Industrial equipment predictive maintenance method and device, storage medium and program product

Abstract

The embodiment of the invention discloses an industrial equipment predictive maintenance method and device, a storage medium and a program product. The method comprises the following steps: each node trains a prediction model based on local data, generates local model parameters and encrypts the local model parameters by adopting a public key to generate encrypted model parameters; the nodes are divided into core nodes and edge nodes, and the core nodes are divided into main nodes and secondary nodes; the edge nodes send the encryption model parameters to the corresponding core nodes for local aggregation to generate local aggregation parameters, then the local aggregation parameters are sent to the main node, and the main node carries out global aggregation to generate a global encryption sum; and the central server decrypts the verified global encryption sum by using a private key to obtain global model parameters, sends the global model parameters to each node for updating the prediction model, and performs predictive maintenance on the industrial equipment by using the updated prediction model. According to the method, while the data security is enhanced, distributed efficient prediction is realized, the maintenance process is optimized, the cost is reduced, and the equipment operation efficiency is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of industrial Internet technology, and in particular to a method, device, storage medium, and program product for predictive maintenance of industrial equipment. Background Art

[0002] With the development of industry, intelligent manufacturing systems are increasing, and predictive maintenance of equipment has become a key technology to improve production efficiency and reduce costs. However, due to the need to protect data privacy and trade secrets, direct data sharing is risky. Existing technical solutions, such as traditional centralized data processing and simple federated learning, cannot fully address the needs of data privacy protection and multi-party collaboration. Summary of the invention

[0003] In view of this, the embodiments of the present disclosure provide a method, device, storage medium, and program product for predictive maintenance of industrial equipment, which can achieve distributed and efficient prediction, optimize maintenance processes, reduce costs, and improve equipment operating efficiency while enhancing data security.

[0004] In a first aspect, the present disclosure provides a method for predictive maintenance of industrial equipment, which adopts the following technical solution:

[0005] Generate a public key and a private key, send the public key to each node, and store the private key in a central server; wherein each node represents one industrial device;

[0006] Each node trains a prediction model based on local data, generates local model parameters and encrypts them using the public key to generate encrypted model parameters;

[0007] Divide multiple nodes into multiple core nodes and multiple edge nodes, and divide multiple core nodes into one main node and several secondary nodes; wherein one core node is associated with several edge nodes, and the main node is associated with several secondary nodes;

[0008] The edge node sends the encryption model parameters to the corresponding core node, and the core node locally aggregates the local encryption model parameters and the received encryption model parameters to generate local aggregated parameters;

[0009] The secondary node sends the local aggregation parameter to the primary node, and the primary node globally aggregates the local local aggregation parameter and the received local aggregation parameter to generate a global encryption sum;

[0010] After the global encryption and verification, the master node encrypts and sends the global encryption to the central server;

[0011] The central server uses a private key to decrypt the global encryption and obtains global model parameters and sends them to each node for updating the prediction model until the global aggregation times reach a preset times threshold or the performance of the prediction model reaches the convergence standard, and the prediction model is used to perform predictive maintenance on industrial equipment.

[0012] Optionally, training a prediction model based on local data to generate local model parameters includes:

[0013] Preprocessing the local data;

[0014] Perform floating point formatting on the preprocessed local data;

[0015] Use non-associative addition and non-associative multiplication to protect the privacy of local data after floating-point formatting;

[0016] The prediction model is trained based on the SGD function and the privacy-protected local data, YY+242226P

[0017] Generate local model parameters.

[0018] Optionally, the expression of the non-associative addition is:

[0019] a⊕b=(a+b)+R(a,b); and / or,

[0020] The expression of the non-associative multiplication is:

[0021] a⊙b=(a×b)+R(a×b);

[0022] Where R(·) is a random perturbation function; a and b represent two data items involved in the operation.

[0023] Optionally, the expression of the non-associative addition is:

[0024] a⊕b=(a+b)+R(a,b); and / or,

[0025] The expression of the non-associative multiplication is:

[0026] a⊙b=(a×b)+R(a×b);

[0027] Where R(·) is a random perturbation function; a and b represent two data items involved in the operation.

[0028] Optionally, the expression of the non-associative addition is:

[0029] a⊕b=(a+b)+R(a,b); and / or,

[0030] The expression of the non-associative multiplication is:

[0031] a⊙b=(a×b)+R(a×b);

[0032] Where R(·) is a random perturbation function; a and b represent two data items involved in the operation.

[0033] Optionally, dividing the multiple core nodes into one primary node and a plurality of secondary nodes includes:

[0034] The primary node is selected from the plurality of core nodes by adopting a rotation mechanism or a random mechanism, and the remaining core nodes are recorded as the secondary nodes.

