Analysis method and system for production element modeling full-process data management

Abstract

The invention discloses an analysis method and system for production element modeling full-process data management, and belongs to the technical field of electric digital data processing, and the method comprises the following steps: S1, obtaining current production order information, carrying out the analysis to obtain a production order information predicted processing load value, and carrying out the matching to obtain a system resource occupation value; s2, judging whether data processing task scheduling is executed or not, if the data processing task scheduling is executed, executing S3, and otherwise, directly performing data processing on the current production order information; s3, analyzing to obtain the processing priority of each piece of production element information, and performing data processing task scheduling on each piece of production element information in combination with the system resource occupancy value of each piece of production element information, thereby obtaining a data processing result and storing the data processing result to a central processing unit; and S4, the central processing unit carries out real-time data processing on a result and executes production element modeling. The problem that the data processing efficiency is affected due to insufficient system resources caused by high-concurrency data exchange in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to an analysis method and system for full-process data management of production factor modeling. Background Art

[0002] With the development of the manufacturing industry, the modeling and data management of production factors (such as orders, equipment status, inventory, and logistics information) need to cover multiple modules such as production, sales, storage, and outbound delivery. The data interactions between these modules are frequent and have high real-time requirements, which places a great demand on system resources. The existing production factor modeling method is implemented through hierarchical clustering calculation.

[0003] For example, the invention patent announcement with announcement number: CN118227716B discloses an industrial production factor optimization method based on a multidimensional database configuration, which includes: obtaining industrial production factor data; performing hierarchical clustering calculations on the industrial production factor data to generate industrial production factor dimension data; performing intra-dimensional measurement analysis on the industrial production factor dimension data to generate industrial production factor measurement data; performing multidimensional database modeling on the industrial production factor dimension data and the industrial production factor measurement data to obtain multidimensional database data; and performing adaptive anomaly calculations on the multidimensional database data to obtain abnormal problem data.

[0004] For example, the invention patent announcement with announcement number: CN113987608B discloses a modeling method for verifying the satisfiability of clothing customization requirements, including: step one, formally modeling the user's personalized requirements; classifying and describing the user's requirements for personalized customized clothing; dividing the requirements into several levels according to the sequence of the clothing processing and manufacturing process; step two, formally modeling the production and manufacturing capabilities; production and manufacturing capabilities include several production factors of one or more of the raw materials, personnel and equipment required to produce products; step three, formalizing the clothing customization requirements into a first-order logic formula, and judging whether the problem is solvable through the satisfiability theory method.

[0005] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above-mentioned prior art has at least the following technical problems: In the existing technology, during the peak order period, the real-time transmission and processing requirements of a large amount of order data will cause the system to respond slowly or even crash, seriously affecting the smooth progress of the production process. However, the existing technology still processes production factors according to the preset plan and does not make timely adjustments. Therefore, there is a problem of high concurrent data exchange leading to insufficient system resources and thus affecting data processing efficiency. Summary of the Invention

[0006] The embodiments of the present application solve the problem in the prior art that high concurrent data exchange leads to insufficient system resources and thus affects data processing efficiency by providing an analysis method and system for full-process data management of production factor modeling, thereby achieving efficient processing of production factor information.

[0007] An embodiment of the present application provides an analysis method for full-process data management of production factor modeling, including the following steps: S1. When the central processing unit receives a production order information processing signal, it obtains the current production order information, analyzes to obtain the expected processing load value of the production order information, and matches it to obtain the system resource occupancy value; S2. Based on the system resource occupancy value, it determines whether to execute data processing task scheduling. If data processing task scheduling is executed, S3 is executed, otherwise the current production order information is directly processed; S3. The production factor information of the current production order information and the data processing intensity parameters of each production factor information are obtained, the processing priority of each production factor information is analyzed, and the data processing task scheduling of each production factor information is performed in combination with the system resource occupancy value of each production factor information, thereby obtaining the data processing result and saving it to the central processing unit; S4. The central processing unit processes the data result in real time and executes production factor modeling.

[0008] An embodiment of the present application provides a full-process data management system for production factor modeling, including: a resource occupancy analysis module, a task scheduling judgment module, a task scheduling analysis module and a production factor modeling module; wherein the resource occupancy analysis module is used to obtain the current production order information after the central processing unit receives the production order information processing signal, analyze and obtain the expected processing load value of the production order information, and match it to obtain the system resource occupancy value; the task scheduling judgment module is used to determine whether to execute data processing task scheduling based on the system resource occupancy value, and if the data processing task scheduling is executed, the task scheduling analysis module is executed, otherwise the current production order information is directly processed; the task scheduling analysis module is used to obtain the production factor information of the current production order information and the data processing intensity parameters of each production factor information, analyze and obtain the processing priority of each production factor information, and perform data processing task scheduling on each production factor information in combination with the system resource occupancy value of each production factor information, thereby obtaining the data processing results and saving them to the central processing unit; the production factor modeling module is used for the central processing unit to process the real-time data results and execute production factor modeling.

