AI analysis method and system applied to paper machine press part data model

Abstract

The invention provides an AI analysis method and system applied to a paper machine press part data model, and belongs to the technical field of paper machine digital production.The AI analysis method comprises the steps that firstly, cross-dimension behavior marks of a paper machine press part data parameter model are obtained, and the cross-dimension behavior marks comprise adjustment of parameters such as line pressure, vacuum and vehicle speed and detection parameters such as the water permeability and the water content of a blanket; the method comprises the steps that firstly, a response and an environment interaction behavior mark are output, then, dynamic behavior inversion is carried out on a cross-dimension behavior mark, dynamic reference logic is generated based on an inversion result, then, closed-loop calibration is carried out on the inversion result and the dynamic reference logic, nodes deviating from the reference are positioned, and a calibration instruction is fed back; finally, an AI analysis process is iteratively optimized according to a result after closed-loop calibration, an iterative analysis report is obtained through integration, 'old master 'parameter optimization and adjustment suggestions are provided for a paper machine, the suitability of paper machine parameters and blanket design is evaluated based on different working conditions, more accurate decision support is provided for blanket optimization and parameter upgrading, and the method is suitable for popularization and application. And the production efficiency and the product quality of the pressing part of the paper machine are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of digital production technology for paper machines, and more specifically, to an AI analysis method and system applied to the data model of the press section of a paper machine. Background Technology

[0002] In the production operation of the paper machine press section, data models play a crucial role in optimizing production processes, improving product quality, and reducing production costs. However, existing data model analysis methods for the paper machine press section have many limitations.

[0003] On the one hand, traditional methods often focus only on data from a single dimension, such as parameter adjustment data or output response data, while ignoring the inherent connections and interactions between data from different dimensions. This isolated analytical approach cannot comprehensively and accurately grasp the actual operating status of the paper machine's press section, making it difficult to identify potential problems and optimization points.

[0004] On the other hand, existing analytical methods lack dynamism and adaptability. The operating environment of the paper machine press section is constantly changing and affected by various factors, such as raw material quality, equipment status, and process parameters. However, traditional methods typically use fixed analytical models and benchmarks, which cannot be dynamically adjusted and calibrated based on real-time changes in data and behavior, leading to discrepancies between the analytical results and the actual situation. Summary of the Invention

[0005] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide an AI analysis method applied to a data model of a paper machine press section, the method comprising: The cross-dimensional behavioral imprint of the paper machine press section data model is obtained. The cross-dimensional behavioral imprint includes parameter adjustment behavioral imprint, output response behavioral imprint and environmental interaction behavioral imprint. Dynamic behavior inversion is performed on the cross-dimensional behavioral imprints of the paper machine press section data model to explore the real-time correlation paths, triggering and transmission mechanisms and synergistic logic between different dimensional imprints, and obtain the dynamic behavior inversion results of the paper machine press section data model. Based on the dynamic behavior inversion results of the paper machine press section data model, a dynamic baseline logic is generated. The correlation adaptation conditions, transmission response templates and cooperative action boundaries of the baseline are adjusted according to the real-time changes of the inversion results to obtain the dynamic baseline logic of the paper machine press section data model. The dynamic behavior inversion results of the paper machine press section data model are compared with the dynamic baseline logic of the paper machine press section data model in a closed loop. The associated nodes, transmission nodes and cooperative nodes that deviate from the baseline in the inversion results are located, node calibration instructions are generated and fed back to the inversion process, and the dynamic behavior inversion results after closed loop calibration are obtained. The AI ​​analysis process of the paper machine press section data model is iteratively optimized based on the dynamic behavior inversion results after closed-loop calibration. The inversion results and dynamic benchmark logic are integrated to obtain the iterative analysis report of the paper machine press section data model.

[0006] Furthermore, embodiments of the present invention also provide an AI analysis system applied to the data model of the press section of a paper machine, characterized in that it includes: A processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to execute the aforementioned AI analysis method applied to a data model of a paper machine press section by executing the machine-executable instructions.

[0007] In another aspect, embodiments of the present invention also provide a computer program product, the computer program product including machine-executable instructions, the machine-executable instructions being stored in a computer-readable storage medium, a processor of a computer device reading the machine-executable instructions from the computer-readable storage medium, the processor executing the machine-executable instructions, causing the computer device to execute the above-described AI analysis method applied to the data model of the paper machine press section.

[0008] Based on the above, by acquiring cross-dimensional behavioral imprints of the paper machine press department data model, covering multiple dimensions such as parameter adjustment, output response, and environmental interaction, dynamic behavior inversion is then performed on the cross-dimensional behavioral imprints to uncover real-time correlation paths, trigger transmission mechanisms, and synergistic logic between different dimensional imprints. This makes the understanding of the dynamic behavior of the data model more accurate and detailed. Dynamic benchmark logic is generated based on the dynamic behavior inversion results, and the benchmark's correlation adaptation conditions, transmission response templates, and synergistic boundary can be adjusted according to the real-time changes in the inversion results, realizing the dynamic adaptability of the benchmark and better matching the actual operational changes of the paper machine press department. Through a closed-loop calibration mechanism, the dynamic behavior inversion results are compared with the dynamic benchmark logic, the nodes that deviate from the benchmark are located, and calibration instructions are generated and fed back to the inversion process, further improving the accuracy and reliability of the analysis. Finally, based on the results of the closed-loop calibration, the AI ​​analysis process is iteratively optimized, and an iterative analysis report is formed, providing a comprehensive, accurate, and dynamically adaptable analytical basis for the production optimization of the paper machine press department, effectively improving the production efficiency and product quality of the paper machine press department. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the execution flow of the AI ​​analysis method applied to the data model of the paper machine press section provided in an embodiment of the present invention.

[0010] Figure 2This is a schematic diagram of exemplary hardware and software components of an AI analysis system for a data model of a paper machine press section, provided in an embodiment of the present invention. Detailed Implementation

[0011] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating an AI analysis method applied to a data model of a paper machine press section, according to an embodiment of the present invention. The following is a detailed description of the AI ​​analysis method applied to the data model of a paper machine press section.

[0012] Step S110: Obtain the cross-dimensional behavioral imprint of the paper machine press section data model. The cross-dimensional behavioral imprint includes parameter adjustment behavioral imprint, output response behavioral imprint and environmental interaction behavioral imprint, thus obtaining the cross-dimensional behavioral imprint of the paper machine press section data model.

[0013] During the actual operation of the paper machine press section, the data model generates various imprint data reflecting its operating status and behavior. Parameter adjustment behavior imprints encompass records of adjustments made to various operating parameters during production, such as adjustments to press roll pressure, changes in machine speed, and control of feed pulp concentration. Output response behavior imprints refer to the changes in various output indicators exhibited by the paper machine press section after parameter adjustments, such as response data for paper moisture content, thickness, and basis weight. Environmental interaction behavior imprints include changes in environmental factors affecting the paper machine press section's operation and its interaction with the external environment, such as fluctuations in workshop temperature and humidity, and changes in raw material characteristics, reflecting the impact of these environmental factors on the press section's operation.

[0014] To obtain the aforementioned cross-dimensional behavioral imprints, real-time data collection is required using various sensors and data acquisition devices equipped in the paper machine's press section. During data collection, for sensitive data, such as operator identification information, data anonymization techniques are employed to anonymize sensitive fields, converting personally identifiable information into a form that cannot be directly linked to a specific individual. Simultaneously, encrypted transmission technology is used to encrypt the collected data during transmission, preventing unauthorized access and leakage. Furthermore, a data access control mechanism must be established to strictly manage the permissions of personnel and systems accessing the behavioral imprint data, ensuring that only authorized personnel and systems can access the relevant data, thereby achieving privacy protection and preventing leakage of sensitive data.

[0015] Step S120: Perform dynamic behavior inversion on the cross-dimensional behavioral imprints of the paper machine press section data model, explore the real-time correlation paths, triggering and transmission mechanisms and synergistic logic between different dimensional imprints, and obtain the dynamic behavior inversion results of the paper machine press section data model.

[0016] The aforementioned dynamic behavior inversion process is a key step in gaining a deeper understanding of the data model operation mechanism of the paper machine press section. By analyzing cross-dimensional behavioral imprints, it reveals the intrinsic connections and operational patterns between parameter adjustment, output response, and environmental interaction.

[0017] Step S121: The parameter adjustment behavior imprint, output response behavior imprint, and environmental interaction behavior imprint in the cross-dimensional behavior imprint of the paper machine press section data model are synchronously split according to the runtime sequence to obtain parameter adjustment behavior imprint segments, output response behavior imprint segments, and environmental interaction behavior imprint segments corresponding to multiple time segments.

[0018] During the continuous operation of the paper machine's press section, various behavioral imprints change over time. To more accurately analyze behavioral characteristics within different time periods, it is necessary to synchronously decompose the three types of behavioral imprints according to the runtime sequence. First, determine the time granularity of the decomposition, for example, by production shift, production batch, or fixed time interval (such as hour) as a time segment. Then, based on the determined time segments, the parameter adjustment behavioral imprint, output response behavioral imprint, and environmental interaction behavioral imprint are respectively decomposed into segments corresponding to each time segment. During the decomposition process, it is essential to ensure that the time segmentation of the three types of behavioral imprints is synchronous, meaning that the three imprint segments corresponding to the same time segment reflect the behavioral situation within the same time period. For example, if the running time of a certain day is divided into multiple time segments by hour, then the parameter adjustment behavioral imprint segment, output response behavioral imprint segment, and environmental interaction behavioral imprint segment corresponding to each time segment are all corresponding behavioral records within that hour. Through the above synchronous decomposition, the behavioral characteristic analysis within different time segments becomes more accurate and targeted.