[0035] Optionally, the global encryption and verification are included, specifically including:

[0036] The master node encrypts the global information and sends it to each secondary node;

[0037] YY+242226P

[0038] Each of the secondary nodes encrypts and audits the global data respectively;

[0039] If the review is passed, the secondary node sends a first signal to the primary node;

[0040] If the review fails, the secondary node sends a second signal to the primary node;

[0041] The master node counts the proportion of the first signal based on the number of received first signals and the number of received second signals;

[0042] If the proportion is greater than a preset proportion threshold, it means that the global encryption has passed the verification;

[0043] If the proportion is not greater than the preset proportion threshold, it means that the global encryption has not passed the verification.

[0044] In a second aspect, the present disclosure also provides an industrial equipment predictive maintenance system, which adopts the following technical solution:

[0045] A key generation and distribution module is used to generate a public key and a private key, send the public key to each node, and store the private key in a central server; wherein each node represents one industrial device;

[0046] A prediction model training module, used for each node to train a prediction model based on local data, generate local model parameters and encrypt them using the public key to generate encrypted model parameters;

[0047] A node division module is used to divide multiple nodes into multiple core nodes and multiple edge nodes, and divide multiple core nodes into one main node and several secondary nodes; wherein one core node is associated with several edge nodes, and the main node is associated with several secondary nodes;

[0048] A local aggregation module, used for the edge node to send the encryption model parameters to the corresponding core node, and the core node locally aggregates the local encryption model parameters and the received encryption model parameters to generate local aggregation parameters;

[0049] A global aggregation module, used for the secondary node to send the local aggregation parameters to the primary node, and the primary node to globally aggregate the local local aggregation parameters and the received local aggregation parameters to generate a global encryption sum;

[0050] A global encryption and sending module, used for the master node to encrypt and send the global data to the central server after the global data is encrypted and verified;

[0051] A global encryption and decryption module is used for the central server to decrypt the global encryption and with a private key, obtain global model parameters and send them to each node, so as to update the prediction model until the global aggregation times reach a preset times threshold or the performance of the prediction model reaches a convergence standard, and the prediction model is used to perform predictive maintenance on industrial equipment.

[0052] In a third aspect, the embodiments of the present disclosure further provide a computer device, which adopts the following technical solution:

[0053] The computer device comprises:

[0054] at least one processor; and,

[0055] a memory communicatively connected to the at least one processor; wherein,

[0056] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any of the above-mentioned industrial equipment predictive maintenance methods.

[0057] In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a computer to execute any of the above-mentioned industrial equipment predictive maintenance methods.

[0058] In a fifth aspect, an embodiment of the present disclosure further provides a computer program product, including a computer program / instruction, which implements the steps of any of the above methods when executed by a processor.

[0059] The predictive maintenance method for industrial equipment provided by the embodiment of the present disclosure ensures the security of the local model parameters generated by each node during the transmission process by using the encryption mechanism of public key and private key, prevents the leakage of sensitive data, and enhances the privacy protection capability of the system. The nodes are divided into core nodes and edge nodes, and the core nodes are further divided into primary nodes and secondary nodes to realize distributed processing. This structure can effectively utilize the computing resources of each node in the network and reduce the burden of the central server. Through local aggregation and global aggregation, the effective integration of model parameters can be achieved while ensuring data security. This method can improve the generalization ability and prediction accuracy of the model. By distributing the computing tasks to each node, the computing resources in the network can be more effectively utilized, the computing pressure of the central server can be reduced, and the operation efficiency of the overall system can be improved. By continuously updating and optimizing the prediction model, the maintenance needs of industrial equipment can be more accurately predicted, thereby realizing predictive maintenance, reducing unexpected downtime, improving the operation efficiency and life of the equipment, and reducing production interruptions and maintenance costs caused by equipment failures, thereby reducing the overall maintenance cost, ensuring the continuous operation of the production line, and improving production efficiency and the economic benefits of the enterprise. By setting a preset number of thresholds or performance convergence criteria, the performance of the prediction model can be monitored to ensure that maintenance is performed when the prediction model performance reaches the optimal level, avoiding unnecessary maintenance operations. The entire process is highly automated, reducing manual intervention, and improving the efficiency and accuracy of maintenance work through intelligent model updates and maintenance. This method also supports the dynamic addition or removal of nodes, has good scalability, and can adapt to industrial equipment networks of different sizes.

[0060] The above description is only an overview of the technical solution of the present disclosure. In order to more clearly understand the technical means of the present disclosure, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present disclosure more obvious and easy to understand, the following is a preferred embodiment.

[0061] The detailed description is as follows with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0063] Figure 1 A flowchart of a method for predictive maintenance of industrial equipment provided by an embodiment of the present disclosure;

[0064] Figure 2A flowchart of a local training method for a prediction model provided in an embodiment of the present disclosure;

[0065] Figure 3 A functional block diagram of an industrial equipment predictive maintenance system provided by an embodiment of the present disclosure;

[0066] Figure 4 A schematic diagram of the structure of a computer device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0067] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0068] It should be clear that the following embodiments of the present disclosure are described by specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other in the absence of conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0069] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0070] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0071] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0072] Reference Figure 1 The present disclosure provides a method for predictive maintenance of industrial equipment, comprising the following steps:

[0073] S1: Generate public and private keys, send the public key to each node, and store the private key in the central server.

