Industrial equipment operation and maintenance management method and system driven by intelligent fusion terminal

Abstract

The invention discloses an intelligent fusion terminal-driven industrial equipment operation and maintenance management method and system, and relates to the technical field of intelligent communication management, and the method comprises the steps: deploying a fusion terminal distribution network, extracting a cognitive vector of each terminal, and evaluating the cognitive ability index of each terminal; extracting a terminal distribution sub-network of each industrial device from the network according to the index; generating a plurality of operation and maintenance control parameter groups based on the sub-network, and calculating to obtain a consistency confidence index; when the consistency confidence index is greater than or equal to a preset threshold value, granting an interaction management authority to the terminal; and if the value is lower than the threshold value, reconstructing the sub-network and redistributing the authority. The technical problems that an existing equipment operation and maintenance management method depends on manual monitoring and is difficult to cope with a complex and changeable industrial environment, so that the equipment collaboration efficiency is low, and the reliability is insufficient are solved, and the technical effect that the equipment collaboration efficiency and reliability are improved through dynamic operation and maintenance optimization control and terminal management driven by the intelligent fusion terminal is achieved.

Description

Technical Field

[0001] This invention relates to the field of intelligent communication management technology, specifically to an industrial equipment operation and maintenance management method and system driven by intelligent converged terminals. Background Technology

[0002] In the industrial production sector, with the continuous growth in equipment complexity and production scale, traditional operation and maintenance management models face numerous challenges. Existing technologies primarily rely on manual inspections and simple sensor monitoring, making it difficult to achieve comprehensive perception and real-time monitoring of equipment operating status. Furthermore, when managing multiple terminals collaboratively, the consistency and reliability of operation and maintenance parameters are difficult to guarantee, leading to low operation and maintenance efficiency and untimely fault response. Simultaneously, unreasonable resource allocation further impacts the effectiveness of operation and maintenance management. Summary of the Invention

[0003] This application provides an intelligent fusion terminal-driven industrial equipment operation and maintenance management method and system to solve the technical problems of existing equipment operation and maintenance management methods relying on manual monitoring, which are difficult to cope with complex and ever-changing industrial environments, resulting in low equipment collaboration efficiency and insufficient reliability.

[0004] The first aspect of this application provides a method for the operation and maintenance management of industrial equipment driven by intelligent converged terminals. The method includes: deploying a converged terminal distribution network; extracting terminal cognitive vectors from each converged terminal to evaluate terminal cognitive ability indicators; extracting terminal distribution sub-networks for each industrial equipment from the converged terminal distribution network based on the terminal cognitive ability indicators; simultaneously generating multiple operation and maintenance control parameter groups corresponding to different industrial equipment categories based on the terminal distribution sub-networks; performing consistency confidence calculations based on the multiple operation and maintenance control parameter groups to obtain a consistency confidence index; granting interactive management permissions for the corresponding industrial equipment to each converged terminal in the terminal distribution sub-network when the consistency confidence index is greater than or equal to a preset confidence index threshold; and reconstructing the terminal distribution sub-network when the consistency confidence index is less than the preset confidence index threshold, and granting interactive management permissions for the corresponding industrial equipment to each converged terminal in the reconstructed terminal distribution sub-network.

[0005] A second aspect of this application provides an intelligent fusion terminal-driven industrial equipment operation and maintenance management system. The system includes: a terminal cognitive vector extraction module, used to deploy a fusion terminal distribution network and evaluate terminal cognitive ability indicators by extracting terminal cognitive vectors from each fusion terminal; a terminal distribution sub-network extraction module, used to extract terminal distribution sub-networks for each industrial device from the fusion terminal distribution network based on the terminal cognitive ability indicators; a consistency confidence calculation module, used to simultaneously generate multiple operation and maintenance control parameter groups corresponding to different industrial device categories based on the terminal distribution sub-networks, and perform consistency confidence calculation based on the multiple operation and maintenance control parameter groups to obtain a consistency confidence index; a management permission granting module, used to grant interactive management permissions for the corresponding industrial device to each fusion terminal in the terminal distribution sub-network when the consistency confidence index is greater than or equal to a preset confidence index threshold; and a sub-network reconstruction module, used to reconstruct the terminal distribution sub-network when the consistency confidence index is less than a preset confidence index threshold, and grant interactive management permissions for the corresponding industrial device to each fusion terminal in the reconstructed terminal distribution sub-network.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The intelligent converged terminal-driven industrial equipment operation and maintenance management method and system provided in this application relates to the field of intelligent communication management technology. By deploying a converged terminal distribution network, evaluating terminal cognitive ability indicators and extracting terminal distribution sub-networks for each device based on these indicators, generating multiple operation and maintenance control parameter groups based on the sub-networks, and evaluating terminal management permissions through consistency confidence calculation, the method determines whether to grant permissions or reconstruct the sub-network based on the consistency confidence index. This achieves intelligent equipment operation and maintenance management, solving the technical problems of existing equipment operation and maintenance management methods that rely on manual monitoring, are difficult to cope with complex and ever-changing industrial environments, and result in low equipment collaboration efficiency and insufficient reliability. The method achieves the technical effect of improving equipment collaboration efficiency and reliability through intelligent converged terminal-driven dynamic operation and maintenance optimization control and terminal management. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 This is a schematic diagram of the operation and maintenance management method for industrial equipment driven by an intelligent fusion terminal, provided in an embodiment of this application. Figure 2A schematic diagram of the structure of an industrial equipment operation and maintenance management system driven by an intelligent fusion terminal provided in an embodiment of this application.

[0009] Figure labeling: Terminal cognitive vector extraction module 11, terminal distribution sub-network extraction module 12, consistency confidence calculation module 13, management permission granting module 14, sub-network reconstruction module 15. Detailed Implementation

[0010] This application provides an intelligent fusion terminal-driven industrial equipment operation and maintenance management method and system to solve the technical problems of existing equipment operation and maintenance management methods relying on manual monitoring, which are difficult to cope with complex and ever-changing industrial environments, resulting in low equipment collaboration efficiency and insufficient reliability.

[0011] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0012] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0013] Example 1, as Figure 1 As shown, this application provides an industrial equipment operation and maintenance management method driven by an intelligent fusion terminal, the method including: P10: Deploy a distributed network of converged terminals, and evaluate the terminal cognitive ability index by extracting the terminal cognitive vector of each converged terminal.

