Intelligent Management System and Method for Water Conservancy Project Equipment Data Based on Digital Twin

Abstract

The present invention relates to the field of water conservancy projects, specifically to an intelligent management system and method for water conservancy project equipment data based on digital twins, including a monitoring cycle calibration module, a response event extraction module, a water conservancy event flow construction module, a warning evaluation model generation module, and a real-time anomaly verification module; the monitoring cycle calibration module is used to generate several historical monitoring cycles of the water conservancy project equipment management platform; the response event extraction module is used to extract water conservancy equipment response events associated with equipment responses during the historical monitoring cycles; the water conservancy event flow construction module is used to generate a water conservancy event flow for the application response of the water conservancy project equipment management platform based on the engineering equipment; the warning evaluation model generation module is used to construct a warning evaluation model corresponding to each water conservancy event flow based on the target water conservancy event; the real-time anomaly verification module is used to determine whether the status of the engineering equipment is abnormal and to verify and update the monitoring cycle to which the engineering equipment belongs after the status is confirmed to be abnormal.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy projects, and specifically to an intelligent management system and method for water conservancy project equipment data based on digital twin. Background Technique

[0002] A water conservancy project is a project built to control and allocate surface water and groundwater in nature to achieve the purpose of eliminating disasters and bringing benefits, and is also called a water project. Water is an indispensable and precious resource for human production and life, but its natural state does not fully meet the needs of humans. Only by building water conservancy projects can the flow of water be controlled, flood disasters be prevented, and the regulation and distribution of water volume be carried out to meet the needs of people's lives and production for water resources. Water conservancy projects need to build different types of hydraulic structures such as dams, levees, spillways, water gates, intakes, channels, ferry crossings, raft channels, and fishways to achieve their goals;

[0003] The existing water conservancy project equipment monitoring platform implements real-time monitoring by setting up monitoring systems with different functions. However, in practice, the occurrence of a water conservancy warning event often affects multiple systems, and there is a coherent cooperation of prior and subsequent responses among the engineering equipment of different systems. Therefore, the maintenance and status monitoring of these engineering equipment are very important. In the prior art, abnormalities are often discovered through fixed maintenance cycles, which have hysteresis and uncertainty. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent management system and method for water conservancy project equipment data based on digital twin to solve the problems raised in the above background technique.

[0005] To solve the above technical problems, the present invention provides the following technical solution: An intelligent management method for water conservancy project equipment data based on digital twin, including the following analysis steps:

[0006] Step S1: Taking each equipment maintenance node recorded in the water conservancy project equipment management platform as the starting monitoring point, and determining the ending monitoring point according to the safety cycle preset by the platform, to generate several historical monitoring cycles of the water conservancy project equipment management platform; extracting water conservancy equipment response events with equipment response associations within the historical monitoring cycles;

[0007] ; The purpose of determining the historical monitoring cycle is to minimize the probability of abnormal damage of the water conservancy project monitoring equipment within this cycle; then the recorded engineering equipment data is more accurate;

[0008] Step S2: Extracting the engineering equipment recorded in the water conservancy equipment response event, and generating a water conservancy event stream for the application response of the water conservancy project equipment management platform based on the engineering equipment;

[0009] Step S3: Extract all the water conservancy equipment response events in the same water conservancy event stream where the recorded engineering equipment parameters are different and are marked as warning events as target water conservancy events, and construct a warning evaluation model corresponding to each water conservancy event stream based on the target water conservancy events;

[0010] Step S4: Based on the warning evaluation model, determine whether the status of the engineering equipment in the real-time monitoring warning response is abnormal, and verify and update the monitoring period to which the engineering equipment belongs after the status is confirmed to be abnormal.

[0011] Only evaluating the equipment status for warning events is because when the water conservancy monitoring is in a normal state, the abnormality of the equipment is easy to be detected and has little impact on the monitoring. However, when a warning event occurs, there are various reasons for the abnormality. It is difficult to judge intuitively whether it is the abnormality caused by the data or the abnormality of the equipment itself. And the abnormality of the equipment will lead to the change of the result, thus causing a greater impact.

[0012] Further, step S2 includes the following specific steps:

[0013] Mark the monitoring system corresponding to the engineering equipment and the execution functions corresponding to the equipment. The execution functions include data acquisition function and instruction operation function; Equipment response association means that due to the data obtained by a certain equipment in a certain monitoring system of the water conservancy engineering equipment management platform triggering a response signal, which causes the equipment in other monitoring systems to respond, the multi-equipment response event corresponding to a certain process. Then, the equipment that responds and is located in different monitoring systems in the corresponding response event is considered to have equipment response association;

[0014] Extract the first responding engineering equipment in the water conservancy equipment response event as the initial engineering equipment, and connect the remaining engineering equipment with equipment response association in the order of response time with the initial engineering equipment as the response starting point to form a water conservancy event stream corresponding to the corresponding water conservancy equipment response event. When the same position corresponds to different execution functions or different equipment names, it is a different water conservancy event stream.

