed to the regulatory module.

[0008] Preferably, the acquisition module further includes: A calculation unit for extracting event triggering conditions based on time-delay data and external influencing factors; An adjustment unit for dynamically adjusting event triggering conditions according to time-lag data and regulation targets.

[0009] Preferably, the priority calculation module is used to determine the speed at which the priority weight of the event at each time point decreases according to the time lag data and the preset control target. The calculation formula is: ; in, Indicates the speed at which the priority weight decreases, Indicates the change in event priority. Indicates the time change.

[0010] Preferably, the control module dynamically adjusts the triggering conditions of the time point events based on the optimized event sequence to achieve precise control, including defining the maximum allowable time delay for each operation according to the time delay of key operations under different business logics. and the minimum allowable delay ; Get the actual triggering time of the key operation of the current event and expected ideal triggering moment ; Adjust the trigger conditions: The specific formula is: < ; in, Indicates the actual trigger time and expected ideal triggering moment The function of the error between Indicates the actual triggering time, represents the ideal triggering moment, Indicates the preset tolerance error value; If the conditions are met, the adjusted trigger conditions remain Otherwise, adjust the trigger condition according to the following formula: ; in, Indicates the final triggering moment obtained by adjustment. represents the ideal triggering moment, represents the maximum permissible time delay, Indicates the shortest time point event in an event sequence; Apply the updated trigger condition to the corresponding subsequent associated time point events.

[0011] Preferably, the control module dynamically adjusts the time point event triggering conditions based on the optimized event sequence to achieve the actual triggering moment in precise control. and ideal triggering moment The function of the error between Specifically expressed as: ; in, Indicates the actual trigger time and ideal triggering moment The function of the error between Indicates the actual triggering time, Indicates the ideal triggering moment.

[0012] Preferably, the acquisition module acquires the time point events and their triggering conditions of different business logics, and extracts the change rate of the event triggering conditions in the relevant time lag data of the key operation. The calculation formula is: ; in, Indicates the rate of change of the event triggering condition, Indicates the change in the event triggering condition. Indicates the time change.

[0013] A multi-time dimension data screening and dilution method, using the multi-time dimension data screening and dilution system, the method comprises: S1. Obtain the time point events and triggering conditions of different business logics, and extract the relevant time lag data of key operations; S2. Determine the priority weight of events at each time point based on the time-lag data and preset control targets; S3, sorting the events using priority weights and generating an optimized event sequence; S4. Based on the optimized event sequence, dynamically adjust the event triggering conditions at the time point to achieve precise control.

[0014] A computer-readable storage medium stores a computer program, wherein when a device where the computer-readable storage medium is located executes the computer program, the steps of the multi-time dimension data screening dilution method are implemented.

