A task integrity judgment method for multi-device cooperative work under complex constraint conditions

Abstract

The application discloses a kind of task integrity judgment methods of multiple equipment collaborative work under complex constraint condition, comprising: establishing comprehensive data acquisition system, real-time acquisition multi-dimensional data of collaborative equipment and pre-processing, combined with constraint modeling, complex constraint condition is quantified as computable constraint condition model;Build multi-agent collaborative evaluation model, and the multi-agent collaborative evaluation model considers each device participating in collaborative work as an intelligent agent, realizes the evaluation of overall task integrity by simulating the cooperation process between devices;Use deep learning technology to automatically extract high-level features from multi-dimensional data;Design distributed real-time analysis system, combined with adaptive optimization mechanism, realize large-scale data processing and model continuous optimization.The method of the application can not only improve the reliability and efficiency of collaborative tasks, but also provide strong support for equipment task decision-making, and has an important role in improving the intelligent level and decision accuracy of operation support.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of equipment operation support digitization, and in particular to a task integrity judgment method for multi-device collaborative work under complex constraints. BACKGROUND

[0002] In equipment operation support, multi-device collaborative work has become an important means to efficiently complete tasks and achieve strategic initiative. However, with the complexity of the battlefield environment and the refinement of task requirements, multi-device collaborative work faces many challenges, the most prominent of which is how to accurately judge the integrity of the task under complex constraints. Traditional task integrity judgment methods mainly rely on single-device state monitoring and simple collaboration rules, which are difficult to cope with complex situations in multi-device collaborative work.

[0003] In recent years, with the development of artificial intelligence and big data technology, some researchers have attempted to apply machine learning and data analysis methods to the evaluation of multi-device collaborative tasks. These methods have improved the accuracy of task integrity judgment to some extent, but still have some obvious shortcomings. First, most existing methods fail to fully consider the impact of complex constraints on task integrity, resulting in a large deviation between the judgment results and the actual situation in actual application. Second, existing methods often ignore the dynamic interaction between devices, making it difficult to accurately capture instantaneous changes and potential risks in the collaborative process.

[0004] In addition, current task integrity judgment systems generally lack effective integration capabilities for multi-source heterogeneous data. In actual production environments, different devices may use different data collection methods and communication protocols. How to effectively integrate heterogeneous data and extract valuable information from it is a key challenge to improve judgment accuracy. At the same time, existing systems also face the problem of computational efficiency and response speed when dealing with large-scale real-time data streams, making it difficult to meet the high requirements for real-time performance in combat scenarios. SUMMARY

[0005] The technical problem to be solved by the present application is to provide a task integrity judgment method for multi-device collaborative work under complex constraints, which comprehensively uses multi-agent systems, deep learning, and distributed computing, etc. advanced technology, through the establishment of a multi-dimensional task evaluation model, realizes the accurate modeling and dynamic adjustment of complex constraints. At the same time, this method also introduces innovative data fusion algorithms and real-time analysis techniques, effectively solving the problems of heterogeneous data processing and real-time response.

[0006] The technical solution adopted by the present application to solve its technical problems is:

[0007] The application provides a task integrity judgment method for multiple devices to work cooperatively under complex constraints, comprising:

[0008] A comprehensive data acquisition system is established to acquire multi-dimensional data of the cooperative devices in real time and pre-process the data, and the complex constraints are quantified into computable constraint condition models by combining constraint modeling;

[0009] Based on the pre-processed data and the constraint condition models, a multi-agent cooperative evaluation model is constructed, which regards each device participating in the cooperative work as an agent, and realizes the evaluation of the overall task integrity by simulating the cooperation process between the devices;

[0010] Deep learning technology is used to automatically extract high-level features from multi-dimensional data;

[0011] A distributed real-time analysis system is designed, and a self-adaptive optimization mechanism is combined to realize large-scale data processing and continuous optimization of the model.

[0012] Further, the method for acquiring multi-dimensional data of the cooperative devices in real time comprises: arranging multiple types of sensor networks according to the characteristics and working environment of the cooperative devices, and monitoring the running parameters, environmental data and task execution state information of the cooperative devices in different cooperative modes and task stages through the sensor networks.

[0013] Further, the method for pre-processing the data comprises: cleaning the originally acquired multi-dimensional data to remove abnormal values, missing values and duplicate data; the abnormal values are detected and corrected by using statistical methods combined with expert knowledge; the missing values are processed by selecting corresponding interpolation methods and data reconstruction methods according to the data characteristics; after data cleaning, standardization processing is performed to unify different dimensional data to the same scale.

[0014] Further, the method for pre-processing the data further comprises: time series synchronization, data structuring and labeling strategy; the time series synchronization processing adds high-precision time stamp records to the data and uses synchronization algorithms to ensure the time consistency between the data of different devices; the data structuring and labeling strategy processing organizes the processed data into a structure convenient for subsequent analysis, and labels the data according to the task integrity evaluation standard, and expert knowledge and automatic algorithms are used in the labeling process.