[0015] Further, step S3 includes the following:

[0016] Step S31: Engineering equipment parameters refer to the data parameters recorded and stored by each engineering equipment. A warning event refers to an event in which a water conservancy equipment response event has an engineering equipment that records the engineering equipment parameters reaching the warning value set by the corresponding monitoring system and transmits a warning signal;

[0017] Step S32: Extract the jth type of engineering equipment parameter A of the ith target water conservancy event recorded under the same water conservancy event stream i j, the engineering equipment parameters are sorted and numbered according to the order of the engineering equipment recorded in the water conservancy event stream. The engineering equipment parameters select the real-time state parameters at the warning response moment, and each type of engineering equipment records a type of engineering equipment parameters;

[0018] Calculate the parameter change rate Y of the j-th type of engineering equipment parameters under the same water conservancy event j , Y j =|A pmax j -A i j | / A pmax j , where A pmax j represents the maximum value among the p target water conservancy events recorded for the j-th type of engineering equipment parameters under the same water conservancy event, p≥i, and p represents the total number of target water conservancy events under the same water conservancy event;

[0019] Step S33: Obtain the parameter change rate Y corresponding to the m types of engineering equipment parameters recorded in the same water conservancy event stream j , m≥j; m represents the number of types of engineering equipment parameters recorded in the target water conservancy event; Use the formula: Z i =(1 / m)∑(Y j ) i , calculate the parameter eigenvalue Z of the i-th target water conservancy event i ; (Y j ) i represents the parameter change rate of the j-th type of engineering equipment parameters recorded in the i-th target water conservancy event;

[0020] Step S34: Extract the parameter eigenvalues of the p target water conservancy events in the same water conservancy event stream, and construct an early warning evaluation model W corresponding to each water conservancy event stream, W = [Z imin , Z imax , where Z imin represents the minimum value of the parameter eigenvalues recorded in the same water conservancy event stream, and Z imax represents the maximum value of the parameter eigenvalues recorded in the same water conservancy event stream.

[0021] When there is a warning in real-time monitoring, if it does not belong to this interval, it is judged that the equipment state is abnormal. Because for water conservancy monitoring, it is a critical judgment response based on the warning value, and a signal will be transmitted when the warning value is exceeded. Then, the parameter characteristics between the historical warning events recorded often do not vary much. Therefore, whether it is less than the minimum value or greater than the maximum value, it can to a certain extent indicate that there is a certain abnormality in the water conservancy engineering equipment itself; resulting in the generation of an abnormal warning, less than the minimum value means abnormal sensitivity, and greater than the maximum value means the equipment is slow to respond.

[0022] Further, step S4 includes the following specific steps:

[0023] Step S41: Obtain the real-time status parameters recorded by each engineering device in the real-time monitoring and early warning response, extract the water conservancy event stream to which the engineering device corresponding to the early warning response belongs, extract the early warning assessment model W corresponding to the same water conservancy event stream in the historical records, calculate the parameter change rate of the corresponding type based on the real-time status parameters, and the real-time parameter eigenvalue Z0 of the overall parameters of each engineering device corresponding to the real-time early warning event to which the early warning response engineering device belongs. The calculation method of the real-time parameter eigenvalue Z0 is the same as that of the parameter eigenvalue Z i The calculation methods are the same;

[0024] Step S42: When the real-time parameter eigenvalue Output the abnormal state of the engineering device in the real-time monitoring and early warning response; otherwise, output the normal state of the engineering device in the real-time monitoring and early warning response; extract the cycle length from the real-time early warning event to the nearest starting monitoring point when the output state is abnormal as the suspicious cycle T1;

[0025] Step S43: Obtain the water conservancy equipment response events that are the same as the engineering device with abnormal recorded status in the suspicious cycle and belong to different water conservancy event streams as the target events to be analyzed. Take the engineering device with abnormal recorded status as the target device, obtain the number D1 of events containing the target device and the number D2 of types of the target events to be analyzed containing the target device. The number of types refers to the same water conservancy event stream as one type; calculate the first eigenvalue F1, F1 = (1 / n1)∑[(D1 / D0)*(D2 / D3)], where n1 represents the number of engineering devices with abnormal recorded status in the real-time early warning event; D0 represents the total number of recorded water conservancy equipment response events in the suspicious cycle, and D3 represents the total number of types of recorded water conservancy equipment response events in the suspicious cycle;

[0026] Obtain the maximum value A 0max and the minimum value A 0min of the engineering device parameters recorded in the target events to be analyzed containing the target device, calculate the second eigenvalue F2, F2 = (1 / n1)∑[(A 0max -A 0min ) / A 0max ;

[0027] Step S44: Extract multiple suspicious cycles with early warning events, construct data pairs with the suspicious cycle length T1, the first eigenvalue F1, and the second eigenvalue F2 in the suspicious cycle. Take F1 and F2 as input variables and T1 as the output variable to construct a cycle prediction model T = k1*F1 + k2*F2 + ε, and substitute multiple groups of data pairs to calculate the corresponding reference coefficients k1, k2, and the error term ε;

[0028] Step S45: When a real-time water conservancy equipment response event occurs, find the engineering equipment with abnormal status in the historical record of the water conservancy event stream to which the real-time water conservancy equipment response event belongs as the real-time prediction engineering equipment. Calculate the first eigenvalue and the second eigenvalue based on the real-time prediction engineering equipment, substitute them into the function relationship model T to output the corresponding period prediction value, and obtain the real-time suspicious period. When the real-time suspicious period is less than the period prediction value, continue to monitor; when the real-time suspicious period is greater than or equal to the period prediction value, transmit a signal to update the real-time monitoring period length to the suspicious period length to remind the equipment for maintenance.