[0074] Among them, each node represents an industrial device.

[0075] S2: Each node trains a prediction model based on local data, generates local model parameters and encrypts them using a public key to generate encrypted model parameters.

[0076] S3: Divide multiple nodes into multiple core nodes and multiple edge nodes, and divide multiple core nodes into one main node and several secondary nodes.

[0077] Among them, one core node is associated with several edge nodes, and the main node is associated with several secondary nodes.

[0078] S4: The edge node sends the encryption model parameters to the corresponding core node, and the core node locally aggregates the local encryption model parameters and the received encryption model parameters to generate local aggregated parameters.

[0079] S5: The secondary node sends the local aggregation parameters to the primary node, and the primary node globally aggregates the local local aggregation parameters and the received local aggregation parameters to generate a global encryption sum.

[0080] S6: After global encryption and verification, the master node will encrypt and send the global data to the central server.

[0081] S7: The central server uses the private key to decrypt the global encryption and obtains the global model parameters and sends them to each node for updating the prediction model until the global aggregation times reaches the preset times threshold or the performance of the prediction model reaches the convergence standard, and the prediction model is used to perform predictive maintenance on industrial equipment.

[0082] The disclosed industrial equipment predictive maintenance method ensures the security of the local model parameters generated by each node during the transmission process by using the encryption mechanism of public key and private key, prevents the leakage of sensitive data, and enhances the privacy protection ability of the system. The nodes are divided into core nodes and edge nodes, and the core nodes are further divided into primary nodes and secondary nodes to realize distributed processing. This structure can effectively utilize the computing resources of each node in the network and reduce the burden of the central server. Through local aggregation and global aggregation, the effective integration of model parameters can be achieved while ensuring data security. This method can improve the generalization ability and prediction accuracy of the model. By distributing the computing tasks to each node, the computing resources in the network can be more effectively utilized, the computing pressure of the central server can be reduced, and the operation efficiency of the overall system can be improved. By continuously updating and optimizing the prediction model, the maintenance needs of industrial equipment can be more accurately predicted, thereby realizing predictive maintenance, reducing unexpected downtime, improving the operation efficiency and life of the equipment, and reducing production interruptions and maintenance costs caused by equipment failures, thereby reducing the overall maintenance cost, ensuring the continuous operation of the production line, and improving production efficiency and the economic benefits of the enterprise. By setting a preset number of thresholds or performance convergence criteria, the performance of the prediction model can be monitored to ensure that maintenance is performed when the prediction model performance reaches the optimal level, avoiding unnecessary maintenance operations. The entire process is highly automated, reducing manual intervention, and improving the efficiency and accuracy of maintenance work through intelligent model updates and maintenance. This method also supports the dynamic addition or removal of nodes, has good scalability, and can adapt to industrial equipment networks of different sizes.

[0083] In summary, this system can enhance data security and protect data privacy while achieving distributed and efficient prediction, optimizing maintenance processes, reducing costs, and improving equipment operation efficiency.

[0084] In S1, a pair of keys, including a public key (pk) and a private key (sk), is generated using a suitable homomorphic encryption algorithm (e.g., RSA, ECC, BGV, or FV). The public key is distributed to each node in the system in a secure manner, such as sending the public key over the network, and the security of the distribution process is ensured to prevent the key from being intercepted or tampered with during transmission. The private key is securely stored on a central server. The private key is the key in the decryption process, so it must be kept secure to prevent unauthorized access. Among them, the public key and the private key can be generated by a designated management agency or by a central server. If the management agency is responsible for generating them, the management agency is responsible for distributing the public key and the private key. If the central server is responsible for generating them, the central server distributes the public key to each node and saves the private key to ensure that only the central server can decrypt the global encryption and obtained subsequently, thereby maintaining the security of the entire system and the privacy of the data.

[0085] When distributing keys, verify node identities through digital signatures or certificate authentication to ensure that each node is trustworthy. Configure each node in the system to ensure that they use the same encryption parameters and protocol versions to ensure the consistency of the encryption process. Implement key management strategies, including key update, rotation, and abandonment processes, to address potential security risks.

[0086] Implement batch processing technology to improve the efficiency of homomorphic operations, that is, when performing homomorphic encryption operations, multiple data points can be processed simultaneously, reducing the time and computing resources required for encryption and decryption. Design a layered network topology to improve the scalability of the system. The core layer handles more complex tasks, while the edge layer is responsible for simple tasks closer to the data source, so that the system can maintain efficient operation when adding nodes. Allow nodes to dynamically join or exit the network according to system requirements, adapt to changes in the number of nodes, increase the flexibility of the system, and adapt to changing workloads. Before the system is officially launched, conduct thorough testing and verification to ensure the security and reliability of the key generation, distribution, and custody processes.

