A distributed industrial automation instrument online monitoring and closed-loop control system

Abstract

The application relates to the technical field of industrial automation control, and discloses a distributed industrial automation instrument online monitoring and closed-loop control system, which is built into a three-level distributed network composed of a field node layer, a regional coordination layer and a cloud management layer and is collocated with multiple function modules for collaborative operation. The system can complete multi-protocol analysis, parameter acquisition and instrument state discrimination; outputs control instructions adapted to working conditions relying on a PID algorithm, realizes parameter self-tuning in combination with an actuator feedback, constructs a closed-loop control, and simultaneously completes node linkage and environmental parameter self-adaptive compensation. The system stores data in an edge cache combined with cloud synchronization, realizes communication encryption authentication through a national encryption algorithm, and automatically identifies and locates faults. The system is stable in operation, high in control precision, excellent in anti-interference and safety performance, and high in intelligent operation and maintenance degree, and can be widely applied to industrial automation scenes with scattered layout and complex working conditions.

Description

Technical Field

[0001] This invention relates to the field of industrial automation control technology, and more specifically, to a distributed industrial automation instrument online monitoring and closed-loop control system. Background Technology

[0002] In industrial automation production scenarios, automated instruments are core equipment for monitoring on-site process parameters and ensuring stable production operation. Currently, the industry generally adopts centralized measurement and control systems to complete data acquisition and field equipment control for industrial instruments. These systems rely on a central control host to uniformly connect with various instruments and actuators on-site, centrally completing data acquisition, status monitoring, and fixed command output. They can meet the basic monitoring and control needs of conventional industrial sites and are currently the most widely used technical solution in the field of industrial instrument measurement and control.

[0003] However, existing centralized monitoring and control systems have many inherent defects: First, the system relies on a single central host, which poses a serious risk of single point of failure. The failure of the main control equipment can cause the entire monitoring and control to fail. The system lacks distributed redundancy and has extremely poor operational stability. Second, the communication protocols of various heterogeneous instruments for pressure, temperature, flow, and level are incompatible. Traditional systems cannot uniformly complete data parsing, and the instrument access adaptability is poor. Third, most systems adopt fixed parameter open-loop control and cannot rely on the feedback of actuators to dynamically adjust the control logic. The closed-loop control capability is lacking, and the control accuracy is difficult to guarantee. Fourth, the system does not collect environmental parameters such as temperature, humidity, vibration, and electromagnetic interference on site, and cannot perform environmental compensation. Interference from on-site operating conditions can easily cause monitoring data deviation. In addition, the system communication link lacks encryption protection mechanisms, and the fault location of instruments and network nodes relies entirely on manual inspection, resulting in low maintenance efficiency and insufficient data transmission security.

[0004] In summary, existing centralized industrial instrumentation and control systems suffer from poor stability, insufficient compatibility, low control accuracy, weak anti-interference capabilities, and inadequate operational security, failing to meet the practical needs of large-scale, complex industrial sites for high reliability, high precision, and unmanned operation and maintenance. Therefore, there is an urgent need for a distributed industrial automation instrumentation online monitoring and closed-loop control system to address the various technical shortcomings of the existing centralized monitoring and control architecture and improve the overall operational performance of industrial instrumentation and control systems. Summary of the Invention

[0005] In view of this, the present invention proposes a distributed industrial automation instrument online monitoring and closed-loop control system, which aims to solve the problems of high single-point failure risk, poor compatibility of heterogeneous instrument access, lack of dynamic closed-loop control capability, poor adaptability to field environmental interference, lack of communication security protection, and reliance on manual fault operation and maintenance in the current technology.

[0006] This invention proposes a distributed industrial automation instrument online monitoring and closed-loop control system, comprising: The distributed network architecture module is configured to build a hierarchical distributed node network, wherein the distributed nodes are respectively connected to industrial automation instruments and actuators. The heterogeneous instrument online monitoring module is connected to the distributed network architecture module, and collects and parses instrument operating data based on the distributed nodes to determine the instrument working status; An automated closed-loop control module is communicatively connected to the heterogeneous instrument online monitoring module and the actuator. The automated closed-loop control module outputs control commands based on the instrument operating data and the instrument working status, and receives the status data returned by the actuator to adjust the control logic. The node collaboration and environment adaptation module is communicatively connected to the distributed node, the heterogeneous instrument online monitoring module, and the automated closed-loop control module, respectively. The node collaboration and environment adaptation module triggers the linkage operation between nodes based on the instrument working status or the control command, and performs compensation adaptation according to the environmental parameters collected by the distributed node. The data management and security operation and maintenance module is communicatively connected to the distributed network architecture module, the heterogeneous instrument online monitoring module, and the automated closed-loop control module. The data management and security operation and maintenance module stores instrument operation data and control logs, encrypts and authenticates the communication process, and diagnoses faults in nodes and instruments.

[0007] Furthermore, the layered distributed node network constructed by the distributed network architecture module includes a three-level structure: a field node layer, a regional coordination layer, and a cloud management layer. The field node layer is equipped with several field distributed nodes, which are located near the industrial automation instrument installation area and directly establish wired communication connections with the corresponding industrial automation instruments and actuators. The regional coordination layer is equipped with a regional aggregation gateway, which establishes a wireless communication connection with the distributed nodes in its jurisdiction to relay collected data and control commands. The cloud management layer is equipped with a cloud management server, which establishes a network communication connection with all regional aggregation gateways to store node parameters and perform network-wide node scheduling.

[0008] Furthermore, the heterogeneous instrument online monitoring module includes a multi-protocol adaptation and parsing unit, a multi-type instrument data acquisition unit, and an instrument status discrimination unit; The multi-protocol adaptation parsing unit communicates with the distributed nodes of the distributed network architecture module to parse and standardize the raw data of different instruments. The multi-type instrument data acquisition unit is signal-connected to the multi-protocol adaptation and parsing unit. The multi-type instrument data acquisition unit acquires process output parameters, power supply status parameters, communication link quality parameters, and measurement deviation data of pressure instruments, temperature instruments, flow instruments, and level instruments. The instrument status discrimination unit is connected to the multi-type instrument data acquisition unit via signal.