[0029] The fact that the suspicious period is less than the period prediction value indicates that the data corresponding to the water conservancy equipment response events during this period are all in a normal state, and the engineering equipment is also at a normal level; while when it is greater than or equal to, it means that the next water conservancy equipment response event may be abnormal. Therefore, the equipment is maintained and repaired at the end of this period, which plays a preventive role. Moreover, the maintenance period of the engineering equipment is adaptively adjusted based on the parameter data of the water conservancy event records, rather than a single fixed maintenance period, improving the intelligence and diversification of the maintenance of water conservancy engineering equipment based on data analysis, and playing a preventive role in the abnormal state of the equipment.

[0030] The intelligent management system for water conservancy engineering equipment based on digital twin. The management system includes a monitoring period calibration module, a response event extraction module, a water conservancy event stream construction module, a warning evaluation model generation module, and a real-time anomaly verification module;

[0031] The monitoring period calibration module is used to generate several historical monitoring periods for the water conservancy engineering equipment management platform;

[0032] The response event extraction module is used to extract the water conservancy equipment response events with equipment response associations during the historical monitoring period;

[0033] The water conservancy event stream construction module is used to generate the water conservancy event stream for the application response of the water conservancy engineering equipment management platform based on the engineering equipment;

[0034] The warning evaluation model generation module is used to construct a warning evaluation model corresponding to each water conservancy event stream based on the target water conservancy event;

[0035] The real-time anomaly verification module is used to judge whether the status of the engineering equipment in the real-time monitoring warning response is abnormal, and verify and update the monitoring period to which the engineering equipment belongs after the status is confirmed to be abnormal.

[0036] Furthermore, the water conservancy event stream construction module includes an equipment response association analysis unit and a water conservancy event stream concatenation unit;

[0037] The device response correlation analysis unit is used for a multi-device response event based on a certain process where data obtained by a certain device in a certain monitoring system of the water conservancy project device management platform triggers a response signal, resulting in the response of devices in other monitoring systems. Then, the devices that respond in the corresponding response event and are located in different monitoring systems are considered to have device response correlation;

[0038] The water conservancy event flow series connection unit is used to series-connect the remaining project devices with device response correlation in the order of response time, starting from the initial project device as the response starting point, to form the water conservancy event flow corresponding to the water conservancy device response event.

[0039] Furthermore, the early warning evaluation model generation module includes a device parameter extraction unit, a parameter change rate calculation unit, a parameter eigenvalue calculation unit, and an early warning evaluation model output unit;

[0040] The device parameter extraction unit is used to extract the project device parameters of the target water conservancy event recorded under the same water conservancy event flow;

[0041] The parameter change rate calculation unit is used to calculate the parameter change rate of the project device parameters under the same water conservancy event flow;

[0042] The parameter eigenvalue calculation unit is used to calculate the parameter eigenvalue of the target water conservancy event;

[0043] The early warning evaluation model output unit is used to extract the parameter eigenvalue of the target water conservancy event in the same water conservancy event flow and construct the early warning evaluation model corresponding to each water conservancy event flow.

[0044] Furthermore, the real-time anomaly verification module includes a real-time device anomaly monitoring unit and a periodic verification unit;

[0045] The real-time device anomaly monitoring unit is used to obtain the real-time status parameters recorded by each project device for real-time monitoring and early warning response, extract the water conservancy event flow to which the project device corresponding to the early warning response belongs, extract the early warning evaluation model corresponding to the same water conservancy event flow in the historical records, calculate the corresponding type of parameter change rate based on the substitution of the real-time status parameters, and the real-time parameter eigenvalue of the parameters of each project device corresponding to the overall real-time early warning event to which the project device for early warning response belongs, and judge the real-time parameter eigenvalue with the model to output the status of the project device for real-time monitoring and early warning response;

[0046] The periodic verification unit is used to analyze the monitoring period with abnormal output device status and verify whether it is necessary to update the monitoring period to remind of device maintenance.

