Intelligent interconnection factory resource collaborative management method and system

Abstract

The invention discloses an intelligent interconnection factory resource collaborative management method and system, and relates to the technical field of factory resource collaborative management, and the method comprises the steps: obtaining historical production monitoring data of each resource production line, determining the production efficiency of semi-finished products, constructing resource particles, generating a historical resource particle fusion monitoring window, and analyzing the historical fusion performance of the resource particles; establishing an association relationship among historical production monitoring data, fusion performance, abnormal influence time and mass, and forming a historical abnormal judgment data set; constructing a real-time fusion monitoring window through real-time semi-finished product production efficiency, analyzing real-time fusion performance, and comparing the real-time fusion performance with historical fusion performance to determine adaptive historical production monitoring data; and taking the abnormal influence time and volume adaptive to the historical data as early warning information, and carrying out early warning on the production line. According to the invention, by dynamically monitoring the resource allocation and accurately identifying the abnormal mode, the resource cooperation efficiency and the production stability of the intelligent interconnection factory are improved, and the risk of production interruption is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of collaborative management of factory resources, and in particular to a method and system for collaborative management of resources in an intelligent interconnected factory. Background Art

[0002] With the rapid development of Industry 4.0 and smart manufacturing, smart interconnected factories have become an important form of modern manufacturing. Smart interconnected factories use the Internet of Things, big data analysis and artificial intelligence technologies to achieve the coordinated operation of multiple production lines and efficient allocation of resources. However, in the actual production process, due to the fluctuations in the semi-finished product production efficiency of the production line, the complexity of resource dependence and the unpredictability of abnormal events, factories face problems such as difficulty in resource coordination, low production efficiency and delayed abnormal response.

[0003] Traditional resource management methods usually rely on static scheduling or dynamic adjustments based on experience, which are difficult to adapt to the real-time and complexity of production data in smart connected factories. For example, in existing technologies, production line monitoring is mostly based on a single indicator (such as output or equipment operating status), lacking a comprehensive analysis of resource allocation ratios, semi-finished product volume requirements, and abnormal impacts. In addition, anomaly detection usually uses simple threshold judgments or manual intervention, which makes it difficult to accurately predict the impact time and volume of anomalies, resulting in delayed warnings or misjudgments, which in turn affects production continuity and resource utilization efficiency. Summary of the invention

[0004] The object of the present invention is to provide a resource collaborative management method and system capable of collaboratively monitoring semi-finished products on a production line.

[0005] The present invention discloses a method for collaborative management of intelligent interconnected factory resources, comprising: Step S100, obtaining the historical production monitoring data corresponding to each resource production line, and determining the semi-finished product production efficiency corresponding to each historical production monitoring data, constructing a number of resource particles for each semi-finished product production efficiency, and constructing a historical resource particle fusion monitoring window based on the semi-finished product production efficiency corresponding to each historical production monitoring data, and determining the historical fusion performance of all resource particles in the historical resource particle fusion monitoring window; Step S200, determining the abnormal impact time and abnormal impact volume corresponding to the historical production monitoring data, establishing a correlation relationship between the historical production monitoring data, the historical fusion performance, the abnormal impact time and the abnormal impact volume, and obtaining a historical abnormal impact judgment data group; Step S300, obtaining the real-time semi-finished product production efficiency corresponding to each resource production line, constructing a real-time resource particle fusion monitoring window, and determining the real-time fusion performance of all resource particles therein, using different historical fusion performances to compare and analyze the real-time fusion performance, determining a number of matching historical fusion performances, and comparing the historical production monitoring data corresponding to each determined historical fusion performance with the real-time production monitoring data, and determining the matching historical production monitoring data; Step S400: identify the abnormal impact time and abnormal impact volume corresponding to the adapted historical monitoring data as resource collaborative warning information, and issue a warning to the corresponding resource production line.

[0006] In an embodiment of the present invention, a method for constructing a resource particle fusion monitoring window includes: Step S101, determine the semi-finished products corresponding to different resource production lines, determine the volume required for each semi-finished product for the final finished product, and calculate the required volume multiple of each type of semi-finished product relative to the finished product; Step S102, based on the semi-finished product production efficiency of the resource production line and the required volume multiple, determine the resource particles generated per unit time; Step S103, constructing a time monitoring window for resource particles, and sequentially constructing a resource particle ratio sequence in the order of consecutive time nodes, each resource particle ratio sequence includes different resource particles corresponding to the time node, and the number of resource particles is configured according to the number generated at the corresponding time node; Step S104, a monitoring time length is preset for the time monitoring window to obtain a resource particle fusion monitoring window.

[0007] In an embodiment of the present invention, a method for determining the fusion performance of resource particles in a resource particle fusion monitoring window includes: Step S105, for the resource particle ratio of each type of resource particles in each resource particle ratio sequence to all resource particles, determine the fluctuation characteristics of the ratio of different resource particles between resource particle ratio sequences, and determine the total number of monitored resource particles of all resource particles within a preset time period; Step S106, identifying the fluctuation characteristics of the resource particle ratio change and the total number of monitored resource particles as fusion performance.

[0008] In an embodiment of the present invention, a method for comparing and analyzing historical fusion performance and real-time fusion performance includes: Step S301, determining the historical resource particle ratio change fluctuation characteristics and the total number of historical monitored resource particles of the historical fusion performance, determining the real-time resource particle ratio change fluctuation characteristics and the total number of real-time monitored resource particles of the real-time fusion performance, and comparing the fluctuation difference characteristics between the resource particle ratio change fluctuation characteristics and the total number difference between the monitored resource particles; Among them, the fluctuation difference characteristics include the fluctuation period of the proportion change of each type of resource particles and the average proportion change fluctuation amplitude; Step S302: If the difference in the proportion change fluctuation period and the average proportion change fluctuation value between each type of resource particles is within a preset range, and the total number difference between the total number of monitored resource particles is also within a preset range, it is determined that the historical fusion performance and the real-time fusion performance are consistent.