[0015] It can be seen from the above technical solution that the present invention has the following beneficial effects: The multi-time dimension data screening and dilution system, method and storage medium obtain time point events and their triggering conditions of different business logics through an acquisition module, and extract relevant time lag data of key operations. The priority calculation module determines the priority weight of events at each time point according to the time lag data and preset control targets. The sorting module is used to sort events by priority weight and generate an optimized event sequence. The control module is used to dynamically adjust the triggering conditions of time point events based on the optimized event sequence to achieve precise control, avoid the adverse effects caused by too long triggering time lag or premature triggering, and can dynamically adjust the timing of advertising resource delivery according to actual business needs and market changes, avoid resource waste or inefficient delivery due to premature or oversensitive triggering, improve advertising effects while optimizing resource utilization efficiency, and can handle multiple It can adjust the trigger conditions of events in multiple time dimensions to ensure that events can be triggered at the best time, improve the overall performance and reliability of the system, reduce system failures and business processing delays caused by inaccurate timing, and flexibly adjust the trigger conditions according to business logic and time-delay data, so that the system has stronger self-adaptation capabilities to cope with the ever-changing external environment and business needs, ensure that the system can still run efficiently in complex and changeable scenarios, and regulate the trigger conditions of time point events driven by different business logics to solve the problem of long time lag or early triggering of key operations in specific situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a connection diagram of the system modules of the present invention; Figure 2 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] like Figure 1 As shown, the present invention provides a technical solution: a multi-time dimension data screening dilution system, the system comprising: The acquisition module is used to obtain the time point events and their triggering conditions of different business logics, and extract the relevant time lag data of key operations; The priority calculation module connected to the acquisition module is used to determine the priority weight of events at each time point according to the time-lag data and the preset control target, including defining the initial priority weight of each event based on the time-lag data and the control target, calculating the decreasing law of the event priority, simulating its priority changing over time, and calculating the priority weight of each event. The specific formula is: ; in, Indicates an event at time The priority weight when Indicates the priority weight when the event starts. represents the base of natural logarithm, Indicates the speed at which the priority weight decreases, Indicates time; A sorting module connected to the priority calculation module is used to sort the events using the priority weights and generate an optimized event sequence; The control module connected to the sorting module is used to dynamically adjust the trigger conditions of time point events based on the optimized event sequence to achieve precise control, including setting the initial trigger conditions and sensitivity coefficients according to the optimized event sequence, and dynamically adjusting the trigger conditions according to the priority and influence of the event. The specific formula is: ; in, Indicates the final adjusted event trigger condition quantization value, Indicates the quantitative value of the trigger condition initially set for the event. represents the sensitivity coefficient, Indicates external change value; Output the adjusted trigger conditions and execute event control operations.

[0019] In the working principle of the present invention, first, the acquisition module collects non-business logic time point events and related trigger conditions from the system. These time point events are not directly affected by business operations, but they have an important impact on the overall operation of the system. The acquired event data is transmitted to the calculation module, which is responsible for calculating the priority of each event according to the preset control target. The calculation process is based on time series data and uses a mathematical model of priority change to analyze the dynamic change of event priority over time. In the priority calculation formula, Indicates an event at time priority at the time of the event, and based on the initial priority at the start of the event and a decay factor , calculate the time The priority value of the decay. Then, the sorting module sorts all events according to priority and generates a priority event sequence. These events are arranged from high to low priority to ensure that the system can prioritize the most important events. Finally, the control module adjusts the response conditions of the system based on the sorting results. By dynamically adjusting the trigger conditions, the control module can optimize the working state of the system in real time according to the influence of priority events and changes in external variables to ensure the rational allocation of resources and maximize system performance.

[0020] The present invention brings significant performance improvements through precise priority calculation and real-time control capabilities. First, the system can accurately evaluate the importance of events based on time series, and dynamically schedule them according to priority, avoiding low-priority events from occupying too many resources and improving resource utilization efficiency. Compared with the traditional fixed priority scheduling method, the priority change model of the present invention can reflect the actual importance of events over time, greatly enhancing the adaptability and flexibility of the system. By dynamically adjusting the trigger conditions in real time, the system can not only make adjustments under different environments and requirements, but also reduce response delays and improve event processing speed and efficiency. In addition, the system's control module is highly adaptable and can be optimized in real time according to changes in the external environment to ensure that the system can still maintain stability and efficient operation under complex and changing conditions. By introducing the sensitivity coefficient and external variables The dynamic adjustment mechanism of the system can adjust the control strategy according to actual needs, further enhancing the accuracy and controllability of the system. In general, the system can improve performance while reducing energy consumption and unnecessary waste of resources, ultimately bringing higher cost-effectiveness and competitiveness.

[0021] The acquisition module obtains the time point events and their triggering conditions of different business logics, and extracts the relevant time lag data of key operations, including obtaining the time point events and their triggering conditions of different business logics, calculating the relationship between the time lag data and the event triggering conditions, and capturing the changes in the time lag data. The specific formula is: ; in, Indicates the external change value that the event triggering condition is subject to. represents the impact factor of the event, Indicates the rate of change of the event triggering condition.

