An engineering change intelligent identification and influence analysis method and system

Abstract

The present application relates to the technical field of intelligent decision-making, in particular to an engineering change intelligent identification and influence analysis method and system. In the present application, by extracting the physical connection information of the field control equipment and associating the operating parameters and the logical constraint parameters, a field device interconnection topology model with signal flow direction as the connection line is constructed, the discrete engineering equipment is converted into a digital map with logical handshake relationship, the parameter modification behavior is monitored in real time and the change source equipment is locked, the history trend consistency and real-time availability of the redundant signal channel are verified by using the grey correlation analysis algorithm and the effectiveness threshold, when it is confirmed that it has effective backup, it is automatically determined as a blocking node, the input signal change is simulated and calculated according to the PID control algorithm combined with the loop gain coefficient, whether the fluctuation occurs physical dissipation is confirmed by comparing the absolute value of the difference of the theoretical output signal and the signal control dead zone threshold, and the effective fluctuation which can actually drive the downstream equipment action is accurately identified.

Description

Technical Field

[0001] This invention relates to the field of intelligent decision-making technology, and in particular to a method and system for intelligent identification and impact analysis of engineering changes. Background Technology

[0002] The field of intelligent decision-making technology refers to a set of technologies that, for complex business and engineering scenarios, utilize multi-source data acquisition and standardized processing, knowledge rule expression and constraint modeling, state recognition and event triggering, reasoning calculation and solution generation, risk item verification and consistency check, result output and closed-loop recording, etc., to judge the state changes of target objects and provide executable decision-making basis.

[0003] Among them, the intelligent identification and impact analysis method for engineering changes refers to first extracting and identifying the changed objects and version differences in the change documents when engineering changes occur, then mapping the changed content to the relevant object range one by one according to the pre-maintained association relationship, and then comparing and verifying the affected content item by item according to the established judgment rules, and writing the identification results and the list of affected items into the change record for subsequent approval and tracking.

[0004] Existing technologies rely on text recognition of change documents and pre-maintained static association lists to determine the scope of impact. They lack in-depth analysis of the logical and physical connections of actual signal flows between field devices, and cannot construct dynamic interconnection models that reflect the real-time operating status of the engineering site. When faced with complex signal cascading and logical dependencies, they struggle to automatically trace change transmission paths, relying only on item-by-item comparisons and verifications based on predetermined rules. They cannot quantify the signal fluctuation amplitude caused by parameter modifications, ignore the dynamic response mechanism of proportional-integral-derivative characteristics in equipment control algorithms to errors, and fail to consider the dissipation and blocking effect of physical dead zones and saturation limits of actuators on small fluctuations. They also struggle to identify the automatic isolation capability of signal channels with redundant backup functions for change risks. Consequently, change impact analysis remains at the level of document recording and is detached from physical reality. They are prone to misjudging invalid disturbances that have been automatically adjusted by the system or shielded by physical characteristics as risk items, resulting in ambiguous scope of impact and inaccurate basis for engineering change approval decisions. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing an intelligent identification and impact analysis method and system for engineering changes.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for intelligent identification and impact analysis of engineering changes, comprising the following steps: S1: Extract information about the field control equipment at the engineering site, and associate each field control equipment with corresponding adjustable operating parameters and logical constraint parameters to construct a field equipment interconnection topology model; S2: Monitor the modification behavior of the adjustable operating parameters of each field control device, calculate the difference in parameter values ​​before and after the modification of the adjustable operating parameters, and lock the field control device to be analyzed by combining the information in the field device interconnection topology model, and extract the corresponding engineering change feature data packet; S3: Read the input signal channel identifier currently used by each field control device to be analyzed in the engineering change feature transmission data packet, determine the redundancy status of the input signal channel, and generate the input channel redundancy status determination result; S4: Determine the equivalent input signal change value sensed by the field control equipment to be analyzed based on the parameter value difference in the data packet transmitted by the engineering change feature, and judge the signal fluctuation state caused by the engineering change in combination with the logic constraint parameters to obtain the signal fluctuation state judgment result. S5: Perform a comprehensive judgment on the input channel redundancy status judgment result and the signal fluctuation status judgment result to obtain the result of defining the scope of impact of engineering changes.

[0007] As a further aspect of the present invention, the field device interconnection topology model includes a unique hardware identifier for the field control device, an initial network structure connected by signal flow direction, adjustable operating parameters, and logical constraint parameters. The engineering change feature transmission data packet includes a unique hardware identifier, a change parameter type identifier, and parameter value differences. The input channel redundancy status determination result is specifically the availability status of redundant signal channels verified by grey relational analysis and validity threshold detection. The signal fluctuation status determination result is specifically the fluctuation dissipation confirmation status obtained by comparing the input signal with the signal control dead zone threshold. The engineering change impact range definition result includes information on affected field control devices and information on devices blocking the impact of the change.

[0008] As a further aspect of the present invention, the steps for obtaining the field device interconnection topology model are as follows: S111: Read and parse the engineering fieldbus configuration file, extract the unique hardware identification code of all field control devices, and establish an initial network structure with field control devices as vertices and signal flow as the connection based on the input and output signal mapping relationship between devices recorded in the configuration file. S112: Traverse each field control device node in the initial network structure, associate the adjustable operating parameters consisting of process control setpoints, PID control coefficients and alarm thresholds, map the adjustable operating parameters to the corresponding field control device nodes, and obtain the network structure of associated operating parameters. S113: Based on the network structure of the associated operating parameters, associate logical constraint parameters with each field control device, including input signal validity threshold, control loop gain coefficient, output signal saturation limit value, signal control dead zone threshold and redundant channel mapping configuration. Integrate the physical connection relationship of all field control devices with the adjustable operating parameters and logical constraint parameter configurations to establish a field device interconnection topology model.

[0009] As a further aspect of the present invention, the step of obtaining the engineering change feature transmission data packet specifically includes: S211: Monitor the real-time modification behavior of adjustable operating parameters for any field control device, mark the target field control device as the source device of engineering change, identify the category of the modified parameter in the adjustable operating parameters, generate the change parameter type identifier, read the adjustable operating parameter values ​​before and after the modification, and calculate the difference in parameter values ​​of the source device of engineering change before and after the modification. S212: Search for and receive the downstream node of the output signal of the engineering change source device in the field device interconnection topology model, determine the downstream node of the output signal of the engineering change source device as the field control device to be analyzed, and extract the index information and connection port information of the field control device to be analyzed as the information of the field control device to be analyzed. S213: Package the unique hardware identifier, change parameter type identifier, and parameter value difference of the field control equipment to be analyzed from the information of the field control equipment to be analyzed, and generate an engineering change feature transmission data package.