[0015] Further, the constraint condition model in the method is specifically:

[0016] The time synchronization constraint is modeled, and the time correlation of each device is quantified into a series of mathematical expressions by establishing a time dependency graph and a time constraint matrix, which represents the absolute time requirement, the relative time relationship and the fault tolerance range;

[0017] Modeling the spatial position constraints, according to the physical layout of the device, the working range, the moving track factors, converting the spatial position constraints into a series of geometric constraints and kinematics equations by constructing a three-dimensional space model and a collision detection algorithm;

[0018] Modeling the resource allocation balance constraints, according to the reasonable allocation and dynamic adjustment of various guarantee resources including energy, equipment and manpower, converting the resource allocation problem into a solvable mathematical optimization problem by establishing a resource flow network model and an optimization equation group;

[0019] Modeling the logical dependency relationship constraints between devices, including device start-stop sequence, process dependency, information interaction, converting these logical dependencies into a series of conditional statements and state equations by constructing a state transition graph and a logical relationship matrix;

[0020] On the basis of constraint modeling, further constraint quantization; the constraint description in constraint modeling is converted into a specific numerical range or mathematical expression, and a dynamic weight is introduced, and different weights are given to different constraint conditions according to the current production status and task importance.

[0021] Further, the method of constructing a multi-agent collaborative evaluation model in the method comprises:

[0022] Design and definition of the agent, each device participating in collaborative work is abstracted as an agent with autonomous decision-making ability; each agent contains the following core components: state vector, perception module, decision module and execution module; the state vector describes the current running state of the device, including position, speed, key parameters of load; the perception module is responsible for receiving and processing information from the environment and other agents; the decision module makes decisions on the next action based on the current state and perception information, combined with the predetermined strategy and learning algorithm; the execution module is responsible for converting the decision into specific operation instructions;

[0023] Interaction mechanism between agents, adopting a multi-level interaction protocol, including information sharing, task negotiation, resource allocation; information sharing mechanism, information sharing between agents, real-time acquisition of global information; task negotiation mechanism allows dynamic coordination between agents on task allocation and execution order to adapt to changes in the production environment; resource allocation mechanism is used to realize the optimal allocation of resources between agents;

[0024] Multi-dimensional evaluation index system, including production efficiency, quality index, collaborative consistency, robustness, adaptability index; the index is quantified by mathematical formula and algorithm, and is weighted and combined according to the specific task requirements to form the final task integrity score;

[0025] The collaborative decision-making framework based on reinforcement learning, each agent learns and optimizes its own decision-making strategy through continuous interaction with the environment and other agents; an improved multi-agent deep reinforcement learning algorithm is used to handle continuous action space and non-static environment, and attention mechanism and graph neural network are used to enable the model to effectively capture the complex dependency between agents;

[0026] Monte Carlo simulation technology is integrated in the model, and through simulation of a large number of random scenes, various possible collaborative working conditions are evaluated to obtain the task integrity judgment result;

[0027] A dynamic constraint adaptation mechanism is used to adjust the behavior boundary and decision-making space of the agent in real time based on the constraint condition model;

[0028] Causal reasoning technology is integrated in the model, and through the construction of causal graphs and intervention experiments, the influence path and degree of different factors on task integrity are analyzed;

[0029] Through the iterative optimization process of model training and verification, historical data is used for offline training, and then model parameters are continuously updated and optimized through online learning to obtain a trained multi-agent collaborative evaluation model.

[0030] Further, the method for automatically extracting high-level features by deep learning technology in the method comprises:

[0031] Different deep learning models are used for different types of data sources; for time series data, a combination structure of long short-term memory network and one-dimensional convolutional neural network is used; for image and video data, an improved convolutional neural network structure is used; for text data, a model based on Transformer is used;

[0032] Attention mechanism is introduced in the feature extraction process, and through embedding attention layers in each deep learning model, the most critical data segments and features for task integrity judgment are automatically identified and focused;

[0033] In the process of fusing multi-source heterogeneous data, a multi-modal feature fusion framework is used to normalize the features extracted from each data source to eliminate the scale difference between different modalities; through a multi-level feature fusion network, the features are fused level by level; in each layer of fusion, a cross-modal attention mechanism is introduced, so that the features of different modalities can be enhanced and supplemented each other; the fused features are mapped to the task integrity evaluation space through a fully connected layer;

[0034] Adversarial training technology is used to add an adversarial sample generator in the feature fusion network;

[0035] Adopt dynamic feature selection mechanism, according to current available data source and data quality, automatically adjust feature extraction and fusion strategy, when some sensor data is missing, automatically enhance the weight of other related data source.

[0036] Further, the method for designing a distributed real-time analysis system and an adaptive optimization mechanism comprises:

[0037] In the distributed real-time analysis system, a distributed architecture based on edge computing and cloud computing is adopted, in the edge layer, each device or device group is equipped with a lightweight preprocessing unit and a feature extraction module, the edge node processes local data in real time, performs preliminary feature extraction and task integrity evaluation; the cloud is responsible for data fusion, deep learning model training and global decision optimization;

[0038] Adopting a stream computing framework to process continuous data streams, through an adaptive load balancing algorithm, dynamically adjusting task allocation according to current data traffic and computing resources, ensuring stable performance under peak load;

[0039] In the adaptive optimization mechanism, online learning and incremental updating mechanism are adopted, and the decision model is continuously adjusted and optimized according to real-time feedback to adapt to the dynamic changes of the production environment.