[0009] Furthermore, the instrument status judgment unit compares the collected data with a preset threshold in real time to generate a normal judgment result or an abnormal judgment result. When a normal judgment result is generated, the instrument status discrimination unit maintains the current acquisition state and continuously uploads the instrument operation data to the distributed network architecture module; When an anomaly determination result is generated, the instrument status determination unit adds an anomaly identifier to the corresponding data and synchronously uploads the data with the anomaly identifier to the distributed network architecture module.

[0010] Furthermore, the automated closed-loop control module includes an instruction generation unit, a data receiving unit, and a control logic self-tuning unit; The instruction generation unit is signal-connected to the instrument status discrimination unit of the heterogeneous instrument online monitoring module, the data receiving unit is communicatively connected to the actuator, and the control logic self-tuning unit is signal-connected to the instruction generation unit and the data receiving unit respectively. The instruction generation unit generates corresponding actuator control instructions based on the instrument's operating data and its normal / abnormal operating status.

[0011] Furthermore, the instruction generation unit has a built-in PID control algorithm, which outputs a steady-state maintenance instruction when the instrument is in normal working condition, and outputs a parameter correction and adjustment instruction when the instrument is in abnormal working condition. The actuator status data returned by the data receiving unit includes the actuator displacement, operating current value, and output response parameters. The control logic self-tuning unit pre-stores the standard response threshold of the actuator, and compares the returned actuator status data with the standard response threshold in real time to generate a matching result or a non-matching result. When generating a matching result, the control logic self-tuning unit maintains the proportional, integral, and derivative parameters of the current PID algorithm unchanged. When a mismatch result is generated, the control logic self-tuning unit corrects the proportional, integral, and derivative parameters of the PID control algorithm in real time to complete the control logic adjustment.

[0012] Furthermore, the node collaboration and environment adaptation module includes a node linkage control unit, an environmental parameter acquisition unit, and a compensation and adaptation execution unit. The node linkage control unit is signal-connected to the distributed nodes of the distributed network architecture module and the automated closed-loop control module. The environmental parameter acquisition unit is set around the distributed nodes to collect temperature, humidity, vibration and electromagnetic interference parameters of the node's operating environment. The compensation and adaptation execution unit is connected to the environmental parameter acquisition unit and the distributed node, respectively.

[0013] Furthermore, when the instrument is in an abnormal working state or receives an abnormal control command, the node linkage control unit triggers at least one node linkage operation among adjacent node redundant acquisition, fault node bypass, and dynamic route reconstruction. The compensation and adaptation execution unit has a pre-stored node environment standard threshold. It compares the collected environmental parameters with the node environment standard threshold in real time to generate a matching result or a non-matching result. When generating matching results, the compensation and adaptation execution unit maintains the current operating parameters of the distributed nodes; When a mismatch result is generated, the compensation and adaptation execution unit performs compensation and adaptation on the operating parameters of the distributed nodes based on table lookup correction or interpolation fitting.

[0014] Furthermore, the data management and security operation and maintenance module includes a data storage unit, a communication encryption and authentication unit, and a fault diagnosis unit; The data storage unit is communicatively connected to the distributed network architecture module, the heterogeneous instrument online monitoring module, and the automated closed-loop control module, respectively. The communication encryption and authentication unit is set on the communication link between the modules and is used to encrypt the transmitted data and authenticate the identity. The fault diagnosis unit is connected to the distributed network architecture module and the heterogeneous instrument online monitoring module via signals.

[0015] Furthermore, the data storage unit stores instrument operation data and control logs based on edge caching and cloud synchronization; The communication encryption and authentication unit encrypts and authenticates communication data based on a combination of SM2 asymmetric encryption and SM4 symmetric encryption. The fault diagnosis unit has pre-stored standard operating parameter thresholds for nodes and instruments. It compares the collected operating parameters with the standard operating parameter thresholds in real time to generate normal or fault results. When a normal result is generated, the fault diagnosis unit continuously monitors the operating status of the nodes and instruments; When a fault result is generated, the fault diagnosis unit locks the faulty object and generates fault location information.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: Through a layered distributed node network architecture, the single-point-of-failure problem of traditional centralized systems is completely solved. Relying on node linkage functions, the system gains redundancy, significantly improving overall operational stability. It can uniformly parse heterogeneous instrument data from different communication protocols, effectively solving the problem of poor equipment compatibility. It can dynamically adjust control parameters based on actuator feedback, constructing a complete closed-loop control system and greatly improving control accuracy. Simultaneously, it can collect on-site environmental parameters in real time and perform compensation and adaptation, reducing the impact of external conditions such as temperature, humidity, vibration, and electromagnetic interference, ensuring the authenticity and reliability of monitoring data. Furthermore, a communication encryption and authentication mechanism is added to strengthen data transmission security and achieve automatic fault diagnosis and location for instruments and nodes, replacing traditional manual inspection methods, effectively improving operation and maintenance efficiency, reducing operation and maintenance costs, and fully meeting the application needs of high reliability, high precision, high security, and intelligent operation and maintenance in large and complex industrial sites. Attached Figure Description

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 Functional block diagram of a distributed industrial automation instrument online monitoring and closed-loop control system provided in an embodiment of the present invention; Figure 2 A flowchart of a distributed industrial automation instrument online monitoring and closed-loop control system provided in an embodiment of the present invention; Figure 3 The diagram shows the hierarchical distributed node network topology of the distributed industrial automation instrument online monitoring and closed-loop control system provided in this embodiment of the invention. Detailed Implementation

