Data screening method based on custom process component

Abstract

The invention discloses a data screening method based on a custom process component, and relates to the technical field of data screening, when a new production scene needs to be processed or an existing process needs to be adjusted, a user can freely combine all functional components in a dragging mode and define a business process meeting specific requirements, and the user experience is improved. And the tedious process of recoding in each change is avoided. Meanwhile, due to the dynamic judgment and path selection functions of the condition nodes, the system can dynamically adjust the follow-up operation path according to the processing result of the actual data, and the adaptability and the response capability of the service process are further improved. Finally, according to the scheme, an efficient, flexible and customizable solution is provided, the development and maintenance cost is greatly reduced, and variable production requirements can be rapidly met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data screening, and particularly relates to a data screening method based on a self-defined process component. BACKGROUND

[0002] In industrial manufacturing, the production process is usually divided into multiple processes, and each process has multiple steps, which are completed by different machines. However, selecting a suitable machine for the next step of processing is a complex task. This selection process is influenced by multiple factors, such as product priority, expiration time, product value, and process requirements. In actual operation, selection, screening, and sorting are core operations, involving sorting, screening, and selecting appropriate resources based on different standards. Existing technologies usually rely on static sorting and screening algorithms, which require repeated coding of the same code when facing different factories and different products, and cannot easily adapt to changing production requirements.

[0003] However, the above-mentioned existing technology faces the following problems. First, whenever a new production scenario needs to be handled or an existing process needs to be adjusted, the existing system usually requires developers to write a large number of sorting and screening algorithms, which results in a long development cycle and low efficiency. Second, the existing system lacks flexibility and cannot quickly adapt to different factory requirements or different production conditions. Each time the function is changed or expanded, the underlying code needs to be manually modified, which cannot meet the rapidly changing business requirements. The existence of these problems limits the universality and flexibility of the existing technology in practical applications. SUMMARY

[0004] The purpose of the present application is to solve the above-mentioned problems and provide a data screening method based on a self-defined process component.

[0005] The present application provides a data screening method based on a self-defined process component, which comprises: A component combination platform is constructed, which contains multiple functional components, each of which is identified by a unique ID; Functional components are defined and connected through a visual interface. Users combine multiple components into a self-defined business process diagram according to their needs by drawing a process diagram; A unique identifier is generated and the process is initialized; The process component is executed. The first component of the process diagram queries and loads data from the database or data source according to the preset conditions, and then each component is executed in order. The execution result is stored through a data structure and passed to the next component, ensuring the continuity and consistency of the data flow; In the execution process, if a component is a conditional node, the system determines whether to continue executing the next component according to the processing result of the current data. If the condition is met, a predetermined component is executed, otherwise another component is executed, realizing the dynamic adjustment of business logic. After each component is executed, the result is stored in a database or a cache system in a specific data structure. The next component extracts the execution result of the previous step from the storage by obtaining a unique identifier and continues to execute the next data processing step. When all components are executed, the final screening, selection and sorting results are output to the downstream system or other application programs as the basis for subsequent processing or decision-making.

[0006] Optionally, the components include but are not limited to process filtering components, process recipe matching components, QTime sorting components, priority sorting components and sorting components, which are used to realize data filtering, sorting and selection operations.

[0007] Optionally, combining multiple components into a custom business flowchart according to requirements includes: The flowchart defines the execution order of each component and the data transmission rule. Users can flexibly adjust the order and condition judgment of the components according to business requirements.

[0008] Optionally, generating a unique identifier and initializing the flow execution includes: Each time the flowchart is executed, the system automatically generates a unique identifier UID for the flow to record and track the execution information of the flow. UID is used to identify the state of data flow and component execution process.

[0009] The step of determining whether to continue executing the next component according to the processing result of the current data is: Calculate the data fluctuation stability index and energy consumption efficiency index according to the execution result of the current component, add the data fluctuation stability index and the energy consumption efficiency index to obtain the executable index of the subsequent flow; and determine whether to continue executing the next component according to the executable index of the subsequent flow.

[0010] Optionally, the calculation step of the data fluctuation stability index is: Extract the original data time series in the execution result of the current component, where each data point represents a data value at a specific time point, and the timestamp corresponds to the data one by one. In the time series, select a fixed size sliding window, calculate the standard deviation of the data in the window, calculate the difference between the current window standard deviation and the previous window standard deviation, and then divide the difference value by the previous window standard deviation to obtain the change rate of the standard deviation between adjacent sliding windows. Calculate the probability distribution of all standard deviation change rates, and calculate the entropy value of the probability distribution of all standard deviation change rates with the formula of information entropy, and divide the entropy value by the theoretical maximum entropy value to obtain the deviation degree; Divide the time series into multiple subintervals, calculate the fluctuation of each subinterval, and measure the similarity of each subinterval to obtain the fractal dimension; Multiply the standard deviation change rate, the deviation degree and the fractal dimension, and take the reciprocal of the multiplication result as the data fluctuation stability index.