[0009] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. The analysis method for full-process data management of production factor modeling provided by the present invention realizes accurate allocation and scheduling of system resources by dynamically analyzing the production order information to estimate the processing load value and match the system resource occupancy value. When system resources are tight, the key production factor information is processed first through task division and resource occupancy value analysis, thus avoiding resource waste and task backlog, and realizing efficient processing of production factor information, effectively solving the problem in the prior art that high concurrent data exchange leads to insufficient system resources and thus affects data processing efficiency.

[0010] 2. The present invention obtains the data processing intensity parameters of each production factor information and analyzes the processing priority of the production factor information, thereby sorting and scheduling the production factor information based on the processing priority, ensuring that high-priority tasks are given priority, improving the overall efficiency of the production process, and further realizing the efficient execution and management of production tasks.

[0011] 3. By inputting the real-time data processing results of each production factor information into the preset visual output model, the visual output results of each production factor information are obtained and output and displayed, so that the status of the production factor information can be observed intuitively, and the production factor information that has not completed data processing is marked as unprocessed production factor information, and visual output is not performed, ensuring the accuracy of the visual results and realizing transparent management of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A flowchart of the analysis method for full-process data management of production factor modeling provided in an embodiment of the present application; Figure 2 A flowchart of the analytical method for full-process data management of production factor modeling provided in an embodiment of the present application; Figure 3 A detailed flow chart of the analysis method for full-process data management of production factor modeling provided in an embodiment of the present application; Figure 4 A structural diagram of the analysis system for full-process data management of production factor modeling provided in an embodiment of the present application. DETAILED DESCRIPTION

[0013] The embodiments of the present application solve the problem in the prior art that high concurrent data exchange leads to insufficient system resources and thus affects data processing efficiency by providing an analysis method and system for full-process data management of production factor modeling, thereby achieving efficient processing of production factor information.

[0014] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0015] like Figure 1 As shown, it is a flow chart of the analysis method for full-process data management of production factor modeling provided by an embodiment of the present application, and the method includes the following steps: S1. When the central processing unit receives the production order information processing signal, it obtains the current production order information, analyzes to obtain the expected processing load value of the production order information, and matches to obtain the system resource occupancy value; S2. Based on the system resource occupancy value, it determines whether to execute data processing task scheduling. If the data processing task scheduling is executed, S3 is executed, otherwise the current production order information is directly processed; S3. The production factor information of the current production order information and the data processing intensity parameters of each production factor information are obtained, the processing priority of each production factor information is obtained by analysis, and the data processing task scheduling of each production factor information is performed in combination with the system resource occupancy value of each production factor information, thereby obtaining the data processing result and saving it to the central processing unit; S4. The central processing unit processes the data result in real time and executes production factor modeling.

[0016] In this embodiment, if Figure 2 The figure shows a process framework diagram for the analysis method for full-process data management of production factor modeling provided by an embodiment of the present application. Upon receiving a production order information processing signal, the estimated processing load value for the production order information is calculated, and then the system resource usage value is checked. A determination is made as to whether data processing task scheduling should be performed. If so, the processing priority of each production factor information is determined, and data processing task scheduling is performed based on the production factor information. If not, data processing is performed directly. Finally, the data processing results are obtained, and production factor modeling is performed.

[0017] After the central processor receives the production order information processing signal, it promptly obtains the current production order information and analyzes it to obtain the expected processing load value. This allows the system resource demand for order processing to be estimated in advance. This matching method then determines the system resource utilization value, providing a key basis for subsequent rational allocation of system resources. This effectively avoids blind resource allocation and improves the accuracy and efficiency of resource allocation.

[0018] The system determines whether to schedule data processing tasks based on system resource usage. If scheduling is not necessary, data processing is performed directly, reducing unnecessary scheduling overhead and quickly responding to production order information processing needs. When system resources are sufficient, orders can be processed directly, accelerating the processing process. When resources are limited, task scheduling is performed to ensure efficient operation under different load conditions.

[0019] After receiving the data processing results, the central processing unit executes production factor modeling, ensuring that the data used in the modeling process is properly scheduled and processed, and can more accurately reflect the actual status of production factors and their mutual influence. This helps provide more reliable data support for production decisions.