[0009] In an embodiment of the present invention, the method for comparing historical production monitoring data with real-time production monitoring data includes: Step S303, determine the historical production monitoring data marked with abnormal production status, determine the task volume requirement corresponding to each historical production monitoring data, summarize and classify the historical production monitoring data based on the equality of the task volume requirement, form a number of comparison production monitoring data groups, compare the historical production monitoring data marked with abnormal production status in the comparison production monitoring data groups with other data, and determine a number of focused production monitoring data based on the comparison results; Step S304, determine the task requirements of the real-time production monitoring data, and determine several corresponding comparison production monitoring data groups, and determine the respective focused production monitoring data, compare the focused production monitoring data with the corresponding data in the real-time production monitoring data, and determine the degree of adaptation between the two.

[0010] In an embodiment of the present invention, a method for determining the production monitoring data to be focused on includes: Step S3031, parameterizing each type of data in the historical production monitoring data, and constructing monitoring data parameter curves respectively; Step S3032, aligning the data parameter curves of the historical production monitoring data marked with abnormal production status and unmarked with abnormal production status in time, and segmenting them according to equivalent production capacity cycles, respectively obtaining several types of normal monitoring data parameter curve segment groups and several types of abnormal monitoring data parameter curve segment groups, each normal monitoring data parameter curve segment group contains several normal monitoring data parameter curve segments, and each abnormal monitoring data parameter curve segment group contains several abnormal monitoring data parameter curve segments; Step S3033, merging normal monitoring data parameter curve segment groups and abnormal monitoring data parameter curve segment groups that belong to the same comparison production monitoring data group and have the same data type, respectively, to obtain a normal monitoring data parameter curve segment set and an abnormal monitoring data parameter curve segment set; Step S3034, performing curvature change analysis and curve parameter value analysis on the monitoring parameter curve segments in the monitoring data parameter curve segment set, wherein the curvature change analysis includes determining the first duration ratio of the curvature change in different curvature intervals, and constructing a curvature interval~first duration ratio correspondence sequence, and the curve parameter value analysis includes determining the second duration ratio of the curve parameter values ​​in different curve parameter value intervals, and constructing a curve parameter value interval~second duration ratio correspondence sequence; Step S3035, based on the comparison relationship between the abnormal monitoring data parameter curve segment set and the normal monitoring data parameter curve segment set of the same data type, establish a comparison relationship of the curvature interval~first duration correspondence sequence, and a comparison relationship of the curve parameter value interval~second duration proportion correspondence sequence, and perform a comparison analysis based on the comparison relationship, and based on the analysis result, determine the prominence of the abnormal monitoring data parameter curve segment set relative to the normal monitoring data parameter curve segment set, if the prominence is greater than or equal to a preset value, then the monitoring data corresponding to the data type is determined to be production-focused monitoring data.

[0011] In the embodiment of the present invention, comparing the historical production monitoring data and the real-time production monitoring data corresponding to each determined historical fusion performance includes: Step S301, determining the focused production monitoring data in the historical production monitoring data and the real-time production monitoring data, and parameterizing each focused production monitoring data, and respectively constructing a focused monitoring data parameter curve, and comparing the corresponding focused monitoring data parameter curves based on the corresponding relationship between the historical production monitoring data and the real-time production monitoring data, the comparison method includes: Step S3011, dynamically translate the focus monitoring data parameter curves with respect to each other, and extract the curve mapping area change between the two during the translation process in real time, confirm the unit mapping area increment of the curve mapping area change relative to different time nodes, and construct a unit mapping area increment curve. If within a preset time period, the value corresponding to the unit mapping area increment curve is less than or equal to the preset value, and the curvature accumulation value is less than or equal to the preset value, then the dynamic translation between the focus monitoring data parameter curves is terminated, and it is determined that the focus monitoring data parameter curves are consistent with each other; Step S3012: If all the relative monitoring data parameter curves are consistent with each other, it is determined that the historical production monitoring data and the real-time production monitoring data are compatible.

[0012] In an embodiment of the present invention, there is also disclosed an intelligent interconnected factory resource collaborative management system, including: The first module is used to obtain the historical production monitoring data corresponding to each resource production line, and determine the semi-finished product production efficiency corresponding to each historical production monitoring data, construct a number of resource particles for each semi-finished product production efficiency, and construct a historical resource particle fusion monitoring window based on the semi-finished product production efficiency corresponding to each historical production monitoring data, and determine the historical fusion performance of all resource particles in the historical resource particle fusion monitoring window; The second module is used to determine the abnormal impact time and abnormal impact volume corresponding to the historical production monitoring data, establish the correlation between the historical production monitoring data, historical fusion performance, abnormal impact time and abnormal impact volume, and obtain the historical abnormal impact judgment data group; The third module is used to obtain the real-time semi-finished product production efficiency corresponding to each resource production line, build a real-time resource particle fusion monitoring window, and determine the real-time fusion performance of all resource particles therein, compare and analyze the real-time fusion performance using different historical fusion performances, determine several matching historical fusion performances, and compare the historical production monitoring data corresponding to each determined historical fusion performance with the real-time production monitoring data to determine the matching historical production monitoring data; The fourth module is used to identify the abnormal impact time and abnormal impact volume corresponding to the adapted historical monitoring data as resource collaborative warning information to issue an early warning to the corresponding resource production line.