[0010] As a further aspect of the present invention, the step of obtaining the input channel redundancy status determination result specifically includes: S311: Read the input signal channel identifier currently used by each field control device to be analyzed, which is recorded in the engineering change feature transmission data packet; query the redundant channel mapping configuration in the logical constraint parameters; confirm whether there are redundant signal channels with the same physical process variable meaning and originating from different upstream devices; and obtain the redundant signal channel identifier. S312: Obtain the historical operation monitoring data of the main input channel and the redundant signal channel corresponding to the redundant signal channel identifier of the field control device to be analyzed within a preset time period, and call the grey relational analysis algorithm to calculate the correlation degree between the historical operation monitoring data of the main input channel and the historical operation monitoring data sequence of the redundant signal channel to obtain the historical data trend correlation degree. S313: Based on the historical data trend correlation, and combined with the input signal validity threshold in the logical constraint parameters, detect whether the real-time numerical status of the redundant signal channel corresponding to the redundant signal channel identifier is within the allowable working range limited by the input signal validity threshold. Based on the verification results of the historical data trend correlation, determine whether the redundant signal channel can be replaced, and generate the input channel redundancy status determination result.

[0011] As a further aspect of the present invention, the step of obtaining the signal fluctuation state determination result specifically includes: S411: Based on the parameter value difference in the data packet of the engineering change feature transmission, determine the input signal change value received by the field control equipment to be analyzed, and substitute the input signal change value with the control loop gain coefficient in the logic constraint parameter into the PID control algorithm to calculate the control response before and after the change of adjustable operating parameters, and obtain the first theoretical output signal value and the second theoretical output signal value of the field control equipment to be analyzed as a theoretical output signal value pair. S412: Based on the output signal saturation limiting value in the logic constraint parameters, perform boundary constraint processing on the first theoretical output signal value and the second theoretical output signal value in the theoretical output signal value pair, and calculate the absolute value of the theoretical output signal difference between the first theoretical output signal value and the second theoretical output signal value after the output signal saturation limiting value processing. S413: Compare the absolute value of the difference between the theoretical output signals with the signal control dead zone threshold in the logic constraint parameters to determine whether the signal fluctuation amplitude caused by the engineering change is covered by the signal control dead zone threshold and stops being transmitted backward, determine whether the signal fluctuation has been dissipated, and generate a signal fluctuation status judgment result.

[0012] As a further aspect of the present invention, the steps for obtaining the results of defining the scope of impact of the engineering change are as follows: S511: Perform a comprehensive condition judgment on the input channel redundancy status judgment result and the signal fluctuation status judgment result. If the input channel redundancy status judgment result shows that the redundant signal channel can be replaced, or the signal fluctuation status judgment result shows that the signal fluctuation amplitude caused by the engineering change is covered by the signal control dead zone threshold and stops being transmitted backward and dissipated, then it is determined that the current field control equipment to be analyzed can block the impact of the change; otherwise, it is determined that it is affected, and the equipment impact attribute classification result is obtained. S512: Based on the classification results of the equipment impact attributes, for nodes marked as affected field control equipment, the updated engineering change feature transmission data packet is sent to the next-level field control equipment connected to the affected field control equipment in the field equipment interconnection topology model, the analysis status fed back by the next-level field control equipment is collected, and multi-level propagation path equipment information is obtained. S513: Summarize all the equipment impact attribute classification results and the equipment information of the multi-level propagation path, integrate the change impact blocking equipment information and the affected on-site control equipment information, confirm the change impact path and the boundary node of the impact termination, and generate the engineering change impact scope definition result.

[0013] An intelligent identification and impact analysis system for engineering changes, the system comprising: The topology modeling module extracts information about the field control equipment at the engineering site and associates corresponding adjustable operating parameters and logical constraint parameters with each field control equipment to construct a field equipment interconnection topology model. The feature capture module monitors the modification behavior of the adjustable operating parameters of each field control device, calculates the difference in parameter values ​​before and after the modification, and, combined with the information in the field device interconnection topology model, locates the field control device to be analyzed and extracts the corresponding engineering change feature transmission data packet. The channel redundancy determination module reads the input signal channel identifier currently used by each field control device to be analyzed from the engineering change feature transmission data packet, determines the redundancy status of the input signal channel, and generates the input channel redundancy status determination result. The fluctuation state analysis module determines the equivalent input signal change value sensed by the field control equipment to be analyzed based on the parameter value difference in the data packet transmitted by the engineering change characteristics, and judges the signal fluctuation state caused by the engineering change in combination with the logical constraint parameters to obtain the signal fluctuation state judgment result. The comprehensive judgment module performs a comprehensive judgment on the input channel redundancy status judgment result and the signal fluctuation status judgment result to obtain the result of defining the scope of impact of engineering changes.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by extracting the physical connection information of field control equipment and associating it with operating parameters and logical constraint parameters, a field equipment interconnection topology model with signal flow as the connection is constructed. Discrete engineering equipment is transformed into a digital map with logical handshake relationships. Parameter modification behavior is monitored in real time, and the source equipment of the change is identified. Grey relational analysis algorithm and validity threshold detection are used to verify the historical trend consistency and real-time availability of redundant signal channels. When a valid backup is confirmed, it is automatically identified as a blocking node. Based on the PID control algorithm and the loop gain coefficient, the changes in the input signal are simulated and calculated. By comparing the absolute value of the difference between the theoretical output signal and the signal control dead zone threshold, it is confirmed whether the fluctuation has undergone physical dissipation. Effective fluctuations that can actually drive the downstream equipment are accurately identified, realizing the quantitative definition of the physical transmission path and impact termination boundary of engineering changes. This ensures that invalid interference isolated by redundancy mechanisms or covered by dead zones can be eliminated when changes occur, providing accurate data support for engineering change risk assessment. Attached Figure Description