[0040] Further, the method also adopts a multi-scenario simulation and risk assessment module, which quickly evaluates the potential risks and benefits of different decision options by executing multiple simulation scenarios in parallel; a visual interface and alarm mechanism are adopted to display the task integrity evaluation results in real time and to timely warn of abnormal situations.

[0041] The present application provides a task integrity judgment system for multiple devices working collaboratively under complex constraints, comprising:

[0042] A memory for storing executable computer programs;

[0043] A processor for executing the executable computer programs stored in the memory to implement the task integrity judgment method for multiple devices working collaboratively under complex constraints.

[0044] The present application has the following advantages:

[0045] The present application provides a task integrity judgment method for multiple devices working collaboratively under complex constraints. The method comprehensively uses advanced technologies such as multi-agent system, deep learning and distributed computing, and realizes accurate modeling and dynamic adjustment of complex constraints by establishing a multi-dimensional task evaluation model. At the same time, the method also introduces innovative data fusion algorithm and real-time analysis technology, effectively solving the problems of heterogeneous data processing and real-time response.

[0046] (1) Improve the accuracy and reliability of task integrity judgment:

[0047] The present application realizes accurate description and dynamic adjustment of complex constraint conditions by establishing a multi-dimensional data acquisition system and innovative constraint modeling technology. Combined with the multi-agent collaborative evaluation model, it can fully capture the dynamic interaction between devices, significantly improving the accuracy and reliability of task integrity judgment. This enables equipment managers to timely identify potential problems and effectively prevent the risk of collaborative task failure.

[0048] (2) Enhance the system's processing capability for heterogeneous data:

[0049] By introducing deep learning technology for feature extraction and fusion, the present application overcomes the limitations of traditional methods in processing multi-source heterogeneous data. This method can automatically extract high-level features from different types of device data, effectively integrate various information, and provide more comprehensive and accurate basis for task integrity judgment. This greatly improves the system's adaptability and judgment effect in complex production environments.

[0050] (3) Realize real-time analysis and rapid decision-making:

[0051] The distributed real-time analysis system designed in the present application, combined with edge computing and cloud computing technology, can efficiently process large-scale data and quickly make decisions. This feature ensures that in the complex environment of multi-device collaboration, the system can respond to various changes in time, providing real-time and accurate task integrity evaluation results for production management, effectively supporting rapid decision-making and timely intervention.

[0052] (4) Improve the long-term adaptability and stability of the system:

[0053] By introducing an adaptive optimization mechanism, the present application realizes continuous learning and updating of the judgment model. This enables the system to continuously optimize itself according to new data and feedback, adapting to the dynamic changes of production environment and task requirements. This feature ensures that the method maintains high accuracy and stability in long-term application, providing reliable support for the continuous improvement of the efficiency of intelligent operation and maintenance of equipment. BRIEF DESCRIPTION OF DRAWINGS

[0054] The present application will be further described below in conjunction with the drawings and examples, in which:

[0055] Figure 1 is a task integrity judgment method for multi-device collaborative work under complex constraint conditions according to an embodiment of the present application.

[0056] Figure 2 is the specific process of an embodiment of the present application.

[0057] Figure 3is a multi-agent collaborative evaluation model construction process of an embodiment of the present application.

[0058] Figure 4 is a deep learning feature extraction and fusion process of an embodiment of the present application.

[0059] Figure 5 is Apache Flink task processing of an embodiment of the present application. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solutions and advantages of the present application clearer and more apparent, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0061] Embodiment 1

[0062] The present application proposes a task integrity judgment method for multiple devices working collaboratively under complex constraints, aiming to solve the problem of task integrity evaluation in a multi-device collaborative environment. The method mainly includes the following four key aspects. As shown in Figure 1

[0063] This method establishes a comprehensive data acquisition system, which collects real-time operation parameters, environmental data and task execution state information of collaborative devices. After preprocessing the collected data, combined with innovative constraint modeling technology, the complex constraints, including time synchronization requirements, spatial position restrictions, resource allocation balance, are quantified into calculable mathematical models. This process realizes the accurate description and dynamic adjustment of complex constraints, laying a foundation for subsequent task integrity judgment.

[0064] S02 Multi-agent collaborative evaluation model:

[0065] Based on the preprocessed data and constraint condition model, a multi-agent collaborative evaluation model is constructed. This model regards each device participating in collaborative work as an agent, and through simulating the collaboration process between devices, it realizes the evaluation of the overall task integrity. This method can effectively capture the dynamic interaction between devices, improving the accuracy and comprehensiveness of task integrity judgment.

[0066] S03 Deep learning feature extraction and fusion:

[0067] Deep learning technology is introduced for feature extraction, automatically extracting high-level features from multi-source heterogeneous data. This step can effectively handle various data generated by different devices, overcoming the limitations of traditional methods in handling heterogeneous data. Through the deep learning model, the system can capture complex patterns and potential risks in the device collaboration process, further improving the accuracy of task integrity judgment. ​

[0068] S04 Distributed Real-Time Analysis and Adaptive Optimization:

[0069] A distributed real-time analysis system is designed, which utilizes a combination of edge computing and cloud computing architecture to achieve efficient processing of large-scale data and rapid decision-making. At the same time, an adaptive optimization mechanism is introduced, which continuously learns and updates the model, enabling the system to continuously optimize the judgment model based on new data and feedback. This ensures that the method maintains long-term high accuracy and stability in the face of dynamic changes in production environment and task requirements.