[0022] In this embodiment, the acquisition module is responsible for acquiring time point events and their triggering conditions of different business logics from the system. Specifically, the acquired event data involves multiple business areas, and each event is triggered at a different time point. These events and their related triggering conditions provide the basis for subsequent calculations. Next, the calculation module calculates the relationship between the event triggering conditions and the time band data based on the acquired time band data and event triggering conditions. The calculation is based on a formula, where Indicates the external change value that the event triggering condition is subject to. represents the impact factor of the event, It represents the rate of change of the event triggering condition. Through the calculated results, the system can evaluate the changes in the external impact of each event in order to further adjust the system response. Through the analysis of the relationship between the time-band data and the event triggering conditions, the system can quantify the influencing factors and change rates of different events, and optimize the order and method of event processing. This process provides the system with the ability of real-time feedback and dynamic regulation, thereby ensuring that various types of events can respond and adjust in a timely manner according to their external changes. The embodiment of the present invention can more accurately analyze the impact of each event on the system by calculating the relationship between the time-band data and the event triggering conditions. In a multi-business logic system, there are diverse changes in the events and triggering conditions of different business logics. Through the method of the present invention, events at different time points can be effectively identified and quantified, ensuring that the system can be timely regulated according to the changes in the events. Compared with the traditional static event processing method, the dynamic calculation method of the present invention provides stronger flexibility and adaptability, so that the system can still operate stably and efficiently when facing complex and changeable external conditions. In addition, the external change value involved in the calculation formula and rate of change The dynamic adjustment can reflect the changing trend of events in real time, improving the predictability and response speed of the system. By optimizing the control of event triggering conditions, the present invention can minimize resource waste, improve overall system performance, and adapt to multiple scenario requirements in different environments.

[0023] The sorting module uses the priority weights to sort the events and generate an optimized event sequence, including using the output of the priority calculation module to determine the priority of each event, calculate the execution rate of each event, and sort the events based on the execution rate. The specific formula is: ; in, Indicates the event execution rate, represents the rate constant, Indicates the priority of the corresponding event. Indicates the influence factor of the change of event triggering conditions on the event execution rate; The event sequence is optimized according to the reaction rate and the optimized event sequence is passed to the regulatory module.

[0024] In this embodiment, the sorting module sorts the events by obtaining the output of the priority calculation module, and further optimizes the order of event processing based on the execution rate of each event. First, the priority calculation module calculates the priority of each event based on the relationship between the time band data and the trigger condition. Then, the sorting module uses this priority data to determine the execution rate of each event. Specifically, through the formula , which is expressed by the rate constant and event priority To calculate the execution rate of events ,in, is the priority associated with the event, is a constant rate, It is a factor that reflects the impact of changes in event triggering conditions on the execution rate. Through this formula, the sorting module can re-sort events according to the priority and execution rate of the events, ensuring that the system executes events in the optimal order. The calculation of the execution rate enables the system to regulate events more finely. The execution order of events not only takes into account their priority, but also adjusts according to the impact of external conditions on the execution rate of events, thereby maintaining the efficient operation of the system in a dynamic environment. The implementation method of the present invention effectively improves the accuracy of event sorting by introducing the calculation of the execution rate of events. By considering the event priority and execution rate, the system can not only process high-priority events, but also optimize the order of events by adjusting the execution rate, avoiding waste of system resources or response delays due to excessive concentration on certain events. Compared with traditional priority sorting methods, the present invention is more flexible and can dynamically adjust the execution order according to environmental changes and event triggering conditions, thereby improving the adaptability of the system to complex and changeable environments. The calculation and sorting of execution rates can help the system prioritize events that require rapid response in the case of high concurrency or resource constraints, thereby improving the system processing efficiency. In addition, the system can also adjust parameters (such as rate constants) through flexible parameter adjustments (such as rate constants). and impact factor Adjustment), further improve the controllability and accuracy of event processing, and ensure the stability and efficiency of the system in various application scenarios.