[0047] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: By extracting and analyzing the water conservancy response events recorded by the water conservancy engineering platform, the present invention constructs a data specification cluster with engineering equipment as the monitoring core, and realizes the monitoring of whether the equipment status is abnormal through parameter fluctuations. In addition, the present invention also processes and analyzes the equipment data of all water conservancy engineering equipment with response fluctuations before early warning events occur, estimates the possibility of equipment abnormalities, and as much as possible realizes the prediction of abnormal losses, reducing the errors and damages of water conservancy monitoring caused by abnormal water conservancy engineering equipment. Moreover, based on the parameter data recorded by water conservancy events, the maintenance cycle of engineering equipment is adaptively adjusted, rather than a single fixed maintenance cycle, improving the intelligence and diversification of water conservancy engineering equipment maintenance based on data analysis, and playing a role in preventing abnormal equipment status. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, and do not constitute a limitation to the present invention. In the accompanying drawings:

[0049] Figure 1 is a schematic structural diagram of the intelligent management system for water conservancy engineering equipment based on digital twin of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0051] Please refer to Figure 1 , the present invention provides a technical solution: an intelligent management method for water conservancy engineering equipment based on digital twin, including the following analysis steps:

[0052] Step S1: Taking each equipment maintenance node recorded by the water conservancy engineering equipment management platform as the starting monitoring point, and determining the ending monitoring point according to the safety cycle preset by the platform, several historical monitoring cycles of the water conservancy engineering equipment management platform are generated; extracting water conservancy equipment response events with equipment response associations within the historical monitoring cycles;

[0053] The safety cycle refers to the minimum interval cycle without recording equipment abnormalities after equipment maintenance; the purpose of determining the historical monitoring cycle is to minimize the probability of abnormal damage to water conservancy engineering monitoring equipment within this cycle; then the corresponding recorded engineering equipment data is more accurate;

[0054] Step S2: Extract the engineering devices recorded in the water conservancy equipment response events, and generate a water conservancy event stream for the application response of the water conservancy engineering equipment management platform based on the engineering devices;

[0055] Step S3: Extract all water conservancy equipment response events with different recorded engineering equipment parameters and marked as warning events in the same water conservancy event stream as target water conservancy events, and construct a warning evaluation model corresponding to each water conservancy event stream based on the target water conservancy events;

[0056] Step S4: Based on the warning evaluation model, determine whether the status of the engineering equipment in the real-time monitoring warning response is abnormal, and check and update the monitoring period to which the engineering equipment belongs after the status is confirmed to be abnormal.

[0057] Only evaluate the equipment status for warning events because when the water conservancy monitoring is in a normal state, the abnormality of the equipment is easy to be discovered and has little impact on the monitoring. However, when a warning event occurs, there are various reasons for the abnormality. It is difficult to judge intuitively whether it is the abnormality caused by the data or the abnormality of the equipment itself. Moreover, the abnormality of the equipment will lead to a change in the result, thus causing a greater impact.

[0058] Step S2 includes the following specific steps:

[0059] Mark the monitoring system corresponding to the engineering equipment and the execution functions corresponding to the equipment. The execution functions include a data acquisition function and an instruction operation function. The data acquisition function is like a sensor, and the instruction operation function is like a controller, etc.; Equipment response association means that due to the data obtained by a certain equipment in a certain monitoring system of the water conservancy engineering equipment management platform triggering a response signal, which causes the equipment in other monitoring systems to respond, the multi-equipment corresponding to the response event based on a certain process. Then, the equipment that responds and is located in different monitoring systems in the corresponding response event has an equipment response association;

[0060] Extract the first responding engineering equipment in the water conservancy equipment response event as the initial engineering equipment, and connect the remaining engineering equipment with equipment response associations in sequence according to the chronological order of the response time starting from the initial engineering equipment to form a water conservancy event stream corresponding to the water conservancy equipment response event. When the same position corresponds to different execution functions or different equipment names, they are different water conservancy event streams.

[0061] As shown in the embodiment: The first responding engineering equipment is a water level sensor. After the water level sensor responds, it transmits a signal to the barrage control equipment, and the barrage control equipment issues a response signal to control the barrage. In this event, the corresponding water conservancy event stream is: water level sensor → barrage control equipment.

[0062] Step S3 includes the following:

[0063] Step S31: The engineering equipment parameters refer to the data parameters recorded and stored by each engineering equipment. For example, the engineering equipment parameters recorded by the water level sensor are the water level values in each water conservancy equipment response event; the warning event refers to the event in which, when the engineering equipment parameters recorded by the engineering equipment in the water conservancy equipment response event reach the warning value set by the corresponding monitoring system, a warning signal is transmitted.

[0064] Step S32: Extract the j-th type of engineering equipment parameter A of the i-th target water conservancy event recorded under the same water conservancy event stream. i j The engineering equipment parameters are sorted and numbered in the order of the engineering equipment recorded by the water conservancy event stream. The engineering equipment parameters select the real-time state parameters at the warning response moment, and each type of engineering equipment records one type of engineering equipment parameter.

[0065] Calculate the parameter change rate Y of the j-th type of engineering equipment parameter under the same water conservancy event stream. j , Y j =|A pmax j -A i j | / A pmax j , where A pmax j represents the maximum value of the p target water conservancy events recorded by the j-th type of engineering equipment parameter under the same water conservancy event stream, p≥i, and p represents the total number of target water conservancy events under the same water conservancy event stream.