[0020] Furthermore, the current production order information is obtained, and the estimated processing load value of the production order information is obtained by analysis. The specific method is as follows: the current production order information is obtained, and each production factor in the current production order information is recorded as each designated production factor; the current production order information includes the information attributes of each production factor; the historical processing information of several production factors with the same attributes as each designated production factor pre-stored in the database is obtained, and recorded as the historical processing information of each associated production factor; the historical processing information of each associated production factor includes the average task density of system processing, the average time consumed by system processing, the average read and write speed of the system, and the average memory occupancy rate of the system for each associated production factor; the estimated processing load reference set of production order information preset in the database is obtained, and compared and analyzed with the average task density of system processing, the average time consumed by system processing, the average read and write speed of the system, and the average memory occupancy rate of the system to obtain the comparison processing results, and the weighting factor is introduced to quantify the comparison processing results and then couple them to obtain the estimated processing load value of the production order information; The reference set of the estimated processing load of production order information includes a setting value of the average task density of system processing, a setting value of the average time duration of system processing, a setting value of the average read and write speed of the system, and a setting value of the average memory usage of the system.

[0021] In this embodiment, it should be noted that the historical processing information for each associated production factor includes the average system processing task density, average system processing time, average system read / write speed, and average system memory usage. The average system processing task density refers to the average historical system processing task density of multiple production factor information with the same attributes as each specified production factor, indicating the number of tasks the system needs to process per unit time. This information can be obtained through task scheduling system log analysis tools (such as ELK Stack). The average system processing time refers to the average historical system processing time of multiple production factors with the same attributes as each specified production factor, indicating the reference time length from the start to the end of the task for the current production factor information. This information can be obtained through timestamp records. The average system read / write speed refers to the average historical system read / write speed of multiple production factors with the same attributes as each specified production factor, which can be detected through the computer's built-in resource monitor. The average system memory usage refers to the average system memory usage of multiple production factors with the same attributes as each specified production factor, indicating the ratio of occupied memory to total available memory in the system. This information can be obtained through querying the task manager.

[0022] The current system resource allocatable value is obtained and compared with the system resource occupancy value to determine whether to execute data processing task scheduling. If the system resource occupancy value is above the system resource allocatable value, the current production order information is divided into various production factor information, and the system resource occupancy value of each production factor information is analyzed to obtain the system resource occupancy value. Data processing task scheduling is then executed. This facilitates the flexibility and adaptability of resource allocation, enables the reasonable arrangement of task scheduling based on actual resource conditions, and improves resource utilization. If the system resource occupancy value is less than the system resource allocatable value, data processing is directly performed, avoiding unnecessary scheduling overhead and accelerating the processing of production order information. This allows the system to operate efficiently under different load conditions, ensuring timely task processing and avoiding excessive resource consumption.

[0023] By analyzing historical processing information for each associated production factor, including the average system processing task density, average system processing time, average system read / write speed, and average system memory utilization, we derive the estimated processing load for production order information. This takes into account the interplay between these parameters. For example, when the average system processing task density is high, a large number of tasks simultaneously compete for limited resources, leading to increased memory utilization. Increased task density lengthens task queues, increasing task wait times and impacting processing time. Average system processing time is closely related to the average system memory utilization. When memory utilization is high, tasks frequently wait for memory to be freed due to insufficient memory, extending task processing time. Long-running tasks continuously occupy memory resources, further exacerbating memory utilization. Slow average system read / write speeds can extend task processing time because tasks must wait for data read / write completion before continuing. Slow read / write speeds also lead to data backlogs in memory, occupying more memory space and increasing memory utilization. When system resources are limited, task density is high, processing time is long, and memory usage is high, the system needs to switch frequently between multiple tasks, generating a large amount of context switching overhead, increasing the system burden, and reducing overall operating efficiency.

[0024] Get the estimated processing load value of production order information. The specific method is: ; Where, Indicates the expected processing load value of production order information. represents the average task density of the system processing of the i-th associated production factor, Indicates the average task density setting value processed by the system, represents the average system processing time of the i-th associated production factor, Indicates the average processing time setting value of the system. represents the average reading and writing speed of the system for the i-th associated production factor, Indicates the system average read and write speed setting value, represents the system average memory occupancy rate of the i-th associated production factor, Indicates the system average memory usage setting value, represents the average task density weighting factor processed by the system, represents the average task density weighting factor processed by the system, Represents the system average read and write speed weighting factor, Indicates the weighting factor of the system's average memory usage.

[0025] The system processing average task density weighting factor, the system processing average task density weighting factor, the system average read / write speed weighting factor, and the system average memory occupancy weighting factor can be obtained from a database. For example, the system processing average task density weighting factor can be obtained by obtaining the historical system processing average task density stored in the database, and the system processing average task density weighting factor corresponding to the historical system processing average task density, thereby constructing a system processing average task density mapping set, wherein the mapping set has a one-to-one or many-to-one correspondence. The system processing average task density weighting factor can be obtained by inputting the required system processing average task density data into the system processing average task density mapping set. The other weighting factors, such as the system processing average task density weighting factor, the system average read / write speed weighting factor, and the system average memory occupancy weighting factor, are obtained in the same manner as the system processing average task density weighting factor and can all be matched and obtained in the corresponding mapping sets. The system processing average task density weighting factor corresponds to the system processing time duration mapping set, the system average read / write speed weighting factor corresponds to the system average read / write speed mapping set, and the system average memory occupancy weighting factor corresponds to the system average memory occupancy mapping set.