[0011] Optionally, the energy consumption efficiency index calculation step comprises: Obtain the energy consumption data at multiple consecutive time points in the current task execution period and the task output data at the corresponding time points, and record them as the instantaneous energy consumption value and the unit output value at each time point respectively; for each time point, calculate the unit energy consumption ratio at the time point, the calculation method is: square the instantaneous energy consumption value at the time point and divide by the result of adding one to the corresponding unit output value, thereby obtaining the unit energy consumption ratio at the time point, forming a time series of unit energy consumption ratios; Perform adjacent value direction change analysis on the unit energy consumption ratio time series, judge the increasing and decreasing trend between each two adjacent data points in the sequence, and identify the change points of "from increasing to decreasing" or "from decreasing to increasing"; count the number of all change points in the entire sequence as the number of inflection points; divide the number of inflection points by the total time interval length corresponding to the sequence to obtain the inflection point density, which is used to reflect the fluctuation frequency in the unit energy consumption ratio change process; Map each data point in the time series to a two-dimensional coordinate point with time as the horizontal axis and unit energy consumption ratio as the vertical axis; calculate the total length of the curve formed by connecting all data points as the real path length, and calculate the straight line distance between the starting point and the ending point of the sequence as the shortest path length; divide the shortest path length by the real path length to obtain the path compression degree; Analyze the unit output value change trend in the task period, and identify the continuous rising time period; for each continuous rising output segment, determine whether the corresponding energy consumption value decreases after the output rises; if there is a time period where energy consumption decreases lags behind output rise, add up the duration of all such lagging periods and divide by the total duration of the entire task period to obtain the energy consumption lag rate; Combine the inflection point density, the path compression degree and the energy consumption lag rate logically to construct the energy consumption efficiency index; wherein, first add the inflection point density and the energy consumption lag rate, and multiply the result by the reciprocal of the path compression degree to form a non-efficiency disturbance factor, then take the reciprocal of the disturbance factor after adding one to obtain the final energy consumption efficiency index.

[0012] Optionally, the step of judging whether to continue executing the next component according to the subsequent process comprises: The executable index is compared with a preset executable index threshold value, and if the executable index is not less than the preset executable index threshold value, it indicates that the condition is established, and the next predetermined component is executed according to the flow chart; If the executable index is less than the preset executable index threshold value, it indicates that the condition is not established, and otherwise another component is executed to realize dynamic adjustment of business logic.

[0013] The present application has the following beneficial effects: The present application provides a data screening method based on self-defined process components. When a new production scenario needs to be processed or an existing process needs to be adjusted, users can freely combine various functional components through a drag-and-drop method to define a business process that meets specific needs, avoiding the tedious process of re-coding each time a change is made. At the same time, the dynamic judgment and path selection function of the condition node enables the system to dynamically adjust the subsequent operation path according to the processing result of the actual data, further improving the adaptability and response capability of the business process. Ultimately, this scheme provides an efficient, flexible and customizable solution, greatly reducing the cost of development and maintenance, and quickly adapting to changing production needs. BRIEF DESCRIPTION OF DRAWINGS

[0014] The present application will be further described below with reference to the accompanying drawings.

[0015] Figure 1 A flow chart of a data screening method based on self-defined process components. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0017] Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0018] The present application provides a data screening method based on self-defined process components. Referring to Figure 1 , Figure 1 A flow chart of a data screening method based on self-defined process components provided by the present application. The method comprises the following steps: S1: Construct a component combination platform, the platform comprising a plurality of functional components, each functional component being identified by a unique ID; S2: defining and connecting functional components through a visual interface, a user combines multiple components into a custom business flowchart according to requirements by drawing a flowchart; S3: generating a unique identifier (UID) and initializing flow execution; S4: executing flow components, the first component of the flowchart queries and loads data from a database or data source according to preset conditions, and then each component is executed in sequence, the execution result is stored through a data structure (such as Map), and is passed to the next component, to ensure the continuity and consistency of data flow; S5: during execution, if a component is a conditional node, the system determines whether to continue executing the next component according to the processing result of the current data, if the condition is established, a predetermined component is executed, otherwise another component is executed, to realize dynamic adjustment of business logic; S6: after each component is executed, the result is stored in a database or a cache system in a specific data structure (such as Map), and the next component extracts the execution result of the previous step from the storage by obtaining a UID, to continue executing the next data processing step.

[0019] S7: when all components are executed, the final screening, selection and sorting result is output to a downstream system or other application program, as a basis for subsequent processing or decision-making.

[0020] Based on the data filtering method based on the self-defined flow component provided in the embodiment of the application, when a new production scene needs to be processed or an existing flow needs to be adjusted, the user can freely combine various functional components through a drag-and-drop manner, define a business flow that meets specific requirements, and avoid the tedious process of re-coding each time. At the same time, the dynamic judgment and path selection function of the conditional node enable the system to dynamically adjust the subsequent operation path according to the processing result of the actual data, further improving the adaptability and response capability of the business flow. Finally, the scheme provides an efficient, flexible and customizable solution, greatly reducing the development and maintenance cost, and quickly adapting to changing production demands.