[0070] Through the organic combination of these four key aspects, the invention realizes the accurate judgment of the integrity of the task of multiple devices working together under complex constraints. This method not only improves the reliability and efficiency of collaborative tasks, but also provides strong support for task decision-making.

[0071] Example 2

[0072] As Figure 2 shown, the specific embodiments of the present invention include the following steps:

[0073] Step 1: Data acquisition and preprocessing

[0074] Data acquisition and preprocessing is the key foundation for realizing the integrity judgment of multiple devices working together under complex constraints. This step aims to obtain comprehensive and accurate collaborative device operation data, and to clean, standardize and synchronize the original data, laying a solid foundation for subsequent constraint modeling and evaluation model construction.

[0075] In the data acquisition stage, a variety of types of sensor networks need to be arranged according to the characteristics of the collaborative devices and the working environment. These sensors not only collect the operating parameters of each device, but also monitor environmental data and task execution state information. The data acquisition system should have high sampling rate, high precision and anti-interference ability to ensure the capture of subtle changes and mutual influences of devices during collaborative work. At the same time, the data acquisition process should cover the running state of devices in different collaborative modes and task stages to fully reflect the dynamic characteristics of multiple devices working together.

[0076] The data preprocessing stage is a key step to transform raw collected data into high-quality data sets that can be used for subsequent analysis. First, the original data is cleaned to remove outliers, missing values and duplicate data. Outlier processing uses statistical methods combined with expert knowledge for detection and correction to ensure that important abnormal state information is not mistakenly deleted. For missing values, appropriate interpolation methods are selected and advanced data reconstruction techniques are used according to the data characteristics. After data cleaning, standardization is performed to unify different dimensional data to the same scale. Standardization helps to eliminate the dimensional differences between different devices and different parameters, improving the training effect of subsequent models.

[0077] Time series synchronization is an important step in multi-device collaborative data processing. Due to the time delay or sampling frequency difference of different data acquisition systems, it is necessary to align all data to the same time axis. This is achieved through high-precision timestamp recording and advanced synchronization algorithms. Synchronization processing ensures the time consistency between different device data, providing a reliable data foundation for subsequent constraint modeling and collaborative evaluation.

[0078] In addition, during the data preprocessing stage, the structure and labeling strategy of the data also need to be considered. The processed data is organized into a structure that is convenient for subsequent analysis, and the data is labeled according to the task integrity evaluation criteria. This process may need to combine expert knowledge and automated algorithms to ensure the accuracy and consistency of the labeling. At the same time, considering the complexity of collaborative tasks, appropriate data compression and storage solutions need to be designed to facilitate efficient processing and fast access of large-scale data.

[0079] Through the above data acquisition and preprocessing steps, a high-quality, multi-dimensional collaborative work dataset is obtained, laying a solid foundation for subsequent constraint modeling and multi-agent collaborative evaluation. This dataset not only contains the running state information of each device in collaborative work, but also has undergone strict cleaning, standardization and synchronization processing, and can truly reflect the dynamic characteristics of multi-device collaborative work and potential task integrity influencing factors.

[0080] Step 2: Constraint condition modeling and quantification

[0081] Constraint condition modeling and quantification is the core step of realizing multi-device collaborative work task integrity judgment under complex constraints. This step aims to convert various complex constraints into quantifiable and calculable mathematical models, providing accurate judgment basis for subsequent task integrity evaluation.

[0082] In the multi-device collaborative work environment, constraints usually exhibit characteristics of diversification and dynamic change. These constraints may involve time synchronization requirements, spatial position limitations, resource allocation balance, logical dependency relationships between devices, and other aspects. In order to effectively model these complex constraints, this method adopts a multi-level, multi-dimensional modeling strategy.

[0083] First, the time synchronization constraint is modeled. This involves device working time sequence, task execution time window, synchronization requirements of key nodes, etc. By establishing a time dependency graph and a time constraint matrix, the time correlation of each device is quantified into a series of mathematical expressions. These expressions not only consider absolute time requirements, but also include relative time relationships and fault tolerance ranges to adapt to time fluctuations in actual production.

[0084] Secondly, the modeling of spatial position constraints involves factors such as the physical layout of equipment, working range, and movement trajectory. By constructing a three-dimensional space model and collision detection algorithm, spatial constraints are converted into a series of geometric constraints and kinematic equations. This modeling method can effectively prevent physical conflicts between devices and ensure the safety of collaborative operation.

[0085] The modeling of resource allocation balance constraints focuses on the reasonable allocation and dynamic adjustment of various resources, including energy, equipment, and manpower. By establishing a resource flow network model and optimization equation set, the resource allocation problem is transformed into a solvable mathematical optimization problem. This method not only considers static resource allocation but also includes dynamic adjustment mechanisms to respond to sudden changes in the production process.

[0086] The logical dependency relationship between devices is another important constraint. This includes device start-stop sequence, process dependency, and information interaction. By constructing a state transition graph and a logical relationship matrix, these complex logical dependencies are converted into a series of conditional statements and state equations. This modeling method can accurately capture the interaction rules between devices and ensure the logical consistency of collaborative work.