[0019] Step S122: Perform intra-dimensional behavior inversion on the parameter regulation behavior imprint segment corresponding to each time segment, analyze the occurrence order, interaction path and linkage triggering relationship of different regulation behaviors in the parameter regulation behavior imprint segment, and obtain the intra-dimensional behavior association logic of parameter regulation.

[0020] For each time-series segment corresponding to the parameter adjustment behavior imprint, behavior inversion within the dimension is required. First, each independent adjustment behavior is extracted from the parameter adjustment behavior imprint segment, such as pressure roller pressure adjustment, speed adjustment, and feed concentration adjustment. Then, the order of occurrence of these adjustment behaviors is determined, i.e., arranged chronologically. Next, the interaction paths between different adjustment behaviors are analyzed. For example, when the feed concentration changes, it may trigger pressure roller pressure adjustment; thus, there is an interaction path between feed concentration adjustment and pressure roller pressure adjustment. Simultaneously, the linkage triggering relationship between adjustment behaviors must be studied, i.e., whether the occurrence of certain adjustment behaviors will trigger a chain reaction of other adjustment behaviors. For example, an increase in speed may simultaneously lead to adjustments in both pressure roller pressure and feed concentration; in this case, speed adjustment is the linkage triggering factor for pressure roller pressure adjustment and feed concentration adjustment. Through a comprehensive analysis of the above occurrence order, interaction paths, and linkage triggering relationships, a behavior correlation logic within the parameter adjustment dimension is constructed. This logic clearly demonstrates the inherent connections and operational patterns among parameter adjustment behaviors within this time-series segment.

[0021] Step S123: Perform inter-dimensional behavior inversion on the parameter adjustment behavior imprint segment and the output response behavior imprint segment corresponding to each time segment, capture the transmission path, temporal correlation law and intensity relationship of the parameter adjustment behavior triggering the output response behavior, and obtain the inter-dimensional behavior correlation path of parameter output.

[0022] Step S1231: Extract the key adjustment behaviors and their occurrence times from the parameter adjustment behavior imprint segments corresponding to each time segment, record the type, implementation magnitude, and duration of the key adjustment behaviors, and obtain the key behavior information of parameter adjustment.

[0023] Within the parameter adjustment behavior imprint segment corresponding to each time segment, there are various adjustment behaviors, but not all of them have a significant impact on the output response. Therefore, it is necessary to extract key adjustment behaviors. Key adjustment behaviors can be determined based on their impact on the main output indicators of the paper machine's press section. For example, adjustment behaviors that significantly affect key quality indicators such as paper moisture content and thickness can be considered key adjustment behaviors. For the extracted key adjustment behaviors, their type should be recorded, such as press roll pressure adjustment, machine speed adjustment, etc.; the implementation range, i.e., the magnitude of the adjustment, such as the change in press roll pressure from an initial value to a target value; and the duration, i.e., the time from the start of the adjustment behavior to its completion and stabilization.

[0024] Step S1232: Extract the key response behaviors and occurrence times from the output response behavior imprint segments corresponding to each time segment, record the type, intensity, and duration of the key response behaviors, and obtain the key output response behavior information.

[0025] Similar to extracting key behaviors for parameter adjustment, key response behaviors need to be extracted from the output response behavior imprint segment corresponding to each time segment. Key response behaviors typically refer to changes in the main output indicators of the paper machine's press section, such as changes in paper moisture content, thickness, and basis weight. Record the type of key response behavior, identifying which output indicator it corresponds to; the intensity of the change, i.e., the degree of change in the output indicator, such as the change in moisture content from an initial value to a target value; and the duration, i.e., the time from the onset of the response behavior to its stabilization. Simultaneously, accurately record the occurrence time of the key response behavior for correlation analysis with the occurrence time of key behaviors for parameter adjustment. Through these operations, the key output response behavior information is obtained.

[0026] Step S1233: Match the key behavior information of parameter adjustment with the key behavior information of output response according to the time of occurrence, establish the time correspondence between adjustment behavior and response behavior, and obtain time sequence correspondence data.

[0027] A one-to-one correspondence is established between the occurrence times of key parameter adjustment behaviors and key output response behaviors. For each key parameter adjustment behavior, the key output response behaviors that occur after its occurrence time are identified, and their temporal order is determined. For example, if a key parameter adjustment behavior occurs at time T1, then key output response behaviors that occur after time T1 are likely related to that adjustment behavior. In this way, a temporal correspondence between adjustment behaviors and response behaviors is established, forming temporal correspondence data. This temporal correspondence data clearly shows when the output response behavior occurs after a parameter adjustment behavior.

[0028] Step S1234: Based on the time-series correspondence data analysis, analyze the triggering association between the parameter adjustment key behavior and the output response key behavior, determine the parameter adjustment key behavior corresponding to each output response key behavior, identify the direct transmission path and indirect transmission path between behaviors, and obtain the behavior transmission path set.

[0029] Based on the aforementioned time-series correspondence data, the triggering association between key parameter adjustment behaviors and key output response behaviors is analyzed. For each key output response behavior, the key parameter adjustment behaviors that may trigger this response behavior are determined according to its occurrence time and its time correspondence with key parameter adjustment behaviors. If an output response behavior is directly triggered by a key parameter adjustment behavior without the intervention of other adjustment behaviors, then there is a direct transmission path between these two behaviors. For example, adjusting the pressure of the pressure roller directly leads to a change in paper thickness, so the transmission path from the pressure roller pressure adjustment behavior to the paper thickness response behavior is a direct transmission path. If an output response behavior is indirectly triggered by multiple key parameter adjustment behaviors through a series of intermediate adjustment behaviors, then there is an indirect transmission path between these behaviors. For example, adjusting the pulp concentration leads to adjusting the pressure roller pressure, which in turn causes a change in the paper moisture content, so the transmission path from the pulp concentration adjustment behavior to the paper moisture content response behavior is an indirect transmission path, which involves the intermediate step of adjusting the pressure roller pressure behavior. By identifying direct and indirect transmission paths, a set of behavior transmission paths is obtained, which comprehensively reflects the transmission path situation between key parameter adjustment behaviors and key output response behaviors.

[0030] Step S1235: Analyze the time interval between the occurrence time of the regulatory behavior and the occurrence time of the response behavior in the time-series correspondence data, summarize the response time patterns corresponding to different types of regulatory behaviors, and obtain the time correlation patterns.

[0031] In the time-series correlation data, for each pair of parameter adjustment key actions and output response key actions with triggering correlation, the time interval between their occurrence times is calculated. For example, if the parameter adjustment key action occurs at time T1 and the corresponding output response key action occurs at time T2, then the time interval is T2-T1. Then, for different types of parameter adjustment key actions, the time intervals of their corresponding output response key actions are statistically analyzed and summarized. Through statistical analysis of a large amount of data, certain patterns can be found in the response times corresponding to different types of adjustment actions. For example, the output response time interval corresponding to the vehicle speed adjustment action is relatively short, while the output response time interval corresponding to the slurry concentration adjustment action is relatively long. The summary of these time correlation patterns helps to predict the occurrence time of the output response based on the type of adjustment action in actual production, thereby enabling better production control and adjustment.

[0032] Step S1236: Compare the implementation magnitude of the parameter adjustment key behavior with the performance intensity of the output response key behavior, explore the relationship between the two, summarize the response intensity pattern corresponding to different implementation magnitude adjustment behaviors, and obtain the intensity relationship.

[0033] This study compares and analyzes the implementation amplitude of key parameter adjustment behaviors with the performance intensity of corresponding key output response behaviors. For the same type of key parameter adjustment behavior, different implementation amplitudes may lead to different performance intensities of the output response behavior. For example, a larger adjustment amplitude of the pressure roller may result in a greater change in paper thickness. Through comparison and analysis of a large amount of data, the interaction between the implementation amplitude of key parameter adjustment behaviors and the performance intensity of key output response behaviors is explored. The variation patterns of the output response intensity corresponding to the adjustment behavior under different implementation amplitudes are summarized, such as linear and nonlinear relationships, thus obtaining the intensity interaction relationship. This intensity interaction relationship has important guiding significance for determining the appropriate parameter adjustment amplitude based on the desired output response intensity during the production process.

[0034] Step S1237: Integrate the set of behavior transmission paths, time correlation rules and intensity relationships according to the structural requirements of inter-dimensional correlation paths to obtain the inter-dimensional behavior correlation paths of parameter output.

[0035] The aforementioned set of behavioral transmission paths, temporal correlation patterns, and intensity relationships are integrated. Following the structural requirements of inter-dimensional correlation paths, the behavioral transmission path is taken as the main body, and the temporal correlation pattern and intensity relationship are taken as attributes of that path. For example, for a given behavioral transmission path, its corresponding temporal correlation pattern (i.e., the time interval pattern between the regulatory behavior and the response behavior on that path) and intensity relationship (i.e., the relationship between the magnitude of the regulatory behavior and the intensity of the response behavior on that path) are clearly defined. Through this integration, a complete inter-dimensional behavioral correlation path for parameter output is formed. This path clearly demonstrates how parameter regulation behavior influences output response behavior through specific transmission paths, under certain temporal correlation patterns and intensity relationships.

[0036] Step S124: Perform multi-dimensional behavior inversion on the environmental interaction behavior imprint segment corresponding to each time segment, together with the parameter adjustment behavior imprint segment and the output response behavior imprint segment, to identify the influence path of environmental interaction behavior on parameter adjustment behavior, the effect mode on output response behavior, and the synergistic effect conditions of the three, and obtain the multi-dimensional synergistic effect logic of environmental parameter output.