[0026] Furthermore, the system resource occupancy value is obtained by matching. The specific method is: obtain the expected processing load intervals of each production order information preset in the database and the reference system resource occupancy value corresponding to the expected processing load interval of each production order information, and match them with the expected processing load value of the production order information. If the expected processing load value of the production order information is within a certain preset expected processing load interval of production order information, then obtain the reference system resource occupancy value corresponding to the expected processing load interval of the production order information as the system resource occupancy value.

[0027] In this embodiment, during the production scheduling process, the execution order and resource allocation of production tasks can be flexibly adjusted according to the real-time resource occupancy and task load, so that production scheduling can be more adaptable to the changing production environment, can quickly respond to changes in production demand, improve the efficiency and effectiveness of production scheduling, improve the efficiency of resource management, and enhance the automation and intelligence level of resource management.

[0028] Furthermore, it is determined whether to execute data processing task scheduling. The specific method is: obtain the current system resource allocatable value and compare it with the system resource occupancy value, thereby determining whether to execute data processing task scheduling; if the system resource occupancy value is above the system resource allocatable value, then execute data processing task scheduling; if the system resource occupancy value is less than the system resource allocatable value, then do not execute data processing task scheduling, and at the same time directly process the current production order information, obtain the data processing result and save it to the central processing unit.

[0029] In this embodiment, by comparing system resource usage with the available system resource allocation, it is possible to accurately determine whether data processing tasks need to be scheduled, thus avoiding resource waste and ensuring efficient resource utilization. By analyzing the system resource usage of each production factor information, task scheduling becomes more precise, helping to prioritize the processing of urgent production factor information, thereby improving overall production efficiency.

[0030] If the system resource occupancy value is above the system resource allocatable value, data processing task scheduling will be executed. The processing strategy can be flexibly adjusted according to the current resource status to ensure the stable operation of the system. This can avoid the situation where some production factors excessively occupy system resources while other production factors cannot be processed in a timely manner.

[0031] If the system resource occupancy value is less than the system resource allocatable value, the data processing task scheduling will not be executed. At the same time, the current production order information will be directly processed, and the data processing results will be obtained and saved to the central processing unit. By directly processing the data, there is no need to go through a complex task scheduling process, which improves processing efficiency and reduces unnecessary waiting time.

[0032] Furthermore, the information of each production factor of the current production order information and the data processing intensity parameters of each production factor information are obtained, and the processing priority of each production factor information is obtained by analysis. The specific steps are: obtaining the information of each production factor of the current production order information and the data processing intensity parameters of each production factor information, each production factor information includes the data block capacity, data generation frequency, number of data processing procedures and task queue length of each production factor information; obtaining the data processing intensity reference set preset in the database, and comparing and analyzing them with the data block capacity, data generation frequency, number of data processing procedures and task queue length of each production factor information to obtain the data processing comparison result, and introducing the corresponding weighting factor to quantify the data processing comparison result and then perform coupling processing to obtain the data processing intensity of each production factor information; obtaining the processing priority of each production factor information based on the data processing intensity of each production factor information and matching it with the database; the data processing intensity reference set includes the expected value of data block capacity, the expected value of data generation frequency, the expected value of number of data processing procedures and the expected value of task queue length.

[0033] In this embodiment, it should be noted that the data block capacity of each production factor information can be obtained by querying in the Windows Explorer, the data generation frequency represents the number of data records generated per unit time, and can be obtained by using data acquisition tools (such as Apache Kafka). The number of data processing steps represents the number of steps involved in producing a product, and can be obtained through the production order information tracking function in the production management system. The task queue length can be viewed through the task scheduling system to check the number of tasks currently waiting to be processed.

[0034] By obtaining the data processing intensity parameters of each production factor information, including data block capacity, data generation frequency, number of data processing steps and task queue length, the processing intensity of production order information is comprehensively evaluated from multiple dimensions, avoiding the one-sidedness brought about by single indicator evaluation and providing a more accurate basis for subsequent priority determination.

[0035] Comparing and analyzing the data processing intensity parameters of each production factor information with the preset data processing intensity reference set in the database, and introducing the empowerment factor quantification and post-coupling processing, can more accurately determine the processing priority of each production factor information, ensure that the production factor information with high processing intensity is given priority, and improve production efficiency and resource utilization.

[0036] Dynamically adjust the processing priority based on the data processing intensity of each production factor information, be able to flexibly adapt to changes in production factor information according to real-time data, adjust resource allocation in a timely manner, and enhance production flexibility and adaptability.