[0015] Figure 1 This is a flowchart of the intelligent identification and impact analysis method for engineering changes in this invention; Figure 2 This is a flowchart illustrating the construction of the field device interconnection topology model for this invention. Figure 3 Flowchart for generating data packets for transmitting engineering change features in this invention; Figure 4 This is a flowchart of the input signal channel redundancy status determination process of the present invention; Figure 5 This is a flowchart for determining the fluctuation status of engineering change signals in this invention. Figure 6 This is a flowchart illustrating the scope of impact of engineering changes in this invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] Please see Figure 1 This invention provides a technical solution, a method for intelligent identification and impact analysis of engineering changes, comprising the following steps: S1: Extract information about the field control equipment at the engineering site, and associate each field control equipment with corresponding adjustable operating parameters and logical constraint parameters to construct a field equipment interconnection topology model; S2: Monitor the modification behavior of the adjustable operating parameters of each field control device, calculate the difference in parameter values ​​before and after the modification of the adjustable operating parameters, and combine the information in the field device interconnection topology model to locate the field control device to be analyzed and extract the corresponding engineering change feature data packet. S3: Read the input signal channel identifier currently used by each field control device to be analyzed in the engineering change feature transmission data packet, determine the redundancy status of the input signal channel, and generate the input channel redundancy status determination result; S4: Determine the equivalent input signal change value sensed by the field control equipment to be analyzed based on the parameter value difference in the data packet transmitted by the engineering change characteristics. Combine the logic constraint parameters to judge the signal fluctuation state caused by the engineering change and obtain the signal fluctuation state judgment result. S5: Perform a comprehensive judgment on the input channel redundancy status judgment result and the signal fluctuation status judgment result to obtain the result of defining the scope of impact of engineering changes.

[0018] The field device interconnection topology model includes the unique hardware identifier of the field control device, the initial network structure with the signal flow as the connection, adjustable operating parameters and logical constraint parameters. The engineering change feature transmission data packet includes the unique hardware identifier, the change parameter type identifier and parameter value difference. The input channel redundancy status judgment result is specifically the availability status of the redundant signal channel verified by grey relational analysis and validity threshold detection. The signal fluctuation status judgment result is specifically the fluctuation dissipation confirmation status obtained by comparing the input signal with the signal control dead zone threshold. The engineering change impact scope definition result includes the information of the affected field control device and the information of the device blocking the change impact.

[0019] Please see Figure 2 The specific steps for obtaining the field device interconnection topology model are as follows: S111: Read and parse the engineering fieldbus configuration file, extract the unique hardware identification code of all field control devices, and establish an initial network structure with field control devices as vertices and signal flow as the connection based on the input and output signal mapping relationship between devices recorded in the configuration file. The central server of the intelligent management platform for engineering construction calls the underlying data interface to locate and read the configuration description files of the on-site equipment stored in the engineering database. These files, stored in XML or IFC standard formats, contain connection definition data for tens of thousands of physical nodes throughout the entire project. The parsing program uses streaming reading technology to scan the data segments of the configuration file line by line, employing a regular expression matching mechanism to accurately identify and extract the unique hardware identifiers of all on-site equipment, such as asset codes, design institute-preset tag numbers, or RFID tag IDs. Based on the signal mapping table defined in the configuration file, the program deeply analyzes the engineering signal flow attributes of each device port, identifies the connection pairs between source and target device ports, and constructs logical handshake relationships between devices. A weighted directed graph data structure is initialized in memory. Each extracted on-site device is instantiated as a vertex object in the graph, and the signal transmission relationships between devices are instantiated as weighted directed edges. The edge weights represent the signal transmission frequency or engineering dependency; these weight values ​​are derived from statistical analysis of data interaction intervals in historical engineering commissioning logs, taking the mode of the frequency distribution. For example, when the air supply status port of the fresh air handling unit AHU101 is resolved to be connected to the input port of the DDC controller DDC101, a unidirectional connection is established between the vertex representing AHU101 and the vertex representing DDC101, and the signal flow direction is marked as from AHU101 to DDC101. All connections are traversed, isolated nodes are checked and network connectivity is verified, broken logical links are automatically repaired, and finally, an initial network structure is constructed with the field devices as vertices and the signal flow direction as the connection. During this process, if a gateway or hub is defined in the configuration file, it is abstracted as a transparent transmission node or its routing table is directly resolved to establish logical connections.

[0020] S112: Traverse each field control device node in the initial network structure, associate the adjustable operating parameters consisting of process control setpoints, PID control coefficients and alarm thresholds, map the adjustable operating parameters to the corresponding field control device nodes, and obtain the network structure of associated operating parameters. Data exchange channels are established by accessing the engineering parameter configuration blocks of each device through the device driver layer interface. For each node, its specification document is retrieved to determine its supported parameter list. Engineering design settings matching the device's tag name are retrieved from the central design database, such as the target design temperature of the constant temperature and humidity control loop. Control algorithm coefficients are read, specifically including proportional gain, integral time constant, and derivative time constant. These coefficients are derived from step response test calculations based on the Ziegler-Nichols method during the device's individual commissioning phase. Simultaneously, safe operating thresholds are read, including high-high limit, high limit, low limit, and low-low limit alarm values. These thresholds are calculated based on the normal distribution characteristics of the past three months' trial operation data of similar engineering models of the same device, taking the mean plus or minus three standard deviations. The obtained adjustable operating parameters are encapsulated into parameter objects and mounted to the corresponding engineering field device nodes via pointers or hash mapping. For example, for a temperature controller node identified as TIC202, parameters such as the design setpoint of 20.0 degrees Celsius, proportional gain of 1.5, integral time of 20 seconds, and alarm limit of 25.0 degrees Celsius are bound to it. A parameter integrity check is performed; if a node is found to be missing critical operating parameters, the node is marked and a warning is logged. Simultaneously, an attempt is made to complete the parameters using a default template whose values ​​are derived from the statistical median of parameter configurations for similar devices in a historical engineering case database. The data types of the parameters are rigorously validated to ensure that floating-point, integer, and Boolean data can be correctly mapped to the device controller address. After traversal and mapping operations, the initial topology skeleton is filled with rich engineering design data, generating a network structure associated with the operating parameters.