[0014] Furthermore, step P10 in this embodiment of the application also includes: P11: Synchronously collect the status awareness dataset of each fusion terminal; P12: Based on the status awareness dataset, extract the terminal cognition vector of each fusion terminal in the process of interacting with the corresponding industrial equipment, including the quality of interactive data transmission, the efficiency of interactive data processing, the computational complexity of interactive data, and the terminal health awareness status; P13: Establish the terminal cognition capability index between each fusion terminal and the corresponding industrial equipment based on the extracted terminal cognition vector.

[0015] Specifically, by deploying a distributed network of converged terminals, the ability of each converged terminal to understand the operational status of industrial equipment is assessed. The core of this process is to comprehensively analyze the interactions between the converged terminals and the equipment, thereby quantifying their level of understanding of the equipment's operational status.

[0016] First, a converged terminal distribution network should be rationally deployed in the industrial environment. This network consists of multiple intelligent converged terminals distributed around industrial equipment to collect operational data and interact with the equipment. After deployment, the crucial terminal cognitive capability assessment phase begins. To achieve this, it is necessary to synchronously collect the state-aware dataset of each converged terminal. The state-aware dataset refers to various data collected by the converged terminal during operation regarding its own state and its interaction with industrial equipment, including equipment operating parameters, environmental parameters, and communication status. Synchronously collecting this data provides a comprehensive and real-time data foundation for subsequent terminal cognitive vector extraction.

[0017] After collecting the state-aware dataset, the next step is to extract the terminal cognition vector for each fusion terminal during its interaction with the corresponding industrial equipment. This terminal cognition vector represents a set of vectors illustrating the multi-dimensional performance characteristics exhibited by the fusion terminal during its interaction with the industrial equipment. These performance characteristics include the following key dimensions: interactive data transmission quality, interactive data processing efficiency, interactive data computational complexity, and terminal health awareness status. Interactive data transmission quality reflects the reliability and stability of data transmission between the fusion terminal and the industrial equipment. For example, by calculating parameters such as the success rate and retransmission rate of data packets, it is possible to assess whether there are issues such as packet loss or errors during data transmission. Interactive data processing efficiency measures the speed and capability of the fusion terminal in processing the collected data. For example, it analyzes the time required for the fusion terminal to process data and the amount of data that can be processed per unit time. Interactive data computational complexity reflects the computational difficulty faced by the fusion terminal when processing interactive data. For example, it analyzes the algorithm complexity used by the fusion terminal when performing data processing tasks. Terminal health awareness status characterizes the health status of the fusion terminal itself, including the normal operation of the hardware and the stability of the software system. This is achieved by monitoring the readings of the fusion terminal's hardware sensors and information such as error logs in the software system.

[0018] By extracting data from the aforementioned four dimensions from the state-aware dataset, a terminal cognition vector is formed for each converged terminal. This vector comprehensively reflects the integrated performance of the converged terminal during interaction with industrial equipment, providing a quantitative basis for subsequent terminal cognition capability assessment.

[0019] Next, based on the extracted terminal cognitive vectors, a terminal cognitive capability index is further established between each converged terminal and its corresponding industrial equipment. The terminal cognitive capability index is a comprehensive evaluation value that reflects the completeness of the terminal's understanding of the equipment's operating status. To establish this index, a series of algorithmic models can be used to weight and integrate the various dimensions of the cognitive vector. For example, weighted average methods and machine learning models can be used to comprehensively analyze and calculate the data from each dimension. For instance, different weights can be assigned to interactive data transmission quality, interactive data processing efficiency, interactive data computational complexity, and terminal health perception status, and a comprehensive terminal cognitive capability index can be calculated based on these weights and the corresponding vector values. The level of this index directly reflects the converged terminal's cognitive ability and operation and maintenance management capabilities regarding the equipment's operating status in the current industrial environment and equipment interaction scenario.

[0020] Through the above process, the embodiments of this application can accurately assess the cognitive capabilities of each fusion terminal, providing a basis for decision-making in subsequent industrial equipment operation and maintenance management.

[0021] P20: Extract the terminal distribution sub-network of each industrial device from the fused terminal distribution network based on the terminal cognitive ability index.

[0022] Furthermore, step P20 in this embodiment of the application also includes: P21: Obtain the set of industrial equipment and identify the set of fusion terminals corresponding to the communication connection of each industrial equipment; P22: Extract the set of terminal cognitive ability indicators corresponding to the set of fusion terminals; P23: Select N fusion terminals from the set of terminal cognitive ability indicators that are greater than the preset terminal cognitive ability indicator threshold, and construct a terminal distribution sub-network for each industrial equipment based on the N fusion terminals.

[0023] It should be understood that, based on the terminal cognitive ability index, the terminals corresponding to each device are screened and evaluated, and the terminal distribution sub-network of each industrial device is extracted from the converged terminal distribution network.

[0024] First, a complete set of industrial equipment needs to be acquired, encompassing all industrial devices requiring operation and maintenance management. These devices may be distributed across different production areas, possessing varying functions and operating parameters. Next, for each industrial device, the set of converged terminals communicating with it needs to be identified. The key to this step is clarifying which converged terminals have direct communication relationships with each industrial device. For example, through the device's communication protocol, network topology, or device management system, it can be determined which converged terminals can directly interact with a specific industrial device, ensuring that all device-related terminals are identified and categorized into the appropriate set, constituting the candidate terminal set for that industrial device.

[0025] After identifying the set of converged terminals corresponding to each industrial device, the next step is to extract the set of terminal cognitive ability indicators for these converged terminals. Terminal cognitive ability indicators are important quantitative metrics for measuring the completeness of a converged terminal's understanding of the industrial device's operating status. As mentioned earlier, they reflect the converged terminal's performance across multiple dimensions, including data transmission quality, processing efficiency, computational complexity, and health awareness. By obtaining these indicators from the distributed network of converged terminals, a terminal cognitive ability indicator value can be generated for each converged terminal. These indicator values ​​constitute the set of terminal cognitive ability indicators, providing a quantitative basis for subsequent screening. For example, for a specific industrial device, its corresponding set of converged terminals may contain multiple terminals, each with a corresponding terminal cognitive ability indicator value. These values ​​collectively constitute the set of terminal cognitive ability indicators for that device.

[0026] Finally, N converged terminals whose cognitive ability indicators are greater than a preset threshold are selected from the extracted set of terminal cognitive ability indicators. The preset threshold is a benchmark value set based on actual application scenarios and operational needs to ensure that the selected converged terminals possess sufficient cognitive ability to effectively participate in the operation and maintenance management of industrial equipment. For example, if the preset threshold is 0.8, and assuming the range of the terminal cognitive ability indicator is 0 to 1, only converged terminals with a cognitive ability indicator greater than 0.8 will be selected. This ensures that each converged terminal in the constructed terminal distribution sub-network can reliably perceive and process the operational data of the industrial equipment.