[0037] Step S1241: Extract the key environmental factors and changes in the environmental interaction behavior imprint fragments corresponding to each time segment, record the type, changes and duration of the key environmental factors, and obtain the key environmental interaction factor information.

[0038] Each time-series segment contains environmental interaction behavior imprints containing information on various environmental factors. Key environmental factors need to be extracted from these segments. These key environmental factors typically refer to those that significantly impact the operation of the paper machine's press section, such as workshop temperature, humidity, and raw material characteristics (e.g., fiber length, hardness). For each extracted key environmental factor, its type is recorded, specifying which type of environmental factor it is; its change pattern, i.e., the numerical change of the environmental factor within that time-series segment, such as temperature rising or falling from one value to another; and its duration, i.e., the length of time the environmental factor remains in a specific state of change. Through these records, key environmental interaction factor information is formed.

[0039] Step S1242: Associate the key environmental interaction factor information with the corresponding parameter adjustment behavior imprint fragments, identify the adjustment of parameter adjustment behavior after changes in key environmental interaction factors, identify the direct and indirect paths of environmental factors affecting parameter adjustment, and obtain the path of environmental influence on parameter adjustment.

[0040] This study correlates key environmental interaction factors with corresponding parameter adjustment behavior imprint fragments. When key environmental interaction factors change, it observes whether corresponding parameter adjustment behaviors occur in the parameter adjustment behavior imprint fragments. If the change in environmental factors directly leads to the implementation of a certain parameter adjustment behavior, then this is the direct path of environmental factors influencing parameter adjustment. For example, when the workshop humidity rises to a certain level, it directly triggers the adjustment behavior of the pressure roller, so the change in workshop humidity leading to the adjustment behavior of the pressure roller is a direct influence path. If the change in environmental factors first affects other intermediate factors, and then the intermediate factors lead to the implementation of parameter adjustment behavior, then this is an indirect path. For example, an increase in workshop temperature leads to a change in the moisture content of raw materials, which in turn triggers the adjustment behavior of the feed concentration, so the change in workshop temperature leading to a change in the moisture content of raw materials and then to the adjustment behavior of the feed concentration is an indirect influence path. By identifying direct and indirect paths, the influence path of the environment on parameter adjustment is obtained, revealing how environmental factors affect parameter adjustment behavior.

[0041] Step S1243: Associate the key environmental interaction factors with the corresponding output response behavior imprint fragments, identify the change characteristics of output response behavior after changes in key environmental interaction factors, summarize the way environmental factors affect output response and the law of influence, and obtain the mode of environmental effect on output response.

[0042] This study correlates key environmental interaction factors with corresponding output response behavior imprint segments. It analyzes the changes in key response behaviors within these segments when these key environmental interaction factors change. For example, does a change in workshop temperature lead to a change in paper moisture content, and what is the direction and extent of this change? Through analysis of extensive data, the study summarizes the ways in which environmental factors affect output responses, such as promoting, inhibiting, linear, and nonlinear effects, as well as the patterns of influence, such as the differences in the degree of influence on output responses across different environmental factor values. This leads to the formation of an environmental-output response influence model, which describes how environmental factors directly or indirectly affect output response behavior.

[0043] Step S1244: Analyze the time sequence of changes in the three key factors of environmental interaction, parameter adjustment behavior and output response behavior, determine the effective implementation conditions, environmental adaptation requirements and parameter output collaboration boundaries of the three factors, and obtain the effective implementation conditions, environmental adaptation requirements and parameter output collaboration boundaries of the three factors.

[0044] This analysis examines the temporal sequence and interrelationships of key environmental interaction factors, parameter adjustment behavior, and output response behavior. It determines the conditions under which these three factors can synergistically achieve stable operation of the paper machine's press section and desired output targets. Effective implementation conditions refer to the specific conditions required for the synergistic effect of these three factors, such as environmental factors changing within a certain range, parameter adjustment behavior being implemented in a specific manner, and output response behavior reaching a certain standard. Environmental adaptation requirements refer to the conditions that environmental factors must meet to accommodate the synergistic effect of parameter adjustment and output response. The parameter-output synergy boundary refers to the scope and limits of the synergistic effect of parameter adjustment behavior and output response behavior; for example, the range of parameter adjustment amplitude and the interval of output response indicators required to guarantee the synergistic effect of the three factors. Through the analysis of the above, the effective implementation conditions, environmental adaptation requirements, and parameter-output synergy boundary of the three factors are clarified.

[0045] Step S1245: Integrate the environmental influence path on parameter adjustment, the environmental response mode to output, the effective implementation conditions for the three to work together, the environmental adaptation requirements and the parameter output collaboration boundary, and organize them according to the structural requirements of the multi-dimensional collaboration logic to obtain the multi-dimensional collaboration logic of environmental parameter output.

[0046] The aforementioned environmental influence paths on parameter regulation, environmental effects on output response patterns, effective implementation conditions for the synergy of these three elements, environmental adaptation requirements, and parameter output synergy boundaries are integrated. Following the structural requirements of multi-dimensional synergy logic, environmental factors, parameter regulation behavior, and output response behavior are treated as three dimensions, clarifying their interaction relationships, synergistic implementation conditions, environmental adaptation requirements, and synergistic boundaries. For example, under specific environmental adaptation requirements, environmental factors act on parameter regulation behavior through specific influence paths, parameter regulation behavior influences output response behavior through transmission paths, and environmental factors also directly influence output response behavior through their action patterns. The three elements achieve synergy within the effective implementation conditions and synergistic boundaries. Through this integration, a multi-dimensional synergistic logic for environmental parameter output is formed, comprehensively reflecting the synergistic laws among the three dimensions.

[0047] Step S125: Integrate the behavior association logic within the parameter adjustment dimension, the behavior association path between parameter output dimensions, and the multi-dimensional synergistic logic of environmental parameter output according to the time sequence to construct a structured inversion model with behavior nodes as the core, association paths as the link, and synergistic logic as the constraint, and obtain a preliminary dynamic behavior inversion model.

[0048] After analyzing the behavioral correlation logic within the parameter adjustment dimension, the behavioral correlation paths between parameter output dimensions, and the multi-dimensional synergistic logic of environmental parameter output for each time segment, the above logical information is integrated according to the chronological order of the time segments. During the integration process, each behavior (including parameter adjustment behavior, output response behavior, and environmental interaction behavior) is used as a behavior node, and the behavioral correlation logic within the parameter adjustment dimension and the behavioral correlation paths between parameter output dimensions are used as the correlation paths connecting each behavior node. Simultaneously, the multi-dimensional synergistic logic of environmental parameter output is used as the constraint condition for the entire model. This constructs a structured inversion model, namely the preliminary dynamic behavioral inversion model. This preliminary dynamic behavioral inversion model can clearly demonstrate the correlation and synergistic effects between various behaviors of the paper machine press section data model in different time segments.

[0049] Step S126: Perform temporal coherence processing on the preliminary dynamic behavior inversion model, connect the model parts corresponding to different time segments, so that the associated paths, transmission mechanisms and collaborative logic in the preliminary dynamic behavior inversion model remain temporally consistent, and obtain the dynamic behavior inversion result of the paper machine press section data model.

[0050] Since the preliminary dynamic behavior inversion model is integrated sequentially according to time segments, there may be certain breaks or inconsistencies between the model parts corresponding to different time segments. Therefore, it is necessary to perform temporal coherence processing on the preliminary dynamic behavior inversion model. Specifically, the correlation paths, transmission mechanisms, and collaborative logic in the model parts of adjacent time segments are analyzed to ensure their temporal continuity and consistency. For example, the output of a behavior node in the previous time segment may be the input of another behavior node in the next time segment, and it is necessary to ensure that the above connection relationship is correctly reflected in the model. For the transmission mechanism and collaborative logic, it is also necessary to check whether there are contradictions or inconsistencies between different time segments, and make corresponding adjustments and optimizations to ensure that the correlation paths, transmission mechanisms, and collaborative logic in the entire preliminary dynamic behavior inversion model are consistent in time. After temporal coherence processing, the final dynamic behavior inversion result of the paper machine press department data model is obtained. This dynamic behavior inversion result of the paper machine press department data model can more accurately reflect the dynamic behavior characteristics of the paper machine press department data model during continuous operation.

[0051] Step S130: Generate dynamic baseline logic based on the dynamic behavior inversion results of the paper machine press section data model, and adjust the correlation adaptation conditions, transmission response template and cooperative action boundary of the baseline according to the real-time changes of the inversion results to obtain the dynamic baseline logic of the paper machine press section data model.

[0052] Dynamic baseline logic is an important basis for evaluating whether the data model of the paper machine press section is operating normally. It can be adjusted in real time according to the changes in the dynamic behavior inversion results of the model, ensuring the adaptability and accuracy of the baseline.

[0053] Step S131: Extract the behavior correlation logic within the parameter adjustment dimension from the dynamic behavior inversion result of the paper machine press section data model, identify the effective range of the correlation between parameter adjustment behaviors, the triggering premise of linkage and mutual compatibility requirements, and obtain the initial basis for the correlation adaptation conditions.

[0054] The behavioral correlation logic within the parameter adjustment dimension is extracted from the dynamic behavior inversion results of the paper machine press section data model. This logic is then analyzed in depth to identify the effective scope of the correlation between parameter adjustment behaviors—that is, under what conditions a certain correlation is effective; the triggering conditions for linkage—that is, what conditions must be met to trigger the linkage between behaviors; and the mutual compatibility requirements—that is, what compatibility conditions must be met between different parameter adjustment behaviors during implementation to avoid conflicts or adverse effects. Through the identification of the above content, the initial basis for correlation adaptation conditions is formed.