[0037] The data processing intensity of each production factor is determined by analyzing the data processing intensity parameters of each production factor. This takes into account the interplay between these parameters. For example, data block capacity affects the load of a single data processing operation. Larger data blocks increase the processing time. A high data generation frequency leads to a rapid growth of the task queue, which in turn increases the task queue length. This increased task queue length increases the waiting time for subsequent tasks, further prolonging the processing time for tasks with a large number of data processing steps. Furthermore, a large number of data processing steps consumes more system resources, impacting the processing efficiency of both the data generation frequency and data block capacity.

[0038] The data processing intensity of each production factor information is obtained by: ; Where, represents the data processing intensity of the j-th production factor information, j represents the number of the production factor information, , Represents the total amount of production factor information, The capacity of the data block representing the information of the j-th production factor, Indicates the expected value of the data block capacity, represents the data generation frequency of the j-th production factor information, represents the expected value of the data generation frequency, represents the number of data processing steps for the j-th production factor information, represents the expected number of data processing steps, The length of the task queue representing the j-th production factor information, Indicates the expected value of the task queue length, represents the data block capacity weighting factor, represents the data generation frequency weighting factor, represents the weighting factor of the number of data processing steps, Indicates the task queue length weighting factor.

[0039] The data block capacity weighting factor, data generation frequency weighting factor, data processing process quantity weighting factor and task queue length weighting factor can be obtained from the database. For example, the data block capacity weighting factor can be obtained by obtaining the expected data block capacity value stored in the database, and the data block capacity weighting factor corresponding to the expected data block capacity value, thereby constructing a data block capacity mapping set, wherein there is a one-to-one or many-to-one correspondence in the mapping set. The data block capacity weighting factor can be obtained by inputting the required data block capacity data into the data block capacity mapping set. The acquisition method of other weighting factors such as the data generation frequency weighting factor, the data processing process quantity weighting factor and the task queue length weighting factor is the same as the acquisition method of the data block capacity weighting factor, and can all be matched in the corresponding mapping set, wherein the data generation frequency weighting factor corresponds to the data generation frequency mapping set, the data processing process quantity weighting factor corresponds to the data processing process quantity mapping set, and the task queue length weighting factor corresponds to the task queue length mapping set.

[0040] Based on the data processing intensity of each production factor information and matching it with the database, the processing priority of each production factor information is obtained. The specific method is: obtain the data processing intensity interval of each production factor information preset in the database and the reference processing priority of the production factor information corresponding to the data processing intensity interval of each production factor information, and match it with the data processing intensity of each production factor information. If there is a certain production factor information whose data processing intensity is within the preset data processing intensity interval of the production factor information, then obtain the reference processing priority of the production factor information corresponding to the data processing intensity interval of the production factor information as the processing priority of the production factor information, thereby obtaining the processing priority of each production factor information.

[0041] Furthermore, data processing tasks are scheduled for each production factor information. The specific steps are as follows: based on the processing priority of each production factor information, the production factor information is sorted in descending order of priority, and the production factor information with the highest priority is selected for priority processing, and the production factor information is marked as priority production factor information, and the remaining production factor information is marked as candidate production factor information; the system resource occupancy value corresponding to the priority production factor information is obtained, and compared with the system resource allocatable value. If the system resource occupancy value corresponding to the priority production factor information is greater than the system resource allocatable value, the system resource is adjusted, otherwise the data processing task scheduling is continued. The specific method for continuing to execute the data processing task scheduling is: performing difference processing on the system resource occupancy value corresponding to the priority production factor information and the system resource allocatable value to obtain the remaining difference of the system resources, and performing real-time update processing; sorting the processing priority of the candidate production factor information in descending order, and comparing the system resource occupancy value of the candidate production factor information with the system resource allocatable value in descending order, thereby analyzing and obtaining the production factor information suitable for execution processing, and performing data processing task scheduling.

[0042] In this embodiment, if Figure 3 As shown, this is a detailed flow chart of the analysis method for full-process data management of production factor modeling provided in an embodiment of the present application. It can be seen from the figure that if data processing task scheduling is performed, the data processing intensity of each production factor information is obtained and analyzed to obtain the processing priority of each production factor information, and the production factor information with the highest priority is selected for priority processing, and the production factor information is marked as priority production factor information, and the remaining production factor information is marked as candidate production factor information, and the system resource occupancy value corresponding to the priority production factor information is obtained and compared with the system resource allocatable value. If the system resource occupancy value corresponding to the priority production factor information is greater than the system resource allocatable value, the system resource adjustment is performed, otherwise the data processing task scheduling is continued, and the processing priority of the candidate production factor information is sorted in descending order. Based on the descending sorting order, the system resource occupancy value of the candidate production factor information is compared with the system resource allocatable value in turn, thereby analyzing and obtaining the production factor information suitable for execution processing, and performing data processing task scheduling.