[0066] Step S33: Obtain the parameter change rates Y corresponding to the m types of engineering equipment parameters recorded by the same water conservancy event stream. j , m≥j; m represents the number of types of engineering equipment parameters recorded in the target water conservancy event; use the formula: Z i =(1 / m)∑(Y j ) i , calculate the parameter eigenvalue Z of the i-th target water conservancy event. i ; (Y j ) i represents the parameter change rate of the j-th type of engineering equipment parameter recorded in the i-th target water conservancy event.

[0067] Step S34: Extract the parameter eigenvalues of the p target water conservancy events in the same water conservancy event stream, and construct a warning evaluation model W corresponding to each water conservancy event stream, W = [Z imin , Z imax , where Z imin represents the minimum value of the parameter eigenvalues recorded by the same water conservancy event stream, and Z imax represents the maximum value of the parameter eigenvalues recorded by the same water conservancy event stream.

[0068] When there is a warning in real-time monitoring, if it does not belong to this interval, it is determined that the device status is abnormal. Because for water conservancy monitoring, it is a critical judgment response based on warning values. When the warning value is exceeded, a signal will be transmitted. Generally, the parameter characteristics between the historical warning events recorded are not very different. Therefore, whether it is less than the minimum value or greater than the maximum value, it can to a certain extent indicate that there is a certain abnormality in the water conservancy project equipment itself, resulting in the generation of abnormal warnings. Being less than the minimum value means abnormal sensitivity, and being greater than the maximum value means the device is slow to respond.

[0069] Step S4 includes the following specific steps:

[0070] Step S41: Obtain the real-time status parameters recorded by each engineering device in the real-time monitoring warning response, extract the water conservancy event stream to which the engineering device corresponding to the warning response belongs, extract the warning evaluation model W corresponding to the same water conservancy event stream in the historical records, calculate the corresponding type of parameter change rate based on the real-time status parameters, and the real-time parameter characteristic value Z0 of the overall parameters of each engineering device corresponding to the real-time warning event to which the warning response engineering device belongs. The calculation method of the real-time parameter characteristic value Z0 is the same as that of the parameter characteristic value Z i The calculation methods are the same;

[0071] As shown in the embodiment: the parameter change rate Y j , Y j =|A pmax j -A i j | / A pmax j Replace the A in the formula with the real-time status parameters i j Select the maximum engineering device parameter of the engineering device corresponding to the same water conservancy event stream in the historical records as A pmax j ;

[0072] Step S42: When the real-time parameter characteristic value , output that the status of the engineering device in the real-time monitoring warning response is abnormal; otherwise, output that the status of the engineering device in the real-time monitoring warning response is normal; extract the cycle length from the real-time warning event when the output status is abnormal to the nearest starting monitoring point as the suspicious cycle T1;

[0073] When there are multiple engineering devices responding in the warning event, outputting that the status of the engineering device in the real-time monitoring warning response is abnormal means all the responding engineering devices;

[0074] Step S43: Obtain the water conservancy equipment response events that are the same as the engineering equipment with abnormal real-time warning event record status during the suspicious period and are in different water conservancy event flows as the target events to be analyzed. Take the engineering equipment with abnormal record status as the target equipment, and obtain the number D1 of events containing the target equipment and the number D2 of types of target events to be analyzed containing the target equipment. The number of types means that the same water conservancy event flow is one type; calculate the first eigenvalue F1, F1 = (1 / n1)∑[(D1 / D0)*(D2 / D3)], where n1 represents the number of engineering equipment with abnormal record status in real-time warning events; D0 represents the total number of water conservancy equipment response events recorded during the suspicious period, and D3 represents the total number of types of water conservancy equipment response events recorded during the suspicious period;

[0075] Obtain the maximum value A of the engineering equipment parameters recorded in the target events to be analyzed that contain the target equipment 0max and the minimum value A 0min , calculate the second eigenvalue F2, F2 = (1 / n1)∑[(A 0max -A 0min ) / A 0max ;

[0076] Step S44: Extract multiple suspicious periods with warning events, construct data pairs by combining the suspicious period length T1 with the first eigenvalue F1 and the second eigenvalue F2 within the suspicious period. Use F1 and F2 as input variables and T1 as the output variable to construct a period prediction model T = k1*F1 + k2*F2 + ε, and substitute multiple groups of data pairs to calculate the corresponding reference coefficients k1, k2, and the error term ε;

[0077] Step S45: When a real-time water conservancy equipment response event occurs, find the engineering equipment with abnormal status in the historical records of the water conservancy event flow to which the real-time water conservancy equipment response event belongs as the real-time prediction engineering equipment. Calculate the first eigenvalue and the second eigenvalue based on the real-time prediction engineering equipment, substitute them into the function relationship model T to output the corresponding period prediction value, and obtain the real-time suspicious period. When the real-time suspicious period is less than the period prediction value, continue monitoring; when the real-time suspicious period is greater than or equal to the period prediction value, transmit a signal to update the real-time monitoring period length to the suspicious period length to remind of equipment maintenance.