[0055] Step S132: Capture the changing trend of the dynamic behavior inversion result of the data model of the paper machine press section in real time, track the real-time changes of the behavior association logic within the parameter adjustment dimension, determine the stability of the change by comparing data of K consecutive monitoring cycles, and adjust the scope of action and triggering premise of the association adaptation conditions according to the changed association logic to obtain the dynamic association adaptation conditions.

[0056] Step S1321: Based on the set monitoring period, continuously monitor the behavior correlation logic within the parameter adjustment dimension of the dynamic behavior inversion result of the paper machine press section data model, capture the addition, change and disappearance of the correlation relationship within the behavior correlation logic within the parameter adjustment dimension, and obtain the correlation logic change data.

[0057] Pre-set the monitoring period, such as hourly or daily. Continuously monitor the behavioral correlation logic within the parameter adjustment dimension of the dynamic behavior inversion results of the paper machine press section data model according to the set monitoring period. During the monitoring process, focus on the addition of new correlations (i.e., whether new parameter adjustment behavioral correlations appear); changes (i.e., whether existing correlations have changed in terms of scope of action or triggering conditions); and disappearance (i.e., whether some original correlations no longer exist). Record the above-mentioned monitoring results to form correlation logic change data.

[0058] Step S1322: Perform trend analysis on the data of the changes in the related logic, identify the direction and duration of changes in the behavior related logic within the parameter adjustment dimension, determine the stability of the changes by comparing data from K consecutive monitoring periods, and classify the change attributes.

[0059] Trend analysis is performed on the data showing changes in the correlation logic to determine the direction of change in the behavioral correlation logic within the parameter adjustment dimension. For example, is the correlation gradually increasing or decreasing, and is the scope of effect expanding or shrinking? Simultaneously, the persistence of the change is analyzed, i.e., how many monitoring cycles a certain trend has lasted. By comparing data over K consecutive monitoring cycles (K can be set according to actual conditions, such as 3, 5, etc.), if the trend of the correlation logic remains consistent over K consecutive monitoring cycles, then the change can be determined to be stable. Based on the stability of the change, the change attribute is divided into persistent change and temporary fluctuation. Persistent change refers to a stable trend that exists over multiple consecutive monitoring cycles; temporary fluctuation refers to a change that occurs only in a few monitoring cycles and then returns to its original state.

[0060] Step S1323: If the change attribute is a continuous change, extract the effective scope of action, linkage triggering premise and mutual compatibility requirements in the behavior association logic within the parameter adjustment dimension after the change, as the basis for adjusting the association adaptation conditions.

[0061] When the changing attribute is determined to be a continuous change, new effective scope of action, linkage triggering premises and mutual compatibility requirements are extracted from the behavioral association logic within the changed parameter adjustment dimension. The extracted content will serve as the basis for adjusting the association adaptation conditions to ensure that the association adaptation conditions can adapt to the continuous changes in the behavioral association logic within the parameter adjustment dimension.

[0062] Step S1324: Expand or narrow the effective scope of the associated adaptation conditions according to the adjustment criteria, and optimize the specific requirements of the linkage triggering premise.

[0063] Based on the above adjustment criteria, the effective scope of the association adaptation conditions will be expanded or narrowed accordingly. If the effective scope of the association logic is expanded after the change, then the effective scope of the association adaptation conditions should also be expanded accordingly; conversely, it should be narrowed. At the same time, the specific requirements of the linkage triggering premises will be optimized to match the linkage triggering premises in the changed association logic, ensuring that the association adaptation conditions can accurately reflect the association relationship between parameter adjustment behaviors.

[0064] Step S1325: If the change attribute is a temporary fluctuation, retain the core content of the original associated adaptation conditions and make local adjustments to the adaptation conditions corresponding to the fluctuation part.

[0065] When the change attribute is a temporary fluctuation, considering that the above changes may be caused by accidental factors and will not have a long-term impact on the behavioral correlation logic within the parameter adjustment dimension, the core content of the original correlation adaptation conditions is retained to maintain the stability of the baseline logic. For the adaptation conditions corresponding to the fluctuating part, only local adjustments are made to cope with the temporary changes, without changing the overall structure and core requirements of the correlation adaptation conditions.

[0066] Step S1326: Integrate the adjusted effective scope of action, triggering conditions, and local adjustment content to generate dynamic correlation and adaptation conditions.

[0067] The effective scope of action, triggering prerequisites, and local adjustments for temporary fluctuations, adjusted according to the above steps, are integrated. Following the structural requirements of the correlation adaptation conditions, these adjusted contents are organically organized to form dynamic correlation adaptation conditions. These dynamic correlation adaptation conditions can be adjusted in real time based on changes in the behavioral correlation logic within the parameter adjustment dimension, ensuring they adapt to the actual operation of the paper machine press department data model.

[0068] Step S133: Analyze the behavioral correlation path between parameter output dimensions in the dynamic behavior inversion result of the paper machine press section data model, extract the time response range, intensity action standard and feedback adjustment law of parameter output transmission, and obtain the initial basis of the transmission response template.

[0069] The behavioral correlation paths between parameter output dimensions in the dynamic behavior inversion results of the paper machine press section data model are analyzed. From these correlation paths, the time response range of parameter output transmission is extracted (i.e., the time interval within which the output response behavior may occur after the parameter adjustment behavior occurs); the intensity standard (i.e., the correspondence standard between the implementation magnitude of the parameter adjustment behavior and the performance intensity of the output response behavior); and the feedback adjustment law (i.e., the law of how the parameter adjustment behavior makes feedback adjustments when the output response behavior reaches a certain intensity or exceeds a certain range). The extracted content is used as the initial basis for constructing a preliminary transmission response template.

[0070] Step S134: Based on the real-time transmission data of the dynamic behavior inversion result of the paper machine press section data model, track the operational changes of the behavior correlation path between parameter output dimensions, determine the change trend by comparing the data differences between two adjacent transmission cycles, and adjust the time range and intensity standard of the transmission response template according to the changed transmission path to obtain the dynamic transmission response template.

[0071] Step S1341: Collect real-time transmission data from the dynamic behavior inversion results of the data model of the paper machine press section, extract the time interval data, intensity matching data and feedback adjustment data in the parameter output transmission process, and obtain real-time transmission characteristic data.

[0072] Real-time transmission data is collected from the dynamic behavior inversion results of the paper machine press section data model. This real-time transmission data reflects the actual operation of the behavioral correlation path between parameter output dimensions. From the real-time transmission data, time interval data during the parameter output transmission process (i.e., the time interval between parameter adjustment behavior and output response behavior); intensity matching data (i.e., the matching relationship data between the implementation magnitude of parameter adjustment behavior and the performance intensity of output response behavior); and feedback adjustment data (i.e., the feedback adjustment data of output response behavior on parameter adjustment behavior). These data are integrated to form real-time transmission characteristic data.

[0073] Step S1342: Perform statistical analysis on the real-time transmission feature data, compare it with historical transmission feature data, identify the changes in time interval, intensity matching and feedback adjustment in the behavioral correlation path between parameter output dimensions, determine the change trend by comparing the data differences between two adjacent transmission cycles, and classify the change attributes.

[0074] Statistical analysis is performed on real-time transmission characteristic data to calculate statistical indicators such as the average and variance of time intervals and the correlation coefficient of intensity matching. These statistical indicators are compared with corresponding indicators in historical transmission characteristic data to identify changes in time intervals, intensity matching, and feedback regulation in the behavioral correlation paths between parameter output dimensions. By comparing the differences in real-time transmission characteristic data between two adjacent transmission cycles (the transmission cycle can be determined based on the actual situation of parameter output transmission, such as each complete parameter adjustment-output response process constituting one transmission cycle), the trend of change is determined to be gradually increasing, gradually decreasing, or remaining basically stable. Based on the stability of the trend, the change attribute is divided into long-term trend changes and short-term random changes. Long-term trend changes refer to a trend that continues to change in one direction over multiple adjacent transmission cycles; short-term random changes refer to random changes that occur only in a few adjacent transmission cycles.

[0075] Step S1343: If the change attribute is a long-term trend change, redetermine the time range of the transmission response based on the behavioral correlation path between the output dimensions of the changed parameters, and adjust the allowable time interval.

[0076] When the change attribute exhibits a long-term trend, the time interval pattern between parameter adjustment behavior and output response behavior is re-analyzed based on the behavioral correlation path between the changed parameter output dimensions to determine a new transmission response time range. Based on this new time range, the allowable time interval in the transmission response template is adjusted to cover the changed time interval situation.

[0077] Step S1344: Adjust the intensity standard of the conduction response based on the changed intensity matching data to optimize the intensity adaptation requirements.

[0078] Simultaneously, based on the revised intensity matching data, the correspondence between the magnitude of parameter adjustment and the intensity of the output response is reassessed, and the intensity standard in the conducted response template is adjusted accordingly. The intensity adaptation requirements are optimized to ensure that the intensity standard accurately reflects the revised intensity matching, enabling the conducted response template to correctly assess whether the intensity of the parameter output conduction meets the requirements.

[0079] Step S1345: If the change attribute is a short-term random change, maintain the core parameters of the original conduction response template and make local adjustments to the adaptation conditions corresponding to the fluctuation part.

[0080] When a change in attribute is determined to be a short-term, accidental change, it indicates that the change in the behavioral correlation path between parameter output dimensions is not a long-term stable trend, but may be caused by sudden, temporary factors. In this case, to ensure the overall stability and continuity of the transmission response template, the core parameters in the original transmission response template should be maintained, such as the basic time response range framework and the main intensity action standards. For the adaptation conditions corresponding to the fluctuation part, such as abnormal time intervals or intensity matching deviations occurring within a specific transmission cycle, only local adjustments should be made. For example, if a short-term fluctuation causes a small deviation in the matching between the parameter adjustment amplitude and the response intensity, a temporary allowable fluctuation range can be set based on the original intensity standard, or the intensity correspondence between specific types of adjustment behavior and response behavior can be fine-tuned without changing the overall intensity standard system.