[0025] The acquisition module also includes a calculation unit for extracting event triggering conditions according to the time-lag data and external influencing factors; and an adjustment unit for dynamically adjusting the event triggering conditions according to the time-lag data and the regulation target.

[0026] In this embodiment, the acquisition module is expanded to include two submodules: a calculation unit and an adjustment unit. First, the calculation unit is responsible for extracting event triggering conditions based on time-delay data and external influencing factors. Time-delay data represents the time delay between events in the system, while external influencing factors refer to the impact of changes in the system's external environment or operating conditions on event triggering. The calculation unit extracts event triggering conditions that meet specific conditions by analyzing the time-delay data and combining external influencing factors, thereby providing the necessary data basis for the execution of the event. Then, the adjustment unit dynamically adjusts the event triggering conditions based on the time-delay data acquired in real time and the preset control target. The control target can be set according to the actual needs of the system, for example, to optimize the response time of the system, reduce resource waste, or improve the accuracy of event processing. The adjustment unit uses the time difference between events reflected by the time-delay data and combines the current control target of the system to dynamically adjust the event triggering conditions in real time. Through this process, the system can flexibly adjust the timing of event triggering according to environmental changes and changes in internal and external conditions, thereby ensuring that the system can operate efficiently under different conditions. The present invention improves the system's perception and control capabilities of event triggering conditions by introducing time-delay data and external influencing factors. By extracting key event trigger conditions, the calculation unit enables the system to more accurately understand the time correlation between different events, avoiding unnecessary false triggers or delayed triggers. At the same time, the introduction of the adjustment unit enables the system to respond to changes in the external environment and control targets in real time, and dynamically adjust the event trigger conditions, thereby improving the adaptability and flexibility of the system. Compared with traditional static trigger condition systems, the system of the present invention has higher accuracy and real-time performance. By dynamically adjusting the event trigger conditions, the system can automatically optimize the order and timing of event processing, thereby reducing resource waste and improving event processing efficiency. Especially in complex and changeable environments, the system can quickly adapt to external changes and make adjustments to ensure that the system always remains in the best working state.

[0027] The priority calculation module is used to determine the speed at which the priority weight of events at each time point decreases based on the time-lag data and the preset control target. The calculation formula is: ; in, Indicates the speed at which the priority weight decreases, Indicates the change in event priority. Indicates the time change.

[0028] In this embodiment, the priority calculation module calculates the priority of each event at each time point according to the time band data and the preset control target, and determines the priority of event processing by calculating the speed of event priority change. Specifically, by analyzing the change of the priority of each event within the time interval, the priority calculation module can calculate the speed of event priority decrease. The calculation formula is ;in, Indicates the change in event priority within a certain period of time. The time change represents the time interval between event triggering. It can help the system determine the priority change trend of each event, so as to arrange the processing order of events more accurately. By calculating the priority drop speed of each event, the system can adjust the event processing strategy in real time to ensure that events with large priority changes can be processed in time. The calculation model enables the system to dynamically respond to event priority changes and optimize the event processing process in real time to ensure that the system can operate efficiently and stably in a complex environment. This embodiment improves the system's ability to respond to event priority changes by calculating the priority drop speed. Compared with the traditional static priority sorting method, the present invention can more flexibly adjust the event processing order by calculating the priority change speed in real time, so that the system can dynamically optimize the processing process according to the change of event priority. This method not only improves the adaptability of the system, but also can effectively manage the execution order of events in a high-concurrency environment, reduce the waste of system resources, and improve the efficiency of event processing. By calculating the priority change rate, the system can monitor and adjust the priority of each event in real time, which helps to maintain the stability of the system in a dynamically changing working environment, ensure that important events are responded to in time, and improve the overall processing efficiency.