[0013] The present invention discloses a method and system for collaborative management of resources in an intelligent interconnected factory, which relates to the technical field of collaborative management of factory resources, obtains historical production monitoring data of each resource production line, determines the semi-finished product production efficiency and constructs resource particles, generates a historical resource particle fusion monitoring window, and analyzes the historical fusion performance of resource particles; establishes a correlation between historical production monitoring data, fusion performance, abnormal impact time and volume, and forms a historical abnormal judgment data group; constructs a real-time fusion monitoring window through real-time semi-finished product production efficiency, analyzes the real-time fusion performance, compares it with the historical fusion performance, and determines the adapted historical production monitoring data; uses the abnormal impact time and volume of the adapted historical data as early warning information to warn the production line. The present invention improves the resource coordination efficiency and production stability of the intelligent interconnected factory and reduces the risk of production interruption by dynamically monitoring resource allocation and accurately identifying abnormal patterns.

[0014] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1A method step diagram of a method for collaborative management of resources of an intelligent interconnected factory disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0017] The following will be combined with the accompanying drawings and specific embodiments to clearly and completely describe the technical solution of the present invention. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and cannot be understood as limiting the scope of protection of the present invention. Those skilled in the art in this field can make some non-essential improvements and adjustments based on the content of the present invention described below. In the present invention, unless otherwise clearly specified and limited, the technical terms used in the present invention should be the common meanings understood by the technical personnel described in the present invention.

[0018] Example: The present invention discloses a method for collaborative management of intelligent interconnected factory resources. Figure 1 ,include: Step S100, obtain the historical production monitoring data corresponding to each resource production line, and determine the semi-finished product production efficiency corresponding to each historical production monitoring data, construct a number of resource particles for each semi-finished product production efficiency, and construct a historical resource particle fusion monitoring window based on the semi-finished product production efficiency corresponding to each historical production monitoring data, and determine the historical fusion performance of all resource particles in the historical resource particle fusion monitoring window.

[0019] Step S100 aims to build a benchmark model of resource allocation and production efficiency through historical production data, providing a basis for subsequent anomaly detection and real-time monitoring. The system first collects production monitoring data of each resource production line during its past operation, including indicators such as output, equipment operation status, raw material consumption and production cycle, to fully reflect the operation status of the production line. By analyzing these data, the system quantifies the production efficiency of each production line in the production of semi-finished products, which is defined as the number of qualified semi-finished products produced per unit time (for example, 100 semi-finished products produced per hour). For the convenience of analysis, the system introduces the concept of "resource particles", each of which represents the output of semi-finished products per unit time, specifically the number of semi-finished products produced in a certain time period (for example, 1 hour), which is abstracted as a quantifiable virtual data unit. For example, if a production line produces 100 semi-finished products per hour, 100 resource particles are generated, and each particle represents the output of 1 semi-finished product. Based on the semi-finished product production efficiency of each production line, the system generates a corresponding number of resource particles and constructs a "historical resource particle fusion monitoring window". This window is an analysis framework of the time dimension, integrating the resource particles of all production lines in the same time period to form a resource particle ratio sequence of continuous time nodes. The sequence records the number and proportion of resource particles in different production lines, reflecting the resource allocation status. The system analyzes the fluctuation characteristics of the proportion of resource particles in the window (for example, the fluctuation period and amplitude) and the total amount change to determine the "historical fusion performance". This performance quantifies the stability and efficiency of resource coordination in historical production. For example, a stable particle ratio indicates a balanced resource allocation, and a sharp fluctuation may indicate a historical anomaly (such as equipment failure). This step simplifies the complex production dynamics into an operational data model through the output representation of resource particles, which is convenient for subsequent comparison and anomaly identification. The historical fusion performance serves as a benchmark to provide a reference for real-time monitoring and ensure that the system can detect production status deviations. The process needs to process large-scale time series data and rely on efficient data analysis technology to lay the foundation for dynamic resource management in smart connected factories.

[0020] Step S200, determine the abnormal impact time and abnormal impact volume corresponding to the historical production monitoring data, establish the correlation between the historical production monitoring data, historical fusion performance, abnormal impact time and abnormal impact volume, and obtain the historical abnormal impact judgment data group.

[0021] Step S200 focuses on extracting abnormal features from historical production monitoring data and establishing an associated model to provide a basis for real-time abnormal prediction. The system first analyzes historical production monitoring data to identify abnormal events, such as equipment failure, sudden drop in production, or resource shortages, by detecting significant deviations in the data (for example, production is lower than expected or equipment parameters are abnormal). For each abnormality, the system quantifies its "abnormal impact time", that is, the duration from the occurrence of the abnormality to recovery (for example, a 2-hour shutdown), and "abnormal impact volume", that is, the specific impact of the abnormality on production, such as the number of semi-finished products reduced in production (expressed in resource particles, for example, a reduction of 200 resource particles is equivalent to 200 semi-finished products). These attributes are extracted from production logs through statistical analysis and time series decomposition, and cross-validated with equipment status and resource consumption data. The system then associates the abnormal features with the historical fusion representation generated in step S100 (the proportion fluctuation and total amount characteristics of resource particles) and historical production monitoring data. For example, an abnormality may be associated with a sudden drop in the proportion of resource particles in a production line, indicating an imbalance in resource allocation.

[0022] Step S300, obtain the real-time semi-finished product production efficiency corresponding to each resource production line, build a real-time resource particle fusion monitoring window, and determine the real-time fusion performance of all resource particles therein, use different historical fusion performances to compare and analyze the real-time fusion performance, determine several matching historical fusion performances, and compare the historical production monitoring data corresponding to each determined historical fusion performance with the real-time production monitoring data to determine the matching historical production monitoring data.