[0021] S113: A network structure based on associated operating parameters is used to associate logical constraint parameters with each field control device, including input signal validity threshold, control loop gain coefficient, output signal saturation limit value, signal control dead zone threshold, and redundant channel mapping configuration. The physical connection relationship of all field control devices is integrated with the adjustable operating parameters and logical constraint parameter configurations to establish a field device interconnection topology model. The system retrieves specification data for each device from the engineering design drawing database, extracting input signal validity thresholds, such as the permissible current range of 4 mA to 20 mA for sensors; extracts the control loop gain coefficient, used to describe the controller output's sensitivity to errors, determined by analyzing the covariance ratio of historical debugging errors to output response; extracts the output signal saturation limit value, i.e., the minimum and maximum percentage limits of the controller's physical output; and extracts the signal control dead zone threshold, i.e., the minimum error range within which the controller will not produce adjustment actions, calculated based on friction torque test data from the actuator's factory test report. Furthermore, it queries the redundancy design table to obtain the redundant channel mapping configuration, which details which physical ports are hot or cold standby, and the switching logic conditions. These logical constraint parameters are deeply integrated with the previously adjustable operating parameters and physical connection relationships to establish a multi-dimensional parameter correlation matrix. For example, for a control valve node, it is associated not only with its opening command but also with its physical stroke limits of 0 to 100 and dead zone limit of 0.5. All data structures are serialized and stored in a high-speed cache database, establishing a complete field device interconnection topology model. In this model, each node contains not only static connection information, but also dynamic operating parameters and static physical constraint logic.

[0022] Please see Figure 3 The specific steps for obtaining the engineering change feature transmission data packet are as follows: S211: Monitor the real-time modification behavior of adjustable operating parameters for any field control device, mark the target field control device as the source device of engineering change, identify the category of the modified parameter in the adjustable operating parameters, generate the change parameter type identifier, read the adjustable operating parameter values ​​before and after the modification, and calculate the difference in parameter values ​​of the source device of engineering change before and after the modification. When an engineering change instruction is detected, the target device is locked and marked as the source device for the engineering change. The parameter parsing subroutine is invoked to identify the category of the modified parameter within the adjustable operating parameter set, including three possibilities: PID parameter category, alarm threshold category, and process setpoint category. The category of the modification is determined, and a corresponding change parameter type identifier is generated, such as the identifier code TYPE_PID_GAIN. Simultaneously, the historical database and real-time memory area are accessed to read the old parameter value before the engineering change instruction was executed and the new parameter value after execution. Floating-point arithmetic logic is used to calculate the difference between the two values. Specifically, the signed difference value is obtained by subtracting the original value from the modified value. For example, if an engineer changes the proportional coefficient of a level controller LIC301 from 1.0 to 1.2, the change type is identified as a proportional coefficient change under the PID parameter category. The old value of 1.0 and the new value of 1.2 are read, and the parameter value difference is calculated. If the design setting is modified, from 50.0 to 52.0, the difference will be... .

[0023] S212: Search for and receive the downstream node of the output signal of the source device of the engineering change in the field device interconnection topology model, determine the downstream node of the output signal of the source device of the engineering change as the field control device to be analyzed, and extract the index information and connection port information of the field control device to be analyzed as the information of the field control device to be analyzed. In the established field device interconnection topology model, starting from the node of the device that caused the engineering change, a breadth-first search or depth-first search algorithm is executed. The outgoing edge list of this node is retrieved to identify the direction of all signal outflow, thus finding the downstream nodes that directly receive the output signal from this device. These downstream nodes are then identified as the field devices to be analyzed. For each device to be analyzed, its device index information, including device ID, installation position number, and network address, is extracted from the topology model; simultaneously, connection port information is extracted to determine which port of the source device the signal originates from and which port of the device to be analyzed receives it. For example, if the change source is a TIC101 temperature controller, its associated devices include the upstream flow transmitter FT102 that provides feedforward signals to it, and the downstream control valve TV101. The system will identify both of these devices as devices to be analyzed and extract their connected slot numbers and channel numbers respectively. This process is not limited to physical direct connections; if the model contains logical soft connections, they will also be traced.

[0024] S213: Package the unique hardware identifier, change parameter type identifier, and parameter value difference of the field control equipment to be analyzed from the field control equipment information to generate an engineering change feature transmission data package; A data transmission object, namely the engineering change feature transmission data packet, is instantiated. The unique hardware identifier from the field equipment information of the project to be analyzed is written into the data packet header as a basis for routing. The change parameter type identifier generated in S211 is written into the data body, clearly informing downstream devices of the nature of the change. The calculated parameter value difference is written into the data segment of the data body to quantify the magnitude of the change. For example, the data packet contains: target device ID is TV101, change type is SETPOINT_CHANGE, and parameter difference value is 2.0. A timestamp and the ID of the change source device are appended to ensure data traceability. If there are multiple devices to be analyzed, an independent data packet is generated for each device, or a composite data packet containing a multicast list is generated. The generated data packet is serialized and encoded, ready to be sent to the analysis engine via the internal message bus.

[0025] Please see Figure 4 The specific steps for obtaining the input channel redundancy status determination result are as follows: S311: Read the input signal channel identifier currently used by each field control device to be analyzed, which is recorded in the engineering change feature transmission data packet; query the redundant channel mapping configuration in the logical constraint parameters; confirm whether there are redundant signal channels with the same physical process variable meaning that originate from different upstream devices; and obtain the redundant signal channel identifier. The process of confirming the existence of redundant signal channels with the same physical process variable meaning originating from different upstream devices is as follows: Based on the input signal channel identifier, the corresponding engineering tag name, engineering unit of measurement type, and engineering range value are indexed and extracted from the redundant channel mapping configuration of the logical constraint parameters. Traverse all candidate signal channels recorded in the redundant channel mapping configuration, and check item by item whether the engineering tag name, engineering unit of measurement type and engineering range of each candidate signal channel are consistent with the parameter content corresponding to the input signal channel identifier; When a specified alternative signal channel is identified that meets the requirements of complete consistency in engineering tag name, engineering unit of measurement type and engineering range, and the physical source port address is inconsistent with the input signal channel identifier, it is determined that there are redundant signal channels with the same physical process variable meaning and originating from different upstream devices. The analysis engine receives engineering change feature transmission data packets and parses out the identifier of the input signal channel currently in use by the field equipment of the project under analysis, for example, the identifier is AI_Channel_01. It then accesses the redundant channel mapping configuration table in the logical constraint parameters. This process first indexes and extracts the corresponding engineering tag name from the mapping table based on the input signal channel identifier, for example, "return air temperature detection"; extracts the engineering unit of measurement type, for example, "degrees Celsius"; and extracts the engineering range value, for example, "0 to 50". Next, it traverses the list of all candidate signal channels recorded in the mapping configuration, comparing the attributes of each candidate channel item by item. It checks whether the engineering tag name of the candidate channel is completely consistent with the main channel, whether the unit of measurement is the same, and whether the range overlaps. More importantly, it checks the physical source port address. Only when the tag name, unit, and range of the candidate channel are completely consistent with the main channel, but the physical source port address is different (meaning it comes from different sensors or transmitters), is a redundant signal channel determined to exist. For example, if the tag number of AI_Channel_02 is also "return air temperature detection", the unit and range are matched, and its physical address comes from the backup sensor TT101_B, which is different from the main channel's TT101_A, then AI_Channel_02 is confirmed as a redundant signal channel, and its identifier is obtained.