[0087] In S2, local data is the running data of each node. Each node maintains the version of local data to ensure data version synchronization.

[0088] Reference Figure 2 The flowchart of the local training method of the prediction model shown in the figure, "Training the prediction model based on local data and generating local model parameters" includes the following steps:

[0089] S21: preprocessing local data;

[0090] S22: Formatting the preprocessed local data into floating point numbers;

[0091] S23: Use non-associative addition and non-associative multiplication to protect the privacy of the local data after the floating point number is formatted;

[0092] S24: Train the prediction model based on the SGD function and the privacy-protected local data to generate local model parameters.

[0093] In S21, each node preprocesses local data, which includes cleaning, standardization and normalization. Preprocessing ensures data quality and provides a consistent input format for model training.

[0094] In S22, the IEEE 754 standard is used to uniformly format local data into floating-point numbers, allowing each node to process and store data in a unified format, ensuring data consistency and accuracy, and preparing for subsequent mathematical operations.

[0095] In S23, non-associative operations are introduced to enhance data privacy, and random perturbations are introduced by modifying addition and multiplication operations. Among them, non-associative addition is: Non-associative multiplication is a⊙b=(a×b)+R(a×b). R(·) is a random perturbation function that depends on a and b, where a and b represent the two data items involved in the operation. Through non-associative addition and non-associative multiplication, even if the calculation results can be observed, the original data cannot be directly inferred, and while protecting data privacy, necessary calculations can be performed on local data.

[0096] In one implementation of S24, the expression of the SGD function is:

[0097]

[0098] Among them, θ t+1 is the prediction model parameter for the next iteration; θ t is the prediction model parameter of the current iteration; θ represents the prediction model parameter; η is the learning rate, which is used to control the step size of the update; is the loss gradient under the current prediction model parameters; ξ t The random perturbation term introduced in each iteration is a random number drawn from a Gaussian distribution. Its function is to add Gaussian noise to the gradient update to protect privacy, which is similar to the Laplace mechanism or Gaussian mechanism in differential privacy. t is the current training iteration number.

[0099] The above SGD function combines the SGD algorithm with non-associative operations. When calculating the gradient of model parameters, a local gradient clipping mechanism is designed to limit the size of the gradient. This can protect data privacy while training the prediction model, ensuring that data privacy will not be leaked even in a distributed training environment.

[0100] In another implementation of S24, the expression of the SGD function is:

[0101]

[0102] Before training begins, an initial privacy budget ε is allocated to each node, which is adjusted according to the sensitivity of the local data to ensure that sensitive data is more protected. t is the ε value used in the tth iteration. In each iteration, the ε value is dynamically adjusted according to the progress of the prediction model training and the current privacy requirements. This dynamic adjustment is implemented based on the model performance feedback mechanism. Specifically, in each iteration, if the performance of the prediction model does not reach the preset target, ε is reduced to increase privacy protection. Conversely, if the performance of the prediction model reaches the preset target, ε is increased to reduce noise and improve the accuracy of the prediction model.

[0103] This method of dynamically adjusting the ε value can find a balance between protecting privacy and optimizing model performance. The SGD function not only includes the update of model parameters, but also integrates privacy protection measures, so that each node can effectively protect data privacy while training the model locally.

[0104] Furthermore, a privacy budget warning mechanism is implemented to monitor the usage of ε and the remaining privacy budget. If the privacy budget is close to exhaustion, the warning system will trigger an adjustment strategy, such as reducing the learning rate or increasing the noise level.

[0105] You can also design a secure data sharing mechanism and use a secure data sharing protocol based on homomorphic encryption. Design a feature selection mechanism based on oblivious transfer (OT) to ensure the privacy of data during feature selection. Implement a secure data quality assessment protocol without exposing the original data.

[0106] After the prediction model training is completed, the prediction model parameters at this time are recorded as local model parameters. Multi-party secure computing technology can also be used to evaluate model performance, such as cross-validation of secure multi-party computing to ensure that the data of each node is not leaked, and the area under the ROC curve (AUC) is calculated to evaluate the prediction ability of the model. During the evaluation process, the data of each node remains encrypted and no sensitive information is leaked. It is also possible to implement an Adam optimizer or other optimization algorithms based on non-associative operations to adapt to non-associative addition and multiplication.

[0107] The local model parameters obtained by public key encryption are generated to generate encrypted model parameters, ready to be shared with other nodes. The formula is: i =Encrypt(pk,§ i ), where i is the sequence number of the node; c i is the encryption model parameter of the i-th node, § i is the local model parameter of the i-th node, and Encrypt is the encryption function.

