CN-122022775-A - Expressway electromechanical system operation and maintenance optimization method based on multi-source data fusion

CN122022775ACN 122022775 ACN122022775 ACN 122022775ACN-122022775-A

Abstract

The invention discloses an operation and maintenance optimization method of an electromechanical system of a highway based on multi-source data fusion, which relates to the field of operation and maintenance management and comprises the steps of S1, identifying and grading the change risk of a roadbed structure of the highway, S2, obtaining the validity check result of engineering reference conditions, carrying out self-adaptive reconstruction on monitoring data of electromechanical equipment, S3, implementing operation and maintenance monitoring, identifying hidden deviation, and S4, allocating operation and maintenance resources. The invention has the refinement capability of risk identification and classification, and improves the pertinence of operation and maintenance decisions through spatial association and risk serialization processing. The reliability of operation and maintenance monitoring is enhanced based on the implicit deviation recognition of the self-adaptive reconstruction monitoring data. And realizing resource optimization and risk prevention and control cooperation through a differentiated operation and maintenance strategy.

Inventors

GAO JIE
LIU CHENG
WU HAORAN
SONG XIAOFENG
SUN SHAOYUN
CHEN XIAOYI
LIN XIANGJIE
LI TENGFEI

Assignees

云南山高投资发展有限公司

Dates

Publication Date: 20260512
Application Date: 20260311

Claims (10)