[0021] In one embodiment, S1: a component combination platform is constructed, the platform includes multiple functional components, each functional component is identified by a unique ID, including: The components include but are not limited to process filtering components, process recipe matching components, QTime sorting components, priority sorting components, and sorting components, etc., for realizing data filtering, sorting and selection operations; It should be noted that the purpose of this step is to build a multifunctional component combination platform, and the core of the platform is to integrate multiple functional components to form a system with high flexibility and scalability. In this platform, each functional component has a unique identifier (ID) to facilitate system management, scheduling and execution. The functional components include but are not limited to process filtering components, process recipe matching components, QTime sorting components, priority sorting components and sorting components, etc. These components work together to meet the data filtering, sorting and selection operations under different business requirements. For example, the process filtering component is used to filter the list of devices that meet the product's process requirements. It can filter the devices that meet the requirements based on specific process parameters such as temperature, humidity, processing time, etc. The process recipe matching component filters suitable equipment according to the product's recipe requirements. It can select the most suitable production equipment according to the recipe requirements of different products (such as raw materials, proportions, etc.). In addition, the QTime sorting component handles the time requirements of products between processes to ensure that each process is completed on time. For example, some process steps need to be completed within a certain time, and if the time limit is exceeded, it may affect product quality. The QTime sorting component will prioritize these products to ensure that time-sensitive processes are handled in a timely manner. The priority sorting component prioritizes tasks based on product importance or customer demand, while the sorting component can sort devices or data based on other criteria to ensure optimal allocation of production resources.

[0022] Through the flexible combination of these components, users can customize processing flows that meet specific production requirements. Whether it's sorting, filtering or selection operations, these functional components can be combined to meet the diverse needs of businesses.

[0023] In one implementation, S2: define and connect functional components through a visual interface, users can combine multiple components into a custom business flowchart according to their needs by drawing a flowchart, including: The flowchart defines the execution order of each component and the data transfer rules. Users can flexibly adjust the order and condition judgment of the components according to business needs.

[0024] It should be noted that in this step, the user defines and connects various functional components through an intuitive visual interface. This interface provides a graphical operating platform, and the user can easily build a business process diagram by dragging and connecting without writing complex code. By drawing the process diagram, the user arranges multiple functional components according to the requirements to form a clear operation flowchart. Each component has a clear position in the flowchart, and the flowchart defines the execution order, data flow direction and transmission rules of each component. In this way, the system can execute each component according to the user's design and ensure that the data flows smoothly from one component to the next. For example, suppose the user needs to design a production process. First, there is a process filtering component that filters equipment according to process requirements. Next, after the formula matching component, it ensures that the equipment meets the formula requirements. Then, the data passes through the QTime sorting component to ensure that the process is completed on time. Finally, it enters the priority sorting component to adjust the execution order according to the product priority. The user adjusts the order of these components in the flowchart by dragging them and defines the connection relationship between them. The data transmission rules between these components are also set in the flowchart to ensure that the data can flow smoothly and each component can obtain the input data it needs. In addition, the user can also flexibly adjust the order and condition judgment of the components according to the actual business requirements. For example, in some cases, the execution order of a certain component may need to be dynamically adjusted according to the results of the previous component. By adding a conditional judgment node, the user can control the execution path of some components in the flowchart. For example, if the filtering result of the process filtering component does not meet certain standards, the process may be diverted to another alternative component for further processing; if the filtering result is qualified, the process will continue to the next step. This visual design method not only reduces the complexity of system development, but also greatly improves the flexibility and ease of use of the system, allowing users to quickly adjust the production process according to specific requirements to meet different business scenarios.

[0025] In one implementation, S3: generating a unique identifier (UID) and initializing process execution includes: Each time the flowchart is executed, the system automatically generates a unique identifier (UID) for the process to record and track the execution information of the process, and the UID is used to identify the state of the data flow and component execution process; It is noted that in this step, each time the user starts and executes the defined flowchart, the system automatically generates a unique identifier (UID) for the flow, which is used to identify and track the execution process of the flow. The UID is a globally unique identifier that ensures that all data, state, and results can be accurately tracked and managed throughout the execution of the flow. Each time the system executes a new business process, it assigns a new UID to the process, allowing all component executions and data transfers in the process to be clearly recorded and distinguished, avoiding confusion between process executions. The UID is not only used to identify the flow itself, but also to track the execution status of each component in the flow. For example, when the flow jumps from one component to the next, the UID will be passed as the "identity" of the flow, ensuring that the input and output data of each component can be correctly associated. For example, in a certain production scheduling flow, when data is passed from the process filtering component to the process recipe matching component, the UID serves as an identifier to ensure that data flows between the two components are in order, and the status of each component can be accurately reflected in the system. For example, assume a production flow in a factory includes screening, sorting, and scheduling operations. When the user starts the flow, the system assigns a UID, such as UID12345, to the flow. Next, all executing components (such as process filtering, recipe matching, sorting, etc.) will associate their execution results and status with UID12345, ensuring that the data of each link can be traced back to this specific execution instance. When the flow is executed, the final result is also output with this UID, providing it to downstream systems or other applications as a basis for subsequent operations. The generation and use of UID not only improve the accuracy of process management, but also provide strong support for data tracking, log recording, problem troubleshooting, etc. Through the UID, the system can ensure the integrity and traceability of each execution flow and data, greatly improving the stability and maintainability of the system.