[0023] Step S300 implements real-time production status monitoring and historical abnormal pattern matching, and the core is to quickly identify potential abnormalities. The system first collects the current semi-finished product production efficiency data of each production line, covering real-time output and resource utilization, reflecting the current operating status. Based on the resource particle framework of step S100, the system converts real-time efficiency into resource particles, each particle represents the semi-finished product output per unit time (for example, 150 semi-finished products produced in 1 hour correspond to 150 resource particles). The system constructs a "real-time resource particle fusion monitoring window" to integrate the resource particles of each production line, form a resource particle ratio sequence of continuous time nodes, and record the number and proportion of particles. The analysis sequence generates a "real-time fusion performance", including the fluctuation characteristics (period, amplitude) and total changes of the resource particle ratio. The system compares the real-time fusion performance with the historical fusion performance of step S100, and uses feature matching algorithms (such as time series similarity or statistical distance) to identify similar historical fusion performances based on characteristics such as fluctuation period, amplitude and total difference. For each matched historical fusion performance, the system extracts the corresponding historical production monitoring data and compares it in detail with the real-time production monitoring data. The comparison includes task volume requirements, production curves and equipment parameters to determine the historical production monitoring data that best matches the current scenario. The output definition of resource particles makes real-time efficiency quantification intuitive and convenient for fluctuation analysis. Multi-level comparison (fusion performance first, then production data) improves matching accuracy and reduces misjudgment. The adapted historical data represents the scenario closest to the current state, usually containing known anomalies, providing a basis for early warning. This step relies on real-time data processing and feature extraction to respond quickly to dynamic environments. Its innovation lies in connecting historical and real-time data through fusion performance, providing a systematic solution for abnormal prediction in complex scenarios, and ensuring the stability of interconnected factories.

[0024] Step S400: identify the abnormal impact time and abnormal impact volume corresponding to the adapted historical monitoring data as resource collaborative warning information, and issue a warning to the corresponding resource production line.

[0025] Step S400 converts historical abnormal features into real-time warning information to prevent production interruptions. Based on the adapted historical production monitoring data of step S300, the system extracts the abnormal impact time (such as 3 hours of downtime) and the abnormal impact volume (such as reducing 500 resource particles, equivalent to 500 semi-finished products) from the data group of step S200. These attributes are encapsulated as "resource collaborative warning information" to clarify the type, duration and impact range of potential abnormalities. The system sends information to relevant production lines through alarm signals, interface prompts or control system integration, prompting measures such as adjusting resources or maintaining equipment. The output definition of resource particles makes the volume intuitively expressed in the number of semi-finished products, which is easy for operators to understand. The accuracy of the warning comes from the prediction of historical abnormal patterns to ensure relevance to the current scenario. Implementation requires docking with the factory communication system to ensure timely information transmission and sort alarms by abnormal severity. This step converts the analysis results into instructions, closes the loop of data and actions, reduces the risk of interruption, and improves resource efficiency. Its success depends on the accuracy of historical data and real-time processing capabilities, reflecting the practicality of the system in a dynamic environment. The early warning mechanism guides current operations through history, improves timeliness of response, and enhances the adaptability of interconnected factories to complex scenarios, providing support for efficient production.

[0026] In an embodiment of the present invention, a method for constructing a resource particle fusion monitoring window includes: Step S101, determine the semi-finished products corresponding to different resource production lines, determine the volume required for each semi-finished product for the final finished product, and calculate the required volume multiple of each type of semi-finished product relative to the finished product; Step S102, based on the semi-finished product production efficiency of the resource production line and the required volume multiple, determine the resource particles generated per unit time; Step S103, constructing a time monitoring window for resource particles, and sequentially constructing a resource particle ratio sequence in the order of consecutive time nodes, each resource particle ratio sequence includes different resource particles corresponding to the time node, and the number of resource particles is configured according to the number generated at the corresponding time node; Step S104, a monitoring time length is preset for the time monitoring window to obtain a resource particle fusion monitoring window.

[0027] In an embodiment of the present invention, a method for determining the fusion performance of resource particles in a resource particle fusion monitoring window includes: Step S105, for the resource particle ratio of each type of resource particles in each resource particle ratio sequence to all resource particles, determine the fluctuation characteristics of the ratio of different resource particles between resource particle ratio sequences, and determine the total number of monitored resource particles of all resource particles within a preset time period; Step S106, identifying the fluctuation characteristics of the resource particle ratio change and the total number of monitored resource particles as fusion performance.

[0028] In an embodiment of the present invention, a method for comparing and analyzing historical fusion performance and real-time fusion performance includes: Step S301, determining the historical resource particle ratio change fluctuation characteristics and the total number of historical monitored resource particles of the historical fusion performance, determining the real-time resource particle ratio change fluctuation characteristics and the total number of real-time monitored resource particles of the real-time fusion performance, and comparing the fluctuation difference characteristics between the resource particle ratio change fluctuation characteristics and the total number difference between the monitored resource particles; Among them, the fluctuation difference characteristics include the fluctuation period of the proportion change of each type of resource particles and the average proportion change fluctuation amplitude; Step S302: If the difference in the proportion change fluctuation period and the average proportion change fluctuation value between each type of resource particles is within a preset range, and the total number difference between the total number of monitored resource particles is also within a preset range, it is determined that the historical fusion performance and the real-time fusion performance are consistent.

[0029] In an embodiment of the present invention, the method for comparing historical production monitoring data with real-time production monitoring data includes: Step S303, determine the historical production monitoring data marked with abnormal production status, determine the task volume requirement corresponding to each historical production monitoring data, and classify the historical production monitoring data based on the equality of the task volume requirement to form a number of comparison production monitoring data groups, compare the historical production monitoring data marked with abnormal production status with others in the comparison production monitoring data groups, and determine a number of focused production monitoring data based on the comparison results.