[0018] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] In some embodiments of this application, see Figures 1-3 This embodiment provides a distributed industrial automation instrument online monitoring and closed-loop control system, including: The distributed network architecture module is configured to build a hierarchical distributed node network, wherein the distributed nodes are respectively connected to industrial automation instruments and actuators. The heterogeneous instrument online monitoring module is connected to the distributed network architecture module, and collects and parses instrument operating data based on the distributed nodes to determine the instrument working status; An automated closed-loop control module is communicatively connected to the heterogeneous instrument online monitoring module and the actuator. The automated closed-loop control module outputs control commands based on the instrument operating data and the instrument working status, and receives the status data returned by the actuator to adjust the control logic. The node collaboration and environment adaptation module is communicatively connected to the distributed node, the heterogeneous instrument online monitoring module, and the automated closed-loop control module, respectively. The node collaboration and environment adaptation module triggers the linkage operation between nodes based on the instrument working status or the control command, and performs compensation adaptation according to the environmental parameters collected by the distributed node. The data management and security operation and maintenance module is communicatively connected to the distributed network architecture module, the heterogeneous instrument online monitoring module, and the automated closed-loop control module. The data management and security operation and maintenance module stores instrument operation data and control logs, encrypts and authenticates the communication process, and diagnoses faults in nodes and instruments.

[0020] Specifically, see Figure 3 The distributed network architecture module constructs a three-layer distributed node network with clear hierarchy and division of labor. From top to bottom, it consists of a cloud management layer, a regional coordination layer, and a field node layer. The field node layer deploys several field distributed nodes, all located near the installation sites of industrial automation instruments and actuators. These nodes connect to front-end measurement and control equipment via wired communication, directly completing instrument operation data acquisition, issuing field control commands, and transmitting actuator status data, ensuring the real-time performance and stability of the underlying field data interaction. The regional coordination layer is equipped with regional aggregation gateways, each covering an independent industrial area. These gateways establish wireless communication links with all field distributed nodes within the area, undertaking the core role of data aggregation and command forwarding within the area, preventing network congestion caused by direct cloud access from underlying nodes. The cloud management layer deploys a cloud management server, which establishes remote network communication connections with all regional aggregation gateways across the entire network. This server uniformly collects the operating parameters of all network nodes and dynamically performs network node load scheduling, network routing optimization, and network status monitoring based on the operating load of each area, achieving integrated network management and control across the entire link from the field end, the area end, to the cloud.

[0021] Understandably, this invention separates data acquisition, regional relay, and network-wide scheduling functions into three independent layers, distributing network computing power. Even if a single field node or a single regional gateway fails, it will not affect the normal operation of the entire network, thus improving system fault tolerance and operational stability at the architectural level. Wired communication in the field ensures the accuracy of underlying data transmission; wireless networking in the region reduces the amount of on-site cabling; and unified scheduling in the cloud enables comprehensive network management. The three-layer architecture, each with its own function, provides stronger network scalability, allowing for flexible addition or removal of field nodes and aggregation gateways according to the scale of the plant. Simultaneously, the layered structure facilitates the location of network faults in different areas, significantly reducing the difficulty of network operation and maintenance, and adapting to the large-scale, decentralized instrumentation and control networking needs of large industrial sites.

[0022] Specifically, see Figure 2 The heterogeneous instrument online monitoring module is responsible for acquiring, processing, and summarizing data from various industrial instruments in the field. The module consists of a multi-protocol adaptation and parsing unit, a multi-type instrument data acquisition unit, and an instrument status discrimination unit. Each unit establishes signal connections sequentially according to the data flow order, forming a coherent data processing link. The multi-protocol adaptation and parsing unit directly communicates with the distributed nodes within the distributed network architecture module. Addressing the issue of inconsistent communication protocols among different brands and types of instruments in industrial settings, it receives raw data packets from various instruments, automatically identifies the protocol, parses the content, and converts the diverse raw data into a standardized data format common to the system, achieving interoperability and compatibility of heterogeneous instrument data. For example, the multi-protocol adaptation and parsing unit supports mainstream industrial instrument communication protocols such as Modbus RTU, HART, Profibus PA, Foundation Fieldbus, and CANopen, automatically identifying packet types and calling the corresponding parsing plugins through a built-in protocol feature library. The multi-type instrument data acquisition unit is connected to the multi-protocol adaptation and parsing unit to collect data from pressure instruments, temperature instruments, flow instruments, and level instruments in a comprehensive manner. The collected data includes not only process output parameters reflecting production conditions but also power supply status parameters, communication link quality parameters, and measurement deviation data to ensure measurement accuracy, characterizing equipment operating conditions. The instrument status determination unit interfaces with the multi-type instrument data acquisition unit to fully receive the standardized data after parsing and acquisition, providing comprehensive and standardized basic data support for subsequent instrument operating status determination and system control logic adjustment.

[0023] Understandably, the multi-protocol adaptation and parsing unit is compatible with various mainstream automation instruments in the field, eliminating the need for separate adapter configurations for different instruments. This significantly improves the system's ability to interface with heterogeneous instruments and effectively addresses the shortcomings of poor compatibility in traditional instrument access. The multi-type instrument data acquisition unit enables the synchronous acquisition of process parameters and equipment status parameters. The three units have clearly defined roles and work collaboratively to ensure the real-time performance and integrity of data acquisition and transmission.

[0024] Specifically, see Figure 2 The instrument status judgment unit receives standardized, multi-dimensional instrument operation data transmitted from various types of instrument data acquisition units. Relying on internally stored preset thresholds specific to each instrument, it continuously and dynamically compares real-time acquired process output parameters, power supply status parameters, communication link quality parameters, and metering deviation data, automatically distinguishing instrument operating conditions and outputting two judgment results. When all real-time acquired data matches the preset threshold range and generates a normal judgment result, the instrument status judgment unit does not change the pre-defined data acquisition frequency and mode, maintaining a stable acquisition state. Simultaneously, it continuously uploads normal instrument operation data without anomalies to the distributed network architecture module in sequence, providing a reliable data source for the system's normal closed-loop control. When any acquired data exceeds the corresponding preset threshold and generates an abnormal judgment result, the instrument status judgment unit immediately binds a unique anomaly identifier to the out-of-limit abnormal data, completing the anomaly data marking and classification. Subsequently, it synchronously uploads fault data carrying the anomaly identifier to the distributed network architecture module, achieving classified uploading of normal and abnormal data. This facilitates rapid data type differentiation by backend modules, enabling targeted control adjustments, fault diagnosis, and other subsequent linkage operations, achieving fully automatic and intelligent judgment of instrument operating status.