[0081] Step S1346: Integrate the adjusted time range, intensity standard, and temporary adjustment information to generate a dynamic transmission response template.

[0082] The time range (including the redefined time allowance under long-term trend changes, or the original time range maintained under short-term random changes), intensity standards (including the optimized intensity adaptation requirements under long-term trend changes, or the core intensity parameters maintained under short-term random changes), and temporary adjustment information for short-term random changes are integrated after the above steps. Following the structured requirements of the transmission response template, the time range and intensity standards form the main framework of the template, with temporary adjustment information embedded as supplementary explanations or conditional clauses, forming a dynamic transmission response template that can be dynamically adjusted in real time according to changes in the behavioral correlation paths between parameter output dimensions.

[0083] Step S135: Analyze the multi-dimensional synergistic logic of environmental parameter output in the dynamic behavior inversion results of the paper machine press section data model, identify the effective implementation conditions, environmental adaptation requirements and parameter output synergistic boundaries of the three, and obtain the initial basis for the synergistic boundary.

[0084] This paper conducts an in-depth analysis of the multi-dimensional synergistic logic of environmental parameter outputs in the dynamic behavior inversion results of the paper machine press section data model. This multi-dimensional synergistic logic encompasses the modes and patterns of synergistic interaction among environmental interaction behavior, parameter adjustment behavior, and output response behavior. From this, we identify the effective implementation conditions for the synergy among these three elements, i.e., under what circumstances can they achieve effective synergy, such as environmental factors being within a specific range or parameter adjustment behavior being implemented according to a specific pattern; environmental adaptation requirements, i.e., what conditions environmental factors must meet to form good synergy with parameter adjustment and output response; and parameter output synergy boundaries, i.e., the scope and limits of parameter adjustment and output response behaviors in the synergistic effect. Through the identification of the above, we obtain the initial basis for the synergistic boundary.

[0085] Step S136: Capture the real-time changes of the multi-dimensional synergistic logic of environmental parameters, determine the impact of changes by monitoring the difference in synergistic effects before and after changes in environmental factors, and adjust the implementation conditions and adaptation requirements of the synergistic boundary according to the changed synergistic logic to obtain the dynamic synergistic boundary.

[0086] The system continuously captures real-time changes in the multi-dimensional synergistic logic of environmental parameter outputs. By monitoring the differences in the synergistic effects of the three factors before and after changes in environmental factors—such as changes in the stability of output response indicators and the efficiency of parameter adjustment—the system determines the degree and direction of the impact of environmental factor changes on the synergistic logic. Based on the changed synergistic logic, the effective implementation conditions of the synergistic boundary are adjusted, such as expanding or narrowing the effective scope of environmental factors and modifying the implementation standards of parameter adjustment behavior. Simultaneously, environmental adaptation requirements are adjusted to match the adaptation conditions of environmental factors with the changed synergistic logic. Through these adjustments, a dynamic synergistic boundary is obtained, which can be updated in real-time according to changes in the multi-dimensional synergistic logic of environmental parameter outputs.

[0087] Step S137: Integrate the dynamic correlation adaptation conditions, dynamic transmission response templates, and dynamic collaborative action boundaries, organize them according to the structured requirements of the baseline logic, add a real-time update trigger mechanism, and generate a dynamic baseline logic for the paper machine press section data model that can follow the changes in the dynamic behavior inversion results.

[0088] The dynamically correlated adaptation conditions, dynamic transmission response templates, and dynamic synergistic boundaries obtained above are integrated. Following the structured requirements of the baseline logic, the dynamically correlated adaptation conditions serve as the benchmark for behavioral correlation within the parameter adjustment dimension, the dynamic transmission response templates serve as the benchmark for behavioral transmission between parameter output dimensions, and the dynamic synergistic boundaries serve as the benchmark for multi-dimensional synergistic effects of environmental parameter outputs. Simultaneously, a real-time update trigger mechanism is added. This mechanism monitors changes in the dynamic behavior inversion results of the paper machine press section data model. When the correlation logic, transmission path, or synergistic logic in the inversion results changes, it automatically triggers updates to the corresponding parts (dynamically correlated adaptation conditions, dynamic transmission response templates, or dynamic synergistic boundaries) in the dynamic baseline logic. Through this integration and mechanism addition, a dynamic baseline logic for the paper machine press section data model that can follow changes in the dynamic behavior inversion results is generated.

[0089] Step S140: Perform closed-loop calibration on the dynamic behavior inversion results of the paper machine press section data model and the dynamic baseline logic of the paper machine press section data model, locate the related nodes, transmission nodes and cooperative nodes that deviate from the baseline in the inversion results, generate node calibration instructions and feed them back to the inversion process, and obtain the dynamic behavior inversion results after closed-loop calibration.

[0090] Closed-loop calibration is a crucial step in ensuring the accuracy of the dynamic behavior inversion results of the data model of the paper machine press section. By comparing the inversion results with the dynamic baseline logic, deviations are identified and calibrated, forming a closed-loop process of continuous optimization.

[0091] Step S141: Compare the behavior correlation logic within the parameter adjustment dimension in the dynamic behavior inversion result of the paper machine press section data model with the dynamic correlation adaptation conditions in the dynamic baseline logic of the paper machine press section data model node by node, identify the nodes in the behavior correlation logic within the parameter adjustment dimension that do not meet the dynamic correlation adaptation conditions, mark them as correlation deviation nodes, and obtain the correlation deviation node set.

[0092] Each node in the behavior association logic within the parameter adjustment dimension of the paper machine press section data model's dynamic behavior inversion results is compared with the corresponding requirements of the dynamic association adaptation conditions in the dynamic baseline logic. The association relationship of each node is checked to ensure it meets the effective scope of the dynamic association adaptation conditions, the triggering prerequisites, and the mutual compatibility requirements. If the association relationship of a node exceeds the effective scope of the dynamic association adaptation conditions, or its triggering prerequisites do not meet the requirements, or it is incompatible with other nodes, then that node is marked as an association deviation node. All marked association deviation nodes are summarized to form an association deviation node set.

[0093] Step S142: Compare the behavior association path between parameter output dimensions in the dynamic behavior inversion result of the paper machine press section data model with the dynamic transmission response template in the dynamic baseline logic of the paper machine press section data model path one by one, identify the nodes in the behavior association path between parameter output dimensions that do not conform to the dynamic transmission response template, mark them as transmission deviation nodes, and obtain the transmission deviation node set.

[0094] Each path in the behavioral correlation path between parameter output dimensions in the dynamic behavior inversion results of the paper machine press section data model is analyzed, and each node in the path is compared with the dynamic conduction response template in the dynamic baseline logic. It is checked whether the conduction time corresponding to the node is within the time allowable range of the dynamic conduction response template, and whether the conduction strength meets the strength standard. If the conduction time of a node exceeds the time allowable range, or the conduction strength does not meet the strength standard, the node is marked as a conduction deviation node. All conduction deviation nodes are summarized to form a conduction deviation node set.

[0095] Step S143: Compare the multi-dimensional collaborative logic of the environmental parameter output in the dynamic behavior inversion result of the paper machine press section data model with the dynamic collaborative boundary in the dynamic baseline logic of the paper machine press section data model, identify the nodes in the multi-dimensional collaborative logic of the environmental parameter output that do not conform to the dynamic collaborative boundary, mark them as collaborative deviation nodes, and obtain the collaborative deviation node set.

[0096] Each node in the multi-dimensional synergistic logic of environmental parameter output is logically compared with the dynamic synergistic boundary. The synergy corresponding to a node is checked to see if it meets the effective implementation conditions, environmental adaptation requirements, and parameter output synergy boundaries of the dynamic synergy boundary. If the synergy of a node does not meet the effective implementation conditions, or the environmental factors do not meet the environmental adaptation requirements, or exceed the parameter output synergy boundary, then that node is marked as a synergistic deviation node. All synergistic deviation nodes are aggregated to form a synergistic deviation node set.

[0097] Step S144: For each deviation node in the set of associated deviation nodes, the set of transmitted deviation nodes, and the set of coordinated deviation nodes, trace the source, explore the upstream node influence path, behavior transmission link, and external factors generated by the deviation node, and obtain the deviation source tracing result.

[0098] Step S1441: Starting from each deviation node, trace back the upstream related nodes in the dynamic behavior inversion result of the paper machine press section data model, identify the relationship and transmission path between the deviation node and the upstream node, determine the range of upstream nodes that may affect the deviation node, and obtain the range of upstream related nodes.

[0099] For each deviation node in the sets of associated deviation nodes, transmitted deviation nodes, and coordinated deviation nodes, the upstream nodes associated with that node are traced back through the dynamic behavior inversion results of the paper machine press section data model. The relationships between the deviation nodes and upstream nodes are analyzed, such as whether they are directly or indirectly related, and the transmission paths between them. Through this reverse tracing, the range of upstream nodes that may affect the deviation node is determined, i.e., the range of upstream associated nodes.

[0100] Step S1442: Analyze the behavior state of each node in the upstream associated node range, determine whether the operation mode of the upstream node conforms to the model preset logic, identify the direct upstream influencing nodes that cause the deviation nodes, and obtain the information of the directly influencing nodes.

[0101] Analyze the behavior of each node within the upstream associated node range to check whether their operating patterns conform to the model's preset normal logic and rules. If an upstream node's operating pattern does not conform to the preset logic, and its abnormal behavior can directly lead to the generation of a deviation node, then this upstream node is the direct upstream influencing node causing the deviation node. Record information such as the type and abnormal behavior of the directly influencing node to obtain the information of the directly influencing node.