[0078] The fact that the suspicious period is less than the period prediction value indicates that the data corresponding to the water conservancy equipment response events during this period are all in a normal state, and the engineering equipment is also at a normal level; while when it is greater than or equal to, it means that the next water conservancy equipment response event may be abnormal. Therefore, the equipment is maintained and repaired at the end of this cycle, which plays a preventive role. Moreover, the maintenance cycle of the engineering equipment is adaptively adjusted based on the parameter data of the water conservancy event records, rather than a single fixed maintenance cycle, improving the intelligence and diversification of the maintenance of water conservancy engineering equipment based on data analysis, and playing a preventive role in the abnormal status of the equipment.

[0079] As shown in the embodiments:

[0080] When the real-time prediction engineering equipment recorded in the same water conservancy event stream is different in different warning events, calculations are performed for all of them;

[0081] For example, if the real-time warning engineering equipment is engineering equipment a1 and engineering equipment a2, when analyzing the first eigenvalue, the number D1 of events containing engineering equipment a1 and the number of types of target events to be analyzed containing engineering equipment a1 in the target event to be analyzed are obtained; and the number D1 of events containing engineering equipment a2 and the number of types of target events to be analyzed containing engineering equipment a2 in the target event to be analyzed; then the formula F1 = (1 / n1)∑[(D1 / D0)*(D2 / D3)] is used, and the average value of [(D1 / D0)*(D2 / D3)] corresponding to the two engineering equipment is taken as the first eigenvalue, and the same is true for the second eigenvalue;

[0082] As long as there is a situation where the real-time suspicious period is greater than or equal to the period estimated value, the transmission signal updates the real-time monitoring period length to the suspicious period length to remind the equipment for maintenance;

[0083] In real-time monitoring, the real-time water conservancy equipment response event of this application can be a normal event or a warning event. In the case of a warning event, the abnormal engineering equipment type is directly warned without verifying the period, and the period data and parameter data corresponding to the warning event are used to analyze and update the period estimation model.

[0084] A digital twin-based intelligent management system for water conservancy engineering equipment, the management system includes a monitoring period calibration module, a response event extraction module, a water conservancy event stream construction module, a warning evaluation model generation module, and a real-time anomaly verification module;

[0085] The monitoring period calibration module is used to generate several historical monitoring periods of the water conservancy engineering equipment management platform;

[0086] The response event extraction module is used to extract water conservancy equipment response events with equipment response associations during the historical monitoring period;

[0087] The water conservancy event stream construction module is used to generate a water conservancy event stream for the application response of the water conservancy engineering equipment management platform based on the engineering equipment;

[0088] The warning evaluation model generation module is used to construct a warning evaluation model corresponding to each water conservancy event stream based on the target water conservancy event;

[0089] The real-time anomaly verification module is used to determine whether the status of the engineering equipment in the real-time monitoring warning response is abnormal, and verify and update the monitoring period to which the engineering equipment status belongs after confirming the abnormality.

[0090] The water conservancy event flow construction module includes a device response correlation analysis unit and a water conservancy event flow series connection unit;

[0091] The device response correlation analysis unit is used for a multi-device response event based on a certain process in which the data obtained by a certain device in a certain monitoring system in the water conservancy project device management platform triggers a response signal, resulting in the response of devices in other monitoring systems. Then, the devices that respond and are located in different monitoring systems in the corresponding response event are considered to have a device response correlation;

[0092] The water conservancy event flow series connection unit is used to series-connect the remaining project devices with device response correlations in the order of response time with the initial project device as the response starting point, forming a water conservancy event flow corresponding to the water conservancy device response event.

[0093] The early warning evaluation model generation module includes a device parameter extraction unit, a parameter change rate calculation unit, a parameter eigenvalue calculation unit, and an early warning evaluation model output unit;

[0094] The device parameter extraction unit is used to extract the project device parameters of the target water conservancy event recorded under the same water conservancy event flow;

[0095] The parameter change rate calculation unit is used to calculate the parameter change rate of the project device parameters under the same water conservancy event flow;

[0096] The parameter eigenvalue calculation unit is used to calculate the parameter eigenvalue of the target water conservancy event;

[0097] The early warning evaluation model output unit is used to extract the parameter eigenvalue of the target water conservancy event in the same water conservancy event flow and construct an early warning evaluation model corresponding to each water conservancy event flow.

[0098] The real-time anomaly verification module includes a real-time device anomaly monitoring unit and a periodic verification unit;

[0099] The real-time device anomaly monitoring unit is used to obtain the real-time status parameters recorded by each project device for real-time monitoring and early warning response, extract the water conservancy event flow to which the project device corresponding to the early warning response belongs, extract the early warning evaluation model corresponding to the same water conservancy event flow in the historical record, calculate the corresponding type of parameter change rate based on the substitution of the real-time status parameters, and the real-time parameter eigenvalue of the overall project device parameters corresponding to the real-time early warning event to which the early warning response project device belongs. Then, judge the real-time parameter eigenvalue with the model and output the status of the project device for real-time monitoring and early warning response;

[0100] The periodic verification unit is used to analyze the monitoring period with abnormal output device status and verify whether it is necessary to update the monitoring period to remind of equipment maintenance.