[0025] Understandably, this instrument status judgment unit achieves fully automated real-time assessment of instrument operating status without any manual intervention, effectively reducing the workload of manual inspections and data verification, and improving the level of monitoring intelligence. Through automatic threshold comparison, it quickly distinguishes between normal and abnormal instrument operating conditions, enabling separate uploading of the two types of data and optimizing the overall transmission efficiency of the distributed network. Simultaneously, it adds unique identifiers to abnormal data, allowing for intuitive differentiation of abnormal data entries, facilitating rapid capture of abnormal information by subsequent automated closed-loop control modules, data management, and safety maintenance modules, thus shortening system fault response and control time. This unit achieves pre-fault screening from the data side, preventing abnormal instrument data from interfering with the system's normal control logic, ensuring the stability of the system's closed-loop control, and adapting to the 24 / 7 unattended automated monitoring operation requirements of industrial sites.

[0026] Specifically, see Figure 2The automated closed-loop control module is the core functional module of the system for automatic control of field equipment. It consists of an instruction generation unit, a data receiving unit, and a control logic self-tuning unit. The instruction generation unit establishes a signal interface with the instrument status discrimination unit in the heterogeneous instrument online monitoring module, enabling real-time acquisition of instrument operating data after status discrimination, as well as normal and abnormal operating status signals, serving as the core basis for instruction compilation. The data receiving unit maintains a direct communication connection with the field actuators. The control logic self-tuning unit, as the module's linkage hub, is connected to both the instruction generation unit and the data receiving unit, streamlining the entire process of instruction issuance, status feedback, and parameter optimization. The instruction generation unit compiles control instructions differentiated according to different instrument operating states. When the instrument is in normal operating condition, it outputs a steady-state maintenance instruction; when the instrument experiences an abnormal operation, it outputs parameter correction control instructions. These instructions are sent to the actuators via the link to complete the action control. The actuator's operating status is then fed back to the control logic self-tuning unit through the data receiving unit, which optimizes the control logic based on this feedback, achieving a fully closed-loop operation of instruction output, equipment execution, status feedback, and parameter optimization.

[0027] Understandably, the rational layout of each unit and smooth signal interaction, along with the generation of control commands based on the differentiated actual operating states of the instruments, ensure that the control actions are more closely aligned with the actual needs of the site, avoiding blind adjustments. The entire architecture constructs a complete closed-loop control system, capable of collecting real-time operational feedback data from the actuators and continuously optimizing the control logic through a control logic self-tuning unit, dynamically correcting control parameters. Compared to traditional fixed control modes, this significantly improves control accuracy and adaptability to operating conditions. The entire control process is fully automated, reducing errors and maintenance workload caused by manual intervention. Simultaneously, the closed-loop linkage mechanism can promptly adjust the control strategy based on the equipment's performance, effectively mitigating the impact of fluctuations in on-site operating conditions, ensuring stable operation of industrial production processes, and meeting the application requirements of high-precision, continuous automatic control in complex industrial scenarios.

[0028] Specifically, see Figure 2The instruction generation unit incorporates a built-in PID control algorithm, enabling it to output differentiated control instructions based on varying instrument operating conditions, adapting to both steady-state operation and fault correction scenarios in industrial settings. When the instrument is in a normal, stable operating state, there is no need for significant adjustments to the operating conditions of the on-site actuators; the instruction generation unit outputs steady-state maintenance instructions to ensure that production process parameters remain constant. When the instrument experiences parameter deviations or operational anomalies, the instruction generation unit promptly outputs parameter correction and control instructions to quickly intervene and adjust the on-site actuators. The data receiving unit collects real-time, multi-dimensional operational feedback data from the actuators, including displacement, operating current, and output response parameters. The control logic self-tuning unit pre-stores standard response thresholds for the actuators that match the on-site operating conditions, continuously comparing the real-time transmitted actuator operating data with these standard thresholds to generate either a matching or non-matching result. If the data matches the standard threshold, it means that the current PID control parameters are suitable for the field conditions. The unit maintains the original proportional, integral, and derivative parameters constant to ensure the system operates smoothly and steadily. If the data does not match the standard threshold, it means that there is a deviation in the existing control parameters. The unit adaptively corrects the three core PID parameters in real time, dynamically optimizes the control logic, and realizes that the control strategy automatically iterates with the field conditions.

[0029] For example, the specific method for correcting PID parameters by the control logic self-tuning unit is as follows: When the returned actuator status data (action displacement, operating current value, output response parameters) does not match the standard response threshold, the current response error e(t) (the difference between the target response value and the actual returned value) and the error change rate Δe(t) are first calculated. Then, an adaptive adjustment rule based on deviation integral is used to update the PID parameters according to the following formula: Proportional coefficient correction: Kp_new=Kp_old×(1+α·e(t)), where α is the proportional correction factor (0<α≤0.1); Integration time correction: Ti_new=Ti_old×(1-β·∫e(t)dt), where β is the integration correction factor (0<β≤0.05), and Ti_new is restricted to the interval [0.5Ti_old,2Ti_old]; Differential time correction: Td_new=Td_old×(1+γ·Δe(t)), where γ is the differential correction factor (0<γ≤0.08), and Td_new is restricted to the interval [0.5Td_old,2Td_old].

[0030] The default values ​​for the correction factors α, β, and γ are 0.05, 0.02, and 0.04, respectively, and they can be remotely adjusted by maintenance personnel via host computer software. After each correction, the control logic self-tuning unit applies the new parameters to the control command generation for the next cycle, while continuing to monitor the data returned by the actuator until the response error falls within the standard threshold range. This adaptive tuning method does not require interruption of the on-site control process and can achieve online, real-time optimization of PID parameters.