[0102] Step S1443: Identify the behavioral transmission link from the directly affecting node to the deviation node, clarify the intermediate nodes, transmission methods and mechanisms in the transmission process, and obtain the details of the deviation transmission link.

[0103] After identifying the directly influencing nodes, the behavioral transmission chain from these nodes to the deviation nodes is further identified. A detailed analysis is conducted to determine if intermediate nodes exist during the transmission process, their type and function; whether the transmission occurs directly or indirectly, through parameter adjustment, environmental changes, or other means; and the mechanism of transmission, such as facilitating, inhibiting, or other types of effects. Through this analysis, a detailed deviation transmission chain is obtained, clearly demonstrating how the deviation is transmitted from the directly influencing nodes to the deviation nodes.

[0104] Step S1444: Investigate the changes in external environmental factors corresponding to the deviation nodes, analyze whether there are abnormal environmental factors and changes in the environmental interaction behavior imprints, determine whether the external environmental factors are the cause of the deviation, and obtain information on external factors.

[0105] Investigate the changes in external environmental factors corresponding to the occurrence of deviation nodes. Review environmental interaction behavior logs to analyze whether there are any abnormal environmental factors or changes, such as environmental factor values ​​suddenly exceeding the normal range or abnormal rates of change. Determine whether these abnormal external environmental factors may have induced the generation of deviation nodes; for example, whether abnormal changes in environmental factors led to abnormal behavior in nodes directly affecting deviation nodes, and thus propagated to deviation nodes. If the above-mentioned external environmental factors exist, record their type, changes, and the way they affect deviation nodes to obtain information on external influencing factors.

[0106] Step S1445: Integrate the upstream associated node range, directly affecting node information, deviation transmission link details, and external factor information to form the deviation tracing result corresponding to each deviation node.

[0107] The upstream related node range, directly influencing node information, deviation transmission link details, and external influencing factor information are integrated and organized according to the structural requirements of deviation tracing results to form a complete deviation tracing result corresponding to each deviation node. This multi-dimensional synergistic result comprehensively reflects the causes, influence paths, and related factors of deviation nodes.

[0108] Step S145: Generate a targeted calibration instruction for each deviation node based on the deviation tracing results. The calibration instruction includes the node adjustment direction, correlation correction requirements, and transmission path optimization suggestions, resulting in a set of node calibration instructions.

[0109] Based on the deviation tracing results, targeted calibration instructions are formulated for each deviation node. The calibration instructions should clearly define the adjustment direction of the node, such as whether to enhance or weaken the node's behavior intensity, or whether to extend or shorten the propagation time; require corrections to relationships, such as adjusting the association method between the node and upstream or downstream nodes, or modifying the triggering prerequisites for linkage; and provide suggestions for propagation path optimization, such as optimizing direct propagation paths or reducing intermediate nodes in indirect propagation paths. The calibration instructions generated for all deviation nodes are then compiled to obtain a node calibration instruction set.

[0110] Step S146: Feed back the set of node calibration instructions to the dynamic behavior inversion process of the data model of the paper machine press section, drive the inversion process to adjust the deviation nodes, correct the correlation of related deviation nodes, the transmission path of the transmission deviation nodes and the collaborative logic of the collaborative deviation nodes, and obtain the dynamic behavior inversion result after closed-loop calibration.

[0111] The set of node calibration instructions is fed back into the dynamic behavior inversion process of the paper machine press department data model. Based on the calibration instructions, the inversion process corrects the association relationships of associated deviation nodes to meet the dynamic association adaptation conditions; optimizes the transmission paths of transmission deviation nodes, adjusting transmission time and intensity to conform to the dynamic transmission response template; and adjusts the collaborative logic of cooperative deviation nodes to conform to the dynamic collaborative action boundary. After these adjustments, a closed-loop calibrated dynamic behavior inversion result is obtained, which improves the accuracy and reliability of the result.

[0112] Step S150: Based on the dynamic behavior inversion results after closed-loop calibration, iteratively optimize the AI ​​analysis process of the paper machine press section data model, integrate the iterative inversion results and dynamic benchmark logic, and obtain the paper machine press section data model iterative analysis report.

[0113] The AI ​​analysis process of the paper machine press section data model is iteratively optimized based on the dynamic behavior inversion results after closed-loop calibration, continuously improving the performance and accuracy of the analysis process, and generating an iterative analysis report.

[0114] Step S151: Analyze the dynamic behavior inversion results after closed-loop calibration, extract the behavior correlation logic within the optimized parameter adjustment dimension, the behavior correlation path between parameter output dimensions, and the multi-dimensional synergistic logic of environmental parameter output from the results, and obtain the core content of model optimization.

[0115] The dynamic behavior inversion results after closed-loop calibration are analyzed to extract the behavior correlation logic within the parameter adjustment dimension, the behavior correlation path between parameter output dimensions, and the multi-dimensional synergistic logic of environmental parameter output after calibration and optimization. The above-mentioned optimized logic and path are the core manifestation of the model operation mechanism and constitute the core content of model optimization.

[0116] Step S152: Based on the core content of the model optimization, adjust the AI ​​analysis process of the paper machine press section data model, optimize the operation logic of the data acquisition link, behavior inversion link, benchmark generation link and calibration link in the analysis process, and obtain the iteratively optimized AI analysis process.

[0117] Based on the core principles of model optimization, adjustments are made to each stage of the AI ​​analysis process. In the data acquisition stage, it may be necessary to optimize sensor sampling frequency and data filtering rules to improve the accuracy and relevance of data collection. In the behavior inversion stage, the parameters of the inversion algorithm and the identification method of associated paths are adjusted according to the optimized logic. In the benchmark generation stage, the update trigger conditions and adjustment criteria of the dynamic benchmark logic are modified. In the calibration stage, the identification algorithm for deviation nodes and the generation rules for calibration instructions are optimized. Through the optimization of the operational logic of the above stages, an iteratively optimized AI analysis process is obtained.

[0118] Step S153: Apply the iteratively optimized AI analysis process to the operation analysis of the data model of the paper machine press section, collect model operation data, benchmark adjustment data and calibration implementation data during the analysis process, and obtain iterative analysis process data.

[0119] The iteratively optimized AI analysis process was applied to the actual operation analysis of the data model in the paper machine's press section. During the analysis, model operation data was collected, such as the operating status of each behavior node and the activity level of related paths; baseline adjustment data, such as the number of updates and adjustment magnitudes of the dynamic baseline logic; and calibration implementation data, such as the execution status of calibration instructions and the reduction in the number of deviation nodes. These data were then summarized to obtain the iterative analysis process data.

[0120] Step S154: Compare the AI ​​analysis process running data before and after iterative optimization, analyze the impact of process optimization on model inversion accuracy, benchmark adaptability and calibration implementation effect, and obtain the process optimization impact analysis.

[0121] By comparing the operational data of the AI ​​analysis process before and after iterative optimization, the effectiveness of the optimization is evaluated. This includes assessing whether the model inversion accuracy has improved (e.g., whether the consistency between the inversion results and actual operation has increased); whether the baseline adaptability has been enhanced (e.g., whether the dynamic baseline logic can adapt to changes in the inversion results more quickly and accurately); and whether the calibration implementation effect has improved (e.g., whether the number of deviation nodes has decreased and the stability of calibrated nodes has improved). Through this analysis, an impact analysis of the process optimization is obtained, summarizing the effectiveness and shortcomings of the iterative optimization.

[0122] Step S155: Integrate the dynamic behavior inversion results after closed-loop calibration, the dynamic baseline logic of the paper machine press section data model, the AI ​​analysis process after iterative optimization, the iterative analysis process data and the impact analysis of process optimization, organize them according to the structured requirements of the analysis report, and generate the iterative analysis report of the paper machine press section data model.

[0123] This report integrates the dynamic behavior inversion results after closed-loop calibration, the dynamic baseline logic of the paper machine press department data model, the iteratively optimized AI analysis process, iterative analysis process data, and the impact analysis of process optimization. Following the structural requirements of an analysis report, including an introduction, methods, results, discussion, and conclusions, the above content is organized and presented to generate an iterative analysis report for the paper machine press department data model. This iterative analysis report comprehensively summarizes the model's iterative optimization process, effects, and related data.

[0124] Based on the same inventive concept, please refer to Figure 2 The diagram shows a schematic block diagram of an AI analysis system 100 for a paper machine press section data model, provided in an embodiment of this application, for executing the AI ​​analysis method applied to the paper machine press section data model described above. The AI ​​analysis system 100 for the paper machine press section data model may include a communication unit 110, a machine-readable storage medium 120, and a processor 130.

[0125] In this embodiment, both the machine-readable storage medium 120 and the processor 130 are located within the AI ​​analysis system 100 applied to the data model of the paper machine press section, and are separately configured. However, it should be understood that the machine-readable storage medium 120 may also be independent of the AI ​​analysis system 100 applied to the data model of the paper machine press section, and may be accessed by the processor 130 via a bus interface. Alternatively, the machine-readable storage medium 120 may also be integrated into the processor 130 and may communicate and interact with external systems via the communication unit 110.

[0126] The processor 130 is the control center of the AI ​​analysis system 100 applied to the data model of the paper machine press section. It connects to various parts of the AI ​​analysis system 100 via various interfaces and lines. By running or executing software programs and / or modules stored in the machine-readable storage medium 120, and by calling data stored in the machine-readable storage medium 120, it performs various functions and processes data of the AI ​​analysis system 100, thereby providing overall monitoring of the AI ​​analysis system 100. Optionally, the processor 130 may include one or more processing cores; for example, the processor 130 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into the processor. The machine-readable storage medium 120 is used to store machine-executable instructions for executing the scheme of this application, and the processor 130 is used to execute the machine-executable instructions stored in the machine-readable storage medium 120 to implement the AI ​​analysis method applied to the data model of the paper machine press section provided in the aforementioned method embodiment.