[0026] In one embodiment, S4: Execute flow components, the first component of the flowchart queries and loads data from the database or data source according to the preset conditions, and then each component is executed in sequence, the execution result is stored through a data structure (such as Map), and is passed to the next component, ensuring the continuity and consistency of data flow; It should be noted that in this step, the core of the execution flow component is to execute each functional component in sequence according to the user-defined flowchart order, ensuring that the operation and data transmission of each component can be accurately performed. First, the first component in the flowchart will query and load the required data from the database or other data sources according to the preset conditions. The data source can be a relational database, a non-relational database, a cache system or other data storage media, depending on the needs of the business scenario. For example, in the production scheduling process, the process filtering component may need to query device information or process requirements from the database to filter out devices that meet the conditions. Once the data is loaded, the first component will perform its scheduled operations (such as filtering, processing, etc.), and then store the execution results in a specific data structure (such as Map). Map is a key-value storage structure that can effectively store processed data and pass it to the next component in the form of key-value. For example, assuming that the first component is a process filtering component, its output result may be a filtered device list, which is stored in Map and passed to the next component in the form of "device list": [device1, device2, device3]. Subsequently, the system will execute the second component according to the flowchart order. The second component will obtain the execution result of the previous component (such as the device list) from the Map, and then continue to execute its operation. For example, the process recipe matching component receives the device list, filters out the devices that meet the conditions according to the characteristics of the devices and the requirements of the recipe, and stores the results in Map again to pass to the next component. The execution result of each component is maintained in the Map structure and passed to the next component through this structure, ensuring the continuity and consistency of data flow between components. This step-by-step execution and data transmission design ensures that each component can obtain accurate input and output correct results, providing effective data for subsequent components. In this way, the data processing process becomes orderly and efficient, and ensures that data collaboration between components in complex business processes is error-free. For example, in a production scheduling process, the process filtering component queries all device information from the database and filters out devices that meet the process requirements, outputting the result as Map; then, the recipe matching component obtains this device list and further filters it according to the recipe requirements, outputting the devices that meet the conditions to the sorting component for priority sorting, ensuring that the device scheduling in the production process meets the expectations. Each component relies on the output data of the previous component, forming a complete data flow. In this way, the execution of the entire process is a continuous and orderly process, with each component executing in sequence to ensure accurate data transmission and processing.

[0027] In one embodiment, S5: during execution, if a component is a conditional node, the system determines whether to continue executing the next component according to the processing result of the current data, if the condition is true, a predetermined component is executed, otherwise another component is executed, realizing dynamic adjustment of business logic; In one implementation, the step of determining whether to continue executing the next component according to the processing result of the current data includes: According to the execution result of the current component, a data fluctuation stability index and an energy consumption efficiency index are calculated, the data fluctuation stability index and the energy consumption efficiency index are added to obtain a performable index of the subsequent process, and whether to continue executing the next component is determined according to the performable index of the subsequent process.

[0028] In one embodiment, the calculation of the data fluctuation stability index includes: The original data time series in the execution result of the current component is extracted, wherein each data point represents a data value at a specific time point, and the time stamp is one-to-one corresponding to the data; In the time series, a fixed-size sliding window is selected, the standard deviation of the data in the window is calculated, the difference between the standard deviation of the current window and the standard deviation of the previous window is calculated, and then the difference is divided by the standard deviation of the previous window to obtain the change rate of the standard deviation between adjacent sliding windows; The probability distribution of all standard deviation change rates is calculated, and the entropy value of the probability distribution of all standard deviation change rates is calculated using the formula of information entropy, and the entropy value is divided by the theoretical maximum entropy value to obtain the deviation degree; The time series is divided into multiple subintervals, the fluctuation of each subinterval is calculated, and the similarity of each subinterval is measured to obtain the fractal dimension; The standard deviation change rate, the deviation degree and the fractal dimension are multiplied, and the reciprocal of the multiplication result is taken as the data fluctuation stability index.

[0029] It should be noted that in the calculation process of the data fluctuation stability index, the data involved in each step is obtained as follows: First, the original data time series is extracted from the current component execution result. This time series reflects the data values at different time points, usually from real-time monitoring devices or sensors. Each data point includes a timestamp and the corresponding data value. For example, if it is temperature sensor data, each data point represents the temperature value at a certain time, and the timestamp records the specific sampling time. Next, in order to calculate the standard deviation, we need to select a fixed size sliding window (for example, the past 10 seconds of data) from the extracted time series. Within this window, we calculate the standard deviation of the data, which represents the fluctuation amplitude of the data in that time period. Then, compare the standard deviation of the current window with the standard deviation of the previous window, calculate the difference between the two, and divide it by the standard deviation of the previous window to get the standard deviation change rate. This data is obtained by calculating the standard deviation of each sliding window. Then, after calculating the standard deviation change rate of each sliding window, the next step is to calculate the probability distribution of these standard deviation change rates. We use this distribution to calculate the information entropy, which measures the complexity and uncertainty of the distribution of standard deviation change rates. Specifically, by calculating the frequency of each standard deviation change rate, we apply the formula of information entropy to calculate the entropy value of the change rate distribution, and then compare it with the theoretical maximum entropy value to get the deviation degree. Finally, the fractal dimension is obtained by dividing the time series into multiple subintervals and calculating the data fluctuation in each subinterval. The specific method is to divide the time series into several smaller subintervals, calculate the fluctuation of data in each subinterval (such as mean or standard deviation), and then compare the fluctuation characteristics of these subintervals to measure their similarity. Through the calculation and comparison of the fluctuations of these subintervals, the fractal dimension of the time series is obtained, which represents the self-similarity and complexity of the data. Through the above steps, the acquisition method of each data set is based on the analysis of time series, ensuring the continuity and stability of the calculation process, and thus obtaining the data fluctuation stability index.