[0087] Based on constraint modeling, further constraint quantification is carried out. This process involves converting abstract constraint descriptions into specific numerical ranges or mathematical expressions. Time synchronization requirements are quantified as allowed time deviation ranges, spatial constraints are quantified as specific coordinate boundary values, and resource allocation balance is quantified as upper and lower limits of resource utilization. This quantification process not only considers the strict limits of constraints but also introduces fuzzy logic and probability models to handle uncertainties and ambiguities in actual production.

[0088] A key innovation in constraint quantification is the introduction of a dynamic weight system. According to the current production status and task importance, different constraints are given different weights. This dynamic weight system can flexibly adjust the importance of each constraint condition, making the task integrity judgment more in line with actual needs.

[0089] In addition, this method also considers the mutual influence and potential conflicts between constraint conditions. By establishing a constraint relationship network, the interaction strength between constraints is analyzed and quantified. This global constraint analysis helps find the optimal balance point when multiple constraint conditions conflict with each other.

[0090] Through the above constraint condition modeling and quantification steps, this method converts various constraints in a complex multi-device collaborative work environment into a comprehensive, accurate, and computable mathematical model system. This model system not only accurately describes various constraint conditions but also has the ability of dynamic adjustment and global optimization, providing a solid theoretical foundation and computational framework for subsequent task integrity judgment.

[0091] Step 3: Multi-agent collaborative evaluation model construction

[0092] The multi-agent collaborative evaluation model construction is the core innovative link of the method, aiming to realize the accurate evaluation of task integrity by simulating the complex dynamic process of multi-device collaborative work. This model integrates knowledge from artificial intelligence, complex system theory, and operations research, providing strong theoretical support and computational framework for task integrity judgment under complex constraints.

[0093] The first step of model construction is the design and definition of agents. In this method, each device participating in collaborative work is abstracted as an agent with autonomous decision-making ability. Each agent contains the following core components: state vector, perception module, decision module, and execution module. The state vector describes the current operating state of the device, including position, speed, load, and other key parameters. The perception module is responsible for receiving and processing information from the environment and other agents. The decision module makes decisions on the next action based on the current state and perception information, combined with predetermined strategies and learning algorithms. The execution module is responsible for converting decisions into specific operation instructions.

[0094] The interaction mechanism between agents is another key element of the model. This method designs a multi-level interaction protocol, including information sharing, task negotiation, resource allocation, etc. The information sharing mechanism ensures that each agent can obtain global information in time, improving the accuracy of decision-making. The task negotiation mechanism allows agents to dynamically coordinate the task allocation and execution order among each other to adapt to changes in the production environment. The resource allocation mechanism realizes the optimal allocation of resources among agents, improving overall efficiency.

[0095] To accurately evaluate task integrity, this method constructs a multi-dimensional evaluation index system. This index system not only includes traditional production efficiency and quality indicators, but also introduces new indicators such as collaboration consistency, robustness, and adaptability. Collaboration consistency measures the degree of coordination of the behavior of multiple agents, robustness reflects the ability of the system to respond to external disturbances, and adaptability evaluates the response speed of the system to environmental changes. These indicators are quantified through mathematical formulas and algorithms, and are combined according to the specific task requirements to form the final task integrity score.

[0096] The core of the model is a reinforcement learning-based collaborative decision-making framework. Each agent learns and optimizes its decision-making strategy through continuous interaction with the environment and other agents. This method uses an improved multi-agent deep reinforcement learning algorithm, MADDPG (Multi-Agent Deep Deterministic Policy Gradient), to handle continuous action spaces and non-static environments. By introducing attention mechanisms and graph neural networks, the model can effectively capture complex dependencies between agents, improving learning efficiency and decision-making quality.

[0097] To handle uncertainties and randomness in actual production, the method integrates Monte Carlo simulation technology into the model. By simulating a large number of random scenarios, the system can evaluate various possible collaborative work situations, resulting in more reliable task integrity judgment results. This simulation-based evaluation method can also predict potential risks and bottlenecks, providing forward-looking decision support for production management.

[0098] Another innovation of the model is the introduction of a dynamic constraint adaptation mechanism. Based on the constraint condition model established in the previous step, the system can adjust the behavior boundaries and decision-making space of agents in real time. This mechanism ensures that when constraints change, the model can quickly adjust evaluation criteria and decision-making strategies, maintaining the accuracy and real-time nature of evaluation results.

[0099] To improve the model's interpretability and credibility, the method also integrates causal reasoning technology. By constructing causal graphs and conducting intervention experiments, the system can analyze the influence path and degree of different factors on task integrity. This not only improves the interpretability of evaluation results, but also provides valuable insights for optimizing collaborative work processes.

[0100] The training and verification of the model is an iterative optimization process. First, offline training is performed using historical data, and then online learning is used to continuously update and optimize model parameters. Verification processes such as cross-validation and A / B testing are used to ensure the model's generalization ability in different scenarios. At the same time, model ensemble technology is introduced to further improve the accuracy and stability of evaluation by integrating the results of multiple sub-models.

[0101] Through the construction of the above multi-agent collaborative evaluation model, this method realizes comprehensive and accurate evaluation of multi-device collaborative work task integrity under complex constraints. The model not only accurately judges the integrity status of the current task, but also predicts potential risks, providing strong support for production management decisions. Its adaptive learning ability and dynamic constraint handling mechanism enable it to effectively respond to various changes and challenges in the actual production environment, providing an innovative technical solution to improve production efficiency and quality.