[0127] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. An AI analysis method applied to a data model of a paper machine press section, characterized in that, The method includes: The cross-dimensional behavioral imprint of the paper machine press section data model is obtained. The cross-dimensional behavioral imprint includes parameter adjustment behavioral imprint, output response behavioral imprint and environmental interaction behavioral imprint. Dynamic behavior inversion is performed on the cross-dimensional behavioral imprints of the paper machine press section data model to explore the real-time correlation paths, triggering and transmission mechanisms and synergistic logic between different dimensional imprints, and obtain the dynamic behavior inversion results of the paper machine press section data model. Based on the dynamic behavior inversion results of the paper machine press section data model, a dynamic baseline logic is generated. The correlation adaptation conditions, transmission response templates and cooperative action boundaries of the baseline are adjusted according to the real-time changes of the inversion results to obtain the dynamic baseline logic of the paper machine press section data model. The dynamic behavior inversion results of the paper machine press section data model are compared with the dynamic baseline logic of the paper machine press section data model in a closed loop. The associated nodes, transmission nodes and cooperative nodes that deviate from the baseline in the inversion results are located, node calibration instructions are generated and fed back to the inversion process, and the dynamic behavior inversion results after closed loop calibration are obtained. The AI ​​analysis process of the paper machine press section data model is iteratively optimized based on the dynamic behavior inversion results after closed-loop calibration. The inversion results and dynamic benchmark logic are integrated to obtain the iterative analysis report of the paper machine press section data model.

2. The AI ​​analysis method applied to the data model of the press section of a paper machine according to claim 1, characterized in that, The dynamic behavior inversion of the cross-dimensional behavioral imprints of the paper machine press section data model is performed to mine the real-time correlation paths, triggering and transmission mechanisms, and synergistic logic between different dimensional imprints, and to obtain the dynamic behavior inversion results of the paper machine press section data model, including: The parameter adjustment behavior imprint, output response behavior imprint, and environmental interaction behavior imprint in the cross-dimensional behavior imprint of the paper machine press section data model are synchronously split according to the runtime sequence to obtain parameter adjustment behavior imprint segments, output response behavior imprint segments, and environmental interaction behavior imprint segments corresponding to multiple time segments. For each time segment, the behavior inversion within the dimension of the parameter regulation behavior imprint segment is performed. The occurrence order, interaction path and linkage triggering relationship of different regulation behaviors in the parameter regulation behavior imprint segment are analyzed to obtain the behavior association logic within the parameter regulation dimension. For each time segment, the parameter adjustment behavior imprint segment and the output response behavior imprint segment are subjected to inter-dimensional behavior inversion to capture the transmission path, temporal correlation law and intensity relationship of parameter adjustment behavior triggering output response behavior, and obtain the inter-dimensional behavior correlation path of parameter output. For each time segment, the environmental interaction behavior imprint segment is compared with the parameter adjustment behavior imprint segment and the output response behavior imprint segment in a multi-dimensional behavior inversion. The influence path of environmental interaction behavior on parameter adjustment behavior, the mode of action on output response behavior and the synergistic effect conditions of the three are identified, and the multi-dimensional synergistic effect logic of environmental parameter output is obtained. The behavioral correlation logic within the parameter adjustment dimension, the behavioral correlation path between parameter output dimensions, and the multi-dimensional synergistic logic of environmental parameter output are integrated according to the time sequence to construct a structured inversion model with behavioral nodes as the core, correlation paths as the link, and synergistic logic as the constraint, thus obtaining a preliminary dynamic behavioral inversion model. The preliminary dynamic behavior inversion model is processed to ensure temporal consistency, connecting the model parts corresponding to different time segments. This ensures that the associated paths, transmission mechanisms, and collaborative logic in the preliminary dynamic behavior inversion model remain temporally consistent, resulting in the dynamic behavior inversion results of the paper machine press section data model.

3. The AI ​​analysis method applied to the data model of the press section of a paper machine according to claim 1, characterized in that, The dynamic baseline logic generated based on the dynamic behavior inversion results of the paper machine press section data model, which adjusts the correlation adaptation conditions, transmission response templates, and cooperative action boundaries of the baseline according to the real-time changes of the inversion results, yields the dynamic baseline logic of the paper machine press section data model, including: Extract the behavior correlation logic within the parameter adjustment dimension from the dynamic behavior inversion results of the paper machine press section data model, identify the effective scope of the correlation between parameter adjustment behaviors, the triggering premise of linkage and mutual compatibility requirements, and obtain the initial basis for the correlation adaptation conditions. The dynamic behavior inversion results of the data model of the paper machine press section are captured in real time. The real-time changes of the behavior association logic within the parameter adjustment dimension are tracked. The stability of the change is determined by comparing the data of K consecutive monitoring cycles. The scope of action and triggering conditions of the association adaptation conditions are adjusted according to the changed association logic to obtain the dynamic association adaptation conditions. The behavioral correlation paths between parameter output dimensions in the dynamic behavior inversion results of the paper machine press section data model are analyzed, and the time response range, intensity standard and feedback adjustment law of parameter output transmission are extracted to obtain the initial basis of the transmission response template. Based on the real-time transmission data of the dynamic behavior inversion result of the paper machine press section data model, the operation changes of the behavior correlation path between parameter output dimensions are tracked. The change trend is determined by comparing the data differences between two adjacent transmission cycles. The time range and intensity standard of the transmission response template are adjusted according to the changed transmission path to obtain the dynamic transmission response template. The dynamic behavior inversion results of the paper machine press section data model are analyzed to identify the multi-dimensional synergistic logic of environmental parameter output in the data model, and the effective implementation conditions, environmental adaptation requirements and parameter output synergistic boundaries of the three are identified to obtain the initial basis for the synergistic boundary. The system captures real-time changes in multi-dimensional synergistic logic of environmental parameters, determines the impact of changes by monitoring the differences in synergistic effects before and after changes in environmental factors, and adjusts the implementation conditions and adaptation requirements of the synergistic boundary according to the changed synergistic logic to obtain the dynamic synergistic boundary. The dynamic correlation adaptation conditions, dynamic transmission response templates, and dynamic collaborative action boundaries are integrated and organized according to the structured requirements of the baseline logic. A real-time update triggering mechanism is added to generate a dynamic baseline logic for the paper machine press section data model that can follow the changes in the dynamic behavior inversion results.

4. The AI ​​analysis method applied to the data model of the press section of a paper machine according to claim 1, characterized in that, The process involves performing closed-loop calibration of the dynamic behavior inversion results of the paper machine press section data model with the dynamic baseline logic of the paper machine press section data model. This includes locating related nodes, transmission nodes, and collaborative nodes in the inversion results that deviate from the baseline, generating node calibration instructions, and feeding them back to the inversion process. The result is the closed-loop calibrated dynamic behavior inversion results, including: The behavior correlation logic within the parameter adjustment dimension in the dynamic behavior inversion result of the paper machine press section data model is compared node by node with the dynamic correlation adaptation conditions in the dynamic baseline logic of the paper machine press section data model. Nodes in the behavior correlation logic within the parameter adjustment dimension that do not meet the dynamic correlation adaptation conditions are identified and marked as correlation deviation nodes, thus obtaining a set of correlation deviation nodes. The behavior association path between parameter output dimensions in the dynamic behavior inversion result of the paper machine press section data model is compared with the dynamic transmission response template in the dynamic baseline logic of the paper machine press section data model path one by one. Nodes that do not conform to the dynamic transmission response template in the behavior association path between parameter output dimensions are identified and marked as transmission deviation nodes, thus obtaining a set of transmission deviation nodes. The multi-dimensional synergistic logic of environmental parameter output in the dynamic behavior inversion result of the paper machine press section data model is compared with the dynamic synergistic boundary in the dynamic baseline logic of the paper machine press section data model. Nodes in the multi-dimensional synergistic logic of environmental parameter output that do not conform to the dynamic synergistic boundary are identified and marked as synergistic deviation nodes, thus obtaining a set of synergistic deviation nodes. For each deviation node in the set of associated deviation nodes, the set of transmitted deviation nodes, and the set of collaborative deviation nodes, the source is traced to mine the upstream node influence path, behavior transmission link, and external factors that caused the deviation node, and the deviation source tracing result is obtained. Based on the deviation tracing results, a targeted calibration instruction is generated for each deviation node. The calibration instruction includes the node adjustment direction, correlation correction requirements, and transmission path optimization suggestions, resulting in a set of node calibration instructions. The set of node calibration instructions is fed back to the dynamic behavior inversion process of the data model of the paper machine press section, driving the inversion process to adjust the deviation nodes, correct the correlation of related deviation nodes, the transmission path of the transmission deviation nodes, and the collaborative logic of the collaborative deviation nodes, so as to obtain the dynamic behavior inversion result after closed-loop calibration.