[0030] It should be noted that the data fluctuation stability index is an index for measuring the stability and regularity of data fluctuation in the system. It reflects whether the data fluctuation is within a controllable range and whether there is abnormal fluctuation within a period of time. Specifically, the data fluctuation stability index considers the change amplitude of the data, the regularity of the fluctuation, and whether there is unstable or unpredictable fluctuation. The value of the index is usually between 0 and 1, and the closer the value is to 1, the more stable the data fluctuation is, and the system is in a normal working state; the closer the value is to 0, the greater the data fluctuation is, and the system may be in an unstable or abnormal state, and there is a risk of continuing to perform subsequent operations. When the data fluctuation stability index is larger, it means that the data fluctuation is smaller, and the system stability is higher, and the current execution process will not be affected by external interference or sudden events, and the system can continue to run smoothly. On the contrary, if the data fluctuation stability index is small, it means that the data fluctuation in the system is large, and there may be a risk of abnormality or failure, and at this time, continuing to execute the subsequent components may lead to unstable results or incorrect decisions, and even may cause problems in production or system crash. For example, assuming that in a production scheduling system, the running state of the equipment will affect the arrangement of the subsequent production steps. If the fluctuation stability index of the equipment is high (for example, the stability index is 0.9), it means that the equipment runs smoothly, and the performance of the equipment is normal, and the system can safely continue to the next step in the production plan; if the fluctuation stability index of the equipment is low (for example, the stability index is 0.3), it means that the equipment runs unstably, and there may be a risk of failure or performance degradation, and at this time, continuing to execute the next step may cause production interruption, product quality problems, or even equipment damage. Therefore, a larger data fluctuation stability index indicates that the system is in good running state and has the condition to continue to execute the next operation; on the contrary, a smaller fluctuation index indicates that the system may need to be adjusted, checked or repaired to avoid problems caused by continuing to execute.

[0031] The reason for calculating the data fluctuation stability index in the above manner instead of using traditional weighted average, variance single index or simple threshold judgment is that this method can more comprehensively, deeply, multi-dimensionally and dynamically depict the essential characteristics of data fluctuation. First, by using the sliding window standard deviation and its change rate, the severity and change speed of local fluctuation in the time series can be captured, reflecting whether there is a sudden oscillation behavior in the current system. Second, by calculating the probability distribution information entropy of the standard deviation change rate and further normalizing it into the deviation degree, the unpredictability and complexity of the fluctuation pattern can be measured, and then it can be judged whether the system has a trend instability. Third, by using the fractal dimension calculation of the time series, it can be identified whether the self-similar structure of the data at multiple scales is disordered, so as to evaluate whether the system is converted from a structural stable state to a chaotic state. By multiplying the three and taking the reciprocal, a nonlinear amplification response mechanism can be constructed: once any dimension fluctuates violently, the entropy value deviates seriously or the fractal dimension becomes high, the index value will be significantly reduced, which can sensitively prompt the system not to continue to execute the subsequent components. Compared with the conventional method, the biggest advantage of this method is dynamic, locally sensitive and multi-dimensional structure understanding ability, which can accurately reflect the situation of "overall statistical stability but local disturbance violent", avoid missing key abnormalities by traditional mean method, and thus provide more reliable and more detailed judgment basis in actual engineering or decision-making system. For example, in a scenario where an industrial equipment maintains overall stability but suddenly has high-frequency oscillation in a certain period, the traditional average fluctuation rate may be insensitive, but this method can capture, quantify and quickly feedback this local non-stable state, thereby improving the active response ability and safety margin of the system. In summary, this method not only has a rigorous logic and progressive calculation, but also truly realizes the fusion judgment of "structure, speed and complexity" in three dimensions, so that the stability index obtained is more meaningful and practical in engineering.

[0032] In an implementation manner, the energy consumption efficiency index is calculated by: The energy consumption data and task output data at a plurality of continuous time points in a current task execution period are obtained, and the instantaneous energy consumption value and unit output value at each time point are recorded respectively; for each time point, the unit energy consumption ratio at the time point is calculated, and the calculation method is: the instantaneous energy consumption value at the time point is squared and divided by the result of the corresponding unit output value plus one, thereby obtaining the unit energy consumption ratio at the time point, forming a time series of unit energy consumption ratio, which is used to represent the influence degree of energy consumption on unit output at each time point.