[0029] The control module dynamically adjusts the trigger conditions of time point events based on the optimized event sequence to achieve precise control, including defining the maximum allowable time delay for each operation according to the time delay of key operations under different business logics. and the minimum allowable delay ; Get the actual triggering time of the key operation of the current event and expected ideal triggering moment ; Adjust the trigger conditions: The specific formula is: < ; in, Indicates the actual trigger time and expected ideal triggering moment The function of the error between Indicates the actual triggering time, represents the ideal triggering moment, Indicates the preset tolerance error value; If the conditions are met, the adjusted trigger conditions remain Otherwise, adjust the trigger condition according to the following formula: ; in, Indicates the final triggering moment obtained by adjustment. represents the ideal triggering moment, represents the maximum permissible time delay, Indicates the shortest time point event in an event sequence; Apply the updated trigger condition to the corresponding subsequent associated time point events.

[0030] In this embodiment, the control module first dynamically adjusts the triggering conditions of each time point event based on the optimized event sequence. By analyzing the time band data and external influencing factors, the control module determines the maximum allowable time lag of each event. and the minimum allowable delay , thus providing a time window for subsequent event triggering. On this basis, the control module obtains the actual triggering time of the current event and expected ideal triggering moment , and adjust the trigger conditions by analyzing the error between the trigger moments. Specifically, the control module adjusts the trigger conditions according to the error function To determine whether the deviation between the actual trigger time and the ideal trigger time exceeds the preset tolerance error If the error meets the set conditions, the trigger condition remains unchanged; otherwise, the control module adjusts the control condition according to the formula To adjust the triggering time to ensure that each event can be triggered within a reasonable time range. Finally, the updated triggering conditions will be applied to subsequent related events to ensure that the scheduling of the entire event sequence is accurate and efficient. This implementation ensures the flexibility and accuracy of event scheduling by dynamically adjusting the event triggering conditions. By setting the maximum allowable time lag and the minimum allowable time lag, the system can fine-tune the triggering time according to the actual situation, avoid delays or premature execution of event processing, and improve the timeliness and accuracy of system response. By limiting the error of the triggering time, the system ensures the stability and accuracy of the triggering conditions. Compared with the traditional fixed triggering condition method, the dynamic adjustment mechanism of the present invention can optimize the triggering time according to real-time feedback in a complex and changing environment, thereby improving the adaptability, reliability and efficiency of the system. In addition, the system can intelligently adjust the triggering conditions of subsequent events to ensure the smooth execution of the event sequence and the efficient use of system resources.

[0031] The control module dynamically adjusts the event trigger conditions at the time point based on the optimized event sequence to achieve the actual trigger moment in precise control. and ideal triggering moment The function of the error between Specifically expressed as: ; in, Indicates the actual trigger time and ideal triggering moment The function of the error between Indicates the actual triggering time, Indicates the ideal triggering moment.

[0032] In this embodiment, the control module uses the error function To calculate the event triggering time and ideal triggering moment This error value reflects the difference between the event triggering time and the ideal expected triggering time, which can help the system determine whether the current triggering conditions meet the requirements. When the error value is large, the system dynamically adjusts the triggering conditions to narrow the gap between the actual triggering time and the ideal triggering time, thereby improving the accuracy and response time of event processing. Specifically, the system calculates the error value , and compare it with the preset tolerance to determine whether the triggering time of the event needs to be adjusted. If the error exceeds the tolerance range, the control module will further optimize the triggering conditions to ensure that the event can be triggered at the expected time to improve the execution efficiency of the overall event sequence. To accurately measure the gap between the event triggering moment and the ideal moment. Compared with the traditional static triggering mechanism, the present invention provides a more flexible and accurate control method. By calculating the error and dynamically adjusting the triggering moment according to the tolerance, the system can effectively reduce the waste of resources or delayed response caused by inaccurate triggering moment, and improve the efficiency and accuracy of event processing. In addition, the simple form of the error function This enables the system to calculate and respond quickly while maintaining efficient control capabilities. This approach can not only improve the accuracy of event triggering, but also maintain good adaptability in complex environments, ensuring that the system can still maintain efficient and stable operation under dynamically changing conditions.