5. The AI ​​analysis method applied to the data model of the press section of a paper machine according to claim 2, characterized in that, The step of performing inter-dimensional behavior inversion on the parameter adjustment behavior imprint fragments and output response behavior imprint fragments corresponding to each time segment, capturing the transmission path, temporal correlation rules, and intensity relationship of parameter adjustment behavior triggering output response behavior, and obtaining the inter-dimensional behavior correlation path of parameter output includes: Extract the key adjustment behaviors and their occurrence times from the parameter adjustment behavior imprint segments corresponding to each time segment, and record the type, implementation magnitude and duration of the key adjustment behaviors to obtain key parameter adjustment behavior information; Extract the key response behaviors and their occurrence times from the output response behavior imprint segments corresponding to each time segment, record the type, intensity, and duration of the key response behaviors, and obtain the key output response behavior information. The key behavior information of parameter adjustment and the key behavior information of output response are mapped according to the time of occurrence to establish the time correspondence between adjustment behavior and response behavior, and the time sequence correspondence data is obtained. Based on the time-series correspondence data analysis, the triggering association between the key behavior of parameter adjustment and the key behavior of output response is determined, the key behavior of parameter adjustment corresponding to each key behavior of output response is determined, the direct and indirect transmission paths between behaviors are identified, and a set of behavior transmission paths is obtained. By analyzing the time interval between the occurrence of regulatory behavior and the occurrence of response behavior in the time-series correspondence data, the response time patterns corresponding to different types of regulatory behavior are summarized, and the time correlation patterns are obtained. By comparing the implementation magnitude of key parameter adjustment behaviors with the performance intensity of key output response behaviors, we can explore the relationship between the two, summarize the response intensity patterns corresponding to different implementation magnitude adjustment behaviors, and obtain the intensity-effect relationship. The behavioral transmission path set, time correlation rules, and intensity relationship are integrated according to the structural requirements of inter-dimensional correlation paths to obtain the behavioral correlation paths between parameter output dimensions.

6. The AI ​​analysis method applied to the data model of the press section of a paper machine according to claim 2, characterized in that, The process involves performing multi-dimensional behavior inversion on the environmental interaction behavior imprint fragments corresponding to each time segment, along with the parameter adjustment behavior imprint fragments and the output response behavior imprint fragments. This identifies the influence path of environmental interaction behavior on parameter adjustment behavior, the mode of action on output response behavior, and the synergistic conditions among the three, resulting in the multi-dimensional synergistic logic of environmental parameter output, including: Extract key environmental factors and their changes from the environmental interaction behavior imprint segments corresponding to each time segment, record the type, changes and duration of key environmental factors, and obtain information on key environmental interaction factors; The key environmental interaction factors are associated with the corresponding parameter adjustment behavior imprint fragments to identify the adjustment of parameter adjustment behavior after changes in key environmental interaction factors, and to identify the direct and indirect paths of environmental factors affecting parameter adjustment, thus obtaining the path of environmental influence on parameter adjustment. By associating the key environmental interaction factors with the corresponding output response behavior imprint fragments, we can identify the characteristics of output response behavior changes after changes in key environmental interaction factors, summarize the ways and laws of how environmental factors affect output response, and obtain the mode of environmental effect on output response. By analyzing the time sequence of changes in key environmental interaction factors, parameter regulation behavior, and output response behavior, the time range of their synergistic effect is determined, and the synergistic effect time range is obtained. By combining the influence path of the environment on parameter adjustment, the mode of the environment on the output response, and the time range of the synergistic effect, the implementation conditions of the synergistic effect of the three are explored, including the changes in environmental factors, the implementation methods of parameter adjustment, and the adaptation standards of the output response, so as to obtain the implementation conditions of the synergistic effect. By integrating the environmental influence path on parameter adjustment, the environmental response mode to output, the time range of synergistic effect, and the conditions for synergistic effect implementation, and organizing them according to the structural requirements of multi-dimensional synergistic effect logic, a multi-dimensional synergistic effect logic for environmental parameter output is obtained.

7. The AI ​​analysis method applied to the data model of the press section of a paper machine according to claim 3, characterized in that, The system captures the changing trend of the dynamic behavior inversion results of the paper machine press section data model in real time, tracks the real-time changes of the behavior correlation logic within the parameter adjustment dimension, determines the stability of the changes through data comparison of three consecutive monitoring periods, and adjusts the scope and triggering conditions of the correlation adaptation conditions according to the changed correlation logic to obtain dynamic correlation adaptation conditions, including: Based on the set monitoring period, the behavior correlation logic within the parameter adjustment dimension in the dynamic behavior inversion result of the paper machine press section data model is continuously monitored, and the addition, change and disappearance of the correlation relationship in the behavior correlation logic within the parameter adjustment dimension are captured to obtain correlation logic change data. Trend analysis is performed on the data of the changes in the related logic to identify the direction and duration of changes in the behavioral related logic within the parameter adjustment dimension. The stability of the changes is determined by comparing data from K consecutive monitoring periods, and the change attributes are classified. If the changing attribute is a continuous change, extract the effective scope of action, linkage triggering premises and mutual compatibility requirements in the behavior association logic within the parameter adjustment dimension after the change, and use them as the basis for adjusting the association adaptation conditions; Based on the adjustment criteria, the effective scope of the associated adaptation conditions may be expanded or narrowed, and the specific requirements for the triggering premise of linkage may be optimized. If the change attribute is a temporary fluctuation, retain the core content of the original associated adaptation conditions and make local adjustments to the adaptation conditions corresponding to the fluctuating part; The effective scope of action, triggering conditions, and local adjustments after integration and adjustment are used to generate dynamic correlation and adaptation conditions. Furthermore, the real-time transmission data based on the dynamic behavior inversion results of the paper machine press section data model tracks the operational changes of the behavioral correlation path between parameter output dimensions, determines the change trend by comparing the data differences between two adjacent transmission cycles, and adjusts the time range and intensity standard of the transmission response template according to the changed transmission path to obtain a dynamic transmission response template, including: Real-time transmission data is collected from the dynamic behavior inversion results of the data model of the paper machine press section. The time interval data, intensity matching data and feedback adjustment data of the parameter output transmission process are extracted to obtain real-time transmission characteristic data. Statistical analysis is performed on the real-time transmission characteristic data, and compared with historical transmission characteristic data to identify changes in time intervals, intensity matching, and feedback adjustment in the behavioral correlation paths between parameter output dimensions. The trend of change is determined by comparing the data differences between two adjacent transmission cycles, and the change attributes are classified. If the change attribute is a long-term trend change, redetermine the time range of the transmission response based on the behavioral correlation path between the output dimensions of the changed parameters, and adjust the allowable time interval. Adjust the strength standard of the conduction response based on the changed strength matching data, and optimize the strength adaptation requirements; If the change is a short-term, accidental change, maintain the core parameters of the original conduction response template, and temporarily adjust the time range and intensity standard corresponding to the changed part; By integrating and adjusting the time range, intensity standards, and temporary adjustment information, a dynamic transmission response template is generated.

8. The AI ​​analysis method applied to the data model of the press section of a paper machine according to claim 4, characterized in that, The process involves tracing the source of each deviation node in the set of associated deviation nodes, the set of transmitted deviation nodes, and the set of coordinated deviation nodes, mining the upstream node influence path, behavioral transmission link, and external factors that caused the deviation node, and obtaining the deviation source tracing results, including: Starting from each deviation node, the upstream related nodes in the dynamic behavior inversion results of the paper machine press section data model are traced back in reverse. The relationship and transmission path between the deviation node and the upstream node are identified, and the range of upstream nodes that may affect the deviation node is determined to obtain the range of upstream related nodes. Analyze the behavior state of each node in the upstream associated node range, determine whether the operation mode of the upstream node conforms to the model's preset logic, identify the direct upstream influencing nodes that cause the deviation nodes, and obtain information on the directly influencing nodes; Identify the behavioral transmission links from directly influencing nodes to deviation nodes, clarify the intermediate nodes, transmission methods, and mechanisms of action in the transmission process, and obtain details of the deviation transmission links; Investigate the changes in external environmental factors corresponding to deviation nodes, analyze whether there are abnormal environmental factors and changes in environmental interaction behavior imprints, determine whether external environmental factors are the cause of deviation, and obtain information on external factors. By integrating the upstream associated node range, directly influencing node information, deviation transmission link details, and external factor information, a deviation tracing result corresponding to each deviation node is formed.

9. The AI ​​analysis method applied to the data model of the press section of a paper machine according to claim 1, characterized in that, The AI ​​analysis process for iteratively optimizing the paper machine press section data model based on the dynamic behavior inversion results after closed-loop calibration integrates the iterative inversion results and dynamic baseline logic to obtain an iterative analysis report of the paper machine press section data model, including: The dynamic behavior inversion results after closed-loop calibration are analyzed, and the behavior correlation logic within the optimized parameter adjustment dimension, the behavior correlation path between parameter output dimensions, and the multi-dimensional synergistic logic of environmental parameter output are extracted from the results to obtain the core content of model optimization. Based on the core content of the model optimization, the AI ​​analysis process of the paper machine press section data model is adjusted, and the operation logic of the data acquisition, behavior inversion, benchmark generation and calibration links in the analysis process is optimized to obtain the iteratively optimized AI analysis process; The iteratively optimized AI analysis process is applied to the operation analysis of the data model of the paper machine press section. Model operation data, benchmark adjustment data and calibration implementation data are collected during the analysis process to obtain iterative analysis process data. By comparing the AI ​​analysis process operation data before and after iterative optimization, the impact of process optimization on model inversion accuracy, benchmark adaptability and calibration implementation effect is analyzed, resulting in a process optimization impact analysis. The dynamic behavior inversion results after closed-loop calibration, the dynamic baseline logic of the paper machine press department data model, the AI ​​analysis process after iterative optimization, the data of the iterative analysis process and the impact analysis of process optimization are integrated and organized according to the structured requirements of the analysis report to generate the iterative analysis report of the paper machine press department data model.

10. An AI analysis system applied to the data model of the press section of a paper machine, characterized in that, include: processor; A machine-readable storage medium for storing machine-executable instructions of the processor; The processor is configured to execute the AI ​​analysis method for the data model of the paper machine press section as described in any one of claims 1 to 9 by executing the machine-executable instructions.

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