[0033] The adjacent value direction change of the unit energy consumption ratio time sequence is analyzed, the increasing and decreasing trend between each two adjacent data points in the sequence is judged, and the change points of "from increasing to decreasing" or "from decreasing to increasing" are identified; the number of all change points in the entire sequence is counted as the number of inflection points; the number of inflection points is divided by the total time interval length corresponding to the sequence to obtain the inflection point density, which is used to reflect the fluctuation frequency in the unit energy consumption ratio change process; Each data point in the time sequence is mapped to a two-dimensional coordinate point with time as the horizontal axis and the unit energy consumption ratio as the vertical axis; the total length of the curve formed by connecting all data points is calculated as the real path length, and the straight line distance between the starting point and the ending point of the sequence is calculated as the shortest path length; the shortest path length is divided by the real path length to obtain the path compression degree, which is used to measure the fluctuation amplitude and path stability of the unit energy consumption ratio change process; The change trend of the unit output value in the task cycle is analyzed, and the continuous rising time period is identified; for each continuous rising output section, it is judged whether the corresponding energy consumption value appears after the output rises; if there is a time period in which the energy consumption lags behind the output rise, the duration of all such lagging sections is added and divided by the total duration of the entire task cycle to obtain the energy consumption lag rate, which is used to measure the reaction speed of the system to the energy consumption control after the efficiency of the task output is improved; The inflection point density, path compression degree and energy consumption lag rate are logically combined to construct the energy consumption efficiency index; first, the inflection point density and the energy consumption lag rate are added and multiplied by the reciprocal of the path compression degree to form a non-efficiency disturbance factor, then the disturbance factor is added by one and the reciprocal is taken to obtain the final energy consumption efficiency index; the value of the index is between 0 and 1, the larger the value, the higher the energy use efficiency, the more agile the system response, and the smaller the fluctuation.

[0034] It should be noted that in the calculation process of the above energy consumption efficiency index, the acquisition method of each data is as follows: the energy consumption data and task output data in the current task execution cycle can be obtained by real-time collection through the built-in production monitoring module of the system, wherein the energy consumption data refers to the current, voltage or other energy consumption information recorded at each time point, which is recorded and uploaded by energy consumption sensors such as electric meter modules, flow meters, etc. at a preset sampling frequency; the task output data is recorded by the production control system of the device (such as PLC, MES, etc.) in real time, and the output or processing amount per unit time at each time point is recorded, which is marked with task ID and execution time, so as to form a "instantaneous energy consumption value-unit output value" pair with time stamp accurate alignment; these data are automatically processed by the system, and are arranged in chronological order to form a structured time sequence, which is used for subsequent ratio calculation, path analysis, trend identification and delay evaluation, etc. operations, to ensure that the basic data of each index comes from original and verifiable data sources in the actual production process, with accuracy and traceability.

[0035] It should be noted that the energy consumption effective index is a comprehensive numerical index for measuring the coordination, efficiency and system response sensitivity between unit output and energy consumption during task execution. The index comprehensively reflects the quality of energy utilization in the current task execution process by analyzing the fluctuation trend of the unit energy consumption ratio (reflected by the inflection point density), the stability of the change trajectory (reflected by the path compression degree) and the timeliness of the energy consumption response output efficiency change (reflected by the energy consumption lag rate). Specifically, if the unit energy consumption ratio fluctuates frequently, the trajectory is tortuous, and the energy consumption adjustment lags behind the change of the task output, it means that the system energy efficiency control has problems such as large fluctuation, slow adjustment, poor feedback, etc. At this time, the energy consumption effective index will tend to a smaller value; on the contrary, if the unit energy consumption ratio changes smoothly, the path is close to a straight line, and the energy consumption decreases rapidly after the task output improves and responds to system adjustment in time, the index will tend to 1, indicating that the system running efficiency is high, the energy consumption strategy is accurate, and the energy use process is stable and reliable. Therefore, the larger the index is, the better the current task performs in terms of unit output energy efficiency, and the system state is in a high-efficiency, low-consumption, fast-response benign interval, which not only ensures the smooth execution of the current task, but also provides a stable system foundation and resource guarantee for the execution of the next task component, with higher process continuity and execution feasibility. For example, in a complex device scheduling process, if the unit energy consumption ratio is stable and decreasing in several consecutive execution cycles, the system response speed is fast and the path change is stable, the energy consumption effective index will always maintain a high level, and the system can automatically judge based on the index whether to wait or reconfigure, and directly schedule the subsequent task to ensure the overall process efficiency and energy cost control. Therefore, the index is not only used for energy efficiency evaluation of a single task, but also a key decision factor in process adaptive promotion.

[0036] It should be noted that the above method is used to calculate the energy consumption effective index, instead of using the conventional method of total energy consumption divided by total output, weighted average energy efficiency, or linear trend fitting, mainly because this method can fully capture the real state and potential risks of energy efficiency in the task execution process from multiple dynamic dimensions, nonlinear characteristics and response lag behavior. First, by constructing and identifying the "inflection point density" through the time series of unit energy consumption ratio, it can reveal whether the system energy efficiency exists frequent and unstable fluctuations, avoiding the problem of covering the stage of severe energy fluctuations by only relying on the average value. Second, the "path compression degree" measures the overall stability and redundancy of the energy efficiency change process, which avoids the disadvantages of traditional trend analysis in nonlinear intervals, so that the deviation risk of system state in the tortuous path can be timely perceived. Third, the "energy consumption lag rate" effectively captures the synchronization problem between output efficiency improvement and energy consumption response, making up for the limitations of traditional calculation methods in considering the lag and inertia adjustment mechanism. The three dimensions are independent and highly complementary, without involving weighted summation and unknown factors, and can extract the internal logic and operating characteristics of system energy efficiency control through structural combination without relying on empirical parameter tuning. The energy consumption effective index obtained in this way not only retains the macro judgment ability of overall efficiency, but also has the sensitivity to micro dynamics such as instantaneous abnormalities, structural changes and reaction delays, which can provide more scientific, accurate and forward-looking decision support for system automation scheduling, adaptive process management and resource allocation, significantly better than the application performance of traditional methods in complex task chains.