[0031] Understandably, this solution relies on multi-dimensional feedback data from the actuator to achieve online self-tuning of PID parameters. The system can differentiate between normal and abnormal instrument conditions and issue differentiated commands, resulting in more targeted control. By comprehensively evaluating the execution effect through three types of data—displacement, current, and response speed—it avoids judgment errors caused by single feedback data and improves the accuracy of closed-loop feedback judgment. Simultaneously, there is no need for maintenance personnel to manually adjust PID parameters on-site; the system can automatically complete threshold comparison and parameter correction, significantly reducing manual debugging costs and maintenance workload. The dynamic self-tuning mode effectively mitigates problems such as control lag and excessive overshoot caused by fluctuations in industrial operating conditions, improves closed-loop control response speed and control accuracy, and ensures that the entire control system always adapts to real-time on-site conditions, guaranteeing stable and controllable operation throughout the entire industrial production process.

[0032] Specifically, see Figure 2 The node collaboration and environment adaptation module is used to achieve distributed node fault linkage self-healing and compensation for interference from harsh on-site environments. It consists of three parts: a node linkage control unit, an environmental parameter acquisition unit, and a compensation and adaptation execution unit. The node linkage control unit enables cross-module signal interaction and establishes stable signal connections with all distributed nodes and the automated closed-loop control module within the distributed network architecture module. It can receive abnormal control commands from the automated closed-loop control module in real time and synchronously acquire the operating status and instrument anomaly information of each distributed node, providing signal triggering basis for subsequent network-wide node collaborative operations. The environmental parameter acquisition unit is installed near each distributed node, closely positioned to monitor the node's operating environment. It continuously collects four key environmental parameters affecting node communication and data acquisition accuracy: ambient temperature, ambient humidity, on-site mechanical vibration intensity, and spatial electromagnetic interference intensity. The compensation and adaptation execution unit interfaces with both the environmental parameter acquisition unit and the distributed nodes. It can receive the collected full-domain environmental data in real time and directly adjust and intervene in the underlying operating parameters of the distributed nodes. It also receives environmental sensing data to complete subsequent parameter compensation work, achieving integrated operation of node network fault collaborative control and environmental interference compensation.

[0033] The pre-stored environmental compensation table in the compensation adaptation execution unit records the node communication gain compensation value and data acquisition accuracy correction coefficient for different parameter ranges of four environmental parameters: temperature, humidity, vibration, and electromagnetic interference. The compensation table is constructed as follows: During the system factory calibration phase, distributed nodes are placed under standard environmental conditions (temperature 25℃, humidity 50%RH, vibration 0.1g, electromagnetic interference 0V / m), and the basic operating parameters of the nodes are recorded. Then, each individual environmental parameter is changed one by one, and the changes in node communication bit error rate, signal strength, and sampling deviation are measured. Based on the basic parameters, the required compensation amount for each parameter range is calculated, forming a multi-dimensional compensation table. The compensation table is stored in the local memory of each distributed node and can be remotely updated by the cloud management server. When an environmental parameter deviates from the standard threshold and the compensation adaptation execution unit determines it to be a mismatch, if the measured environmental parameter happens to correspond to a stored parameter point in the compensation table, the compensation value is directly retrieved from the table for correction; if the measured parameter is between two adjacent parameter points in the compensation table, the compensation value is calculated using linear interpolation. The specific interpolation method is as follows: Let A(x1,y1) and B(x2,y2) be the two parameter points adjacent to the measured parameter in the compensation table, where x is the environmental parameter value and y is the corresponding compensation value. If the measured parameter value is x, then the compensation value y = y1 + (y2 - y1) × (x - x1) / (x2 - x1). For cases where all four parameters—temperature, humidity, vibration, and electromagnetic interference—deviate simultaneously, one-dimensional interpolation is first performed on each independent parameter to obtain a single compensation value. Then, a weighted sum is calculated based on the weighting coefficients of each parameter's impact on node operation (temperature weight 0.4, humidity weight 0.2, vibration weight 0.2, electromagnetic interference weight 0.2) to obtain the final comprehensive compensation value. The compensation adaptation execution unit then superimposes the calculated comprehensive compensation value onto the distributed node's communication transmission power, sampling filter coefficients, and other operating parameters to achieve adaptive cancellation of environmental interference.

[0034] Understandably, by setting up a dedicated node-linked control unit, a signal channel is established between the control module and the distributed nodes across the network. This enables rapid response to abnormal system conditions and provides signal support for subsequent self-healing operations such as redundant node acquisition, fault bypass, and route reconstruction, preventing the continuous spread of single-node failures from affecting the entire network operation. Relying on environmental parameter acquisition units deployed close to the nodes, the system can accurately perceive the real-world operating conditions around the nodes, solving the problem of traditional systems' inability to quantify and monitor environmental interference. Combined with a compensation and adaptation execution unit, it can specifically offset operational deviations caused by the external environment, improving the stability of node data acquisition and communication transmission. This module combines network fault self-healing and environmental interference resistance capabilities, adapting to the high-interference, high-reliability operational requirements of industrial sites.

[0035] Specifically, see Figure 2The node linkage control unit, relying on the pre-signal connection, can sense two types of abnormal operating condition signals in real time: abnormal signals from the instrument's own operation and abnormal control commands issued by the automated closed-loop control module. Upon receiving either type of abnormal signal, it can autonomously trigger the node collaborative linkage mechanism without manual intervention. The unit can flexibly trigger at least one of three linkage operations—adjacent node redundant acquisition, fault node bypass, and dynamic route reconstruction—based on the severity of the on-site fault, achieving network-level fault self-healing: adjacent node redundant acquisition supplements the missing data of the faulty node; fault node bypass isolates the failed node to prevent fault propagation; and dynamic route reconstruction re-plans the data transmission path to ensure uninterrupted network communication.