[0102] Step 4: Deep Learning Feature Extraction and Fusion

[0103] Based on the multi-agent collaborative evaluation model, this method further introduces deep learning technology for feature extraction and fusion to better handle complex data structures and implicit patterns in multi-device collaborative work. This step aims to automatically extract high-level features from massive heterogeneous data and organically integrate these features to provide more comprehensive and in-depth information support for task integrity judgment.

[0104] First, a series of specialized deep learning models are designed for different types of data sources. For time series data, a combination structure of Long Short-Term Memory (LSTM) and One-Dimensional Convolutional Neural Network (1D-CNN) is adopted. This structure can effectively capture long-term dependencies and local features in time series data. For image and video data, an improved Convolutional Neural Network (CNN) structure is used to extract spatial and motion features. For text data, a Transformer-based model is used to understand complex semantic information.

[0105] A key innovation in feature extraction is the introduction of an attention mechanism. By embedding attention layers in each deep learning model, the system can automatically identify and focus on the most critical data segments and features for task integrity judgment. This not only improves the efficiency and accuracy of feature extraction, but also enhances the model's interpretability, allowing managers to better understand the basis for the judgment results.

[0106] To handle the fusion of multi-source heterogeneous data, this method designs a multi-modal feature fusion framework. This framework first normalizes the features extracted from each data source to eliminate scale differences between different modalities. Then, through a multi-level feature fusion network, feature fusion is achieved step by step. In each layer of fusion, a cross-modal attention mechanism is introduced, allowing features from different modalities to enhance and complement each other. Finally, the fused features are mapped to the task integrity evaluation space through a fully connected layer.

[0107] To improve the robustness and generalization ability of feature fusion, this method also introduces an adversarial training technique. By adding an adversarial sample generator to the feature fusion network, the system can learn more stable and universal feature representations, so that it can still maintain high accuracy in judgment when facing new or abnormal collaborative work scenarios.

[0108] Moreover, considering the uncertainty of data quality and availability in real equipment operation environments, the method designs a dynamic feature selection mechanism. This mechanism can automatically adjust the feature extraction and fusion strategy according to the current available data sources and data quality. When some sensor data is missing, the system can automatically enhance the weight of other related data sources to ensure the continuity and reliability of task integrity judgment.

[0109] Through the above deep learning feature extraction and fusion steps, the method realizes the intelligent processing of complex data in multi-device cooperative work. This not only greatly improves the accuracy and comprehensiveness of task integrity judgment, but also provides more rich and accurate input information for the multi-agent collaborative evaluation model. This deep learning-based feature processing method, combined with the aforementioned multi-agent model, forms a powerful task integrity evaluation system that can effectively cope with various challenges of multi-device cooperative work under complex constraints.

[0110] Step 5: Distributed Real-time Analysis and Decision Optimization

[0111] Based on deep learning feature extraction and fusion, the method further realizes distributed real-time analysis and decision optimization to meet the strict requirements of real-time and large-scale data processing in equipment combat environments. This step aims to transform the complex models and algorithms mentioned above into an efficient and scalable real-time system, ensuring that the task integrity of multi-device cooperative work under complex constraints can be timely and accurately evaluated and optimized.

[0112] First, a distributed architecture based on the combination of edge computing and cloud computing is designed. At the edge layer, each device or device group is equipped with a lightweight preprocessing unit and feature extraction module. These edge nodes can process local data in real time, perform preliminary feature extraction and task integrity evaluation, greatly reducing data transmission volume and the computational burden of the central node. The cloud is responsible for more complex data fusion, deep learning model training, and global decision optimization. This hierarchical architecture not only guarantees the real-time response capability of the system, but also realizes the high-precision requirement of global optimization.

[0113] In order to handle high-concurrency data streams, the method uses the stream computing framework Apache Flink to process continuous data streams, achieving millisecond-level data processing delay. At the same time, by implementing an adaptive load balancing algorithm, the system can dynamically adjust task allocation according to the current data traffic and computing resources, ensuring stable performance under peak load.

[0114] The decision optimization module incorporates online learning and incremental updating mechanisms. The system can continuously adjust and optimize the decision model based on real-time feedback, adapting to the dynamic changes in the production environment. At the same time, by implementing hot updating technology for the model, it ensures that model optimization does not affect the continuous operation of the system.

[0115] To improve the reliability and robustness of decision-making, the method also integrates multi-scenario simulation and risk assessment modules. By executing multiple simulation scenarios in parallel, the system can quickly assess the potential risks and benefits of different decision options, providing comprehensive decision support information for managers.

[0116] Finally, by designing an intuitive visualization interface and alarm mechanism, the method realizes real-time display of task integrity assessment results and timely warning of abnormal situations. This allows production managers to intuitively understand the current collaborative work status and respond quickly when potential problems arise.

[0117] Through the above steps of distributed real-time analysis and decision optimization, the method successfully transforms the complex task integrity judgment model into an efficient and reliable real-time system. This not only meets the needs of industrial production for real-time and large-scale data processing, but also provides strong technical support for intelligent management of multi-device collaborative work under complex constraints.