[0037] In one embodiment, judging whether to continue executing the next component according to the performable index of the subsequent process includes: Comparing the performable index with a preset performable index threshold value, if the performable index is not less than the preset performable index threshold value, it means that the condition is established, and the next predetermined component is executed according to the flowchart; If the performable index is less than the preset performable index threshold value, it means that the condition is not established, otherwise another component is executed to realize the dynamic adjustment of business logic.

[0038] It should be noted that in the process component-based data screening method, in order to realize the flexibility and intelligence of business logic, the system sets "can proceed index" as the core index for dynamic judgment of execution path. Specifically, when the process is executed to the component node containing condition judgment, the system will first calculate the "can proceed index" of the current task, and compare the index with the preset "can proceed index threshold". If the can proceed index is greater than or equal to the threshold, it means that the stability, energy efficiency and other comprehensive indexes of the current data processing result have reached the expected standard of the system, that is, it indicates that the subsequent task has a good execution basis, at this time the system will continue to execute the next scheduled component according to the normal path set in the flowchart, and realize the natural advancement of the business flow; on the contrary, if the can proceed index is lower than the threshold, it means that the current processing result has a certain degree of instability, non-efficiency or risk of not being suitable for immediate operation, then the system will automatically trigger the "alternative path" or "branch component" set in the flowchart, and may perform abnormal processing, supplementary verification, data back, etc. Operation, thereby realizing the dynamic adjustment and abnormal response of the business process. For example, if the current component in the flowchart is processing a certain process data screening, the calculated data fluctuation stability index and energy consumption efficiency index are low, and the comprehensive can proceed index is less than the threshold, then the system may skip the subsequent sorting component and enter a data optimization component to correct abnormal data, to ensure the quality and robustness of the overall process. This path shunting mechanism based on index judgment breaks through the rigid constraints of the traditional "fixed process" execution logic, so that the process can flexibly adjust the path when facing different task states, data quality or system response ability differences, to ensure that the entire execution process is more intelligent, stable and adaptive.

[0039] In one embodiment, S6: after each component is executed, the result is stored in a database or cache system in a specific data structure (such as Map), and the next component extracts the execution result of the previous step from the storage by obtaining UID, and continues to execute the next data processing step.

[0040] It should be noted that, in the execution process of the component flow, in order to ensure efficient data transmission and state consistency between components, the system will encapsulate and store the execution results of each component according to a unified data structure after the execution of each component is completed. Common data structures include Map, JSON object or other key-value pair structures, which can clearly identify the processing results and state identifiers of each field. The system will store the data structure in the preset database or cache system with the current flow unique identifier (UID) as the index. The storage operation not only includes the output data of the component itself, but also can contain auxiliary information such as execution timestamp, status code, exception mark, performance index, etc. to facilitate subsequent component calling or fault backtracking. When the next component is ready to execute, the system will automatically retrieve the corresponding previous component result data based on the UID and pass the data as input to the current component, realizing seamless data connection and state continuation between components. For example, in a flow from a "process filtering component" to a "priority sorting component", the filtering component will store the retained data record in the form of Map after completion, with the key as the field name and the value as the filtered data set, and the UID as the storage index; when the sorting component starts, the system will automatically obtain the Map structure from the cache according to the UID, and read the valid data field to perform sorting operation. This mechanism effectively avoids the coupling problem caused by directly passing data between components through hard coding, improves the modularization level, scalability and concurrent processing capability of the system, and is especially suitable for stability guarantee in complex scenarios such as parallel execution, multi-flow scheduling and state reconstruction.

[0041] In one embodiment, after the execution of the entire custom process component, the system uniformly packages and outputs the processing results of the last component, generating the final screening, selection and sorting results. The output result is usually in the form of structured data, such as a table, a JSON file or a database record, containing key data fields processed by each functional component, such as the target record set screened out, the process recipe parameters matched, the priority queue sorted, etc. These results will be transmitted to the downstream system through the preset interface, such as the production scheduling system, the quality management platform or the business decision module, for supporting subsequent automated execution or manual intervention. To ensure the integrity and compatibility of data output, the system will also attach execution logs, process status codes and related marker fields, so that the downstream system can accurately identify the processing stage and context of the current data. For example, in the semiconductor manufacturing scenario, the wafer batch screened by the "process filtering component" and the processing sequence sorted by the "QTime sorting component" will be finally output to the MES system for driving the next stage of equipment operation instruction arrangement. This output mechanism not only ensures the consistency of data in the processing chain, but also provides traceable and verifiable data basis for subsequent systems, realizing the full-process closed loop from custom process configuration to intelligent decision output, and improving the flexibility, transparency and automation level of the system as a whole.

[0042] The above describes one embodiment of the present application in detail, but the content described is only the preferred embodiment of the present application and cannot be considered as limiting the scope of the implementation of the present application. Any equivalent changes and improvements made in the scope of the present application should still belong to the scope of the patent coverage of the present application.