[0030] Step S303 aims to filter out the most representative data for anomaly detection through systematic analysis of historical production monitoring data, and provide accurate reference for subsequent comparison with real-time data. The system first identifies records marked with abnormal production status from historical production monitoring data. These anomalies may include equipment failure, sudden drop in output or resource shortage, etc., and distinguish them through abnormal labels (such as fault codes or output abnormality flags). Each piece of historical production monitoring data contains indicators such as output, equipment status, and resource consumption. The system extracts "task volume demand", that is, the expected semi-finished product output of the production task (expressed in resource particles, each particle corresponds to the semi-finished product output per unit time, for example, 100 semi-finished products per hour correspond to 100 resource particles). The task volume demand reflects the production goals and resource allocation requirements of the production line, and is an important basis for comparing historical and real-time data. Based on the equivalence of task volume demand, the system summarizes and classifies the historical production monitoring data, and classifies the data with similar task volume demand into the same "comparison production monitoring data group". For example, data with a task volume demand of 500 semi-finished products per hour are grouped together to ensure that the data in the group are comparable in terms of production goals. In each comparison production monitoring data group, the system will compare the historical production monitoring data marked as abnormal with other non-abnormal data in detail, and analyze their difference characteristics, such as the shape of the production curve, the fluctuation of the resource particle ratio (based on the output per unit time), or the change of equipment operating parameters. Through the comparison, the system identifies the unique pattern of abnormal data relative to normal data, for example, abnormal data may show a sharp fluctuation in the resource particle ratio or a sudden drop in production. Based on these differences, the system determines the "focused production monitoring data", that is, the data subset with outstanding characteristics in anomaly detection.

[0031] Step S304, determine the task requirements of the real-time production monitoring data, and determine several corresponding comparison production monitoring data groups, and determine the respective focused production monitoring data, compare the focused production monitoring data with the corresponding data in the real-time production monitoring data, and determine the degree of adaptation between the two.

[0032] Step S304 evaluates the similarity between the current production status and the historical abnormal pattern by accurately matching the real-time production monitoring data with the historical data, providing a basis for early warning. The system first collects real-time production monitoring data, including current output, equipment status and resource consumption, and determines the real-time "task demand" based on these data, that is, the expected semi-finished product output of the current production task (expressed in resource particles, for example, 200 semi-finished products per hour correspond to 200 resource particles). The task demand is the key indicator for matching. According to the real-time task demand, the system selects the corresponding historical data group from the comparison production monitoring data group generated in step S303 to ensure that the historical and real-time data are comparable in production goals. For example, if the real-time task demand is 500 semi-finished products per hour, the system selects the comparison production monitoring data group with similar historical task demand. In the selected data group, the system further extracts the "focused production monitoring data" determined in step S303, which represents the typical characteristics under abnormal conditions.

[0033] In an embodiment of the present invention, a method for determining the production monitoring data to be focused on includes: Step S3031, parameterize each type of data in the historical production monitoring data, and construct monitoring data parameter curves respectively.

[0034] In step S3031, the system parameterizes the sensor node data in the historical production monitoring data, constructs the monitoring data parameter curve, and converts the raw data of different sensors into a format that can be quantified and analyzed. Sensor data includes indicators such as temperature (such as equipment bearing temperature), pressure (such as hydraulic system pressure), vibration (such as motor vibration frequency) or current (such as equipment load current), which reflect the real-time status and potential abnormalities of equipment operation. The parameterization process converts these heterogeneous data into standardized numerical representations, such as normalizing the temperature to the deviation value relative to the reference temperature, or quantizing the vibration frequency to the statistical characteristic value of the amplitude (such as the root mean square value). To ensure the comparability of different sensor data, normalization or standardization processing is usually used. Subsequently, the system draws the monitoring data parameter curve with time as the horizontal axis and the parameterized value as the vertical axis, such as the curve of the change of vibration intensity of a certain sensor node over time. The principle of this step is to convert the dynamic behavior of sensor data into a continuous time series representation, which is convenient for analyzing the trend, periodic fluctuation or abnormal point (such as sudden increase in vibration) of equipment status. By generating smooth parametric curves, the system supports mathematical analysis (such as frequency analysis or differentiation) to lay the foundation for distinguishing normal from abnormal sensor data patterns. This process is the core starting point for subsequent anomaly detection, ensuring that sensor data can be used for pattern recognition in a structured manner.

[0035] Step S3032, align the data parameter curves of historical production monitoring data marked with abnormal production status and unmarked abnormal production status in time, and divide them according to equivalent production capacity cycles, to obtain several types of normal monitoring data parameter curve segment groups and several types of abnormal monitoring data parameter curve segment groups, each normal monitoring data parameter curve segment group contains several normal monitoring data parameter curve segments, and each abnormal monitoring data parameter curve segment group contains several abnormal monitoring data parameter curve segments.

[0036] Step S3032 Time-align the sensor data parameter curves marked as abnormal states (such as equipment failure or abnormal vibration indicated by sensor data) and unmarked abnormal states, and segment them according to equivalent production cycles to generate comparable normal and abnormal curve segments. Time alignment ensures that curves from different sensor nodes or production batches are synchronized to a unified time frame to eliminate the impact of data acquisition time differences, such as by aligning the start time of production tasks (such as 08:00 per shift). After alignment, the system segments the curve into multiple segments according to the production cycle (such as an 8-hour shift or equipment operation cycle), each segment representing an independent production operation unit, ensuring that the comparison is based on similar equipment operating conditions. After segmentation, the system divides the curve segments into two categories: normal monitoring data parameter curve segment group (from sensor data without abnormalities, such as stable temperature or vibration) and abnormal monitoring data parameter curve segment group (from abnormal data, such as sudden temperature rise or vibration abnormality). Each group contains multiple segments of the same type of sensor data (such as vibration or current). The principle of this step is to isolate the performance of sensor data under comparable conditions through time alignment and period segmentation, reducing the noise introduced by production schedules or acquisition time differences. This structured processing facilitates subsequent analysis of the differences between normal and abnormal sensor data patterns, providing a reliable data foundation for accurate anomaly detection.