1. The operation and maintenance optimization method for the electromechanical system of the highway based on multi-source data fusion is characterized by comprising the following steps of: S1, constructing an engineering reference condition dynamic verification system, and identifying and grading the change risk of the highway subgrade structure to form a primary, secondary and tertiary risk point sequence; s2, obtaining a validity check result of engineering reference conditions, and carrying out self-adaptive reconstruction on monitoring data of the electromechanical equipment; s3, based on the reconstructed electromechanical equipment monitoring data, operation and maintenance monitoring is implemented, and hidden deviation is identified; and S4, optimizing a differential operation and maintenance strategy based on the implicit deviation, and allocating operation and maintenance resources.
2. The method for optimizing the operation and maintenance of the electromechanical system of the highway based on the multi-source data fusion, which is characterized by comprising the following steps of: The engineering reference condition dynamic verification system comprises a multidimensional risk judgment process based on roadbed displacement characteristics, repair characteristics and geological characteristics; The roadbed displacement characteristics are used for representing structural displacement behaviors of the expressway roadbed in the vertical and horizontal directions; the repairing feature is used for representing engineering repairing behaviors implemented on the roadbed structure, and frequency and scale of the engineering repairing behaviors; the geological features are used for representing geological environment conditions of the area where the roadbed is located and influence of the geological environment conditions on structural stability; the quantization parameters of the roadbed displacement characteristics comprise accumulated settlement amount, accumulated lifting amount, settlement rate and lifting rate; The quantization parameters of the repair feature comprise repair times and repair areas; The quantization parameters of the geological features comprise discrete grading parameters of foundation bearing capacity and discrete grading parameters of regional settlement.
3. The method for optimizing the operation and maintenance of the electromechanical system of the expressway based on the multi-source data fusion of claim 1, wherein the method is characterized in that the method for identifying and grading the change risk of the roadbed structure of the expressway to form a primary, a secondary and a tertiary risk point sequence comprises the following specific steps: taking road section units of the expressway as basic risk identification objects, carrying out expressway roadbed structure change risk identification on each basic risk identification object based on an engineering reference condition dynamic verification system, and comprising the following steps: The quantization parameter of any roadbed displacement feature exceeds the corresponding engineering reference threshold; the quantization parameter of any item of repair feature exceeds the corresponding engineering reference threshold; The quantization parameter of any geological feature exceeds the corresponding engineering reference threshold; when the at least one condition is met, judging the basic risk identification object as a roadbed structure change risk point, and entering a subsequent risk classification flow; Performing item risk quantification on each roadbed structure change risk point respectively to obtain a displacement risk index, a repair risk index and a geological risk correction coefficient of each roadbed structure change risk point; carrying out joint judgment on the displacement risk index, the repair risk index and the geological risk correction coefficient to generate a comprehensive risk index of a corresponding risk point; In the calculation process of the comprehensive risk index, fusing the displacement risk index and the repair risk index according to preset weights, and introducing a geological risk correction coefficient on the basis of the fusion result; Dividing the risk points into at least three levels of risk grades based on the comprehensive risk indexes, wherein the at least three levels of risk grades comprise a primary risk point, a secondary risk point and a tertiary risk point; the roadbed structure change risk points with the comprehensive risk indexes higher than the first threshold value are judged to be first-level risk points, the roadbed structure change risk points with the comprehensive risk indexes between the first threshold value and the second threshold value are judged to be second-level risk points, and the roadbed structure change risk points with the comprehensive risk indexes lower than the second threshold value and still meeting the risk identification conditions are judged to be third-level risk points; and ordering the risk points according to the risk level and the spatial position relation to form a primary risk point sequence, a secondary risk point sequence and a tertiary risk point sequence.
4. The method for optimizing operation and maintenance of the electromechanical system of the highway based on multi-source data fusion of claim 1, wherein the method for obtaining the validity check result of the engineering reference condition is characterized by comprising the following steps: acquiring electromechanical equipment monitoring data, and simultaneously acquiring engineering reference conditions of a corresponding road section, wherein the engineering reference conditions at least comprise design elevation and design roadbed bearing capacity; Comparing the collected electromechanical equipment monitoring data with engineering reference conditions, analyzing the degree of deviation of the monitoring data from a designed calibration range, and generating corresponding deviation parameters; verifying the validity of the engineering reference condition based on the deviation parameter to obtain a validity verification result of the engineering reference condition, wherein the validity verification result comprises validity, abnormality and failure; comparing the deviation parameter with a preset threshold system, wherein the preset threshold system at least comprises a first deviation threshold and a second deviation threshold, the first deviation threshold is used for representing an acceptable deviation upper limit of the engineering reference condition, and the second deviation threshold is used for representing a failure judgment boundary of the engineering reference condition; when the deviation parameter does not exceed the first threshold value, judging that the corresponding engineering reference condition is in a valid state; When the deviation parameter exceeds the first threshold value and does not exceed the second threshold value, judging that the corresponding engineering reference condition is in an abnormal state; and when the deviation parameter exceeds a second threshold value, judging that the corresponding engineering reference condition is in a failure state.
5. The method for optimizing the operation and maintenance of the electromechanical system of the highway based on the multi-source data fusion of claim 1, wherein the method for adaptively reconstructing the monitoring data of the electromechanical device is characterized by comprising the following steps: Determining a reference credibility level of the monitoring data of the corresponding electromechanical equipment according to the validity check result of the engineering reference condition, wherein the reference credibility level comprises three states of effective reference, limited reference and non-referent, and respectively corresponds to the validity check result of the engineering reference condition, the validity check result of abnormality and invalidation; Based on the reference trust level, adaptively selecting a processing strategy of the electromechanical equipment monitoring data, and when engineering reference conditions are in an effective state, taking the engineering reference conditions as complete constraint conditions, and carrying out consistency check and standardization processing on the electromechanical equipment monitoring data so as to maintain structural consistency between the monitoring data and engineering design conditions; When the engineering reference condition is in an abnormal state, reducing the constraint weight of the engineering reference condition in the process of monitoring data processing, only carrying out reference constraint on trend characteristics in the electromechanical equipment monitoring data, and carrying out inhibition processing on monitoring data components deviating from the engineering reference condition; When the engineering reference condition is in a failure state, stopping taking the corresponding engineering reference condition as a monitoring data processing basis, and carrying out self-adaptive reconstruction on the current monitoring data based on the historical state of the electromechanical equipment monitoring data.