[0118] Through the above specific embodiments, the task integrity judgment method for multi-device collaborative work under complex constraints proposed by the present invention successfully solves the challenges faced by traditional judgment techniques in complex industrial environments. This method comprehensively uses innovative technologies such as multi-dimensional data acquisition and preprocessing, constraint condition modeling and quantization, multi-agent collaborative evaluation model construction, deep learning feature extraction and fusion, and distributed real-time analysis and decision optimization, achieving high-precision evaluation and prediction of the task integrity of multi-device collaborative work. The method of the present invention is not only suitable for multi-device collaborative management in equipment operation and support, but can also be extended to complex system management in emerging fields such as intelligent factories, flexible production lines, intelligent warehouse logistics, and collaborative robots. By improving the accuracy and real-time performance of task integrity judgment, the method helps to optimize multi-device collaboration strategies, identify potential risks in advance, and reduce the failure rate of collaboration, thereby significantly improving overall production efficiency and quality. The present invention provides a comprehensive, accurate, and real-time task integrity judgment method for multi-device collaborative work management under complex constraints. It not only solves the current problems of collaborative management in equipment operation, but also provides a new technical path for the development of future intelligent manufacturing. The application of this method will significantly improve production efficiency, reduce operating costs, and enhance the market competitiveness of enterprises.

[0119] To sum up, the method fuses advanced technologies such as data mining, machine learning and multi-agent system, realizes comprehensive analysis and accurate judgment of multi-device collaborative task under complex constraint conditions by establishing a multi-dimensional task integrity evaluation model. The method can not only improve the reliability and efficiency of collaborative task, but also provide strong support for equipment task decision, and has an important role in improving the intelligent level of operation support and decision accuracy.

[0120] The application range of the present application is wide, which can cover equipment task evaluation, intelligent manufacturing, flexible production line, intelligent warehousing logistics, collaborative robot and other fields, and provide new technical support for equipment auxiliary decision, industrial automation and further development of intelligent manufacturing.

[0121] It should be understood that the size of the serial number of each step in the above embodiment does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0122] It should be understood that those skilled in the art can improve or change according to the above description, and all these improvements and changes shall belong to the protection scope of the appended claims of the present application.

Claims

1. A method for determining the integrity of a task involving multi-device collaborative operation under complex constraints, characterized in that, include: Establish a comprehensive data acquisition system to collect and preprocess multidimensional data from collaborative devices in real time. Combined with constraint modeling, complex constraints are quantified into computable constraint models. Based on the preprocessed data and constraint model, a multi-agent collaborative evaluation model is constructed. The multi-agent collaborative evaluation model regards each device participating in the collaborative work as an agent and realizes the evaluation of the integrity of the overall task by simulating the collaboration process between devices. Using deep learning techniques to automatically extract high-level features from multi-dimensional data; Design a distributed real-time analysis system, combined with an adaptive optimization mechanism, to achieve large-scale data processing and continuous model optimization; The methods for constructing a multi-agent cooperative evaluation model in this approach include: The design and definition of intelligent agents abstracts each device participating in collaborative work as an intelligent agent with autonomous decision-making capabilities. Each intelligent agent contains the following core components: a state vector, a perception module, a decision-making module, and an execution module. The state vector describes the current operating state of the device, including key parameters such as position, speed, and load. The perception module is responsible for receiving and processing information from the environment and other intelligent agents. The decision-making module makes a decision on the next action based on the current state and perception information, combined with a predetermined strategy and learning algorithm. The execution module is responsible for translating the decision into specific operation instructions. The interaction mechanism between intelligent agents adopts a multi-layered interaction protocol, including information sharing, task negotiation, and resource allocation. The information sharing mechanism allows intelligent agents to share relevant information and obtain global information in real time. The task negotiation mechanism allows intelligent agents to dynamically coordinate task allocation and execution order to adapt to changes in the production environment. The resource allocation mechanism is used to achieve optimized allocation of resources among intelligent agents. A multi-dimensional evaluation index system, including production efficiency, quality indicators, synergy consistency, robustness, and adaptability indicators; the indicators are quantified through mathematical formulas and algorithms, and weighted and combined according to specific task requirements to form the final task integrity score; Based on a collaborative decision-making framework using reinforcement learning, each agent continuously learns and optimizes its decision-making strategy through ongoing interaction with the environment and other agents. An improved multi-agent deep reinforcement learning algorithm is used to handle continuous action spaces and non-static environments. Attention mechanisms and graph neural networks are employed to enable the model to effectively capture the complex dependencies between agents. Integrating Monte Carlo simulation technology into the model, various possible collaborative working situations are evaluated through the simulation of a large number of random scenarios, and the results of the task integrity judgment are obtained. A dynamic constraint adaptation mechanism is adopted, which adjusts the behavioral boundaries and decision space of the agent in real time based on the constraint model; The model integrates causal reasoning techniques, and analyzes the influence paths and extent of different factors on task integrity by constructing causal graphs and conducting intervention experiments. Through an iterative optimization process of model training and validation, offline training is performed using historical data, and then the model parameters are continuously updated and optimized through online learning to obtain a well-trained multi-agent collaborative evaluation model.

2. The method for determining the integrity of a task under complex constraints involving multi-device collaborative operation, as described in claim 1, is characterized in that... The method for real-time acquisition of multi-dimensional data from collaborative devices includes: deploying various types of sensor networks based on the characteristics and working environment of the collaborative devices, and monitoring the operating parameters, environmental data, and task execution status information of the collaborative devices in different collaborative modes and task stages through the sensor networks.