[0043] Sorting in descending order based on the priority of production factor information can ensure that high-priority tasks are processed first and improve the processing efficiency of key tasks. By comparing the system resource occupancy value of priority production factor information with the system resource allocatable value, resource allocation can be dynamically adjusted to avoid resource waste. When system resources are insufficient, the resource adjustment mechanism is triggered to release resources by suspending low-priority tasks, ensuring the smooth execution of high-priority tasks and maintaining system stability.

[0044] By analyzing the resource requirements of candidate production factor information in sequence and scheduling tasks reasonably, the overall processing capacity and throughput of the system are improved. The task to be executed next is analyzed and determined in the order of data processing completion, ensuring the logic and orderliness of task processing. While ensuring that high-priority production factor information is processed first, the utilization of resources is improved, the time for processing production factor information is saved, and processing efficiency is improved.

[0045] Furthermore, data processing task scheduling is performed. The specific method is: based on descending sorting order, the system resource occupancy value of the candidate production factor information is compared with the system resource allocatable value. If the system resource occupancy value of a certain candidate production factor information or the sum of the system resource occupancy values ​​of adjacent candidate production factor information is below the system resource allocatable value, then the candidate production factor information or the adjacent candidate production factor information is marked as adapted for execution processing production factor information, and data processing task scheduling is performed to complete data processing.

[0046] In this embodiment, it should be noted that the adjacent candidate production factor information refers to production factor information with adjacent priorities.

[0047] By comparing the system resource occupancy value and the system resource allocatable value of the candidate production factor information in turn, the executable production factor information is accurately matched to ensure the accuracy of resource allocation. At the same time, it ensures the timely processing of high-priority production factor information and the effective use of system resources, thereby improving resource utilization and achieving efficient resource utilization.

[0048] Furthermore, system resource adjustments are performed, and the specific method is as follows: if the system resource occupancy value corresponding to the priority production factor information is greater than the system resource allocatable value, system resource adjustments are performed, and the priority of the order being executed is obtained; the order priority threshold preset in the database is obtained, and compared with the priority of the production factor information being executed, if the priority of the production factor information being executed is below the production factor information priority threshold, system resource adjustments are performed, and the production factor information being executed is marked as pending production factor information; the priority of the pending production factor information is arranged in descending order, and the data processing execution tasks of the pending production factor information are suspended in descending order until the system resource allocatable value is above the system resource occupancy value corresponding to the priority production factor information, and the task suspension processing is stopped; if the priorities of the production factor information being executed are all greater than the production factor information priority threshold, no system resource adjustments are performed.

[0049] In this embodiment, if the priority of the production factor information being executed is greater than the production factor information priority threshold, the system resources will not be adjusted, and the production factor information being executed will be processed with priority, and the system resource remaining value will be obtained in real time until the system resource remaining value is above the system resource occupancy value, and then the data processing task scheduling of each production factor information will continue.

[0050] Furthermore, the central processing unit processes the data results in real time and executes production factor modeling. The specific method is: the central processing unit receives the data processing results in real time, and the data processing results include the real-time data processing results of each production factor information; the real-time data processing results of each production factor information are input into the preset visualization output model to obtain the visualization output results of each production factor information, and pre-configuration updates are performed; real-time pre-configuration updates are performed based on the data processing results. When all production factor information has completed visualization output, the visualization output results of each production factor information are integrated into the front-end visualization page, and a full-brightness unified update is performed to obtain the visualization model page of the production factor information and display it.

[0051] In this embodiment, it should be noted that pre-configured update refers to inputting the real-time data processing results of the processed production factor information into a preset visualization output model to obtain the visualization output results of each production factor information, and then inputting the visualization output results of each production factor information into the pre-set production factor modeling model to perform priority loading of data.

[0052] It should be noted that the real-time data processing results of each production factor information are input into the preset visual output model to obtain the visual output results of each production factor information, and pre-configuration is performed. At this time, if the central processing unit receives the real-time data processing results of the production factor information and pre-configuration is performed, the production factor information at this time is in the on state (highlighted); if the central processing unit does not receive the real-time data processing results of the production factor information, the production factor information at this time is in the off state (gray).

[0053] The visual output results of each production factor information are displayed in real time, allowing production management personnel to promptly understand the processing progress and status of production factors, facilitating real-time monitoring and decision-making, ensuring the integrity and accuracy of the visual results, and avoiding misleading decisions caused by incomplete data.

[0054] By visualizing processed production factor information first, the real-time and flexibility of production management is improved. Production managers can prioritize processed production factors and adjust production strategies in a timely manner. Once all production factor information has been visualized, it is integrated into the front-end visualization page and updated uniformly. This ensures the comprehensiveness and consistency of the production factor model page, simplifies the data management process, and avoids confusion caused by inconsistent data. Through the visualization model page, production managers can more intuitively understand the relationship between production factors and the overall production status, allowing them to make accurate decisions more quickly.