[0026] S312: Obtain the historical operation monitoring data of the main input channel and the redundant signal channel corresponding to the redundant signal channel identifier of the field control equipment to be analyzed within a preset time period, call the grey relational analysis algorithm to calculate the correlation degree between the historical operation monitoring data of the main input channel and the historical operation monitoring data sequence of the redundant signal channel, and obtain the historical data trend correlation degree. Based on the identified main input channel identifier and redundant signal channel identifier, a query request is initiated to the historical database to obtain the historical operation monitoring data sequence of both within a preset time period, such as the past hour. A grey relational analysis algorithm is then used to evaluate the consistency of the two sets of data. The calculation logic is as follows: First, the normalized historical operation monitoring data sequence of the main input channel is defined as... (in The historical operational monitoring normalized data sequence of redundant signal channels is as follows: Introducing the resolution coefficient This coefficient is derived from the statistical analysis of the variance of historical environmental noise, and is usually taken as the normalized value of the standard deviation of historical noise (e.g., 0.5). A larger value indicates a higher tolerance for absolute differences in the data when calculating the correlation. (Iterate through all sampling times.) Calculate the absolute difference between the main channel and the redundant channel data. And select the minimum difference among all times. and the maximum difference Based on the above parameters, the historical data trend correlation is obtained by applying the following grey relational degree calculation formula. := in, This represents the correlation degree of the historical data trends obtained from the final calculation. This represents the total number of historical data sampling points used in the calculation. Representing the Normalized values ​​of the main input channel at each sampling time Representing the Normalized values ​​of redundant signal channels at each sampling time Indicates the first The absolute deviation between the two at any given moment This represents the minimum absolute deviation across the entire sequence. This represents the maximum absolute deviation across the entire sequence. This formula quantifies the geometric similarity between two signal waveforms by calculating the average correlation coefficient at each point. For example, it obtains... Data from each sampling time point. Normalized sequence of the main input channel. Redundant signal channel normalized sequence First, calculate the absolute difference sequence at each time point: Identify the minimum difference from the difference sequence. Maximum difference Set the resolution coefficient. (Based on noise statistics). Substitute into the formula to calculate the constant term in the denominator: Molecular calculations: Calculate the correlation coefficient for each item: For the time intervals with a difference of 0.02 (points 1, 3, and 5): Correlation coefficient = For moments with a difference of 0.01 (points 2 and 4): Correlation coefficient = Finally, the correlation degree is obtained by calculating the average value. : Based on the correlation degree classification criteria determined by cluster analysis of paired samples from historical signals, the correlation degree... The numerical range is divided into three level intervals: the first interval is The first interval is defined as "weak correlation," indicating a large difference in trends; the second interval is... Defined as "intermediate correlation," indicating that the trends are generally consistent but with deviations; the third interval is... Defined as "strong correlation," indicating a high degree of synchronization in trends. The values ​​obtained in this calculation... Accurately fall into the third interval ( The strong correlation interval indicates that the redundant channel is highly correlated with the historical trend of the main channel and is qualified as an effective backup.

[0027] S313: Based on the correlation of historical data trends and combined with the input signal validity threshold in the logical constraint parameters, detect whether the real-time numerical status of the redundant signal channel corresponding to the redundant signal channel identifier is within the allowable working range limited by the input signal validity threshold. Based on the verification results of the correlation of historical data trends, determine whether the redundant signal channel can be replaced and generate the input channel redundancy status judgment result. The system reads the calculated historical data trend correlation and retrieves the input signal validity threshold from the logical constraint parameters. First, it checks whether the current real-time value of the redundant signal channel is within the validity threshold range. For example, it checks whether the current is between 4 mA and 20 mA, or whether the value is within the range, and whether there is any open circuit or short circuit fault. The input channel redundancy status determination result is divided into two possibilities: "replaceable" and "non-replaceable". The determination logic is as follows: If the real-time status is abnormal, it is directly determined as "non-replaceable". If the real-time status is normal, the historical data trend correlation is compared with a preset correlation qualification benchmark value. This benchmark value (e.g., 0.75) is determined based on the statistical average of the correlation between the primary and backup channels in historical successful switchover cases. Only when the real-time value is valid and the historical correlation is higher than the benchmark value is the redundant signal channel deemed to be functionally intact and highly consistent with the primary channel data, thus being classified as "replaceable" and generating a "replaceable" input channel redundancy status determination result; otherwise, it is classified as "non-replaceable" and a "non-replaceable" result is generated. This step ensures that the backup channel is truly available and accurate when engineering changes cause abnormalities in the main channel data or require switching, thus avoiding engineering control accidents caused by blind switching.

[0028] Please see Figure 5 The specific steps for obtaining the signal fluctuation state determination result are as follows: S411: Determine the change value of the input signal received by the field control equipment to be analyzed based on the difference in parameter values ​​in the data packet of engineering change characteristics. Substitute the change value of the input signal into the PID control algorithm along with the control loop gain coefficient in the logic constraint parameters. Calculate the control response before and after the change of adjustable operating parameters to obtain the first theoretical output signal value and the second theoretical output signal value of the field control equipment to be analyzed, which are used as the theoretical output signal value pair. Based on the parameter differences in the data packet transmitting engineering change characteristics, the signal changes at the input end of the device under analysis are calculated. If the change is an output change of upstream equipment, this change is directly used as the input signal change value. Combined with the internal control algorithm logic of the device under analysis, typically a PID algorithm, engineering simulation calculations are performed. The control loop gain coefficient and the PID parameters before the change are read, and the input signal change value is substituted into the PID algorithm formula along with the current process variables. The calculation logic is as follows: Define the theoretical output signal value at the time the change occurs as... ,in This is the current computation step. A proportional gain is introduced. (represent ), integral gain (represent and differential gain (represent ).in, A larger value indicates a more drastic response from the system to the current error; The value is used to eliminate steady-state error; The value is used to suppress future trends in error. The output is calculated based on the following discrete PID formula: in, Representing the The theoretical output signal value calculated at each time step. Representing the The input error signal at any given time (i.e., the change in the input signal value). This represents the sampling period of the control system (derived from the device configuration file). Index for accumulating integrals. This represents the error value from the previous moment. For example, the following parameter is obtained: proportional gain. Integral gain Differential gain Sampling period Seconds. Assuming in Always in steady state, error ;exist At any given time, due to engineering changes (such as changes to the TIC101 design settings), the input signal may experience a step change, resulting in an error. Substitute into the formula to calculate. Components of time: Proportional term: Integral term: (It is assumed here that the initial integral accumulation is 0). Differential term: The final calculated theoretical output signal value is obtained. The above calculations are performed based on the parameter states before and after the change, respectively, to obtain the first theoretical output signal value and the second theoretical output signal value of the equipment in the field of the project to be analyzed. As a theoretical output signal value pair, this value pair accurately reflects the driving force of the engineering change on the control output at the theoretical level.