[0033] The acquisition module obtains the time point events and their triggering conditions of different business logics, and extracts the change rate of event triggering conditions in the relevant time lag data of key operations The calculation formula is: ; in, Indicates the rate of change of the event triggering condition, Indicates the change in the event triggering condition. Indicates the time change.

[0034] In this embodiment, the acquisition module is responsible for acquiring the time band data related to the event, which includes the event triggering conditions and the time point of the event triggering. By analyzing these data, the acquisition module can extract the change rate of the event triggering conditions. The calculation formula is ,in, Represents the change in the event triggering condition. Indicates the amount of time change. By calculating the rate of change, the system can track the changes of event trigger conditions over time in real time, thereby providing more accurate data support for event scheduling and triggering. This calculation enables the system to dynamically adjust the trigger conditions to ensure that in different environments, events can be optimized according to the real-time changes in trigger conditions. Rate of change It is an important indicator to measure the speed of change of event trigger conditions, which can reflect the severity of the change of trigger conditions and provide data basis for subsequent regulation. This implementation method provides the system with dynamic regulation and optimization capabilities by introducing the change rate calculation of event trigger conditions. , the system can monitor and evaluate the changing trends of event trigger conditions in real time. Compared with the traditional static trigger condition setting method, the present invention provides a more flexible and accurate trigger control mechanism that can effectively cope with complex dynamic environments. By monitoring the rate of change of event trigger conditions, the system can quickly adapt to changes in the environment and external factors, and adjust the trigger conditions of events in real time. This dynamic adaptability enables the system to efficiently and accurately adjust the trigger sequence when processing a large number of events, avoiding delays or erroneous processing caused by inappropriate trigger conditions, and improving the efficiency and accuracy of event processing.

[0035] like Figure 2 As shown, a multi-time dimension data screening and dilution method is also provided, using the multi-time dimension data screening and dilution system, the method comprises: S1. Obtain the time point events and triggering conditions of different business logics, and extract the relevant time lag data of key operations; S2. Determine the priority weight of events at each time point based on the time-lag data and preset control targets; S3, sorting the events using priority weights and generating an optimized event sequence; S4. Based on the optimized event sequence, dynamically adjust the event triggering conditions at the time point to achieve precise control.

[0036] The method in this embodiment is based on a multi-time dimension data screening and dilution system, and is executed in combination with various functional modules of the system. In step S1, the system collects time point events and trigger conditions of different business logics through the acquisition module, and extracts relevant time lag data of key operations. These time lag data are important bases for the system to judge the time relationship between events. In step S2, the priority calculation module calculates the priority of each event according to the acquired time lag data and the preset control target, and reflects the importance of the event through weight setting. This process can flexibly adjust the weight of each event according to actual needs to ensure that key events can be given priority. In step S3, the sorting module uses the calculated priority weight to sort all events and generate an optimized event sequence. The sorted event sequence can ensure that the system gives priority to important events during execution and improves processing efficiency and accuracy. In step S4, based on the optimized event sequence, the control module dynamically adjusts the trigger conditions of the time point events. This process ensures that the system can achieve precise control in different industries or environments to meet actual business needs. The control module adjusts the trigger conditions according to the difference between the actual event triggering moment and the ideal triggering moment, thereby optimizing system performance. This method can effectively improve the event processing efficiency and accuracy of the multi-time dimension data screening and dilution system through the implementation of four steps. First, the acquisition module obtains the time point events and trigger conditions, and extracts the time lag data, so that the relationship between the events can be clearly understood. Then, with the help of the priority calculation module, the system can flexibly adjust the priority weight of the event according to the time lag data and the control target to ensure that the most critical events are given priority. The sorting module generates an optimized event sequence through priority sorting, so that the system can process events in the most effective order to avoid low-priority events from occupying too many resources. Finally, the control module dynamically adjusts the trigger conditions according to the optimized event sequence, so that the system can quickly adapt to changes in the external environment and internal needs, ensuring that the event trigger conditions are accurate and the response is timely. Compared with traditional methods, this method has higher flexibility and adaptability, and can optimize the event processing order and timing in real time in a complex dynamic environment, avoid unnecessary waste of resources, and improve the overall performance and efficiency of the system.