[0027] After selecting N eligible converged terminals, a terminal distribution subnetwork is constructed for each industrial device. The key to this step is organizing the selected converged terminals into an effective network structure to achieve efficient monitoring and management of the industrial equipment. For example, by defining communication paths, data transmission protocols, and collaborative working mechanisms between terminals, a terminal distribution subnetwork capable of responding to changes in equipment operating status in real time can be built. This subnetwork will serve as the infrastructure for subsequent operation and maintenance management, ensuring that each industrial device receives accurate and efficient operation and maintenance support.

[0028] Through this process, the system can extract suitable terminals from a vast converged terminal distribution network based on the terminals' cognitive ability indicators, and construct a terminal distribution sub-network adapted to each industrial device. This process not only improves the intelligence of terminal selection but also effectively enhances the accuracy and efficiency of equipment operation and maintenance.

[0029] P30: Based on the terminal distribution subnetwork, multiple operation and maintenance control parameter groups corresponding to the industrial equipment category are generated simultaneously. Consistency confidence calculation is performed based on the multiple operation and maintenance control parameter groups to obtain the consistency confidence index.

[0030] Furthermore, based on the terminal distribution subnetwork, multiple operation and maintenance control parameter groups corresponding to the industrial equipment categories are simultaneously generated. In this embodiment, step P30 further includes: P31: The terminal distribution subnetwork synchronously reads the multimodal operation monitoring dataset of the corresponding industrial equipment through each fusion terminal; P32: Determine the operation and maintenance target, and parse the multimodal operation monitoring dataset in the corresponding fusion terminal to obtain a set of parameter operation and maintenance control parameters based on the operation and maintenance target, resulting in multiple sets of operation and maintenance control parameters; P33: The multimodal operation monitoring dataset is obtained by parsing the data in the corresponding fusion terminal through a target-driven parsing model built locally in the fusion terminal. The target-driven parsing model includes a locally downloaded Gaussian regression model, and a target function corresponding to the operation and maintenance target is constructed based on the Gaussian regression model for parsing.

[0031] Optionally, multiple operation and maintenance control parameter groups can be generated simultaneously based on the terminal distribution sub-network, and consistency confidence calculation can be performed based on these operation and maintenance control parameter groups. That is, based on the actual needs and terminal capabilities of each industrial device, highly adaptable operation and maintenance control parameter groups can be generated, and through consistency evaluation, it can be ensured that these parameter groups can maintain consistency and provide effective decision support during the equipment operation and maintenance process.

[0032] First, the terminal distribution subnetwork synchronously reads the multimodal operation monitoring dataset of the corresponding industrial equipment through each fusion terminal. This multimodal operation monitoring dataset contains various types of data collected by the fusion terminals from the industrial equipment, including operating parameters such as temperature, pressure, vibration frequency, current, and voltage, as well as non-traditional data such as images and sounds. By synchronously reading this multimodal data, a comprehensive understanding of the operating status of the industrial equipment can be obtained. For example, a fusion terminal may simultaneously collect temperature sensor data and vibration and sound signals from the equipment during operation. These data together constitute the multimodal operation monitoring dataset, providing a rich information foundation for the subsequent generation of operation and maintenance control parameter sets.

[0033] Next, the operational goals are determined, such as maximizing efficiency, minimizing energy consumption, prioritizing reliability, or balancing lifespan. Then, based on these goals, the multimodal operation monitoring dataset is parsed, and multiple sets of operational control parameters are generated. Specifically, different analysis methods can be used to extract relevant features from the data based on the requirements of each goal, and control parameters that meet the goal can be generated. For example, if the operational goal is to maximize efficiency, the fusion terminal will parse the multimodal operation monitoring dataset, extract parameters related to equipment operating efficiency, such as equipment speed and power, and generate corresponding sets of operational control parameters based on these parameters. If the operational goal is to minimize energy consumption, the fusion terminal will focus on energy-related parameters of the equipment, such as current and voltage, and generate sets of operational control parameters accordingly. In this way, each fusion terminal can generate multiple sets of operational control parameters based on different operational goals. These parameters will serve as the basis for operational control, ensuring that the equipment can execute optimal strategies when achieving specific operational goals.

[0034] For example, during the parsing of multimodal operation monitoring datasets by the corresponding converged terminal, the operation and maintenance control parameter set can be obtained through a target-driven parsing model built locally on the converged terminal. The target-driven parsing model is a tool used to extract key information related to operation and maintenance objectives from raw data. In this embodiment, the target-driven parsing model includes a locally downloaded Gaussian regression model. Gaussian regression is a regression analysis method based on probability statistics, capable of predicting the probability distribution of output values ​​based on input data. Specifically, the converged terminal downloads the Gaussian regression model locally and constructs an objective function corresponding to the operation and maintenance objectives based on this model to perform the parsing.

[0035] Specifically, each fusion terminal first downloads a pre-trained Gaussian regression model from the central management system or a local server. This model, pre-trained based on historical operational data and expert knowledge, is capable of performing effective regression analysis on multimodal operational monitoring data. The Gaussian regression model is then initialized locally on the fusion terminal, including loading model parameters and configuring input / output interfaces. For example, the model's input interface is configured to receive data such as temperature, pressure, and vibration frequency from the multimodal operational monitoring dataset, and the output interface is configured to generate operation and maintenance control parameters.

[0036] Next, based on specific operational goals, such as maximizing efficiency, minimizing energy consumption, prioritizing reliability, and balancing lifespan, objective functions are constructed locally on the converged terminal. For example, for the operational goal of maximizing efficiency, the objective function can be defined as the ratio of device output power to input energy consumption; for the goal of minimizing energy consumption, the objective function can be defined as the device's energy consumption index. The construction of the objective function needs to consider the actual operating parameters and performance indicators of the device. For example, if the goal is to improve the device's operating efficiency, the objective function can be expressed as the ratio of device output power to input energy consumption; if the goal is to reduce energy consumption, the objective function can be expressed as the product of the device's current, voltage, and operating time.