[0029] S412: Based on the output signal saturation limiting value in the logic constraint parameters, perform boundary constraint processing on the first theoretical output signal value and the second theoretical output signal value in the theoretical output signal value pair, and calculate the absolute value of the theoretical output signal difference between the first theoretical output signal value and the second theoretical output signal value after the output signal saturation limiting value processing. Read the output signal saturation limit value from the logic constraint parameters, for example, the upper limit is 100.0 and the lower limit is 0.0. Perform boundary checks on the first and second theoretical output signal values ​​calculated in S411. The output value has three possibilities: below the lower limit, within the limit range, or above the upper limit. If the value exceeds the upper limit, it is forced to the upper limit value; if it is below the lower limit, it is forced to the lower limit value; if it is within the range, it remains unchanged. For example, if the calculated second theoretical output is 105.0 and the upper limit is 100.0, it is corrected to 100.0. After processing, use the absolute value operation logic to calculate the difference between the corrected first and second theoretical outputs. That is, calculate |corrected output 2 - corrected output 1|. Assuming the corrected values ​​are 45.0 and 47.85 respectively, the absolute value of the difference is... .

[0030] S413: Compare the absolute value of the difference between the theoretical output signals with the signal control dead zone threshold in the logic constraint parameters to determine whether the signal fluctuation amplitude caused by the engineering change is covered by the signal control dead zone threshold and stops being transmitted, determine whether the signal fluctuation has been dissipated, and generate the signal fluctuation status judgment result. Obtain the signal control dead zone threshold from the logic constraint parameters. This threshold defines the minimum signal change that the actuator can respond to, for example, 0.5. Compare the absolute value of the theoretical output signal difference calculated in S412 with the signal control dead zone threshold. There are two possibilities for the comparison result: the absolute value of the difference is less than or equal to the dead zone threshold, or the absolute value of the difference is greater than the dead zone threshold. If the absolute value of the difference is less than or equal to the dead zone threshold, it indicates that the signal fluctuation caused by the engineering change is too small to overcome the friction or dead zone of the actuator and cannot drive the downstream equipment to move. Therefore, the fluctuation is determined to be covered and dissipated. If the absolute value of the difference is greater than the dead zone threshold, it indicates that the fluctuation is sufficient to cause a downstream response. Based on this, a signal fluctuation status determination result is generated. For example, if the absolute value of the difference is 2.85 and the dead zone threshold is 0.5, 2.85 is greater than 0.5, indicating that the signal fluctuation has not been dissipated and will continue to propagate downstream; if the absolute value of the difference is 0.2, which is less than 0.5, it indicates that the signal fluctuation has dissipated and propagation has terminated. This result will directly determine whether it is necessary to continue tracking the next level of equipment.

[0031] Please see Figure 6 The specific steps for obtaining the results of defining the scope of impact of engineering changes are as follows: S511: Perform a comprehensive condition judgment on the input channel redundancy status judgment result and the signal fluctuation status judgment result. If the input channel redundancy status judgment result shows that the redundant signal channel can be replaced, or the signal fluctuation status judgment result shows that the signal fluctuation amplitude caused by the engineering change is covered by the signal control dead zone threshold and stops being transmitted backward and dissipated, then it is determined that the current field control equipment to be analyzed can block the impact of the change; otherwise, it is determined that it is affected and the equipment impact attribute classification result is obtained. The system aggregates the input channel redundancy status and signal fluctuation status judgment results and performs a comprehensive logical judgment. The equipment impact attribute classification result has two possibilities: "blocking node" or "affected node." The input channel redundancy status is checked. If the result shows "replaceable," it means that even if an upstream change causes an anomaly in the main channel, the equipment can automatically switch to the redundant channel, thus isolating the impact of the change. The current equipment is determined to block the impact of the change and is classified as a "blocking node." If redundancy is not replaceable, the signal fluctuation status is then checked. If the result shows that the fluctuation is "dissipated," meaning the fluctuation amplitude is covered by the dead zone, the current equipment is also determined to block the impact of the change and is classified as a "blocking node." Only when there is no effective redundancy and the fluctuation is not dissipated is the current on-site equipment in the analyzed project determined to be "affected" and classified as an "affected node." For example, if a controller has no redundancy and the calculated fluctuation amplitude of 2.85 is greater than the dead zone of 0.5, then the controller is marked as an affected on-site equipment.

[0032] S512: Based on the classification results of equipment impact attributes, for nodes marked as affected field control equipment, the updated engineering change feature transmission data packet is sent to the next-level field control equipment connected to the affected field control equipment in the field equipment interconnection topology model, the analysis status feedback from the next-level field control equipment is collected, and multi-level propagation path equipment information is obtained. For the engineering field equipment nodes marked as "affected," their positions in the topology model are identified, and their next-level connected engineering field equipment is searched. The engineering change feature transmission data packet is updated, using the output changes of the currently affected equipment as new change source data, and sent to the next-level equipment. The next-level equipment repeats the above analysis process (S212 to S511). Analysis status feedback from the next-level equipment is collected, including whether it is affected and whether the fluctuation is dissipated. This process propagates in a chain or tree structure until it encounters a blocking node or the end of the network. All paths and equipment traversed are recorded, and multi-level propagation path equipment information is obtained. For example, the change impact propagates from the temperature controller TIC101 to the downstream control valve TV101 (because the fluctuation is not dissipated and there is no redundancy), and then propagates to the flow transmitter FT102 controlled by TV101, forming a complete propagation chain.