[0037] A computer-readable storage medium is also provided, wherein the computer-readable storage medium stores a computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the steps of the multi-time dimension data screening dilution method are implemented. The computer-readable storage medium in this embodiment stores a computer program for implementing the multi-time dimension data screening dilution method. When the program is executed, it operates according to the following steps: obtaining time point events and their triggering conditions of different business logics, and extracting relevant time lag data of key operations: this step obtains time point events and related triggering condition data from external data sources through the program, and extracts time lag data, which are important basis for subsequent priority calculation and regulation. According to the time lag data and preset regulation targets, the priority weight of each time point event is determined: the program calculates the priority weight of each time point event according to the extracted time lag data and regulation targets to ensure that important events can be processed first. Use priority weights to sort events and generate optimized event sequences: the program uses priority weights to sort events and generate optimized event sequences to ensure that the system can process events in the most effective order and avoid low-priority events from occupying too many resources. Based on the optimized event sequence, dynamically adjust the event triggering conditions at the time point to achieve precise control: According to the optimized event sequence, the program adjusts the event triggering conditions to ensure that each event can be triggered at the most appropriate time, thereby ensuring the efficient operation of the entire system. The execution of these steps relies on the automated operation of computer programs, which can greatly improve the efficiency and accuracy of event processing and enable the system to be flexibly adjusted in a dynamic environment.

[0038] The beneficial effect of this embodiment is that by implementing the multi-time dimension data screening dilution method as a computer program, this method can be automatically applied to different computer systems. The execution of the computer program can efficiently and accurately complete the multi-time dimension data screening dilution process, avoid human operation errors, and greatly improve the efficiency and accuracy of event processing. By storing the program in a computer-readable storage medium, the user only needs to run the program through a suitable device to implement the multi-time dimension data screening dilution method. The program can adjust the priority and trigger conditions of the event according to the real-time data, so that the system can flexibly respond to different industry application scenarios and ensure the accuracy and efficiency of event processing.

[0039] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-time dimension data screening and dilution system, characterized in that: The system comprises: The acquisition module is used to obtain the time point events and their triggering conditions of different business logics, and extract the relevant time lag data of key operations; The priority calculation module connected to the acquisition module is used to determine the priority weight of events at each time point according to the time-lag data and the preset control target, including defining the initial priority weight of each event based on the time-lag data and the control target, calculating the decreasing law of the event priority, simulating its priority changing over time, and calculating the priority weight of each event. The specific formula is: ; in, Indicates an event at time The priority weight when Indicates the priority weight when the event starts. represents the base of natural logarithm, Indicates the speed at which the priority weight decreases, Indicates time; A sorting module connected to the priority calculation module is used to sort the events using the priority weights and generate an optimized event sequence; The control module connected to the sorting module is used to dynamically adjust the trigger conditions of time point events based on the optimized event sequence to achieve precise control, including setting the initial trigger conditions and sensitivity coefficients according to the optimized event sequence, and dynamically adjusting the trigger conditions according to the priority and influence of the event. The specific formula is: ; in, Indicates the final adjusted event trigger condition quantization value, Indicates the quantitative value of the trigger condition initially set for the event. represents the sensitivity coefficient, Indicates external change value; Output the adjusted trigger conditions and execute event control operations.