[0037] Then, the multimodal operation monitoring dataset is input into the corresponding Gaussian regression model. The model predicts the output value corresponding to the input data through regression analysis. For example, inputting temperature and pressure data from the device yields the expected energy consumption or efficiency of the device in its current state. Then, based on the output value of the Gaussian regression model, optimization calculations are performed using the objective function. For example, by adjusting the device's operating parameters, the objective function is optimized. Specifically, gradient descent or other optimization algorithms can be used to adjust parameters according to the model's output value until the objective function reaches the preset optimization objective, generating a set of operation and maintenance control parameters. These parameters guide the device to achieve the best operation and maintenance performance in its current operating state. For example, the generated set of operation and maintenance control parameters might include the device's optimal operating temperature range, pressure threshold, current limit, etc. In this way, the fusion terminal can, based on different operation and maintenance objectives, use the Gaussian regression model and objective function to parse the set of operation and maintenance control parameters that best suits the specific operation and maintenance objectives from the multimodal operation monitoring dataset.

[0038] Next, the system will perform consistency confidence calculations based on these operation and maintenance control parameter groups to evaluate the coordination and consistency between different control parameter groups. This ensures that operation and maintenance control strategies under multiple objectives can operate in a coordinated manner without conflicts or contradictions. This consistency confidence index provides a quantitative basis for subsequent operation and maintenance decisions, helping operation and maintenance personnel or the system select the most suitable solution from multiple possible control schemes to ensure optimal equipment operation.

[0039] Furthermore, based on the multiple operation and maintenance control parameter groups, a consistency confidence calculation is performed to obtain a consistency confidence index. Step P30 in this embodiment further includes: P34: Determine at least one key operation and maintenance control parameter; P35: Extract multiple key operation and maintenance control parameters from the multiple operation and maintenance control parameter groups according to the key operation and maintenance control parameter; P36: Perform normalized parameter vector processing on the multiple key operation and maintenance control parameters to obtain multiple key operation and maintenance control parameter vectors, and perform similarity calculation on the key operation and maintenance control parameter vectors corresponding to each pair of fused terminals to construct a similarity matrix; P37: Perform consistency confidence calculation by calculating the average similarity of the similarity matrix to obtain a consistency confidence index.

[0040] In one possible embodiment of this application, the process of performing consistency confidence calculation based on multiple operation and maintenance control parameter groups is further refined to obtain a consistency confidence index, ensuring that the generated control strategy has consistency and coordination under multiple operation and maintenance objectives.

[0041] First, at least one key operation and maintenance (O&M) control parameter needs to be identified. Key O&M control parameters are core indicators that directly affect the operating status and maintenance effectiveness of industrial equipment, such as the equipment's temperature threshold, pressure range, energy consumption, and vibration frequency. The purpose of selecting these key O&M control parameters is to focus on the factors most important to equipment operation and maintenance in subsequent calculations, thereby improving the efficiency and accuracy of the calculations. For example, in the O&M scenario of a motor, key O&M control parameters might include the motor's operating temperature, current intensity, and speed.

[0042] Next, based on the identified key operation and maintenance (O&M) control parameters, specific key O&M control parameter values ​​are extracted from each of the multiple O&M control parameter groups. The purpose of this step is to extract the key parameters from each O&M control parameter group to simplify subsequent analysis and focus on the most important factors. For example, if the key O&M control parameters are the equipment's temperature threshold and pressure range, then the specific values ​​of these two parameters are extracted from each O&M control parameter group to form a set of key parameters. Assuming each O&M control parameter group contains multiple parameters, such as temperature, pressure, and current, through filtering, only the specific values ​​of the two key parameters, temperature and pressure, are retained, forming a simplified parameter set.

[0043] Then, the extracted key operation and maintenance control parameters are processed into normalized parameter vectors, resulting in multiple key operation and maintenance control parameter vectors. Normalization refers to converting all key operation and maintenance control parameters into the same standardized format, allowing comparisons to be made under the same dimensions. For example, by converting each key parameter value to a value between 0 and 1, a normalized key operation and maintenance control parameter vector can be obtained. For instance, a max-min normalization method can be used, which involves subtracting the minimum value of a parameter from its value and then dividing by the difference between its maximum and minimum values. In this way, each group of operation and maintenance control parameters corresponds to a key operation and maintenance control parameter vector, which will be used for subsequent similarity calculations.

[0044] Next, the similarity of the key operation and maintenance control parameter vectors corresponding to each pair of converged terminals is calculated, and a similarity matrix is ​​constructed. Similarity calculation can be implemented in various ways, such as calculating the cosine similarity or Euclidean distance between vectors. Cosine similarity measures the degree of similarity between two vectors by calculating the cosine of the angle between them; the closer the value is to 1, the higher the similarity. Euclidean distance measures the straight-line distance between two vectors; the smaller the distance, the higher the similarity. By comparing the similarity between each pair of vectors, a similarity matrix can be obtained, which reflects the degree of similarity between different converged terminals in terms of key operation and maintenance control parameters. For example, for two converged terminals A and B, the cosine similarity between their key operation and maintenance control parameter vectors can be calculated, and the results can be recorded in the corresponding positions of the similarity matrix.

[0045] Finally, a consistency confidence index is obtained by calculating the average similarity of the similarity matrix. Each element in the similarity matrix represents the similarity between two converged terminals, and the entire matrix reflects the coordination of multiple terminals on key operation and maintenance control parameters. Therefore, by calculating the average similarity of the similarity matrix, an overall evaluation index, namely the consistency confidence index, can be obtained, which reflects the degree of consistency among all converged terminals on key operation and maintenance control parameters. If the consistency confidence index is high, it indicates that the operation and maintenance control parameter groups generated by different converged terminals have high consistency on key parameters, and thus these parameter groups can be considered reliable and can be used for subsequent operation and maintenance management. For example, setting the threshold for the consistency confidence index to 0.8, if the calculated consistency confidence index is greater than or equal to 0.8, the operation and maintenance control parameter group is considered reliable, and the corresponding interactive management permissions can be granted; conversely, if the consistency confidence index is low, it may mean that there are inconsistent or conflicting control strategies, and the terminal distribution subnetwork needs to be reconstructed.

[0046] This process provides data support for subsequent equipment operation and maintenance decisions, ensuring that the system can make stable and consistent operation and maintenance decisions in complex environments, thereby improving the operating efficiency and reliability of the equipment.

[0047] Furthermore, step P37 in this embodiment of the application also includes: P37-1: Generate cognitive weight coefficients based on the terminal cognitive ability indicators corresponding to each fusion terminal; P37-2: Introduce the cognitive weight coefficients to perform weighted calculations on the similarity matrix to obtain a weighted similarity matrix, and perform consistency confidence calculations based on the average similarity of the consistency confidence weighted similarity matrix to re-obtain the consistency confidence index.