[0037] Step S3033, merging normal monitoring data parameter curve segment groups and abnormal monitoring data parameter curve segment groups that belong to the same comparison production monitoring data group and have the same data type, respectively, to obtain normal monitoring data parameter curve segment sets and abnormal monitoring data parameter curve segment sets.

[0038] In step S3033, the system merges the normal and abnormal sensor data parameter curve segment groups that belong to the same comparison production monitoring data group and are of the same type to form normal and abnormal monitoring data parameter curve segment sets. The comparison production monitoring data group consists of sensor data with the same task volume requirements (for example, equipment sensor data that supports the production of 500 semi-finished products per hour), ensuring that the curve segments within the group are comparable in terms of production goals and equipment loads. Within each group, the curve segments of the same type of sensor data (such as vibration frequency or temperature) are aggregated into the normal and abnormal states of the same comparison group. For example, all normal vibration frequency curve segments are merged into a normal monitoring data parameter curve segment set, and abnormal vibration frequency curve segments form the corresponding abnormal set. The merging process is to group these segments, retain their time and parameter characteristics, and form a comprehensive data set covering multiple production runs. The principle of this step is to enhance the statistical robustness of the data through aggregation, so that the system can analyze the collective behavior of sensor data in multiple runs rather than a single instance. The merged curve segment set provides a larger sample size, which facilitates the identification of significant patterns between normal and abnormal conditions (such as the peak characteristics of the vibration curve under abnormal conditions), laying the foundation for subsequent comparative analysis and ensuring the reliability and representativeness of the results.

[0039] Step S3034, performing curvature change analysis and curve parameter value analysis on the monitoring parameter curve segments in the monitoring data parameter curve segment set, the curvature change analysis includes determining the proportion of the first duration of the curvature change in different curvature intervals, and constructing a curvature interval~first duration correspondence sequence, the curve parameter value analysis includes determining the proportion of the second duration of the curve parameter values ​​in different curve parameter value intervals, and constructing a curve parameter value interval~second duration proportion correspondence sequence.

[0040] Step S3034 performs curvature change analysis and curve parameter value analysis on the normal and abnormal monitoring data parameter curve segment sets to quantify the temporal dynamics and amplitude characteristics of the sensor data. Curvature change analysis evaluates the rate of change of the curve shape, reflecting the fluctuation characteristics of sensor parameters (such as vibration frequency or temperature) over time. The system calculates the curvature of each curve segment, divides it into predefined curvature intervals (such as low, medium, and high curvatures), and calculates the first duration ratio, that is, the percentage of time each curvature interval is in the segment set, and generates a sequence of corresponding relationships between the second duration ratios of the curvature intervals. The principle of this step is to decompose the sensor data behavior into two dimensions: dynamic (curvature, capturing parameter mutations or recovery) and static (parameter value, reflecting parameter levels). These sequences represent the sensor operation mode in a structured manner, which is convenient for identifying abnormal features, such as vibration curvature spikes caused by faults or long-term high temperature conditions. This quantitative analysis provides key indicators for subsequent comparisons of normal and abnormal states, supporting accurate abnormal mode detection.

[0041] Step S3035, based on the comparison relationship between the abnormal monitoring data parameter curve segment set and the normal monitoring data parameter curve segment set of the same data type, establish a comparison relationship of the curvature interval~first duration correspondence sequence, and a comparison relationship of the curve parameter value interval~second duration proportion correspondence sequence, and perform a comparison analysis based on the comparison relationship, and based on the analysis result, determine the prominence of the abnormal monitoring data parameter curve segment set relative to the normal monitoring data parameter curve segment set, if the prominence is greater than or equal to a preset value, then the monitoring data corresponding to the data type is determined to be production-focused monitoring data.

[0042] Among them, the expression for calculating the prominence degree is: ; in, To highlight the degree, Highlight weight coefficients for preset curvature changes, Highlight weight coefficients for preset curve parameter values, is the first duration difference highlight judgment function. If the curvature interval ~ first duration correspondence relationship sequence, the first The first duration difference corresponding to the curvature intervals is greater than or equal to the preset value, then Output 1, otherwise output 0. is the number of curvature intervals in the sequence of curvature intervals~first duration correspondences, is the first duration difference performance impact adjustment coefficient, is the first duration difference performance effect adjustment constant, is the second duration difference highlight judgment function. If the curve parameter value interval ~ the second duration ratio correspondence relationship sequence, the If the difference of the second duration of the curve parameter value interval is greater than or equal to the preset value, then Output 1, otherwise output 0. is the adjustment coefficient for the second duration difference performance impact, is the adjustment constant for the impact of the second duration difference performance, and N is the number of curve parameter value intervals in the curve parameter value interval~second duration proportion corresponding relationship sequence.

[0043] In the embodiment of the present invention, comparing the historical production monitoring data and the real-time production monitoring data corresponding to each determined historical fusion performance includes: Step S301, determining the focused production monitoring data in the historical production monitoring data and the real-time production monitoring data, and parameterizing each focused production monitoring data, and respectively constructing a focused monitoring data parameter curve, and comparing the corresponding focused monitoring data parameter curves based on the corresponding relationship between the historical production monitoring data and the real-time production monitoring data, the comparison method includes: Step S3011, dynamically translate the focus monitoring data parameter curves with respect to each other, and extract the curve mapping area change between the two during the translation process in real time, confirm the unit mapping area increment of the curve mapping area change relative to different time nodes, and construct a unit mapping area increment curve. If within a preset time period, the value corresponding to the unit mapping area increment curve is less than or equal to the preset value, and the curvature accumulation value is less than or equal to the preset value, then the dynamic translation between the focus monitoring data parameter curves is terminated, and it is determined that the focus monitoring data parameter curves are consistent with each other; Step S3012: If all the relative monitoring data parameter curves are consistent with each other, it is determined that the historical production monitoring data and the real-time production monitoring data are compatible.