[0101] It should be noted that, in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0102] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent management method for water conservancy project equipment data based on digital twin, characterized in that, Including the following analysis steps: Step S1: Taking each equipment maintenance node recorded in the water conservancy project equipment management platform as the starting monitoring point, determining the termination monitoring point according to the preset safety cycle of the platform, and generating several historical monitoring cycles of the water conservancy project equipment management platform; extracting the water conservancy equipment response events with equipment response associations during the historical monitoring cycles; Step S2: Extracting the engineering equipment recorded in the water conservancy equipment response events, and generating a water conservancy event stream corresponding to the application response of the water conservancy project equipment management platform based on the engineering equipment; The said Step S2 includes the following specific steps: Marking the monitoring system corresponding to the engineering equipment and the execution functions corresponding to the equipment. The execution functions include a data acquisition function and an instruction operation function. The equipment response association means that the data acquired by a certain equipment in a certain monitoring system of the water conservancy project equipment management platform triggers a response signal, resulting in a response event of equipment in other monitoring systems. The multi-equipment response event corresponding to the response based on a certain process. Then, the equipment that responds and is located in different monitoring systems in the corresponding response event is the equipment with an equipment response association; Extracting the first responding engineering equipment in the water conservancy equipment response event as the initial engineering equipment, and connecting the remaining engineering equipment with equipment response associations in sequence according to the chronological order of the response time starting from the initial engineering equipment to form a water conservancy event stream corresponding to the corresponding water conservancy equipment response event. When the same position corresponds to different execution functions or different equipment names, they are different water conservancy event streams; Step S3: Extracting all water conservancy equipment response events with different recorded engineering equipment parameters and marked as warning events in the same water conservancy event stream as the target water conservancy events, and constructing a warning evaluation model corresponding to each water conservancy event stream based on the target water conservancy events; The said Step S3 includes the following: Step S31: The engineering equipment parameters refer to the data parameters recorded and stored by each engineering equipment. The warning event refers to an event in which, when the engineering equipment parameters recorded by the engineering equipment in the water conservancy equipment response event reach the warning value set by the corresponding monitoring system, a warning signal is transmitted; Step S32: Extract the engineering equipment parameter A of the j-th type of the i-th target water conservancy event recorded under the same water conservancy event stream i j , where the engineering equipment parameters are sorted and numbered in the order of the engineering equipment recorded in the water conservancy event stream. The engineering equipment parameters are the real-time state parameters at the early warning response moment, and each type of engineering equipment records one type of engineering equipment parameter; Calculate the parameter change rate Y of the engineering equipment parameters of the j-th category under the same water conservancy event flow j , Y j = |A pmax j - A i j | / A pmax j , where A pmax j represents the maximum value among the p target water conservancy events recorded for the engineering equipment parameters of the j-th category under the same water conservancy event flow, p ≥ i, and p represents the total number of target water conservancy events under the same water conservancy event flow; Step S33: Obtain the parameter change rate Y corresponding to the m types of engineering equipment parameters in the same water conservancy event record j , where m ≥ j; m represents the number of types of engineering equipment parameters recorded in the target water conservancy event; use the formula: Z i = (1 / m)∑(Y j ) i , to calculate the parameter eigenvalue Z i of the i-th target water conservancy event; (Y j ) i represents the parameter change rate of the j-th type of engineering equipment parameter recorded in the i-th target water conservancy event; Step S34: Extract the parameter eigenvalue of p target water conservancy events in the same water conservancy event stream, and construct an early warning evaluation model W corresponding to each water conservancy event stream, W = [Z imin , Z imax , where Z imin represents the minimum value of the parameter eigenvalue recorded in the same water conservancy event stream, and Z imax represents the maximum value of the parameter eigenvalue recorded in the same water conservancy event stream; Step S4: Judging whether the state of the engineering equipment in the real-time monitoring warning response is abnormal based on the warning evaluation model, and verifying and updating the monitoring cycle to which the engineering equipment belongs after the state is confirmed to be abnormal; The said Step S4 includes the following specific steps: Step S41: Obtain the real-time status parameters recorded by each engineering device for real-time monitoring and early warning response, extract the water conservancy event stream to which the engineering device corresponding to the early warning response belongs, extract the early warning assessment model W corresponding to the same water conservancy event stream in the historical records, substitute the real-time status parameters to calculate the parameter change rate of the corresponding type, and the real-time parameter eigenvalue Z0 of the parameters of each engineering device corresponding to the real-time early warning event to which the engineering device for early warning response belongs. The real-time parameter eigenvalue Z0 is calculated in the same way as the parameter eigenvalue Z i The calculation method is the same; Step S42: When the real-time parameter eigenvalue is present, output that the engineering equipment status is abnormal in the real-time monitoring warning response; On the contrary, output that the state of the engineering equipment in the real-time monitoring warning response is normal; Extracting the cycle length from the real-time warning event when the output state is abnormal to the nearest starting monitoring point as the suspicious cycle T1; Step S43: Obtain the water conservancy equipment response events that are the same as the engineering equipment with abnormal real-time warning event record status during the suspicious period and are in different water conservancy event flows as the target events to be analyzed. Take the engineering equipment with abnormal record status as the target equipment, and obtain the number D1 of events containing the target equipment and the number D2 of types of target events to be analyzed containing the target equipment in the target events to be analyzed. The number of types means that the same water conservancy event flow is one type; calculate the first eigenvalue F1, F1 = (1 / n1)∑[(D1 / D0)*(D2 / D3)], where n1 represents the number of engineering equipment with abnormal record status in the real-time warning events; D0 represents the total number of water conservancy equipment response events recorded during the suspicious period, and D3 represents the total number of types of water conservancy equipment response events recorded during the suspicious period; Obtain the maximum value A of the engineering equipment parameters recorded in the target event to be analyzed that contains the target device 0max and the minimum value A 0min , calculate the second eigenvalue F2, F2 = (1 / n1)∑[(A 0max -A 0min ) / A 0max ; Step S44: Extract multiple suspicious periods with warning events, and construct data pairs with the suspicious period length T1, the first eigenvalue F1, and the second eigenvalue F2 during the suspicious period. F1 and F2 are used as input variables, and T1 is used as the output variable to construct a period prediction model T = k1*F1 + k2*F2 + ε, and substitute multiple groups of data pairs to calculate the corresponding reference coefficients k1, k2, and the error term ε; Step S45: When a real-time water conservancy equipment response event occurs during monitoring, find the engineering equipment with abnormal status in the historical records of the water conservancy event flow to which the real-time water conservancy equipment response event belongs as the real-time prediction engineering equipment. Calculate the first eigenvalue and the second eigenvalue based on the real-time prediction engineering equipment, substitute them into the function relationship model T to output the corresponding period prediction value, and obtain the real-time suspicious period. When the real-time suspicious period is less than the period prediction value, continue monitoring; when the real-time suspicious period is greater than or equal to the period prediction value, transmit a signal to update the real-time monitoring period length to the suspicious period length to remind of equipment maintenance.