[0108] In S3, nodes are divided into core nodes and edge nodes. In a distributed system, core nodes usually undertake more computing and coordination tasks, are responsible for aggregating data from edge nodes, perform more complex computing tasks, and participate in the global decision-making process. Edge nodes are usually located near the data source, responsible for collecting and preliminarily processing data, and sending the processed data to the core nodes, but do not directly participate in global aggregation or decision-making. For example, there are three factories participating in the federated learning of the prediction model. Each factory has production equipment for daily industrial manufacturing and control equipment for managing these production equipment. The production equipment is divided into edge nodes, and the control equipment is divided into core nodes. Each core node manages a group of edge nodes to form a hierarchical network structure, which helps to reduce communication overhead and improve data processing efficiency. Through the setting of multiple core nodes, the system can better achieve fault tolerance and load balancing. If a core node fails, other core nodes can take over its tasks to ensure stable operation of the system.

[0109] The core nodes are further divided into master nodes and secondary nodes. There is one master node, which is responsible for the final global aggregation, and there are several secondary nodes. A rotation mechanism or a random mechanism is used to select the master node from multiple core nodes, and the remaining core nodes are recorded as secondary nodes to avoid a single node from concentrating power for a long time, reduce the risk of the system being attacked or failing, and increase the fairness and robustness of the system. It also helps to balance the load of each node and improve the efficiency and scalability of the entire system.

[0110] Furthermore, the system dynamically adjusts the roles and responsibilities of nodes based on the current workload and the health of the nodes. When dividing the roles of nodes, security needs to be considered to ensure the security and reliability of core nodes (especially master nodes) because they play a key role in the system. Through such role division, the distributed system can process large amounts of data more efficiently while maintaining the stability and reliability of the system. Each node performs tasks according to its own role and responsibilities, and together achieves the goals of the entire system.

[0111] In S4, the edge node sends its own encrypted model parameters to the corresponding core node. The core node uses a secure aggregation protocol based on homomorphic encryption to locally aggregate its own encrypted model parameters and the received encrypted model parameters. The core node can implement an anomaly detection mechanism during the local aggregation process to identify and handle possible malicious updates. After the core node completes the local aggregation, it generates local aggregation parameters, which are encrypted.

[0112] In S5, the secondary node sends its local aggregation parameters to the primary node, which have been securely processed through homomorphic encryption. The primary node uses the secure aggregation protocol of homomorphic encryption to globally aggregate its own local aggregation parameters and the received local aggregation parameters to generate a global encrypted sum, denoted as Cglobal, which is the encrypted aggregation result of all node encryption model parameters.

[0113] The master node design is a decentralized aggregation mechanism based on secret sharing, which can increase the robustness and anti-attack capability of the system. During the global aggregation process, the master node continues to perform anomaly detection to ensure the accuracy and security of the aggregation results.

[0114] In S6, the master node sends the global encrypted sum to each secondary node (which can be sent by broadcasting), and each secondary node audits the global encrypted sum, and the audit includes checking the encryption integrity and correctness of the global encrypted sum; if the audit is passed, the secondary node sends a first signal to the master node (the first signal is used to indicate a vote in favor); if the audit is not passed, the secondary node sends a second signal to the master node (the second signal is used to indicate a vote against). The master node counts the proportion of the first signal based on the number of first signals and the number of second signals received; if the proportion is greater than a preset proportion threshold (for example, 2 / 3), it means that the global encrypted sum has passed the verification, and it is determined that all core nodes have reached a consensus; if the proportion is not greater than the preset proportion threshold, it means that the global encrypted sum has not passed the verification, and it is determined that all core nodes have not reached a consensus, detect the location of the error, and correct the error. If necessary, manual intervention or external arbitrator intervention is used. If necessary, the prediction model is retrained and the nodes are divided. This voting mechanism combines fully homomorphic encryption with the Byzantine fault-tolerant protocol to achieve reliable distributed data aggregation and analysis while protecting the privacy of individual data. It can resist some malicious nodes (for example, malicious nodes accounting for 1 / 3 of the total number of core nodes), protect the privacy of individual data and achieve reliable distributed data aggregation. It is suitable for industrial Internet of Things environments that require high security and privacy protection, such as smart manufacturing, supply chain management, energy management and other fields.

[0115] In S7, the central server uses the private key to decrypt Cglobal and obtain the global model parameters. The central server sends the global model parameters back to all nodes (can be sent by broadcast), each node uses the global model parameters to update its local prediction model, each node evaluates the performance of the updated model locally, and the central server collects performance feedback to check whether the model meets the convergence standard or other performance indicators. If abnormal data (such as extreme values) is detected during the model update or performance evaluation process, the system will trigger an alarm. Use zero-knowledge proof technology to prove the existence of anomalies without revealing which specific device has the anomaly, protecting data privacy.