[0036] For example, the dynamic route reconstruction employs a routing algorithm based on link quality assessment. Each distributed node periodically sends probe packets to its neighboring nodes to measure the packet loss rate and signal strength (RSSI), and reports the measurement results to the regional aggregation gateway. When a node is determined to be unavailable due to a fault or environmental interference, the regional aggregation gateway triggers a route reconstruction process: using the affected source node and destination node (aggregation gateway or neighboring node) as endpoints, it calculates a new path that minimizes the link cost (defined as the weighted sum of packet loss rate and hop count) within the currently active node set using Dijkstra's shortest path algorithm. The gateway distributes the calculated new routing table to the relevant nodes, the nodes update their local routing caches, and subsequent data packets are forwarded along the new path. When the faulty node recovers, the gateway reassesses the network topology and gradually switches back to the original path or maintains the optimized path.

[0037] The compensation and adaptation execution unit incorporates standard threshold values ​​for node environments adapted to industrial field conditions. It dynamically compares the real-time temperature, humidity, vibration, and electromagnetic interference parameters transmitted by the environmental parameter acquisition unit with these standard threshold values, classifying the system into two operating conditions. When environmental parameters are within the standard threshold range and a matching result is generated, it indicates that the current environment will not interfere with node operation. The compensation and adaptation execution unit directly maintains the original communication and collected operating parameters of the distributed nodes, ensuring stable steady-state operation of the system. When environmental parameters exceed the threshold and a mismatch result is generated, two methods—table lookup correction and interpolation fitting—are used to specifically correct the node operating parameters, completing adaptive compensation and adaptation for environmental interference and eliminating operational deviations caused by the external environment.

[0038] Understandably, relying on the multi-mode node linkage mechanism, the system can quickly complete network self-healing when instruments or control commands malfunction, effectively isolating faulty nodes, supplementing missing monitoring data, and optimizing network transmission paths. This prevents single-point failures from causing network-wide paralysis, significantly improving the fault tolerance and operational reliability of the distributed network. Simultaneously, the system can monitor environmental interference changes around the clock, automatically distinguishing between normal and abnormal environmental conditions. Combined with both lookup table correction and interpolation fitting compensation methods, it can adapt to environmental interference of varying intensities, achieving higher parameter compensation accuracy and effectively offsetting the negative impacts of temperature and humidity fluctuations, mechanical vibrations, and electromagnetic interference on node acquisition and communication. Overall, it achieves autonomous network fault repair and autonomous environmental interference compensation, further enhancing the system's adaptability and operational stability in complex and harsh industrial environments.

[0039] Specifically, see Figure 2 The data management and security operation and maintenance module provides the system with full-domain data collection, communication security protection, and automated fault diagnosis assurance. It serves as the backend support module for the system's stable and reliable operation, and is composed of a data storage unit, a communication encryption and authentication unit, and a fault diagnosis unit. The data storage unit establishes bidirectional communication connections with the distributed network architecture module, the heterogeneous instrument online monitoring module, and the automated closed-loop control module. It can collect operating parameters of all network nodes, multi-dimensional monitoring data from instruments, system control commands, and closed-loop control logs, uniformly collecting all system operation data and achieving centralized archiving and retention of multi-source heterogeneous data. This provides complete and reliable data support for subsequent fault tracing, production data analysis, and control strategy review. The communication encryption and authentication unit is deployed across the communication links between various functional modules, covering all uplink data and downlink command transmission channels. It encrypts data transmitted throughout the link and authenticates the identity of access devices, blocking unauthorized access terminals and malicious access requests. The fault diagnosis unit establishes signal connections with the distributed network architecture module and the heterogeneous instrument online monitoring module, respectively, to obtain the operating status of network nodes and the operating parameters of instrument monitoring in real time. It monitors network link faults, distributed node hardware faults and automated instrument operating faults around the clock. Relying on real-time signal perception, it realizes online monitoring of the status of all devices and networks in the system, and builds a background operation and maintenance system that integrates data storage, communication security and fault monitoring.

[0040] The setting method for all thresholds in this system (including the preset thresholds used by the instrument status discrimination unit, the actuator standard response thresholds used by the control logic self-tuning unit, the node environment standard thresholds used by the compensation and adaptation execution unit, and the node and instrument standard operating parameter thresholds used by the fault diagnosis unit) is as follows: During the system initialization phase, automatic calibration is performed through on-site no-load testing. Specifically, each distributed node and connected instrument is placed in an actual industrial field environment. Under the condition that there is no abnormal interference on-site and the actuator is running under no-load, steady-state operating data is continuously collected for no less than 30 minutes. The average value of each parameter is taken as the basic reference value, and then upper and lower limit thresholds are generated according to the allowable deviation range of the process (e.g., ±5%). For the actuator standard response threshold, a standard step command is sent under the system no-load state, and the response curves of the actuator's displacement, operating current value, and output response parameters are recorded. ±10% of the steady-state value is used as the matching window threshold. All thresholds are stored in the cloud management server and regional aggregation gateway, allowing maintenance personnel to manually adjust them through the human-machine interface. The system also supports adaptive updates based on historical data (such as the normal operation data of the last 30 days) during operation to adapt to equipment aging and changes in operating conditions.

[0041] Understandably, by fully integrating with the three core business modules, unified storage and archiving of system-wide operational data is achieved, improving the standardization of data management. The communication encryption and authentication unit provides comprehensive protection for all module communication links, preventing data tampering, information leakage, and unauthorized device intrusion at the transmission level, addressing the communication security shortcomings of industrial measurement and control systems and meeting the stringent data security transmission requirements of industrial sites. The fault diagnosis unit can monitor the operating status of network nodes and instruments online in real time, eliminating the need for on-site inspections by maintenance personnel, proactively identifying hidden operational faults, significantly reducing manual maintenance costs, and shortening fault detection and response time. The overall system backend maintenance capabilities are improved, enabling the entire distributed measurement and control system to possess both business control and security maintenance capabilities, further enhancing system operational security and the level of intelligent maintenance.