[0033] S513: Summarize all equipment impact attribute classification results and multi-level propagation path equipment information, integrate change impact blocking equipment information and affected on-site control equipment information, confirm change impact path and impact termination boundary nodes, and generate engineering change impact scope definition results; The system integrates all equipment impact attribute classification results and multi-level propagation path equipment information. All "affected" nodes are highlighted on the topology map and connected to form lines showing the change's impact path. Simultaneously, all "blocking nodes" are identified, forming the boundary where the impact terminates. This information is structured and integrated to generate the final engineering change impact scope definition. This result is presented in list or graphical data form, detailing which equipment is affected, which is safe, and where the impact stops. For example, the result shows that a change in the control output of engineering change source TIC101 causes a corresponding opening action in its downstream valve TV101. This valve action triggers a change in flow rate within the pipeline during the actual process, resulting in a fluctuation in the measured value at flow transmitter FT102. However, since subsequent calculations show that the flow rate change is less than the dead zone range set by its downstream equipment, this fluctuation does not have a substantial impact on further downstream equipment; therefore, the impact scope is limited to TIC101, TV101, and FT102. This result provides engineers with accurate risk assessment basis for engineering changes, avoiding unplanned downtime caused by blind changes.

[0034] An intelligent identification and impact analysis system for engineering changes, the system comprising: The topology modeling module extracts information about the field control equipment at the engineering site and associates corresponding adjustable operating parameters and logical constraint parameters with each field control equipment to construct a field equipment interconnection topology model. The feature capture module monitors the modification behavior of the adjustable operating parameters of each field control device, calculates the difference in parameter values ​​before and after the modification, and, combined with the information in the field device interconnection topology model, locates the field control device to be analyzed and extracts the corresponding engineering change feature transmission data packet. The channel redundancy determination module reads the input signal channel identifier currently used by each field control device to be analyzed from the engineering change feature transmission data packet, determines the redundancy status of the input signal channel, and generates the input channel redundancy status determination result. The fluctuation state analysis module determines the equivalent input signal change value sensed by the field control equipment under analysis based on the parameter value difference in the data packet transmitted by the engineering change characteristics. It then judges the signal fluctuation state caused by the engineering change by combining the logical constraint parameters and obtains the signal fluctuation state judgment result. The comprehensive judgment module performs a comprehensive judgment on the input channel redundancy status judgment result and the signal fluctuation status judgment result to obtain the result of defining the scope of impact of engineering changes.

[0035] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for intelligent identification and impact analysis of engineering changes, characterized in that, Includes the following steps: S1: Extract information about the field control equipment at the engineering site, and associate each field control equipment with corresponding adjustable operating parameters and logical constraint parameters to construct a field equipment interconnection topology model; S2: Monitor the modification behavior of the adjustable operating parameters of each field control device, calculate the difference in parameter values ​​before and after the modification of the adjustable operating parameters, and lock the field control device to be analyzed by combining the information in the field device interconnection topology model, and extract the corresponding engineering change feature data packet; S3: Read the input signal channel identifier currently used by each field control device to be analyzed in the engineering change feature transmission data packet, determine the redundancy status of the input signal channel, and generate the input channel redundancy status determination result; S4: Determine the equivalent input signal change value sensed by the field control equipment to be analyzed based on the parameter value difference in the data packet transmitted by the engineering change feature, and judge the signal fluctuation state caused by the engineering change in combination with the logic constraint parameters to obtain the signal fluctuation state judgment result. S5: Perform a comprehensive judgment on the input channel redundancy status judgment result and the signal fluctuation status judgment result to obtain the result of defining the scope of impact of engineering changes.

2. The intelligent identification and impact analysis method for engineering changes according to claim 1, characterized in that, The field device interconnection topology model includes a unique hardware identifier for the field control device, an initial network structure with signal flow as the connection, adjustable operating parameters, and logical constraint parameters. The engineering change feature transmission data packet includes a unique hardware identifier, a change parameter type identifier, and parameter value differences. The input channel redundancy status determination result is specifically the availability status of redundant signal channels verified by grey relational analysis and validity threshold detection. The signal fluctuation status determination result is specifically the fluctuation dissipation confirmation status obtained by comparing the input signal with the signal control dead zone threshold. The engineering change impact range definition result includes information on affected field control devices and information on devices blocking the impact of the change.

3. The intelligent identification and impact analysis method for engineering changes according to claim 2, characterized in that, The specific steps for obtaining the field device interconnection topology model are as follows: S111: Read and parse the engineering fieldbus configuration file, extract the unique hardware identification code of all field control devices, and establish an initial network structure with field control devices as vertices and signal flow as the connection based on the input and output signal mapping relationship between devices recorded in the configuration file. S112: Traverse each field control device node in the initial network structure, associate the adjustable operating parameters consisting of process control setpoints, PID control coefficients and alarm thresholds, map the adjustable operating parameters to the corresponding field control device nodes, and obtain the network structure of associated operating parameters. S113: Based on the network structure of the associated operating parameters, associate logical constraint parameters with each field control device, including input signal validity threshold, control loop gain coefficient, output signal saturation limit value, signal control dead zone threshold and redundant channel mapping configuration. Integrate the physical connection relationship of all field control devices with the adjustable operating parameters and logical constraint parameter configurations to establish a field device interconnection topology model.

4. The intelligent identification and impact analysis method for engineering changes according to claim 3, characterized in that, The specific steps for obtaining the engineering change feature transmission data packet are as follows: S211: Monitor the real-time modification behavior of adjustable operating parameters for any field control device, mark the target field control device as the source device of engineering change, identify the category of the modified parameter in the adjustable operating parameters, generate the change parameter type identifier, read the adjustable operating parameter values ​​before and after the modification, and calculate the difference in parameter values ​​of the source device of engineering change before and after the modification. S212: Search for and receive the downstream node of the output signal of the engineering change source device in the field device interconnection topology model, determine the downstream node of the output signal of the engineering change source device as the field control device to be analyzed, and extract the index information and connection port information of the field control device to be analyzed as the information of the field control device to be analyzed. S213: Package the unique hardware identifier, change parameter type identifier, and parameter value difference of the field control equipment to be analyzed from the information of the field control equipment to be analyzed, and generate an engineering change feature transmission data package.