2. A multi-time dimension data screening and dilution system according to claim 1, characterized in that: The acquisition module acquires the time point events and their triggering conditions of different business logics, and extracts the relevant time lag data of key operations, including acquiring the time point events and their triggering conditions of different business logics, calculating the relationship between the time lag data and the event triggering conditions, and capturing the changes in the time lag data. The specific formula is: ; in, Indicates the external change value that the event triggering condition is subject to. represents the impact factor of the event, Indicates the rate of change of the event triggering condition.

3. The multi-time dimension data screening and dilution system according to claim 1, characterized in that: The sorting module sorts the events using the priority weights and generates an optimized event sequence, including using the output of the priority calculation module to determine the priority of each event, calculate the execution rate of each event, and sort the events based on the execution rate. The specific formula is: ; in, Indicates the event execution rate, represents the rate constant, Indicates the priority of the corresponding event. Indicates the influence factor of the change of event triggering conditions on the event execution rate; The event sequence is optimized according to the reaction rate and the optimized event sequence is passed to the regulatory module.

4. The multi-time dimension data screening and dilution system according to claim 1, characterized in that: The acquisition module also includes: A calculation unit for extracting event triggering conditions based on time-delay data and external influencing factors; An adjustment unit for dynamically adjusting event triggering conditions according to time-lag data and regulation targets.

5. The multi-time dimension data screening and dilution system according to claim 1, characterized in that: The priority calculation module is used to determine the speed at which the priority weight of the event at each time point decreases according to the time lag data and the preset control target. The calculation formula is: ; in, Indicates the speed at which the priority weight decreases, Indicates the change in event priority. Indicates the time change.

6. The multi-time dimension data screening and dilution system according to claim 1, characterized in that: The control module dynamically adjusts the trigger conditions of time point events based on the optimized event sequence to achieve precise control, including defining the maximum allowable time delay for each operation according to the time delay of key operations under different business logics. and the minimum allowable delay ; Get the actual triggering time of the key operation of the current event and expected ideal triggering moment ; Adjust the trigger conditions: The specific formula is: < ; in, Indicates the actual trigger time and expected ideal triggering moment The function of the error between Indicates the actual triggering time, represents the ideal triggering moment, Indicates the preset tolerance error value; If the conditions are met, the adjusted trigger conditions remain Otherwise, adjust the trigger condition according to the following formula: ； in, Indicates the final triggering time obtained by adjustment, represents the ideal triggering moment, represents the maximum permissible time delay, Indicates the shortest time point event in an event sequence; Apply the updated trigger condition to the corresponding subsequent associated time point events.

7. The multi-time dimension data screening and dilution system according to claim 6, characterized in that: The control module dynamically adjusts the event triggering conditions at the time point based on the optimized event sequence to achieve the actual triggering moment in precise control and ideal triggering moment The function of the error between Specifically expressed as: ; in, Indicates the actual trigger time and ideal triggering moment The function of the error between Indicates the actual triggering time, Indicates the ideal triggering moment.

8. The multi-time dimension data screening and dilution system according to claim 2, characterized in that: The acquisition module acquires the time point events and their triggering conditions of different business logics, and extracts the change rate of the event triggering conditions in the relevant time lag data of key operations The calculation formula is: ; in, Indicates the rate of change of the event triggering condition, Indicates the change in the event triggering condition. Indicates the time change.

9. A multi-time dimension data screening and dilution method, using the multi-time dimension data screening and dilution system according to any one of claims 1 to 8, characterized in that: The method comprises: S1. Obtain the time point events and triggering conditions of different business logics, and extract the relevant time lag data of key operations; S2. Determine the priority weight of events at each time point based on the time-lag data and preset control targets; S3, sorting the events using priority weights and generating an optimized event sequence; S4. Based on the optimized event sequence, dynamically adjust the event triggering conditions at the time point to achieve precise control.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the steps of the multi-time dimension data screening dilution method according to claim 9 are implemented.

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