Claims

1. A data filtering method based on a self-defined process component, characterized in that, The method comprises the following steps: Constructing a component combination platform, the platform comprising a plurality of functional components, each functional component being identified by a unique ID; Defining and connecting the functional components through a visual interface, a user combining a plurality of components into a custom business flowchart according to requirements through the manner of drawing a flowchart; Generating a unique identifier and initializing flow execution; Executing the flow components, the first component of the flowchart querying and loading data from a database or a data source according to preset conditions, and then each component being executed in sequence, the execution result being stored through a data structure and being passed to the next component to ensure the continuity and consistency of data flow; In the execution process, if a component is a conditional node, the system judges whether to continue executing the next component according to the processing result of the current data, if the condition is established, a predetermined component is executed, otherwise another component is executed, to realize dynamic adjustment of business logic; After each component is executed, the result is stored in a database or a cache system in a specific data structure, the next component extracts the execution result of the previous step from the storage by obtaining a unique identifier, and continues to execute the next data processing step; When all components are executed, the final screening, selection and sorting result is output to a downstream system or other application program as a basis for subsequent processing or decision-making.

2. The data filtering method based on self-defined flow components according to claim 1, characterized in that, The components include but are not limited to process filtering components, process recipe matching components, QTime sorting components, priority sorting components and sorting components, for realizing data screening, sorting and selection operations.

3. The data filtering method based on self-defined flow components according to claim 1, characterized in that, Combining a plurality of components into a custom business flowchart according to requirements comprises: The flowchart defines the execution order of each component and the data transmission rule, and the user can flexibly adjust the order and conditional judgment of the components according to business requirements.

4. The data filtering method based on self-defined flow components according to claim 1, characterized in that, Generating a unique identifier and initializing flow execution comprises: Each time the flowchart is executed, the system automatically generates a unique identifier UID for the flow, which is used to record and track the execution information of the flow, and the UID is used to identify the state in the data flow and component execution process.

5. The data filtering method based on self-defined flow components according to claim 1, characterized in that, The step of judging whether to continue executing the next component according to the processing result of the current data comprises: Calculating a data fluctuation stability index and an energy consumption efficiency index according to the execution result of the current component, adding the data fluctuation stability index and the energy consumption efficiency index to obtain a performable index of the subsequent flow; and judging whether to continue executing the next component according to the performable index of the subsequent flow.

6. The data filtering method based on self-defined flow components according to claim 5, characterized in that, The calculation step of the data fluctuation stability index comprises: Extracting a time series of original data in the execution result of the current component, wherein each data point represents a data value at a specific time point, and the timestamp corresponds to the data one by one; In the time series, a fixed-size sliding window is selected, the standard deviation of the data in the window is calculated, the difference between the current window standard deviation and the previous window standard deviation is calculated, and then the difference value is divided by the previous window standard deviation to obtain the change rate of the standard deviation between adjacent sliding windows; Calculating the probability distribution of all standard deviation change rates, and calculating the entropy value of the probability distribution of all standard deviation change rates by using the formula of information entropy, and dividing the entropy value by the theoretical maximum entropy value to obtain a deviation degree; The time series is divided into multiple subintervals, the fluctuation of each subinterval is calculated, and the similarity of each subinterval is measured to obtain the fractal dimension; The reciprocal of the multiplication result is taken as the data fluctuation stability index.

7. The data filtering method based on self-defined flow components according to claim 5, characterized in that, The calculation step of the energy consumption efficiency index is: Obtain the energy consumption data at multiple consecutive time points in the current task execution period and the task output data at the corresponding time points, denoted as the instantaneous energy consumption value and the unit output value at each time point, respectively; For each time point, calculate the unit energy consumption ratio at the time point, the calculation method is: square the instantaneous energy consumption value at the time point, and divide by the result of adding one to the corresponding unit output value, to obtain the unit energy consumption ratio at the time point, forming a time series of unit energy consumption ratio; The adjacent value direction change of the unit energy consumption ratio time series is analyzed, the increasing and decreasing trend between each two adjacent data points in the sequence is judged, and the change points of "from increasing to decreasing" or "from decreasing to increasing" are identified; The number of all change points in the entire sequence is counted as the number of inflection points; The inflection point density is obtained by dividing the total time interval length corresponding to the sequence, which is used to reflect the fluctuation frequency in the unit energy consumption ratio change process; Take time as the horizontal axis and unit energy consumption ratio as the vertical axis, map each data point in the time series to a two-dimensional coordinate point; Calculate the total length of the curve formed by connecting all data points as the real path length, and calculate the straight line distance between the starting point and the ending point of the sequence as the shortest path length; The shortest path length is divided by the real path length to obtain the path compression degree; Analyze the trend of unit output value in the task period, identify the continuous rising period; For each continuous rising output segment, determine whether the corresponding energy consumption value decreases after the output rises; If there is a time period where energy consumption lags behind output rise, the duration of all such lagging periods is added and divided by the total duration of the task period to obtain the energy consumption lag rate; The inflection point density, path compression degree and energy consumption lag rate are logically combined to construct the energy consumption efficiency index; Wherein, first add the inflection point density and the energy consumption lag rate, and multiply the result by the reciprocal of the path compression degree to form a non-efficiency disturbance factor, then take the reciprocal of the disturbance factor after adding one to obtain the final energy consumption efficiency index.

8. The data filtering method based on self-defined flow components according to claim 1, characterized in that, The executable index is compared with the preset executable index threshold value, if the executable index is not less than the preset executable index threshold value, it means that the condition is established, then the next predetermined component is executed according to the flowchart; If the executable index is less than the preset executable index threshold value, it means that the condition is not established, otherwise another component is executed to realize the dynamic adjustment of business logic. ​

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