63 results about "Planned maintenance" patented technology

The invention discloses a predictive maintenance method for an optical storage and charging integrated power station based on deep learning, and the method comprises the steps: constructing an efficient equipment state evaluation and prediction model based on multi-source data fusion, an intelligent prediction algorithm and a closed-loop optimization feedback mechanism, collecting multi-source data, and carrying out the fusion processing, an improved Attention-LSTM model is utilized to evaluate and predict the state of equipment, a transfer learning method is adopted to improve generalization ability, Bayesian optimization and an adaptive sliding window technology are combined at the same time, dynamic threshold adjustment is performed, a deep reinforcement learning algorithm based on a Markov decision process is adopted to optimize a maintenance strategy, and the maintenance efficiency is improved. Weibull distribution is introduced for failure probability modeling, the maintenance cost and the fault risk are balanced, continuous optimization and dynamic adaptive adjustment of a predictive maintenance scheme are realized through a closed-loop feedback mechanism, the prediction accuracy and the intelligent level of maintenance decision are remarkably improved, planned maintenance and sudden fault maintenance are reduced, and the maintenance efficiency is improved. And the reliability of the charging station is improved.

The invention discloses a quick-freezing equipment fault prediction and health management system based on digital twinning, and particularly relates to the field of quick-freezing equipment operation and maintenance. The theoretical fault index generation module is used for generating a multi-dimensional theoretical fault index matrix; the real-time fault index calculation module generates a real-time fault index; the deviation threshold value library establishing unit forms a dynamically updated deviation threshold value library; the residual life prediction module calculates the residual running time of the equipment from the current state to the fault; the health management module calculates equipment health scores and divides equipment health states into four grades; the visual display module provides a visual interface for a user; compared with the traditional operation and maintenance that the residual life cannot be estimated, the method can enable the operation and maintenance personnel to plan maintenance and replace parts in advance and reasonably arrange a production plan, reduces the risk of sudden failure, and enables the equipment operation and maintenance to be changed from passive response to active prevention.

The invention discloses a safety production standardized comprehensive management system and method, and relates to the technical field of safety production management, the system comprises the following components: a data acquisition module, a data preprocessing module, a model construction and training module, a prediction analysis module and a maintenance management module; according to the method, the time sequence data in the full life cycle of the equipment are continuously collected, the time sequence data comprise key parameters such as operation duration, start-stop times and maintenance records, the improved LSTM neural network is utilized to construct the equipment safety life prediction model, and the model can not only predict the overall life of the equipment, but also can predict the service life of the equipment. The method can accurately predict the residual safe use cycle of the easily-worn part, the accurate prediction capability enables an enterprise to plan a maintenance plan in advance, production interruption and safety accidents caused by sudden equipment faults are avoided, and the accuracy and foresight of equipment safety management are remarkably improved.

A unit maintenance plan double-layer optimization method considering new energy grid connection comprises the steps that an upper-layer optimization model is established, and the minimum unit operation cost and the minimum unit conventional maintenance cost in the planned maintenance period serve as target functions; solving a unit start-stop plan during a maintenance plan period by using a genetic algorithm, and arranging a historical maintenance plan as an initial population; establishing a lower-layer optimization model, and taking the minimum unit fault risk cost and the minimum wind and light abandoning and load shedding expected cost in the maintenance plan period as a target function; according to the obtained unit start-stop plan, solving the optimal output and scheduling arrangement of the unit during the maintenance plan by using an improved particle swarm algorithm; and solving the total economic cost of the system according to the obtained optimal output and scheduling arrangement of the unit, and obtaining an optimal maintenance plan according to optimization iteration. According to the method, a basis can be provided for a maintenance plan of a power grid worker, the load loss amount is minimum, the economical efficiency is optimal, and specific guidance is provided for maintenance of equipment with different voltage grades in a power grid.

The invention discloses a railway turnout fault diagnosis method based on power data, and relates to the technical field of rail transit. The method comprises the steps that power data when a train passes through a railway turnout and displacement of a switch rail and a stock rail are obtained and preprocessed, and standardized feature vector data are obtained; acquiring temperature data of a time node corresponding to the standardized feature vector data; correcting the standardized feature vector data according to the temperature data; and inputting the corrected standardized feature vector data into the trained deep cavity convolution long-short-term memory network attention model, and outputting the railway turnout fault type, thereby effectively improving the diagnosis accuracy of the railway turnout fault type, building a scientific foundation for operation and maintenance decisions through the accurate diagnosis result and prediction result, and improving the reliability of the railway turnout fault type diagnosis. And operation and maintenance personnel can plan a maintenance strategy in advance, so that sudden faults of equipment are effectively avoided, and safe and stable operation of railway transportation is stabilized.