[0116] The central server or master node can publish appropriate statistical information, such as average output, total energy consumption, etc., without disclosing the specific data of individual nodes to ensure data privacy and system transparency.

[0117] If the global aggregation times do not reach the preset times threshold or the prediction model performance does not reach the convergence standard, repeat the above steps and continue to update the prediction model until the global aggregation times reaches the preset times threshold or the prediction model performance reaches the convergence standard.

[0118] When publishing global model parameters, functional encryption technology is used to allow each node to use the updated prediction model in the encrypted domain to try predictions, ensuring the security of data during the prediction process. A prediction API with differential privacy protection is implemented to further protect the prediction model and user privacy.

[0119] The final prediction model is used to predict the failure of industrial equipment, so as to carry out routine maintenance of industrial equipment and ensure production safety and efficiency. A safe incremental learning mechanism can be designed so that the prediction model can be updated and optimized according to the newly generated data. A safe model rollback mechanism is implemented so that the prediction model can be restored to the previous stable state when the performance of the prediction model degrades.

[0120] In the above-mentioned technical solution for constructing a predictive maintenance method for industrial equipment, the hardware architecture design of the intelligent data connection acquisition device is a basic and key link. For this purpose, a hardware architecture is designed to ensure efficient and secure data collection and processing. First, a high-performance processor, such as the ARM Cortex-A series, is selected to provide powerful computing power and support complex data processing and analysis tasks. At the same time, a dedicated encryption hardware accelerator is integrated to enhance the security of data during collection and transmission. The hardware architecture also includes an expandable storage system that can cope with the needs of large-scale data storage and provide sufficient data support for subsequent data analysis and model training. In terms of data collection, multi-interface data collection capabilities are implemented to support common industrial communication protocols such as Modbus, Profinet, etc., to ensure seamless connection and data collection with various industrial equipment.

[0121] In order to improve the efficiency and flexibility of data collection, a configurable data collection interface is designed, and a real-time data collection and caching mechanism is implemented to quickly respond to and process the data generated by industrial equipment. In terms of network connection, the device supports wired and wireless network connections, including Ethernet, WiFi, 5G, etc., to ensure that data can be stably and quickly transmitted to the core node or central server.

[0122] Security is a top priority in the design, so a secure network communication protocol such as TLS / SSL is used to protect the security of data transmission. Network fault detection and automatic recovery are performed to ensure the stability and reliability of network connections. In order to give full play to the advantages of edge computing, the hardware architecture integrates edge computing capabilities, realizes local data preprocessing and analysis functions, and a dynamically loadable edge application framework, which enables the device to flexibly deploy and update edge computing applications according to actual needs.

[0123] Through these carefully designed hardware features, intelligent data connection and collection equipment can not only efficiently collect and process industrial equipment data, but also provide strong hardware support for industrial equipment predictive maintenance systems under the premise of ensuring data security and privacy. This lays a solid foundation for achieving efficient and safe operations in fields such as intelligent manufacturing, supply chain management, and energy management.

[0124] The design of the software system is crucial for intelligent data connection and acquisition equipment. To this end, an efficient and secure software system is developed to ensure the high-performance operation and data processing of the equipment. First of all, the operating system and runtime environment choose systems such as FreeRTOS or RTThread that are suitable for real-time operation to ensure the response speed and stability of the system. At the same time, a secure boot and firmware update mechanism is implemented to ensure the continuous update and security maintenance of the equipment during its life cycle. Application isolation and resource management mechanisms are also used to optimize system performance and prevent potential security threats.

[0125] In terms of data collection and preprocessing, a configurable data collection engine is developed to adapt to different industrial equipment and data formats, realize data cleaning and standardized processing, ensure data quality, and lay a solid foundation for subsequent analysis and model training. Data compression and efficient storage are also used to reduce storage space requirements and improve data transmission efficiency.

[0126] Secure communication and data transmission are key components of the system. This communication protocol based on fully homomorphic encryption allows calculations to be performed directly on encrypted data without decryption, thereby protecting data privacy while achieving data usefulness. Data fragmentation and breakpoint continuation, as well as data integrity verification and anti-tampering mechanisms can also be used to ensure the security and reliability of data during transmission.

[0127] The device management and monitoring functions are set up, including remote device configuration and management interfaces, so that system administrators can easily perform device maintenance and configuration. The implementation of such device health monitoring and fault diagnosis functions helps to discover and solve potential device problems in advance. Through secure logging and auditing, key system operations and status changes are recorded, providing additional protection for the safe operation of the system.

[0128] Through the design and implementation of these software systems, intelligent data connection acquisition devices can not only collect and process data efficiently and securely, but also integrate seamlessly with other components in the predictive maintenance system of industrial equipment to jointly build a robust and reliable industrial Internet of Things solution.