6. The method for optimizing operation and maintenance of the electromechanical system of the highway based on multi-source data fusion according to claim 1, wherein said implementing operation and maintenance monitoring, identifying the hidden deviation, comprises the following steps: Based on the reconstructed electromechanical equipment monitoring data, constructing a current running state sequence of the equipment, and representing the continuous running behavior of the equipment; In the operation and maintenance monitoring process based on the reconstructed electromechanical equipment monitoring data, carrying out risk correction on preset operation constraints corresponding to the electromechanical equipment by combining the risk point grade corresponding to the road section where the electromechanical equipment is located, and determining an explicit anomaly range for judging whether explicit anomaly occurs to the equipment based on the operation constraints after the risk correction; The explicit abnormal range is used for limiting the safe operation range of the monitoring data of the electromechanical equipment, and when the monitoring data exceeds the range, the explicit abnormal state is directly judged; Under the condition that the monitoring data does not exceed the explicit abnormal range, extracting deviation features reflecting the change of the running state of the equipment based on the reconstructed electromechanical equipment monitoring data, and analyzing the deviation features according to preset implicit deviation judging conditions; The implicit deviation judging condition is used for representing structural change trend of the equipment running state under the condition that no out-of-range abnormality occurs; comparing and analyzing the current running state sequence with the historical stable running state characteristics of the corresponding electromechanical equipment, and extracting deviation characteristics; continuously and cumulatively analyzing the deviation feature to judge whether the deviation feature keeps the same direction change in a plurality of monitoring periods; When the deviation feature does not trigger the explicit abnormal range but meets any preset implicit deviation judging condition, marking the corresponding equipment state as an implicit deviation; The implicit deviation determination conditions include a trend deviation determination condition, an accumulated deviation determination condition, and a distribution deviation determination condition; The trend deviation judging condition comprises that in a continuous monitoring period, when the direction of the monitored data change amount is kept the same direction and the accumulated change amount exceeds a set proportion threshold value, the trend deviation is judged; the cumulative deviation judging condition comprises the steps of accumulating and summing the absolute values of the differences of the monitoring data of each cycle from the historical reference mean value in a continuous monitoring period, and judging the cumulative deviation when the cumulative deviation exceeds a set cumulative threshold value; the distribution deviation judging condition comprises that in a designated monitoring window, the average value of the current monitoring data sequence is calculated, compared with the average value of the historical stable operation data, the average value is subjected to difference and absolute value processing, and when the absolute value of the difference value of the average value exceeds a set threshold value, the distribution deviation is judged; and outputting the identified hidden deviation as an operation and maintenance monitoring result.
7. The method for optimizing the operation and maintenance of the electromechanical system of the highway based on the multi-source data fusion, according to claim 1, is characterized by comprising the following steps of: acquiring a roadbed structure change risk point sequence of a road section corresponding to the electromechanical equipment, and marking risk association information of the electromechanical equipment according to the risk point grade; the risk association information comprises a risk point identifier, a comprehensive risk intensity value, a risk point space position and a relative sequence in a roadbed sequence; and based on the hidden deviation state and the risk association information, the inspection frequency and the maintenance frequency of the electromechanical equipment are adjusted.
8. The method for optimizing the operation and maintenance of the electromechanical system of the highway based on the multi-source data fusion of claim 3, wherein the specific processing conditions are as follows: Calculating four dimensionless components, namely an accumulated sedimentation risk component, an accumulated lifting risk component, a sedimentation rate risk component and an escape rate risk component, respectively for each roadbed structure change risk point, and carrying out weighted summation on the accumulated sedimentation risk component, the accumulated lifting risk component, the sedimentation rate risk component and the escape rate risk component to obtain a displacement risk index; Calculating a repair frequency risk component and a repair scale risk component respectively for each roadbed structure change risk point, and carrying out weighted summation on the repair frequency risk component and the repair scale risk component to obtain a repair risk index; and determining a corresponding geological risk correction coefficient according to the foundation bearing capacity and the regional settlement according to the geological feature quantization parameters, wherein the geological risk correction coefficient is used for reflecting the influence degree of the geological environment of the roadbed on the structural stability and is used for integrally correcting the displacement risk and the repair risk of the roadbed.
9. The method for optimizing operation and maintenance of an electromechanical system of an expressway based on multi-source data fusion according to claim 3, wherein the method for forming the primary risk point sequence, the secondary risk point sequence and the tertiary risk point sequence is characterized by comprising the following steps: Binding corresponding space reference information for each level of risk points, wherein the space reference information at least comprises a line number, a center line mileage stake number and a roadbed structure unit identifier of the expressway where the risk point is located; uniformly mapping each risk point into the same line coordinate system through the space reference information to form a risk point positioning result which can be used for space calculation; After the space positioning of the risk points is completed, the system takes the linear direction of the expressway as a main sorting direction, and performs preliminary sorting on the risk points in the same risk level from small to large according to mileage stake marks, so that the natural sequence of the risk points in space is obtained, and the preliminary sorting is used for determining the basic arrangement sequence of the risk points along the line direction and provides a basis for subsequent space association analysis; after the preliminary sorting based on mileage is completed, further calculating the space distance between adjacent risk points, and judging whether the adjacent risk points are positioned in the same continuous roadbed structure unit by combining the roadbed structure unit information of each risk point; When the spatial distance of the plurality of risk points is smaller than a preset distance threshold, judging that the potential association relation exists in the structure of the part of risk points, and treating the part of risk points as a risk association section; for risk points within the same risk-associated segment, ranking risk points upstream of the segment; after the internal sequencing of the risk associated sections is completed, re-splicing the risk associated sections according to the spatial positions of the lines of the risk associated sections, and finally forming a continuous risk point sequence chain in the same risk level to form a risk point sequence of the corresponding risk level; through the processing process, a primary risk point sequence, a secondary risk point sequence and a tertiary risk point sequence are respectively formed.
10. The method for optimizing operation and maintenance of an electromechanical system of an expressway based on multi-source data fusion according to claim 5, wherein the adaptively reconstructing the current monitoring data based on the historical state of the electromechanical device monitoring data comprises the following steps: Extracting at least one historical state data set from historical monitoring data according to a preset time scale aiming at target electromechanical equipment, wherein the historical state data set is used for representing typical running state distribution of the electromechanical equipment under normal running conditions; Constructing a historical state reference interval corresponding to the electromechanical equipment based on the historical state data set, wherein the historical state reference interval is used for describing a reasonable operation interval of the electromechanical equipment under the constraint of no external engineering reference condition; comparing the currently collected electromechanical equipment monitoring data with a historical state reference interval, and identifying abnormal components deviating from the historical state reference interval in the current monitoring data; for the abnormal component, based on a historical data segment closest to the current monitoring moment state in the historical state data set, carrying out substitution processing on the abnormal component so that the reconstructed monitoring data meets the historical state reference interval constraint; and outputting the reconstructed monitoring data as a self-adaptive reconstruction result.