The invention discloses a water conservancy pipeline data management system based on cloud computing, and relates to the technical field of intelligent management, the system comprises an analysis module, a prediction module and a decision module, through first analysis and second analysis, time features and spatial features are obtained, fault probability, fault degree and fault interval duration are obtained, and early warning is carried out. According to the invention, the analysis module carries out deep time and space dimension analysis on pipeline historical data, can find potential fault risks in advance, provides powerful guarantee for stable operation of a water conservancy pipeline system, can help managers plan maintenance tasks in advance and reasonably arrange maintenance resources by accurately predicting the fault probability and degree, and improves the maintenance efficiency of the water conservancy pipeline system. And unnecessary maintenance work is avoided, so that the maintenance efficiency is remarkably improved, and the maintenance cost is reduced. Meanwhile, according to the fault interval duration, the potential risk of the associated pipeline is predicted, the maintenance sequence and resource allocation are optimized, priority maintenance of the key pipeline is ensured, and the resource utilization efficiency is further improved.

A planned maintenance system communicates with a set of user platforms that includes an onboard display device of a machine. The planned maintenance system changes a maintenance schedule for the machine based on input data received from any of the user platforms, or usage data associated with the machine. The planned maintenance system provides output data reflecting changes to the maintenance schedule to the user platforms, including the onboard display device. Accordingly, the set of user platforms, including the onboard display device of the machine, can be synchronized with consistent information about the maintenance schedule for the machine.

Implementations for operational simulations of planned maintenance effects for a plurality of vehicles are provided. One aspect provides a computing system comprising processing circuitry configured to provide a plurality of operational models that comprises a route model, a preparation model, and a planned maintenance model. The processing circuitry is further configured to, using the plurality of operational models, execute an operational simulation that generates interactions between a route simulation and a planned maintenance simulation for a plurality of routes in a route schedule, wherein executing the operational simulation comprises simulating performance of a scheduled maintenance task for a vehicle of the plurality of vehicles and determining an effect of the simulated performance of the scheduled maintenance task on the route simulation. The processing circuitry is further configured to output the effect of the simulated performance.

The invention discloses a distribution network line fault inquiry and early warning system based on an OMS platform. The distribution network line fault inquiry and early warning system comprises the OMS platform, a database module of the OMS platform, a safety certification module, a fault inquiry module, a fault early warning module, a planned maintenance module, a planned approval module and a man-machine interaction module. Wherein the database module is used for storing planned power failure, fault power failure and first-aid repair power failure data, the safety certification module is used for providing safety guarantee for accessing the database module, the fault query module is used for querying the fault statistical condition of the same line according to query requirements, and the fault early warning module is used for judging whether to perform power failure color early warning on a query line or not. The plan maintenance module is used for automatically generating a power failure maintenance plan when power failure color early warning exists, the plan approval module is used for rapidly completing power failure maintenance plan approval work, and the man-machine interaction module comprises a user interface and a management interface; the method has the advantages of high working efficiency and more reasonable power cut plan arrangement, and can effectively improve the power supply reliability of the power distribution network.

The invention discloses a health monitoring system of a speed reducer, and belongs to the field of mechanical equipment state monitoring. Comprising a sensor module, a data acquisition module, a data processing module, an early warning module and a display module, the sensor module comprises a displacement sensor, a pressure sensor, a rotating speed sensor, a vibration sensor and a temperature sensor, and the displacement sensor and the pressure sensor are arranged at the position of a brake piston of the speed reducer and used for monitoring piston displacement and brake pressure in real time; the rotating speed sensor is arranged at the output end of the reducer and used for monitoring the output rotating speed of the reducer. Real-time monitoring of key parameters of the speed reducer is achieved through multi-sensor fusion, potential faults can be found in time, and fundamental transformation from passive maintenance to active early warning and from planned maintenance to predicted maintenance is achieved. The brake performance and the abrasion degree of a friction plate are accurately evaluated through combined monitoring of displacement and pressure of a brake piston; and early fault diagnosis is realized through spectrum analysis of the vibration signals.

Embodiments disclosed herein include a method for maintaining a processing tool. In an embodiment, the method comprises performing a planned maintenance (PM) process on the processing tool. The method may continue with processing substrates on the processing tool, and performing an in-situ metrology and cleaning process on the processing tool with an optimization substrate. The process may be used to determine if a processing parameter of the processing tool is within a specified range. In an embodiment, the method may continue with restarting substrate processing on the processing tool when the processing parameter is within the specified range.