[0055] like Figure 4 As shown, it is a structural diagram of the analysis system for full-process data management of production factor modeling provided by an embodiment of the present application. The analysis system for full-process data management of production factor modeling provided by an embodiment of the present application includes: a resource occupancy analysis module, a task scheduling judgment module, a task scheduling analysis module and a production factor modeling module; wherein, the resource occupancy analysis module is used to obtain the current production order information after the central processing unit receives the production order information processing signal, analyze and obtain the expected processing load value of the production order information, and match it to obtain the system resource occupancy value; the task scheduling judgment module is used to determine whether to execute data processing task scheduling based on the system resource occupancy value. If the data processing task scheduling is executed, the task scheduling analysis module is executed, otherwise the current production order information is directly processed; the task scheduling analysis module is used to obtain the production factor information of the current production order information and the data processing intensity parameter of each production factor information, analyze and obtain the processing priority of each production factor information, and perform data processing task scheduling on each production factor information in combination with the system resource occupancy value of each production factor information, thereby obtaining the data processing result and saving it to the central processing unit; the production factor modeling module is used for the central processing unit to process the real-time data result and execute production factor modeling.

[0056] To sum up, this embodiment dynamically analyzes the production order information to estimate the processing load value and matches the system resource occupancy value, thereby realizing accurate allocation and scheduling of system resources. When system resources are tight, through task division and resource occupancy value analysis, key production factor information is prioritized, thus avoiding resource waste and task backlog, thereby realizing efficient processing of production factor information, and effectively solving the problem in the prior art that high concurrent data exchange leads to insufficient system resources and thus affects data processing efficiency.

[0057] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0058] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0059] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0060] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0061] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0062] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. An analytical method for full-process data management of production factor modeling, characterized by: The following steps are involved: S1. After receiving the production order information processing signal, the central processing unit obtains the current production order information, analyzes the production order information to obtain the expected processing load value, and matches it to obtain the system resource usage value; S2. Based on the system resource usage value, determine whether to execute data processing task scheduling. If the data processing task scheduling is executed, execute S3; otherwise, directly process the current production order information; S3. Obtaining information on each production factor of the current production order and a data processing intensity parameter for each production factor, analyzing and determining the processing priority of each production factor, and scheduling data processing tasks for each production factor based on the system resource usage of each production factor, thereby obtaining a data processing result and saving it to a central processing unit; S4, the central processing unit processes the data in real time and performs production factor modeling.

2. The analysis method for full-process data management of production factor modeling according to claim 1, characterized in that: The method of obtaining the current production order information and analyzing the estimated processing load value of the production order information is as follows: Obtain the current production order information, and record each production factor in the current production order information as each designated production factor; The current production order information includes information attributes of each production factor; Obtaining historical processing information of several production factors pre-stored in the database that have the same attributes as the designated production factors, and recording the information as historical processing information of the associated production factors; The historical processing information of each associated production factor includes the average task density of system processing of each associated production factor, the average system processing time, the average system read and write speed, and the average system memory occupancy rate; Obtain a reference set of production order information processing load estimates preset in the database, and compare and analyze them with the system's average task density, average system processing time, average system read and write speed, and average system memory usage. Obtain the comparison results, then introduce weighting factors to quantify the comparison results and perform post-coupling processing to obtain the production order information processing load estimate. The production order information estimated processing load reference set includes a system processing average task density setting value, a system processing average time duration setting value, a system average read and write speed setting value, and a system average memory occupancy setting value.

3. The analysis method for full-process data management of production factor modeling according to claim 1, characterized in that: The matching method is as follows: Obtain the expected processing load range of each production order information preset in the database and the reference system resource occupancy value corresponding to the expected processing load range of each production order information, and match them with the expected processing load value of the production order information. If the expected processing load value of the production order information is within a certain preset expected processing load range of production order information, obtain the reference system resource occupancy value corresponding to the expected processing load range of the production order information as the system resource occupancy value.

4. The analysis method for full-process data management of production factor modeling according to claim 1, characterized in that: The specific method for determining whether to execute data processing task scheduling is as follows: Obtain the current system resource allocation value and compare it with the system resource occupancy value to determine whether to execute data processing task scheduling; If the system resource occupancy value is above the system resource allocatable value, the data processing task scheduling will be executed; If the system resource occupancy value is less than the system resource allocatable value, the data processing task scheduling will not be executed. At the same time, the current production order information will be directly processed, and the data processing results will be obtained and saved to the central processing unit.