[0042] Specifically, see Figure 2The data storage unit adopts a hierarchical storage mode combining edge caching and cloud synchronization, managing the operation data of all-domain instruments and the closed-loop control logs of the system in layers. Field edge nodes cache real-time collected instrument condition data and short-term control logs locally, meeting the system's need for rapid local data retrieval and reducing network congestion caused by directly uploading massive amounts of real-time data to the cloud. Simultaneously, edge-cached data is periodically uploaded to the cloud for off-site backup, achieving bidirectional linkage between local edge storage and centralized cloud storage, balancing real-time data retrieval with data storage integrity. For example, the synchronization strategy between edge nodes and the cloud is as follows: under normal circumstances, timed synchronization is used, with a configurable synchronization period (default 24 hours); when an abnormal instrument status, actuator response overshoot, or node failure is detected, event-driven synchronization is immediately triggered, prioritizing the uploading of relevant data from one hour before and after the abnormal period to the cloud.

[0043] The communication encryption and authentication unit employs a combination of domestically developed cryptographic algorithms for protection. It utilizes the SM2 asymmetric encryption algorithm to verify the access identity of all network modules and distributed nodes, and securely distributes communication keys. The SM4 symmetric encryption algorithm provides high-speed encryption for instrument data, control commands, and feedback status data during transmission. This dual encryption achieves end-to-end encryption of the communication link and trusted device authentication. The fault diagnosis unit pre-stores standard operating parameter thresholds for each distributed node and type of automated instrument. It continuously captures real-time operating parameters from the field and compares them against the standard thresholds, outputting two judgment statuses: normal and fault. If the result is normal, the unit maintains continuous online monitoring, constantly inspecting the equipment's operating condition. If the result is faulty, the unit quickly and accurately locates the faulty node or instrument, automatically generates corresponding fault location information, and uploads it to the upper-level system module, providing accurate data for subsequent maintenance and repair. For example, the specific method of locking the fault object is as follows: the fault diagnosis unit, based on the device ID (node ​​number or instrument serial number) associated with the out-of-limit parameter, and in conjunction with the device topology relationship table in the distributed network architecture module, accurately locates the fault location to the specific field distributed node or automated instrument, and outputs the location information in the format of "device type + installation location + fault code".

[0044] Understandably, the storage approach combining edge caching and cloud synchronization alleviates data transmission pressure on cloud servers, reduces data access latency, and avoids the risk of data loss associated with single local storage, thus improving the reliability of industrial data storage. The adoption of a combined SM2 and SM4 national cryptographic encryption scheme balances identity authentication security with data encryption transmission efficiency, effectively preventing the theft, tampering, and malicious access of communication data by unauthorized devices, thus strengthening the system's communication security defenses. The fault diagnosis unit, relying on automatic threshold comparison, enables 24 / 7 unattended fault monitoring, quickly pinpointing the specific location of the fault and outputting location information, reducing manual maintenance costs.

[0045] In a specific embodiment of this application, the above steps are implemented in the following ways: This system is applied in an industrial automated continuous production workshop, where various heterogeneous automated instruments and their actuators, such as those for temperature, pressure, flow, and level, are deployed on-site. The equipment is scattered, and the working environment is subject to interference factors such as temperature and humidity fluctuations, mechanical vibration, and electromagnetic interference. Furthermore, the communication protocols of these instruments are not standardized. During system operation, the system first establishes a network connection between the field devices using a hierarchical distributed node network. The distributed nodes collect raw data from various instruments, which is then sent to the heterogeneous instrument online monitoring module for multi-protocol parsing and parameter acquisition. The instrument status discrimination unit, based on preset thresholds, distinguishes the instrument operating status and categorizes and uploads the data. The automated closed-loop control module outputs corresponding control commands based on the instrument operating conditions, while simultaneously collecting feedback data from the actuators and comparing it with standard thresholds to dynamically tune the PID controller. The control parameters are used to achieve closed-loop regulation; the node collaboration and environment adaptation module synchronously collects environmental parameters, initiates node linkage operation when abnormal operating conditions occur, and compensates and adapts the node operating parameters based on the threshold comparison results; the data management and security operation and maintenance module uses edge caching combined with cloud synchronization to store operating data and control logs, and completes communication encryption and identity authentication through national cryptographic algorithms throughout the process. At the same time, it compares the operating parameter thresholds to carry out fault diagnosis and locate the fault. The whole process runs in a loop to realize the full-process automated measurement, control and operation and maintenance of workshop instruments and equipment.

[0046] When the system is deployed for the first time, the following initialization steps are performed: each distributed node establishes a physical connection with the instruments and actuators, and the nodes register with the regional aggregation gateway and report their own capability information; the aggregation gateway synchronizes the registration information to the cloud management server; maintenance personnel configure the protocol type, preset threshold, PID initial parameters and environmental standard thresholds of each instrument through the human-machine interface; the system automatically runs an no-load test and collects 30 minutes of steady-state data to calibrate the basic thresholds; after completing the above steps, the system switches to online monitoring and closed-loop control mode.

[0047] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0048] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0049] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0050] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A distributed industrial automation instrument online monitoring and closed-loop control system, characterized in that, include: The distributed network architecture module is configured to build a hierarchical distributed node network, wherein the distributed nodes are respectively connected to industrial automation instruments and actuators. The heterogeneous instrument online monitoring module is connected to the distributed network architecture module, and collects and parses instrument operating data based on the distributed nodes to determine the instrument working status; An automated closed-loop control module is communicatively connected to the heterogeneous instrument online monitoring module and the actuator. The automated closed-loop control module outputs control commands based on the instrument operating data and the instrument working status, and receives the status data returned by the actuator to adjust the control logic. The node collaboration and environment adaptation module is communicatively connected to the distributed node, the heterogeneous instrument online monitoring module, and the automated closed-loop control module, respectively. The node collaboration and environment adaptation module triggers the linkage operation between nodes based on the instrument working status or the control command, and performs compensation adaptation according to the environmental parameters collected by the distributed node. The data management and security operation and maintenance module is communicatively connected to the distributed network architecture module, the heterogeneous instrument online monitoring module, and the automated closed-loop control module. The data management and security operation and maintenance module stores instrument operation data and control logs, encrypts and authenticates the communication process, and diagnoses faults in nodes and instruments.