[0129] To improve system performance, we can analyze system bottlenecks, especially for computing-intensive operations, such as optimizing memory usage and management strategies to ensure that system resources are used most efficiently. We can also use intelligent energy management methods to extend the battery life of devices, which is especially important in energy-constrained environments.

[0130] In terms of enhancing system reliability, we adopt data backup and recovery mechanisms to prevent data loss, ensure that the system can continue to operate when some components fail through failover and fault tolerance, and conduct long-term stability testing and optimization to ensure that the system can maintain high performance after long-term operation. We conduct comprehensive security vulnerability scanning and repair to ensure that the system is not affected by known vulnerabilities, implement intrusion detection and defense to prevent unauthorized access and potential network attacks, and conduct security emergency response so that we can respond quickly and effectively when security incidents occur.

[0131] Through these comprehensive measures, intelligent data connection and collection equipment can not only meet the needs of predictive maintenance of industrial equipment, but also provide efficient and reliable services while protecting data privacy. This provides a solid technical foundation for smart manufacturing, supply chain management, energy management and other fields in the industrial Internet of Things environment.

[0132] Reference Figure 3 The present disclosure provides an industrial equipment predictive maintenance system, comprising:

[0133] The key generation and distribution module 101 is used to generate a public key and a private key, send the public key to each node, and store the private key in a central server; wherein each node represents one industrial device;

[0134] The prediction model training module 102 is used for each node to train the prediction model based on local data, generate local model parameters and encrypt them with a public key to generate encrypted model parameters;

[0135] The node division module 103 is used to divide the multiple nodes into multiple core nodes and multiple edge nodes, and divide the multiple core nodes into one main node and several secondary nodes; wherein one core node is associated with several edge nodes, and the main node is associated with several secondary nodes;

[0136] Local aggregation module 104, used for edge nodes to send encryption model parameters to corresponding core nodes, and core nodes to locally aggregate local encryption model parameters and received encryption model parameters to generate local aggregation parameters;

[0137] The global aggregation module 105 is used for the secondary node to send the local aggregation parameters to the primary node, and the primary node globally aggregates the local local aggregation parameters and the received local aggregation parameters to generate a global encryption sum;

[0138] A global encryption and sending module 106, which is used for the master node to encrypt and send the global data to the central server after the global data is encrypted and verified;

[0139] The global encryption and decryption module 107 is used for the central server to decrypt the global encryption and with a private key, obtain the global model parameters and send them to each node, so as to update the prediction model until the global aggregation times reach a preset times threshold or the performance of the prediction model reaches the convergence standard, and the prediction model is used to perform predictive maintenance on industrial equipment.

[0140] The various variations and specific examples of the industrial equipment predictive maintenance method provided above are also applicable to the industrial equipment predictive maintenance system provided in the present disclosure. Through the above detailed description of the industrial equipment predictive maintenance method, those skilled in the art can clearly understand the implementation method of the industrial equipment predictive maintenance system. For the sake of brevity of the specification, it will not be described in detail here.

[0141] The computer device according to the embodiment of the present disclosure includes a memory and a processor. The memory is used to store non-temporary computer-readable instructions. Specifically, the memory may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include a random access memory (RAM) and / or a cache memory (cache), etc. The non-volatile memory may, for example, include a read-only memory (ROM), a hard disk, a flash memory, etc.

[0142] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the computer device to perform desired functions. In one embodiment of the present disclosure, the processor is used to run the computer-readable instructions stored in the memory, so that the computer device performs all or part of the steps of the industrial equipment predictive maintenance method of each embodiment of the present disclosure.

[0143] Those skilled in the art should be able to understand that in order to solve the technical problem of how to obtain a good user experience, the present embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the protection scope of the present disclosure.

[0144] like Figure 4 A schematic diagram of the structure of a computer device provided in an embodiment of the present disclosure is shown, which is a schematic diagram of the structure of a computer device suitable for implementing the embodiment of the present disclosure. Figure 4 The computer device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0145] like Figure 4 As shown, the computer device may include a processor (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) or a program loaded from a storage device into a random access memory (RAM). In the RAM, various programs and data required for the operation of the computer device are also stored. The processor, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0146] Typically, the following devices can be connected to the I / O interface: input devices such as sensors or visual information acquisition devices; output devices such as display screens; storage devices such as tapes, hard disks, etc.; and communication devices. The communication device can allow the computer device to communicate with other devices (such as edge computing devices) wirelessly or by wire to exchange data. Figure 4 A computer device having various devices is shown, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0147] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processor, all or part of the steps of the industrial equipment predictive maintenance method of the embodiment of the present disclosure are executed.

[0148] For detailed description of this embodiment, please refer to the corresponding description in the above embodiments.

[0149] I will not go into details here.

[0150] According to the computer-readable storage medium of the embodiment of the present disclosure, non-transitory computer-readable instructions are stored thereon. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the industrial equipment predictive maintenance method of each embodiment of the present disclosure are executed.