5. The analysis method for full-process data management of production factor modeling according to claim 1, characterized in that: The specific steps of obtaining the information of each production factor of the current production order information and the data processing intensity parameter of each production factor information and analyzing to obtain the processing priority of each production factor information are as follows: Obtaining information on each production factor of the current production order information and data processing intensity parameters of each production factor information, wherein the information on each production factor information includes data block capacity, data generation frequency, number of data processing steps, and task queue length; Obtain the preset data processing intensity reference set in the database, and compare and analyze it with the data block capacity, data generation frequency, number of data processing steps, and task queue length of each production factor information to obtain the data processing comparison results. Then, introduce the corresponding weighting factors to quantify the data processing comparison results and perform coupling processing to obtain the data processing intensity of each production factor information; Based on the data processing intensity of each production factor information and matching it with the database, the processing priority of each production factor information is obtained; The data processing intensity reference set includes an expected value of data block capacity, an expected value of data generation frequency, an expected value of the number of data processing steps, and an expected value of the task queue length.

6. The analysis method for full-process data management of production factor modeling according to claim 1, characterized in that: The specific steps of scheduling data processing tasks for each production factor information are as follows: Based on the processing priority of each production factor information, the production factor information is sorted in descending order of priority, and the production factor information with the highest priority is selected for priority processing, and the production factor information is marked as priority production factor information, and the remaining production factor information is marked as candidate production factor information; Obtain the system resource occupancy value corresponding to the priority production factor information and compare it with the system resource allocatable value. If the system resource occupancy value corresponding to the priority production factor information is greater than the system resource allocatable value, adjust the system resources. Otherwise, continue to execute the data processing task scheduling. The specific method of continuing to execute the data processing task scheduling is as follows: Perform difference processing on the system resource occupancy value corresponding to the priority production factor information and the system resource allocatable value to obtain the remaining difference of the system resources, and perform real-time update processing; The processing priority of the candidate production factor information is sorted in descending order, and the system resource occupancy value of the candidate production factor information is compared with the system resource allocatable value based on the descending sorting order. The production factor information suitable for execution is analyzed and the data processing task scheduling is performed.

7. The analysis method for full-process data management of production factor modeling according to claim 6, characterized in that: The specific method for executing data processing task scheduling is as follows: Based on the descending sorting order, the system resource occupancy value of the candidate production factor information is compared with the system resource allocatable value. If the system resource occupancy value of a certain candidate production factor information or the sum of the system resource occupancy values ​​of adjacent candidate production factor information is below the system resource allocatable value, the candidate production factor information or the adjacent candidate production factor information is marked as adapted for execution processing production factor information, and data processing task scheduling is executed to complete data processing.

8. The analysis method for full-process data management of production factor modeling according to claim 6, characterized in that: The specific method for adjusting system resources is as follows: If the system resource occupancy value corresponding to the priority production factor information is greater than the system resource allocatable value, the system resources are adjusted and the priority of the order being executed is obtained; Obtain the order priority threshold preset in the database and compare it with the priority of the production factor information being executed. If the priority of the production factor information being executed is below the production factor information priority threshold, adjust the system resources and mark the production factor information being executed as pending production factor information. Arrange the priorities of the production factor information to be processed in descending order, and suspend the execution of the data processing tasks of the production factor information to be processed in descending order, until the system resource allocation value is greater than the system resource occupancy value corresponding to the priority production factor information, and then stop the execution of the task suspension processing; If the priority of all production factor information being executed is greater than the production factor information priority threshold, no system resource adjustment will be performed.

9. The analysis method for full-process data management of production factor modeling according to claim 1, characterized in that: The central processor processes the data in real time and performs production factor modeling, specifically in the following ways: The central processing unit receives data processing results in real time, wherein the data processing results include real-time data processing results of each production factor information; Input the real-time data processing results of each production factor information into the preset visual output model to obtain the visual output results of each production factor information and perform pre-configuration updates; Real-time pre-configuration updates are performed based on data processing results. After all production factor information has been visualized, the visualization output results of each production factor information are integrated into the front-end visualization page, and a full-brightness unified update is performed to obtain the visualization model page of the production factor information and display it.

10. A system using the analysis method for full-process data management of production factor modeling according to any one of claims 1 to 9, characterized in that: include: Resource occupancy analysis module, task scheduling judgment module, task scheduling analysis module and production factor modeling module; The resource occupancy analysis module is configured to obtain the current production order information after the central processing unit receives the production order information processing signal, analyze the production order information to obtain the expected processing load value, and match it to obtain the system resource occupancy value; The task scheduling judgment module is used to judge whether to execute data processing task scheduling based on the system resource occupancy value. If the data processing task scheduling is executed, the task scheduling analysis module is executed; otherwise, the current production order information is directly processed; The task scheduling analysis module is used to obtain the information of each production factor of the current production order information and the data processing intensity parameter of each production factor information, analyze and obtain the processing priority of each production factor information, and schedule the data processing task of each production factor information in combination with the system resource occupancy value of each production factor information, thereby obtaining the data processing results and saving them to the central processing unit; The production factor modeling module is used for the central processor to process data results in real time and execute production factor modeling.

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