Description

Expressway electromechanical system operation and maintenance optimization method based on multi-source data fusion Technical Field The invention relates to the field of operation and maintenance management, in particular to an operation and maintenance optimization method of an electromechanical system of a highway based on multi-source data fusion. Background Expressways are an important component of traffic networks, and their electromechanical systems play a key role in modern highway management. The electromechanical system mainly comprises a plurality of subsystems such as a traffic monitoring system, a charging system, a lighting system, a signal control system, a traffic guiding system and the like, and the subsystems play a vital role in the normal operation of the expressway. With the increase of traffic flow and the increase of traffic safety requirements, the scale of the electromechanical system of the expressway is continuously enlarged, and the complexity of the system is gradually increased. To ensure reliability and safety of electromechanical systems in long, high-intensity operation, they must be continuously monitored and maintained. However, since the highway electromechanical system generally includes a large number of scattered devices and sensors, the conventional operation and maintenance management method faces problems of data island, information asymmetry, and the like. In order to effectively improve operation and maintenance efficiency and ensure traffic safety, more and more highway management institutions are focusing on adopting modern information technology and data analysis technology, particularly multisource data fusion technology to improve the operation and maintenance level of electromechanical systems. The multi-source data fusion technology provides a new idea for health management and fault prediction of equipment by integrating data from different sources, including operation data, roadbed monitoring data, environment monitoring data, historical maintenance records and the like of electromechanical equipment. The technology can cross the limit between different data sources, so that originally independent monitoring systems can work cooperatively, and more accurate and comprehensive information is provided for the running state evaluation and operation and maintenance decision of equipment. Along with the rapid development of technologies such as big data, internet of things and artificial intelligence, more and more highway managers begin to adopt an intelligent operation and maintenance management system. The system not only can collect and analyze the operation data of the electromechanical equipment in real time, but also can utilize data mining and machine learning algorithms to conduct fault prediction, performance evaluation and life cycle management. By monitoring the full life cycle of the equipment, the hidden deviation and the potential risk of the equipment are timely identified, so that accurate fault early warning and maintenance intervention are realized. However, in the long-term operation process of the electromechanical system of the expressway, the road subgrade is affected by engineering factors such as settlement, lifting and repair, and the spatial structure of the road subgrade can be gradually changed. Although the electromechanical device body is not moved in the operation and maintenance records, the relative geometrical relationship between the device and the road structure, the lane position and the line of sight range has changed. The existing operation and maintenance system generally establishes a monitoring and judging model based on the initial installation condition of the equipment, only focuses on the operation parameters and state data of the equipment, and does not continuously check the validity of engineering reference conditions on which the equipment depends, so that the corresponding physical meaning of the same monitoring data is changed in different operation stages, and the system cannot recognize. Since the change does not appear as parameter abnormality or alarm triggering, the operation and maintenance judgment deviation generated by the change is difficult to find in time in the prior art. Disclosure of Invention Aiming at the defects of the prior art, the invention provides an operation and maintenance optimization method of an electromechanical system of a highway based on multi-source data fusion, which is realized by the following technical scheme that the operation and maintenance optimization method of the electromechanical system of the highway based on multi-source data fusion comprises the following steps: s1, constructing an engineering reference condition dynamic verification system, and identifying and grading the change risk of the highway subgrade structure to form a primary, secondary and tertiary risk point sequence. S2, obtaining a validity check result of engineering reference conditions, and carr