[0048] Optionally, the accuracy and relevance of consistency confidence calculation can be further improved by introducing cognitive weight coefficients to perform weighted calculations on the similarity matrix.

[0049] First, a cognitive weight coefficient is generated based on the terminal cognitive ability index corresponding to each converged terminal. The terminal cognitive ability index is a crucial quantitative indicator measuring the completeness of the converged terminal's understanding of the industrial equipment's operating status. It reflects the converged terminal's performance across multiple dimensions, including data transmission quality, processing efficiency, computational complexity, and health awareness. To reflect the influence of different terminals in the decision-making process, the system needs to assign a cognitive weight coefficient to each terminal. For example, assuming the terminal cognitive ability index ranges from 0 to 1, a converged terminal with a cognitive ability index of 0.9 might be assigned a higher weight coefficient, such as 0.9, while a terminal with a cognitive ability index of 0.6 might be assigned a lower weight coefficient, such as 0.6. This method of generating weight coefficients ensures that terminals with stronger cognitive abilities contribute more to the results in subsequent calculations, thereby improving the overall accuracy of the calculations.

[0050] Next, a cognitive weight coefficient is introduced to weight the similarity matrix, resulting in a weighted similarity matrix. Specifically, for each element in the similarity matrix (i.e., the similarity value between any two fusion terminals), its corresponding cognitive weight coefficient is multiplied. For example, assuming the similarity value between fusion terminals A and B is 0.8, the cognitive weight coefficient of terminal A is 0.9, and the cognitive weight coefficient of terminal B is 0.7, then the weighted similarity value is 0.8 × (0.9 + 0.7) / 2 = 0.76. In this way, a new weighted similarity matrix is ​​obtained, where each element considers the cognitive ability of the corresponding fusion terminal. This allows the system to adjust the similarity matrix, making the impact of terminals with stronger cognitive abilities more prominent in consistency assessment, thus more accurately reflecting the actual role of terminals in operation and maintenance control.

[0051] Finally, a consistency confidence index is recalculated based on the average similarity of the weighted similarity matrix. Similar to the previous average similarity calculation, the average of all elements in the weighted similarity matrix is ​​calculated to obtain the weighted average similarity. Then, the consistency confidence index is recalculated based on this weighted average similarity. For example, if the weighted average similarity is 0.76, and the consistency confidence index obtained through a certain calculation method (such as standardization) is 0.85, this indicates that the consistency of the operation and maintenance control parameter group has been more accurately assessed after considering the cognitive ability of the converged terminal. If this recalculated consistency confidence index meets the preset threshold requirement, such as being greater than or equal to 0.8, the operation and maintenance control parameter group can be considered to have high reliability and consistency and can be used for subsequent operation and maintenance management; otherwise, further adjustment or optimization of the operation and maintenance control parameter group may be necessary.

[0052] These steps not only allow us to assess consistency between terminals, but also to weight each terminal based on its cognitive ability, ensuring that terminals with stronger cognitive abilities play a more important role in consistency calculations, thereby further improving the coordination and accuracy of overall operation and maintenance decisions.

[0053] P40: When the consistency confidence index is greater than or equal to the preset confidence index threshold, grant each converged terminal in the terminal distribution sub-network the corresponding industrial equipment interaction management permission.

[0054] Furthermore, step P40 in this embodiment of the application also includes: P41: Generate a digital permission token and distribute the permission token to each converged terminal in the terminal distribution sub-network; P42: Distribute a set of operation and maintenance control parameters through any converged terminal to execute the operation and maintenance management of the corresponding industrial equipment.

[0055] Specifically, when the consistency confidence index is greater than or equal to a preset confidence index threshold, it indicates that the converged terminals in the terminal distribution subnetwork have high consistency and reliability in key operation and maintenance control parameters. The system will then decide to grant each converged terminal in the terminal distribution subnetwork the corresponding industrial equipment's interactive management permissions. This mechanism ensures that terminals are only allowed to operate and manage equipment under conditions of multi-party coordination and high consistency, thereby improving the intelligence and accuracy of equipment operation and maintenance.

[0056] First, generate digital permission tokens. A digital permission token is an electronic credential used to prove that a converged terminal has the authority to interact with and manage specific industrial equipment. This token can contain information such as device identifier, terminal identifier, scope of authority, and validity period, and its security and immutability are ensured through encryption technology. In this step, a unique permission token needs to be generated for each converged terminal, ensuring that each terminal only has the authority to perform the corresponding operation under certain conditions.

[0057] Next, the generated authorization token is distributed to each converged terminal in the terminal distribution subnetwork. This process can be accomplished through a secure communication protocol to ensure the confidentiality and integrity of the token during transmission. For example, Transport Layer Security (TLS) can be used to encrypt the token data and transmit it through a secure communication channel between the converged terminal and the management system. After receiving the authorization token, each converged terminal stores it in a local secure storage area for verification of its own permissions in subsequent operation and maintenance management.

[0058] Then, based on the previously generated operation and maintenance control parameter group, one of the converged terminals is selected as the initiator, and it is assigned to issue operation commands and execute relevant operation and maintenance management tasks. The key to this step is ensuring that the converged terminal can effectively monitor and control the industrial equipment according to the issued operation and maintenance control parameter group. For example, a converged terminal can monitor the equipment's operating status in real time based on the temperature thresholds and pressure ranges in the operation and maintenance control parameter group, and issue an alarm or automatically adjust the equipment's operating parameters when the parameters exceed the set range. Furthermore, the converged terminal can also periodically inspect and maintain the equipment according to the maintenance cycle and alarm conditions in the operation and maintenance control parameter group to ensure the normal operation of the equipment.

[0059] Through the above steps, interactive management permissions can be effectively granted to converged terminals in the terminal distribution subnetwork, ensuring that these terminals can perform precise operation and maintenance management of industrial equipment according to the issued operation and maintenance control parameter sets. This process not only ensures the security and reliability of authorization through digital permission tokens, but also enables real-time monitoring and management of equipment through specific operation and maintenance control parameter sets, improving the intelligence level and operational efficiency of the entire industrial equipment operation and maintenance management system.

[0060] Furthermore, step P42 in this embodiment of the application also includes: P42-1: Read the permission fusion terminal of the corresponding industrial equipment; P42-2: Analyze the real-time task queue of the permission fusion terminal, obtain the first permission fusion terminal, and perform the operation and maintenance management of the corresponding industrial equipment according to the first permission fusion terminal.