[0044] In an embodiment of the present invention, there is also disclosed an intelligent interconnected factory resource collaborative management system, including: The first module is used to obtain the historical production monitoring data corresponding to each resource production line, and determine the semi-finished product production efficiency corresponding to each historical production monitoring data, construct a number of resource particles for each semi-finished product production efficiency, and construct a historical resource particle fusion monitoring window based on the semi-finished product production efficiency corresponding to each historical production monitoring data, and determine the historical fusion performance of all resource particles in the historical resource particle fusion monitoring window; The second module is used to determine the abnormal impact time and abnormal impact volume corresponding to the historical production monitoring data, establish the correlation between the historical production monitoring data, historical fusion performance, abnormal impact time and abnormal impact volume, and obtain the historical abnormal impact judgment data group; The third module is used to obtain the real-time semi-finished product production efficiency corresponding to each resource production line, build a real-time resource particle fusion monitoring window, and determine the real-time fusion performance of all resource particles therein, compare and analyze the real-time fusion performance using different historical fusion performances, determine several matching historical fusion performances, and compare the historical production monitoring data corresponding to each determined historical fusion performance with the real-time production monitoring data to determine the matching historical production monitoring data; The fourth module is used to identify the abnormal impact time and abnormal impact volume corresponding to the adapted historical monitoring data as resource collaborative warning information to issue an early warning to the corresponding resource production line.

[0045] The present invention discloses a method and system for collaborative management of resources in an intelligent interconnected factory, which relates to the technical field of collaborative management of factory resources, obtains historical production monitoring data of each resource production line, determines the semi-finished product production efficiency and constructs resource particles, generates a historical resource particle fusion monitoring window, and analyzes the historical fusion performance of resource particles; establishes a correlation between historical production monitoring data, fusion performance, abnormal impact time and volume, and forms a historical abnormal judgment data group; constructs a real-time fusion monitoring window through real-time semi-finished product production efficiency, analyzes the real-time fusion performance, compares it with the historical fusion performance, and determines the adapted historical production monitoring data; uses the abnormal impact time and volume of the adapted historical data as early warning information to warn the production line. The present invention improves the resource coordination efficiency and production stability of the intelligent interconnected factory and reduces the risk of production interruption by dynamically monitoring resource allocation and accurately identifying abnormal patterns.

[0046] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for collaborative management of intelligent interconnected factory resources, characterized in that: include: Step S100, obtaining the historical production monitoring data corresponding to each resource production line, and determining the semi-finished product production efficiency corresponding to each historical production monitoring data, constructing a number of resource particles for each semi-finished product production efficiency, and constructing a historical resource particle fusion monitoring window based on the semi-finished product production efficiency corresponding to each historical production monitoring data, and determining the historical fusion performance of all resource particles in the historical resource particle fusion monitoring window; Step S200, determining the abnormal impact time and abnormal impact volume corresponding to the historical production monitoring data, establishing a correlation relationship between the historical production monitoring data, the historical fusion performance, the abnormal impact time and the abnormal impact volume, and obtaining a historical abnormal impact judgment data group; Step S300, obtaining the real-time semi-finished product production efficiency corresponding to each resource production line, constructing a real-time resource particle fusion monitoring window, and determining the real-time fusion performance of all resource particles therein, using different historical fusion performances to compare and analyze the real-time fusion performance, determining a number of matching historical fusion performances, and comparing the historical production monitoring data corresponding to each determined historical fusion performance with the real-time production monitoring data, and determining the matching historical production monitoring data; Step S400: identify the abnormal impact time and abnormal impact volume corresponding to the adapted historical monitoring data as resource collaborative warning information, and issue a warning to the corresponding resource production line.

2. The method for collaborative management of intelligent interconnected factory resources according to claim 1, characterized in that: Methods for constructing a resource particle fusion monitoring window include: Step S101, determine the semi-finished products corresponding to different resource production lines, determine the volume required for each semi-finished product for the final finished product, and calculate the required volume multiple of each type of semi-finished product relative to the finished product; Step S102, based on the semi-finished product production efficiency of the resource production line and the required volume multiple, determine the resource particles generated per unit time; Step S103, constructing a time monitoring window for resource particles, and constructing a resource particle ratio sequence in sequence according to the order of consecutive time nodes, each resource particle ratio sequence includes different resource particles corresponding to the time node, and the number of resource particles is configured according to the number generated at the corresponding time node; Step S104, a monitoring time length is preset for the time monitoring window to obtain a resource particle fusion monitoring window.

3. The method for collaborative management of intelligent interconnected factory resources according to claim 2, characterized in that: The method for determining the fusion performance of resource particles within the resource particle fusion monitoring window includes: Step S105, for the resource particle ratio of each type of resource particles in each resource particle ratio sequence to all resource particles, determine the fluctuation characteristics of the ratio of different resource particles between resource particle ratio sequences, and determine the total number of monitored resource particles of all resource particles within a preset time period; Step S106, identifying the fluctuation characteristics of the resource particle ratio change and the total number of monitored resource particles as fusion performance.

4. The method for collaborative management of intelligent interconnected factory resources according to claim 3, characterized in that: Methods for comparing and analyzing historical fusion performance with real-time fusion performance include: Step S301, determining the historical resource particle ratio change fluctuation characteristics and the total number of historical monitored resource particles of the historical fusion performance, determining the real-time resource particle ratio change fluctuation characteristics and the total number of real-time monitored resource particles of the real-time fusion performance, and comparing the fluctuation difference characteristics between the resource particle ratio change fluctuation characteristics and the total number difference between the monitored resource particles; Among them, the fluctuation difference characteristics include the fluctuation period of the proportion change of each type of resource particles and the average proportion change fluctuation amplitude; Step S302: If the difference in the proportion change fluctuation period and the average proportion change fluctuation value between each type of resource particles is within a preset range, and the total number difference between the total number of monitored resource particles is also within a preset range, it is determined that the historical fusion performance and the real-time fusion performance are consistent.