5. The intelligent identification and impact analysis method for engineering changes according to claim 4, characterized in that, The specific steps for obtaining the input channel redundancy status determination result are as follows: S311: Read the input signal channel identifier currently used by each field control device to be analyzed, which is recorded in the engineering change feature transmission data packet; query the redundant channel mapping configuration in the logical constraint parameters; confirm whether there are redundant signal channels with the same physical process variable meaning and originating from different upstream devices; and obtain the redundant signal channel identifier. S312: Obtain the historical operation monitoring data of the main input channel and the redundant signal channel corresponding to the redundant signal channel identifier of the field control device to be analyzed within a preset time period, and call the grey relational analysis algorithm to calculate the correlation degree between the historical operation monitoring data of the main input channel and the historical operation monitoring data sequence of the redundant signal channel to obtain the historical data trend correlation degree. S313: Based on the historical data trend correlation, and combined with the input signal validity threshold in the logical constraint parameters, detect whether the real-time numerical status of the redundant signal channel corresponding to the redundant signal channel identifier is within the allowable working range limited by the input signal validity threshold. Based on the verification results of the historical data trend correlation, determine whether the redundant signal channel can be replaced, and generate the input channel redundancy status determination result.

6. The intelligent identification and impact analysis method for engineering changes according to claim 5, characterized in that, The specific steps for obtaining the signal fluctuation state determination result are as follows: S411: Based on the parameter value difference in the data packet of the engineering change feature transmission, determine the input signal change value received by the field control equipment to be analyzed, and substitute the input signal change value with the control loop gain coefficient in the logic constraint parameter into the PID control algorithm to calculate the control response before and after the change of adjustable operating parameters, and obtain the first theoretical output signal value and the second theoretical output signal value of the field control equipment to be analyzed as a theoretical output signal value pair. S412: Based on the output signal saturation limiting value in the logic constraint parameters, perform boundary constraint processing on the first theoretical output signal value and the second theoretical output signal value in the theoretical output signal value pair, and calculate the absolute value of the theoretical output signal difference between the first theoretical output signal value and the second theoretical output signal value after the output signal saturation limiting value processing. S413: Compare the absolute value of the difference between the theoretical output signals with the signal control dead zone threshold in the logic constraint parameters to determine whether the signal fluctuation amplitude caused by the engineering change is covered by the signal control dead zone threshold and stops being transmitted backward, determine whether the signal fluctuation has been dissipated, and generate a signal fluctuation status judgment result.

7. The intelligent identification and impact analysis method for engineering changes according to claim 6, characterized in that, The specific steps for obtaining the results of defining the scope of impact of the engineering change are as follows: S511: Perform a comprehensive condition judgment on the input channel redundancy status judgment result and the signal fluctuation status judgment result. If the input channel redundancy status judgment result shows that the redundant signal channel can be replaced, or the signal fluctuation status judgment result shows that the signal fluctuation amplitude caused by the engineering change is covered by the signal control dead zone threshold and stops being transmitted backward and dissipated, then it is determined that the current field control equipment to be analyzed can block the impact of the change; otherwise, it is determined that it is affected, and the equipment impact attribute classification result is obtained. S512: Based on the classification results of the equipment impact attributes, for nodes marked as affected field control equipment, the updated engineering change feature transmission data packet is sent to the next-level field control equipment connected to the affected field control equipment in the field equipment interconnection topology model, the analysis status fed back by the next-level field control equipment is collected, and multi-level propagation path equipment information is obtained. S513: Summarize all the equipment impact attribute classification results and the equipment information of the multi-level propagation path, integrate the change impact blocking equipment information and the affected on-site control equipment information, confirm the change impact path and the boundary node of the impact termination, and generate the engineering change impact scope definition result.

8. The intelligent identification and impact analysis method for engineering changes according to claim 4, characterized in that, The correlation coefficient between the historical operation monitoring data of the main input channel and the historical operation monitoring data sequence of the redundant signal channel is calculated. The formula used is: ； in, This represents the total number of historical data sampling points used in the calculation. Representing the Normalized values ​​of the main input channel at each sampling time Representing the Normalized values ​​of redundant signal channels at each sampling time Indicates the first The absolute deviation between the two at any given moment This represents the minimum absolute deviation across the entire sequence. This represents the maximum absolute deviation across the entire sequence. The resolution coefficient.

9. The intelligent identification and impact analysis method for engineering changes according to claim 5, characterized in that, The process of confirming whether there are redundant signal channels with the same physical process variable meaning originating from different upstream devices specifically involves: Based on the input signal channel identifier, the corresponding engineering tag name, engineering unit of measurement type, and engineering range value are indexed and extracted from the redundant channel mapping configuration of the logical constraint parameters. Traverse all candidate signal channels recorded in the redundant channel mapping configuration, and check item by item whether the engineering tag name, engineering unit of measurement type and engineering range of each candidate signal channel are consistent with the parameter content corresponding to the input signal channel identifier; When a specified alternative signal channel is identified that meets the requirements of complete consistency in engineering tag name, engineering unit of measurement type, and engineering range, and the physical source port address is inconsistent with the input signal channel identifier, it is determined that there are redundant signal channels with the same physical process variable meaning and originating from different upstream devices.

10. An intelligent identification and impact analysis system for engineering changes, characterized in that, The intelligent identification and impact analysis method for engineering changes according to any one of claims 1-9, wherein the system comprises: The topology modeling module extracts information about the field control equipment at the engineering site and associates corresponding adjustable operating parameters and logical constraint parameters with each field control equipment to construct a field equipment interconnection topology model. The feature capture module monitors the modification behavior of the adjustable operating parameters of each field control device, calculates the difference in parameter values ​​before and after the modification, and, combined with the information in the field device interconnection topology model, locates the field control device to be analyzed and extracts the corresponding engineering change feature transmission data packet. The channel redundancy determination module reads the input signal channel identifier currently used by each field control device to be analyzed from the engineering change feature transmission data packet, determines the redundancy status of the input signal channel, and generates the input channel redundancy status determination result. The fluctuation state analysis module determines the equivalent input signal change value sensed by the field control equipment to be analyzed based on the parameter value difference in the data packet transmitted by the engineering change characteristics, and judges the signal fluctuation state caused by the engineering change in combination with the logical constraint parameters to obtain the signal fluctuation state judgment result. The comprehensive judgment module performs a comprehensive judgment on the input channel redundancy status judgment result and the signal fluctuation status judgment result to obtain the result of defining the scope of impact of engineering changes.

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