[0061] It should be understood that the process of performing operation and maintenance management tasks for industrial equipment through converged terminals can be further refined, especially how to select appropriate converged terminals to perform these tasks.

[0062] First, the permissions of the corresponding industrial equipment's converged terminals are read. This step is to identify which converged terminals have the authority to perform the corresponding equipment's operation and maintenance management. Each industrial device may interact with multiple converged terminals; therefore, it is necessary to read the device's relevant permission information, such as checking whether each terminal meets the authorization conditions based on previously assigned permission tokens, to determine which terminals are qualified to manage and control the device. Only those terminals that, after passing a consistency confidence calculation, possess sufficiently high cognitive ability and are authorized, are considered as authorized converged terminals. For example, suppose the operation and maintenance management permissions for industrial equipment A are granted to converged terminals 1, 2, and 3; the permission tokens of these terminals will clearly indicate their management permissions for equipment A. By reading these permission tokens, it is possible to quickly determine which terminals are qualified to perform operation and maintenance tasks.

[0063] Next, the real-time task queues of the permission-integrated terminals are analyzed to determine the first permission-integrated terminal. The real-time task queue refers to the list of tasks currently being processed or awaiting processing by each integrated terminal. Each permission-integrated terminal may handle multiple tasks simultaneously, and the task queue reflects the execution order and priority of each task. By analyzing these task queues, the current load of each terminal can be assessed, thereby selecting the terminal most suitable for executing the current maintenance task. For example, if the task queue of integrated terminal 1 already contains multiple high-priority tasks, while the task queue of integrated terminal 2 is relatively idle, then integrated terminal 2 may be selected as the first permission-integrated terminal. The selection criteria for the first permission-integrated terminal may include the length of the task queue, the priority of the tasks, and the current load of the terminal. For example, a load balancing algorithm can be used to prioritize the terminal with the shortest task queue and the lowest current load to execute the maintenance task.

[0064] Finally, the selected first-authority fusion terminal executes the corresponding industrial equipment's operation and maintenance management. This first-authority fusion terminal will perform specific operation and maintenance management operations on the industrial equipment based on the issued operation and maintenance control parameter set. For example, if the first-authority fusion terminal is fusion terminal 2, it will monitor the equipment's operating status in real time based on parameters such as temperature thresholds and pressure ranges in the operation and maintenance control parameter set, and issue alarms or automatically adjust the equipment's operating parameters when the parameters exceed the set range. Furthermore, fusion terminal 2 can also periodically inspect and maintain the equipment according to the maintenance cycle and alarm conditions in the operation and maintenance control parameter set to ensure the equipment's normal operation.

[0065] By introducing real-time task queue analysis and prioritization, the system can allocate maintenance tasks more intelligently and flexibly in complex industrial environments. This approach not only ensures the rationality of task allocation but also improves the overall operational efficiency of the system, making equipment management more refined and efficient.

[0066] P50: When the consistency confidence index is less than the preset confidence index threshold, the terminal distribution sub-network is reconstructed, and each fusion terminal in the terminal distribution reconstruction sub-network is granted the corresponding industrial equipment interactive management permission.

[0067] Furthermore, step P50 in this embodiment of the application also includes: P51: When the consistency confidence index is less than the preset confidence index threshold, an abnormal fusion terminal is located. The abnormal fusion terminal is a fusion terminal in the terminal distribution sub-network whose consistency deviation is greater than the element and the deviation threshold. P52: Reconstruct fusion terminals from the fusion terminal distribution network that are greater than the preset terminal cognitive ability index threshold. P53: Reconstruct the terminal distribution sub-network according to the reconstructed fusion terminals to obtain a terminal distribution reconstruction sub-network.

[0068] Optionally, when the consistency confidence index is less than a preset confidence index threshold, it indicates that there is significant inconsistency in key operation and maintenance control parameters among the converged terminals in the current terminal distribution subnetwork, which may affect the reliability of equipment operation and maintenance management. In this case, it is first necessary to locate the abnormal converged terminals. Abnormal converged terminals refer to converged terminals in the terminal distribution subnetwork whose consistency deviation exceeds a preset deviation threshold. These terminals may cause significant deviations in their management and control of the equipment due to unstable cognitive abilities, poor data transmission quality, or other factors. Therefore, it is necessary to locate these abnormal terminals by analyzing their behavior and cognitive abilities. For example, if the difference between a converged terminal's key parameters and the subnetwork average exceeds a preset deviation threshold, such as 0.2, then the terminal is identified as an abnormal converged terminal. By locating these abnormal terminals, the terminal objects that need to be adjusted or replaced can be identified.

[0069] Next, reconstructed fusion terminals with a cognitive ability index greater than the preset threshold are selected from the fusion terminal distribution network. Specifically, the system uses the previously generated terminal cognitive ability index to filter out terminals with stronger cognitive abilities to replace or supplement abnormal terminals in the current sub-network. For example, if the preset terminal cognitive ability index threshold is 0.8, then terminals with a cognitive ability index greater than 0.8 can be selected from the entire network as candidate reconstruction terminals. These terminals perform better in terms of data transmission quality, processing efficiency, computational complexity, and health awareness status, thus improving the overall performance of the sub-network.

[0070] Finally, the terminal distribution subnetwork is reconstructed based on the selected reconstructed and converged terminals, resulting in a reconstructed terminal distribution subnetwork. This involves adding new reconstructed and converged terminals to the subnetwork, replacing or supplementing existing abnormal terminals, and readjusting the subnetwork's topology and communication connections. For example, if, after locating abnormal terminals, a high degree of consistency deviation is found between terminals A and B, two terminals C and D with strong cognitive abilities can be selected from the entire network and added to the subnetwork to replace terminals A and B. In this way, the reconstructed subnetwork better meets consistency requirements, making device management more stable and efficient.

[0071] This process not only identifies the objects that need adjustment by locating abnormal terminals, but also optimizes the performance of the sub-network by reselecting and replacing terminals, enabling the system to dynamically adapt to changes in the industrial environment and ensuring that the operation and maintenance management of equipment always maintains a high level of intelligence and stability.