5. The method for collaborative management of intelligent interconnected factory resources according to claim 1, characterized in that: Methods for comparing historical production monitoring data with real-time production monitoring data include: Step S303, determine the historical production monitoring data marked with abnormal production status, determine the task volume requirement corresponding to each historical production monitoring data, summarize and classify the historical production monitoring data based on the equality of the task volume requirement, form a number of comparison production monitoring data groups, compare the historical production monitoring data marked with abnormal production status in the comparison production monitoring data groups with other data, and determine a number of focused production monitoring data based on the comparison results; Step S304, determine the task requirements of the real-time production monitoring data, and determine several corresponding comparison production monitoring data groups, and determine the respective focused production monitoring data, compare the focused production monitoring data with the corresponding data in the real-time production monitoring data, and determine the degree of adaptation between the two.

6. The method for collaborative management of intelligent interconnected factory resources according to claim 5, characterized in that: Methods for determining focus on production monitoring data include: Step S3031, parameterizing each type of data in the historical production monitoring data, and constructing monitoring data parameter curves respectively; Step S3032, aligning the data parameter curves of the historical production monitoring data marked with abnormal production status and unmarked with abnormal production status in time, and segmenting them according to equivalent production capacity cycles, respectively obtaining several types of normal monitoring data parameter curve segment groups and several types of abnormal monitoring data parameter curve segment groups, each normal monitoring data parameter curve segment group contains several normal monitoring data parameter curve segments, and each abnormal monitoring data parameter curve segment group contains several abnormal monitoring data parameter curve segments; Step S3033, merging normal monitoring data parameter curve segment groups and abnormal monitoring data parameter curve segment groups that belong to the same comparison production monitoring data group and have the same data type, respectively, to obtain a normal monitoring data parameter curve segment set and an abnormal monitoring data parameter curve segment set; Step S3034, performing curvature change analysis and curve parameter value analysis on the monitoring parameter curve segments in the monitoring data parameter curve segment set, wherein the curvature change analysis includes determining the first duration ratio of the curvature change in different curvature intervals, and constructing a curvature interval~first duration ratio correspondence sequence, and the curve parameter value analysis includes determining the second duration ratio of the curve parameter values ​​in different curve parameter value intervals, and constructing a curve parameter value interval~second duration ratio correspondence sequence; Step S3035, based on the comparison relationship between the abnormal monitoring data parameter curve segment set and the normal monitoring data parameter curve segment set of the same data type, establish a comparison relationship of the curvature interval~first duration correspondence sequence, and a comparison relationship of the curve parameter value interval~second duration proportion correspondence sequence, and perform a comparison analysis based on the comparison relationship, and based on the analysis result, determine the prominence of the abnormal monitoring data parameter curve segment set relative to the normal monitoring data parameter curve segment set, if the prominence is greater than or equal to a preset value, then the monitoring data corresponding to the data type is determined to be production-focused monitoring data.

7. The method for collaborative management of intelligent interconnected factory resources according to claim 1, characterized in that: Comparing the historical production monitoring data and real-time production monitoring data corresponding to each determined historical fusion performance includes: Step S301, determining the focused production monitoring data in the historical production monitoring data and the real-time production monitoring data, and parameterizing each focused production monitoring data, and respectively constructing a focused monitoring data parameter curve, and comparing the corresponding focused monitoring data parameter curves based on the corresponding relationship between the historical production monitoring data and the real-time production monitoring data, the comparison method includes: Step S3011, dynamically translate the focus monitoring data parameter curves with respect to each other, and extract the curve mapping area change between the two during the translation process in real time, confirm the unit mapping area increment of the curve mapping area change relative to different time nodes, and construct a unit mapping area increment curve. If within a preset time period, the value corresponding to the unit mapping area increment curve is less than or equal to the preset value, and the curvature accumulation value is less than or equal to the preset value, then the dynamic translation between the focus monitoring data parameter curves is terminated, and it is determined that the focus monitoring data parameter curves are consistent with each other; Step S3012: If all the relative monitoring data parameter curves are consistent with each other, it is determined that the historical production monitoring data and the real-time production monitoring data are compatible.

8. An intelligent interconnected factory resource collaborative management system, characterized in that: The method for collaborative management of intelligent interconnected factory resources according to any one of claims 1 to 7 comprises: The first module is used to obtain the historical production monitoring data corresponding to each resource production line, and determine the semi-finished product production efficiency corresponding to each historical production monitoring data, construct a number of resource particles for each semi-finished product production efficiency, and construct a historical resource particle fusion monitoring window based on the semi-finished product production efficiency corresponding to each historical production monitoring data, and determine the historical fusion performance of all resource particles in the historical resource particle fusion monitoring window; The second module is used to determine the abnormal impact time and abnormal impact volume corresponding to the historical production monitoring data, establish the correlation between the historical production monitoring data, historical fusion performance, abnormal impact time and abnormal impact volume, and obtain the historical abnormal impact judgment data group; The third module is used to obtain the real-time semi-finished product production efficiency corresponding to each resource production line, build a real-time resource particle fusion monitoring window, and determine the real-time fusion performance of all resource particles therein, compare and analyze the real-time fusion performance using different historical fusion performances, determine several matching historical fusion performances, and compare the historical production monitoring data corresponding to each determined historical fusion performance with the real-time production monitoring data to determine the matching historical production monitoring data; The fourth module is used to identify the abnormal impact time and abnormal impact volume corresponding to the adapted historical monitoring data as resource collaborative warning information to issue an early warning to the corresponding resource production line.

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