3. The method for determining the integrity of a task under complex constraints involving multi-device collaborative operation, as described in claim 1, is characterized in that... The data preprocessing methods in this method include: cleaning the original multidimensional data to remove outliers, missing values, and duplicate data; handling outliers by using statistical methods combined with expert knowledge for detection and correction; handling missing values ​​by selecting appropriate interpolation methods and data reconstruction methods based on data characteristics; and after data cleaning, standardization processing to unify data of different dimensions to the same scale.

4. The method for determining the integrity of a task under complex constraints involving multi-device collaborative operation, as described in claim 3, is characterized in that... The data preprocessing methods in this method also include: time series synchronization, data structuring and annotation strategies; the time series synchronization process adds high-precision timestamp records to the data and uses synchronization algorithms to ensure time consistency between data from different devices; the data structuring and annotation strategy process organizes the processed data into a structure that facilitates subsequent analysis and annotates the data according to the criteria for task integrity, using expert knowledge and automated algorithms in the annotation process.

5. The method for determining the integrity of a task under complex constraints involving multi-device collaborative operation, as described in claim 1, is characterized in that... The constraint model in this method is specifically as follows: The time synchronization constraints are modeled by establishing a time dependency graph and a time constraint matrix, and the time correlation of each device is quantified into a series of mathematical expressions, representing the absolute time requirements, relative time relationships and fault tolerance range. The spatial position constraints are modeled by constructing a three-dimensional spatial model and using collision detection algorithms, based on the physical layout, working range, and movement trajectory of the equipment. The spatial position constraints are then transformed into a series of geometric constraints and kinematic equations. Modeling the resource allocation balance constraints, and based on the rational allocation and dynamic adjustment of various guarantee resources, including energy, equipment and manpower, the resource allocation problem is transformed into a solvable mathematical optimization problem by establishing a resource flow network model and a set of optimization equations. Model the logical dependency constraints between devices, including device start-up and shutdown sequence, process dependency, and information interaction. By constructing a state transition diagram and a logical relationship matrix, these logical dependencies are transformed into a series of conditional statements and state equations. Based on constraint modeling, further constraint quantification is performed; The constraint descriptions in constraint modeling are transformed into specific numerical ranges or mathematical expressions, and dynamic weights are introduced to assign different weights to different constraints based on the current production status and task importance.

6. The method for determining the integrity of a task under complex constraints involving multi-device collaborative operation, as described in claim 1, is characterized in that... The methods for automatically extracting high-level features using deep learning techniques in this approach include: Different deep learning models are used for different types of data sources; for time series data, a combination of long short-term memory network and one-dimensional convolutional neural network is used; for image and video data, an improved convolutional neural network structure is used; and for text data, a Transformer-based model is used. An attention mechanism is introduced during feature extraction. By embedding attention layers in various deep learning models, the most critical data segments and features for judging the integrity of the task are automatically identified and focused on. In the process of fusing multi-source heterogeneous data, a multimodal feature fusion framework is adopted to normalize the features extracted from each data source to eliminate scale differences between different modalities. Through a multi-layer feature fusion network, the features are fused step by step. In each fusion layer, a cross-modal attention mechanism is introduced so that features from different modalities can enhance and complement each other. The fused features are mapped to the task integrity evaluation space through a fully connected layer. Adversarial training techniques are employed, and an adversarial example generator is added to the feature fusion network. A dynamic feature selection mechanism is adopted to automatically adjust the feature extraction and fusion strategy based on the available data sources and data quality. When some sensor data is missing, the weight of other relevant data sources is automatically increased.

7. The method for determining the integrity of a task under complex constraints involving multi-device collaborative operation, as described in claim 1, is characterized in that... The methods for designing a distributed real-time analysis system and an adaptive optimization mechanism in this approach include: The distributed real-time analysis system adopts a distributed architecture based on a combination of edge computing and cloud computing. At the edge layer, each device or group of devices is equipped with a lightweight preprocessing unit and feature extraction module. Edge nodes process local data in real time, perform preliminary feature extraction and task integrity assessment; the cloud is responsible for data fusion, deep learning model training and global decision optimization. A streaming computing framework is used to process continuous data streams. An adaptive load balancing algorithm dynamically adjusts task allocation based on the current data traffic and computing resources to ensure stable performance even under peak load. The adaptive optimization mechanism employs online learning and incremental update mechanisms to continuously adjust and optimize the decision-making model based on real-time feedback, adapting to the dynamic changes in the production environment.

8. The method for determining the integrity of a task under complex constraints involving multi-device collaborative operation, as described in claim 1, is characterized in that... The method also employs a multi-scenario simulation and risk assessment module, which quickly assesses the potential risks and benefits of different decision options by executing multiple simulation scenarios in parallel; and uses a visual interface and alarm mechanism to display the task integrity assessment results in real time and provide timely warnings of abnormal situations.

9. A task integrity assessment system for multi-device collaborative operation under complex constraints, characterized in that, include: Memory, used to store executable computer programs; A processor, when executing an executable computer program stored in a memory, implements the task integrity determination method for multi-device collaborative operation under complex constraints as described in any one of claims 1 to 8.

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