[0072] In summary, the embodiments of this application have at least the following technical effects: This application achieves intelligent equipment operation and maintenance management by assessing terminal cognitive capabilities and dynamically adjusting management permissions, thereby reducing manual intervention; it optimizes collaborative management among multiple industrial devices based on terminal cognitive capability indicators and consistency confidence calculations, improving overall operation and maintenance efficiency; it adjusts the terminal distribution sub-network structure based on the consistency confidence index to ensure the efficiency and consistency of operation and maintenance control strategies; and it ensures that device management permissions are granted to terminals under appropriate conditions through intelligent calculation of the consistency confidence index, thereby enhancing system stability and security.

[0073] It achieves the technical effect of improving equipment collaboration efficiency and reliability through dynamic operation and maintenance optimization control and terminal management driven by intelligent converged terminals.

[0074] Example 2, based on the same inventive concept as the intelligent fusion terminal-driven industrial equipment operation and maintenance management method in the foregoing examples, such as... Figure 2 As shown, this application provides an industrial equipment operation and maintenance management system driven by an intelligent fusion terminal. The system and method embodiments in this application are based on the same inventive concept. The system includes: The terminal cognitive vector extraction module 11 is used to deploy a converged terminal distribution network. By extracting the terminal cognitive vector of each converged terminal, the terminal cognitive ability index is evaluated.

[0075] The terminal distribution sub-network extraction module 12 is used to extract the terminal distribution sub-network of each industrial device from the fused terminal distribution network based on the terminal cognitive ability index.

[0076] The consistency confidence calculation module 13 is used to simultaneously generate multiple operation and maintenance control parameter groups corresponding to the industrial equipment category based on the terminal distribution sub-network, and perform consistency confidence calculation based on the multiple operation and maintenance control parameter groups to obtain a consistency confidence index.

[0077] The management permission granting module 14 is used to grant interactive management permissions for the corresponding industrial equipment to each converged terminal in the terminal distribution sub-network when the consistency confidence index is greater than or equal to a preset confidence index threshold.

[0078] The sub-network reconstruction module 15 is used to reconstruct the terminal distribution sub-network when the consistency confidence index is less than a preset confidence index threshold, and to grant each fusion terminal in the terminal distribution reconstruction sub-network the corresponding industrial equipment interaction management permission.

[0079] Furthermore, the terminal cognitive vector extraction module 11 is also used to perform the following steps: The status awareness dataset of each fusion terminal is collected synchronously. Based on the status awareness dataset, the terminal cognition vector of each fusion terminal interacting with the corresponding industrial equipment is extracted, including the quality of interactive data transmission, the efficiency of interactive data processing, the computational complexity of interactive data, and the terminal health awareness status. Based on the extracted terminal cognition vector, a terminal cognition capability index between each fusion terminal and the corresponding industrial equipment is established.

[0080] Furthermore, the terminal distribution sub-network extraction module 12 is also used to perform the following steps: Obtain a set of industrial equipment and identify the set of fusion terminals corresponding to the communication connection of each industrial equipment; extract the set of terminal cognitive ability indicators corresponding to the set of fusion terminals; select N fusion terminals from the set of terminal cognitive ability indicators that are greater than a preset terminal cognitive ability indicator threshold, and construct a terminal distribution sub-network for each industrial equipment based on the N fusion terminals.

[0081] Furthermore, the consistency confidence calculation module 13 is also used to perform the following steps: The terminal distribution subnetwork synchronously reads the multimodal operation monitoring dataset of the corresponding industrial equipment through each fusion terminal; determines the operation and maintenance target, and parses the multimodal operation monitoring dataset in the corresponding fusion terminal to obtain a set of parameter operation and maintenance control parameters based on the operation and maintenance target, resulting in multiple sets of operation and maintenance control parameters; wherein, the multimodal operation monitoring dataset is parsed in the corresponding fusion terminal by obtaining a target-driven parsing model built locally in the fusion terminal, the target-driven parsing model including a locally downloaded Gaussian regression model, and constructs an objective function corresponding to the operation and maintenance target based on the Gaussian regression model to perform parsing.

[0082] Furthermore, the consistency confidence calculation module 13 is also used to perform the following steps: Identify at least one key operation and maintenance control parameter; extract corresponding key operation and maintenance control parameters from the multiple operation and maintenance control parameter groups according to the key operation and maintenance control parameter; perform normalized parameter vector processing on the multiple key operation and maintenance control parameters to obtain multiple key operation and maintenance control parameter vectors; perform similarity calculation on the key operation and maintenance control parameter vectors corresponding to every two converged terminals to construct a similarity matrix; calculate the consistency confidence index by calculating the average similarity of the similarity matrix.

[0083] Furthermore, the consistency confidence calculation module 13 is also used to perform the following steps: Based on the terminal cognitive ability index corresponding to each fusion terminal, a cognitive weight coefficient is generated; the cognitive weight coefficient is introduced to perform weighted calculation on the similarity matrix to obtain a weighted similarity matrix; and a consistency confidence calculation is performed based on the average similarity of the consistency confidence weighted similarity matrix to obtain a new consistency confidence index.

[0084] Furthermore, the management permission granting module 14 is also used to perform the following steps: This includes generating a digital permission token, distributing the permission token to each converged terminal in the terminal distribution subnetwork, and executing the operation and maintenance management of the corresponding industrial equipment by distributing a set of operation and maintenance control parameters through any converged terminal.

[0085] Furthermore, the management permission granting module 14 is also used to perform the following steps: Read the permission fusion terminal of the corresponding industrial equipment; analyze the real-time task queue of the permission fusion terminal to obtain the first permission fusion terminal, and perform operation and maintenance management of the corresponding industrial equipment according to the first permission fusion terminal.

[0086] Furthermore, the sub-network reconstruction module 15 is also used to perform the following steps: When the consistency confidence index is less than a preset confidence index threshold, an abnormal fusion terminal is located. The abnormal fusion terminal is a fusion terminal in the terminal distribution sub-network whose consistency deviation is greater than the element and the deviation threshold. A reconstructed fusion terminal with a consistency deviation greater than the preset terminal cognitive ability index threshold is selected from the fusion terminal distribution network. The terminal distribution sub-network is reconstructed based on the reconstructed fusion terminal to obtain a terminal distribution reconstructed sub-network.