[0151] The above-mentioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or mobile hard disk), media with built-in rewritable non-volatile memory (e.g., memory card) and media with built-in ROM (e.g., ROM box).

[0152] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0153] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0154] In the present disclosure, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. The block diagrams of the devices, devices, equipment, and systems involved in the present disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0155] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0156] It should also be noted that in the system and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0157] Various changes, substitutions, and modifications of the techniques described herein may be made without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of the present disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and actions described above. Currently existing or later to be developed processes, machines, manufactures, compositions of events, means, methods, or actions that perform substantially the same functions or achieve substantially the same results as the corresponding aspects described herein may be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or actions within their scope.

[0158] The above description of the disclosed aspects is provided to enable any person skilled in the art to make

[0159] Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0160] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A predictive maintenance method for industrial equipment, It is characterized in that include: Generate a public key and a private key, send the public key to each node, and store the private key in a central server; wherein each node represents one industrial device; Each node trains a prediction model based on local data, generates local model parameters and encrypts them using the public key to generate encrypted model parameters; Divide multiple nodes into multiple core nodes and multiple edge nodes, and divide multiple core nodes into one main node and several secondary nodes; wherein one core node is associated with several edge nodes, and the main node is associated with several secondary nodes; The edge node sends the encryption model parameters to the corresponding core node, and the core node locally aggregates the local encryption model parameters and the received encryption model parameters to generate local aggregated parameters; The secondary node sends the local aggregation parameter to the primary node, and the primary node globally aggregates the local local aggregation parameter and the received local aggregation parameter to generate a global encryption sum; After the global encryption and verification, the master node encrypts and sends the global encryption to the central server; The central server uses a private key to decrypt the global encryption and obtains global model parameters and sends them to each node for updating the prediction model until the global aggregation times reach a preset times threshold or the performance of the prediction model reaches the convergence standard, and the prediction model is used to perform predictive maintenance on industrial equipment.

2. The method for predictive maintenance of industrial equipment according to claim 1, It is characterized in that This includes training a prediction model based on local data and generating local model parameters, including: Preprocessing the local data; Perform floating point formatting on the preprocessed local data; Use non-associative addition and non-associative multiplication to protect the privacy of local data after floating-point formatting; The prediction model is trained based on the SGD function and the privacy-protected local data to generate local model parameters.

3. The method for predictive maintenance of industrial equipment according to claim 2, It is characterized in that The expression of the non-associative addition is: and / or, The expression of the non-associative multiplication is: a⊙b=(a×b)+R(a×b); Where R(·) is a random perturbation function; a and b represent two data items involved in the operation.

4. The method for predictive maintenance of industrial equipment according to claim 3, It is characterized in that The expression of the SGD function is: Among them, θ t+1 is the prediction model parameter for the next iteration; θ t is the prediction model parameter of the current iteration; η is the learning rate; is the loss gradient under the current prediction model parameters; ξ t is the random perturbation term introduced in each iteration; t is the number of current training iterations.

5. The method for predictive maintenance of industrial equipment according to claim 3, It is characterized in that The expression of the SGD function is: Among them, θ t+1 is the prediction model parameter for the next iteration; θ t is the prediction model parameter of the current iteration; η is the learning rate; is the loss gradient under the current prediction model parameters; ξ t is the random perturbation term introduced in each iteration; ∈ t is the ε value used at the tth iteration; ε is the privacy budget; and t is the current number of training iterations.

6. The method for predictive maintenance of industrial equipment according to claim 1, It is characterized in that The multiple core nodes are divided into one main node and several secondary nodes, including: The primary node is selected from the plurality of core nodes by adopting a rotation mechanism or a random mechanism, and the remaining core nodes are recorded as the secondary nodes.

7. The method for predictive maintenance of industrial equipment according to claim 1, It is characterized in that Including encrypting and verifying the global data, specifically including: The master node encrypts the global information and sends it to each secondary node; Each of the secondary nodes encrypts and audits the global data respectively; If the review is passed, the secondary node sends a first signal to the primary node; If the review fails, the secondary node sends a second signal to the primary node; The master node counts the proportion of the first signal based on the number of received first signals and the number of received second signals; If the proportion is greater than a preset proportion threshold, it means that the global encryption has passed the verification; If the proportion is not greater than the preset proportion threshold, it means that the global encryption has not passed the verification.

8. A computer device, It is characterized in that The computer device comprises: at least one processor; and, A memory in communication with the at least one processor; wherein YY+242226P The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the industrial equipment predictive maintenance method described in any one of claims 1-7.

9. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores computer instructions, which are used to enable a computer to execute the industrial equipment predictive maintenance method described in any one of claims 1-7.

10. A computer program product comprising computer instructions, It is characterized in that When the computer instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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