2. The distributed industrial automation instrument online monitoring and closed-loop control system according to claim 1, characterized in that, The layered distributed node network constructed by the distributed network architecture module includes a three-level structure: on-site node layer, regional coordination layer, and cloud management layer. The field node layer is equipped with several field distributed nodes, which are located near the industrial automation instrument installation area and directly establish wired communication connections with the corresponding industrial automation instruments and actuators. The regional coordination layer is equipped with a regional aggregation gateway, which establishes a wireless communication connection with the distributed nodes in its jurisdiction to relay collected data and control commands. The cloud management layer is equipped with a cloud management server, which establishes a network communication connection with all regional aggregation gateways to store node parameters and perform network-wide node scheduling.

3. The distributed industrial automation instrument online monitoring and closed-loop control system according to claim 1, characterized in that, The heterogeneous instrument online monitoring module includes a multi-protocol adaptation and parsing unit, a multi-type instrument data acquisition unit, and an instrument status discrimination unit. The multi-protocol adaptation parsing unit communicates with the distributed nodes of the distributed network architecture module to parse and standardize the raw data of different instruments. The multi-type instrument data acquisition unit is signal-connected to the multi-protocol adaptation and parsing unit. The multi-type instrument data acquisition unit acquires process output parameters, power supply status parameters, communication link quality parameters, and measurement deviation data of pressure instruments, temperature instruments, flow instruments, and level instruments. The instrument status discrimination unit is connected to the multi-type instrument data acquisition unit via signal.

4. The distributed industrial automation instrument online monitoring and closed-loop control system according to claim 3, characterized in that, The instrument status discrimination unit compares the collected data with the preset threshold in real time and generates a normal judgment result or an abnormal judgment result. When a normal judgment result is generated, the instrument status discrimination unit maintains the current acquisition state and continuously uploads the instrument operation data to the distributed network architecture module; When an anomaly determination result is generated, the instrument status determination unit adds an anomaly identifier to the corresponding data and synchronously uploads the data with the anomaly identifier to the distributed network architecture module.

5. A distributed industrial automation instrument online monitoring and closed-loop control system according to claim 3, characterized in that, The automated closed-loop control module includes an instruction generation unit, a data receiving unit, and a control logic self-tuning unit. The instruction generation unit is signal-connected to the instrument status discrimination unit of the heterogeneous instrument online monitoring module, the data receiving unit is communicatively connected to the actuator, and the control logic self-tuning unit is signal-connected to the instruction generation unit and the data receiving unit respectively. The instruction generation unit generates corresponding actuator control instructions based on the instrument's operating data and its normal / abnormal operating status.

6. A distributed industrial automation instrument online monitoring and closed-loop control system according to claim 5, characterized in that, The instruction generation unit has a built-in PID control algorithm. When the instrument is in normal working condition, it outputs a steady-state maintenance instruction; when the instrument is in abnormal working condition, it outputs a parameter correction and adjustment instruction. The actuator status data returned by the data receiving unit includes the actuator displacement, operating current value, and output response parameters. The control logic self-tuning unit pre-stores the standard response threshold of the actuator, and compares the returned actuator status data with the standard response threshold in real time to generate a matching result or a non-matching result. When generating a matching result, the control logic self-tuning unit maintains the proportional, integral, and derivative parameters of the current PID algorithm unchanged. When a mismatch result is generated, the control logic self-tuning unit corrects the proportional, integral, and derivative parameters of the PID control algorithm in real time to complete the control logic adjustment.

7. A distributed industrial automation instrument online monitoring and closed-loop control system according to claim 1, characterized in that, The node collaboration and environment adaptation module includes a node linkage control unit, an environmental parameter acquisition unit, and a compensation and adaptation execution unit. The node linkage control unit is signal-connected to the distributed nodes of the distributed network architecture module and the automated closed-loop control module. The environmental parameter acquisition unit is set around the distributed nodes to collect temperature, humidity, vibration and electromagnetic interference parameters of the node's operating environment. The compensation and adaptation execution unit is connected to the environmental parameter acquisition unit and the distributed node, respectively.

8. A distributed industrial automation instrument online monitoring and closed-loop control system according to claim 7, characterized in that, When the instrument is in an abnormal working state or receives an abnormal control command, the node linkage control unit triggers at least one node linkage operation among adjacent node redundant acquisition, fault node bypass, and dynamic route reconstruction. The compensation and adaptation execution unit has a pre-stored node environment standard threshold. It compares the collected environmental parameters with the node environment standard threshold in real time to generate a matching result or a non-matching result. When generating matching results, the compensation and adaptation execution unit maintains the current operating parameters of the distributed nodes; When a mismatch result is generated, the compensation and adaptation execution unit performs compensation and adaptation on the operating parameters of the distributed nodes based on table lookup correction or interpolation fitting.

9. A distributed industrial automation instrument online monitoring and closed-loop control system according to claim 1, characterized in that, The data management and security operation and maintenance module includes a data storage unit, a communication encryption and authentication unit, and a fault diagnosis unit. The data storage unit is communicatively connected to the distributed network architecture module, the heterogeneous instrument online monitoring module, and the automated closed-loop control module, respectively. The communication encryption and authentication unit is set on the communication link between the modules and is used to encrypt the transmitted data and authenticate the identity. The fault diagnosis unit is connected to the distributed network architecture module and the heterogeneous instrument online monitoring module via signals.

10. A distributed industrial automation instrument online monitoring and closed-loop control system according to claim 9, characterized in that, The data storage unit stores instrument operation data and control logs based on edge caching and cloud synchronization. The communication encryption and authentication unit encrypts and authenticates communication data based on a combination of SM2 asymmetric encryption and SM4 symmetric encryption. The fault diagnosis unit has pre-stored standard operating parameter thresholds for nodes and instruments. It compares the collected operating parameters with the standard operating parameter thresholds in real time to generate normal or fault results. When a normal result is generated, the fault diagnosis unit continuously monitors the operating status of the nodes and instruments; When a fault result is generated, the fault diagnosis unit locks the faulty object and generates fault location information.

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