[0087] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0088] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0089] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. An industrial equipment operation and maintenance management method driven by intelligent converged terminals, characterized in that, The method includes: Deploy a distributed network of converged terminals, and evaluate the terminal cognitive ability index by extracting terminal cognitive vectors from each converged terminal. Based on the terminal cognitive ability index, extract the terminal distribution sub-network of each industrial device from the fused terminal distribution network; The terminal distribution subnetwork simultaneously generates multiple operation and maintenance control parameter groups corresponding to the industrial equipment category, and performs consistency confidence calculation based on the multiple operation and maintenance control parameter groups to obtain a consistency confidence index. When the consistency confidence index is greater than or equal to a preset confidence index threshold, the corresponding industrial equipment interactive management permission is granted to each converged terminal in the terminal distribution sub-network. When the consistency confidence index is less than a preset confidence index threshold, the terminal distribution subnetwork is reconstructed, and each fusion terminal in the terminal distribution reconstruction subnetwork is granted the corresponding industrial equipment interactive management permission.

2. The method as described in claim 1, characterized in that, By extracting terminal cognitive vectors from each fusion terminal, the terminal cognitive ability index is evaluated. The methods include: Synchronously collect the status awareness dataset of each fusion terminal; Based on the state-aware dataset, extract the terminal cognition vector of each fusion terminal during the interaction process with the corresponding industrial equipment, including the interaction data transmission quality, interaction data processing efficiency, interaction data computation complexity, and terminal health perception status. Based on the extracted terminal cognition vectors, a terminal cognition capability index is established between each fusion terminal and the corresponding industrial equipment with which it is communicated.

3. The method as described in claim 1, characterized in that, The method for extracting the terminal distribution sub-network of each industrial device from the fused terminal distribution network based on the terminal cognitive ability index includes: Acquire a set of industrial equipment and identify the set of converged terminals corresponding to the communication connections of each industrial equipment; Extract the set of terminal cognitive ability indicators corresponding to the fused terminal set; N fusion terminals with values ​​greater than a preset threshold for terminal cognitive ability indicators are selected from the set of terminal cognitive ability indicators, and a terminal distribution sub-network for each industrial device is constructed based on the N fusion terminals.

4. The method as described in claim 1, characterized in that, The method for simultaneously generating multiple operation and maintenance control parameter groups for corresponding industrial equipment based on the terminal distribution subnetwork includes: The terminal distribution subnetwork synchronously reads the multimodal operation monitoring dataset of the corresponding industrial equipment through each fusion terminal; Determine the operation and maintenance objectives, and parse the multimodal operation monitoring dataset on the corresponding fusion terminal to obtain a set of parameter operation and maintenance control parameters based on the operation and maintenance objectives, thereby obtaining multiple sets of operation and maintenance control parameters. Specifically, the multimodal operation monitoring dataset is obtained by parsing the data on the corresponding fusion terminal through a target-driven parsing model built locally on the fusion terminal. The target-driven parsing model includes a locally downloaded Gaussian regression model, and a target function corresponding to the operation and maintenance target is constructed based on the Gaussian regression model for parsing.

5. The method as described in claim 1, characterized in that, The consistency confidence index is obtained by calculating the consistency confidence based on the multiple sets of operation and maintenance control parameters. The method includes: Identify at least one key operation and maintenance control parameter; According to the key operation and maintenance control parameters, extract the corresponding key operation and maintenance control parameters from the multiple operation and maintenance control parameter groups respectively; The multiple key operation and maintenance control parameters are processed into normalized parameter vectors to obtain multiple key operation and maintenance control parameter vectors. A similarity calculation is performed on the key operation and maintenance control parameter vectors corresponding to each pair of fusion terminals to construct a similarity matrix. Consistency confidence index is obtained by calculating the average similarity of the similarity matrix.

6. The method as described in claim 1, characterized in that, When the consistency confidence index is greater than or equal to a preset confidence index threshold, the corresponding industrial equipment interactive management permission is granted to each converged terminal in the terminal distribution sub-network. This includes generating a digital permission token and distributing the permission token to each converged terminal in the terminal distribution subnetwork; A set of operation and maintenance control parameters can be issued from any converged terminal to execute the operation and maintenance management of the corresponding industrial equipment.

7. The method as described in claim 6, characterized in that, The method also includes executing the operation and maintenance management of corresponding industrial equipment by issuing a set of operation and maintenance control parameters through any converged terminal: Access control for corresponding industrial equipment is integrated into the terminal. The real-time task queue of the permission fusion terminal is analyzed to obtain the first permission fusion terminal, and the operation and maintenance management of the corresponding industrial equipment is performed according to the first permission fusion terminal.

8. The method as described in claim 3, characterized in that, When the consistency confidence index is less than a preset confidence index threshold, the terminal distribution subnetwork is reconstructed, the method including: When the consistency confidence index is less than the preset confidence index threshold, an abnormal fusion terminal is located. The abnormal fusion terminal is a fusion terminal in the terminal distribution sub-network whose consistency deviation is greater than the element and the deviation threshold. Re-select reconstructed converged terminals from the converged terminal distribution network that are greater than the preset terminal cognitive ability index threshold; The terminal distribution subnetwork is reconstructed based on the reconstructed fusion terminal to obtain the terminal distribution reconstructed subnetwork.

9. The method as described in claim 5, characterized in that, The method further includes calculating the consistency confidence index by calculating the average similarity of the similarity matrix, and obtaining the consistency confidence index by means of: Generate cognitive weight coefficients based on the terminal cognitive ability indicators corresponding to each converged terminal; The cognitive weight coefficient is introduced to perform a weighted calculation on the similarity matrix to obtain a weighted similarity matrix. Consistency confidence is then calculated based on the average similarity of the consistency confidence weighted similarity matrix to obtain a new consistency confidence index.

10. An industrial equipment operation and maintenance management system driven by intelligent converged terminals, characterized in that, The system includes: The terminal cognitive vector extraction module is used to deploy a converged terminal distribution network. By extracting the terminal cognitive vector of each converged terminal, the terminal cognitive ability index is evaluated. The terminal distribution sub-network extraction module is used to extract the terminal distribution sub-network of each industrial device from the fused terminal distribution network based on the terminal cognitive ability index. The consistency confidence calculation module is used to simultaneously generate multiple operation and maintenance control parameter groups corresponding to the industrial equipment category based on the terminal distribution sub-network, and perform consistency confidence calculation based on the multiple operation and maintenance control parameter groups to obtain a consistency confidence index. The management permission granting module is used to grant each converged terminal in the terminal distribution sub-network the corresponding industrial equipment's interactive management permission when the consistency confidence index is greater than or equal to a preset confidence index threshold. The sub-network reconstruction module is used to reconstruct the terminal distribution sub-network when the consistency confidence index is less than a preset confidence index threshold, and to grant each fusion terminal in the terminal distribution reconstruction sub-network the corresponding industrial equipment's interactive management permissions.

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