2. The intelligent management system for water conservancy project equipment data based on digital twin, if using the intelligent management method for water conservancy project equipment data based on digital twin described in claim 1, is characterized in that, The management system includes a monitoring period calibration module, a response event extraction module, a water conservancy event flow construction module, a warning evaluation model generation module, and a real-time anomaly verification module; The monitoring period calibration module is used to generate several historical monitoring periods of the water conservancy engineering equipment management platform; The response event extraction module is used to extract the water conservancy equipment response events with equipment response associations during the historical monitoring periods; The water conservancy event flow construction module is used to generate the water conservancy event flow for the application response of the water conservancy engineering equipment management platform based on the engineering equipment; The water conservancy event flow construction module includes an equipment response association analysis unit and a water conservancy event flow concatenation unit; The equipment response association analysis unit is used for the response events of multiple devices based on a certain process when the data obtained by a certain device in a certain monitoring system of the water conservancy engineering equipment management platform triggers a response signal, resulting in the response of devices in other monitoring systems. Then, the devices that respond and are located in different monitoring systems in the corresponding response events are those with equipment response associations; The water conservancy event flow concatenation unit is used to concatenate the remaining engineering equipment with equipment response associations in the order of response time with the initial engineering equipment as the response starting point to form the water conservancy event flow of the corresponding water conservancy equipment response event; The early warning assessment model generation module is used to construct an early warning assessment model corresponding to each water conservancy event stream based on the target water conservancy event; The early warning assessment model generation module includes an equipment parameter extraction unit, a parameter change rate calculation unit, a parameter eigenvalue calculation unit, and an early warning assessment model output unit; The equipment parameter extraction unit is used to extract the engineering equipment parameters of the target water conservancy event recorded under the same water conservancy event stream; The parameter change rate calculation unit is used to calculate the parameter change rate of the engineering equipment parameters under the same water conservancy event stream; The parameter eigenvalue calculation unit is used to calculate the parameter eigenvalue of the target water conservancy event; The early warning assessment model output unit is used to extract the parameter eigenvalues of the target water conservancy event in the same water conservancy event stream and construct an early warning assessment model corresponding to each water conservancy event stream; The real-time anomaly verification module is used to judge whether the state of the engineering equipment in the real-time monitoring early warning response is abnormal, and verify and update the monitoring period to which the engineering equipment state belongs after the confirmation of abnormality; The real-time anomaly verification module includes a real-time equipment anomaly monitoring unit and a period verification unit; The real-time equipment anomaly monitoring unit is used to obtain the real-time state parameters recorded by each engineering equipment in the real-time monitoring early warning response, extract the water conservancy event stream to which the engineering equipment corresponding to the early warning response belongs, extract the early warning assessment model corresponding to the same water conservancy event stream in the historical record, calculate the corresponding type of parameter change rate based on the substitution of the real-time state parameters, and the real-time parameter eigenvalue of the overall engineering equipment parameters corresponding to the real-time early warning event to which the early warning response engineering equipment belongs, and judge the real-time parameter eigenvalue and the model, and output the state of the engineering equipment in the real-time monitoring early warning response; The period verification unit is used to analyze the monitoring period with the output of abnormal equipment state and verify whether it is necessary to update the monitoring period to remind of equipment maintenance.

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