The invention discloses motor dismounting equipment in the technical field of motor dismounting, and the motor dismounting equipment comprises a telescopic supporting frame, a bracket and a buffer assembly, the telescopic supporting frame comprises a fixed supporting frame fixedly mounted on a supporting base and a movable supporting frame connected with the fixed supporting frame in an up-down sliding manner; a pressing handle connecting rod mechanism used for controlling the movable supporting frame to ascend and descend is arranged outside the fixed supporting frame, the bracket comprises a lifting plate fixedly installed at the top end of the movable supporting frame and protective baffles rotationally installed on the two sides of the lifting plate, and an adjusting piece used for controlling the two ends of the lifting plate to stretch out and draw back is arranged at the bottom of the lifting plate. The problems that in the prior art, when 50-kilogram-level light motor device components are subjected to left-right displacement and lifting maintenance in space, large hoisting is difficult to implement, the space is limited when professional electric hoists and hand-drive hoists are used for hoisting, other tools need to be operated by multiple persons and are difficult to flexibly arrange in narrow space, and the maintenance cost is low are solved. The maintenance and inspection progress is limited; the planned maintenance and inspection time is prolonged; and the potential safety hazard of manual brute force carrying exists.

The invention discloses a multi-parameter fusion driven cable full life cycle monitoring method, which relates to the technical field of cable monitoring, and comprises the following steps: acquiring risk data of the surrounding environment of a cable and historical operation data of the cable, and performing calculation and analysis to obtain a fault risk prediction index; the fault risk prediction index is used for reflecting the possibility of cable fault risk; dividing the priority of cable maintenance according to a comparative analysis result of the fault risk prediction index of the cable and a preset fault risk upper limit threshold and a fault risk lower limit threshold; according to the comparison result of the fault risk prediction index and the preset fault risk threshold value, the maintenance priority is divided into the emergency maintenance level, the planned maintenance level and the maintenance-free level, the emergency maintenance situation is immediately alarmed and dispatched for maintenance, the planned maintenance is processed after a scheme is formulated within the specified time, normal operation is kept if maintenance is not needed, and the maintenance efficiency is improved. Operation and maintenance resource distribution is optimized, unnecessary shutdown is reduced, and the operation reliability and the service life of the cable are improved.

The utility model discloses a rotary drum stirring suspension shaft assembly which comprises a suspension shaft, a bearing box, a bearing gland and a self-aligning roller bearing, the self-aligning roller bearing is connected to one end part of the suspension shaft in a sleeving manner; the bearing box covers the outer parts of the self-aligning roller bearing and the suspension shaft; and a bearing gland is arranged at an opening, close to the self-aligning roller bearing, of the bearing box. The shaft sleeve is transformed into a rolling bearing supporting structure, so that the abrasion of the suspension shaft is obviously limited. Therefore, the risk of abrasion and leakage of the box body is reduced. The equipment operation period is prolonged, the maintenance frequency is reduced, the bearing is convenient to disassemble and replace during planned maintenance, the drum core does not need to be hung, and the maintenance risk and the maintenance time are reduced.

The utility model discloses a hydro-generator thrust bearing elastic oil tank which comprises an oil tank seat and supporting iron, the supporting iron is installed in the oil tank seat, the upper end of the supporting iron is fixedly connected with the oil tank seat, a cavity is formed between the interior of the oil tank seat and the supporting iron, the side wall of the oil tank seat is of a corrugated pipe structure, and the bottom supporting face in the oil tank seat is a downwards-concave arc face. The lower end part of the supporting iron is a downwards convex cambered surface; and the curvature radius of the bottom supporting surface is greater than that of the lower end part. According to the utility model, the lower end part of the supporting iron is improved to be the downwards convex cambered surface, the bottom supporting surface in the oil tank seat is improved to be the downwards concave cambered surface, and the curvature radius of the bottom supporting surface is greater than that of the lower end part, so that the supporting iron can be adaptively swung and finely adjusted after the elastic oil tank loses efficacy, and the thrust bearing bush is stressed more uniformly; the elastic fuel tank can still insist to run for a period of time after failure, and preparation time is provided for planned maintenance.

The embodiment of the invention discloses a solar cell surplus capacity determination method and device, equipment and a storage medium, and the method comprises the steps: obtaining the yield information and shutdown information of single-machine equipment of a plurality of processes in a first preset time period, and determining the single-machine restoration daily capacity of each single-machine equipment; aiming at each working procedure in the plurality of working procedures, acquiring planned maintenance information of all single-machine equipment in the working procedure and determining working procedure restoration daily productivity of the working procedure; based on the first pass yield of the current working procedure and the first pass yield of all working procedures after the current working procedure, the working procedure reduction daily productivity of the current working procedure is converted into the working procedure reduction daily productivity of the last working procedure; according to the method, the required capacity of each process is determined based on the incidence relation between the minimum value in the conversion capacity and the first pass yield, then the surplus capacity of each process is determined, information of multiple processes in the solar cell production process can be collected and analyzed in real time, therefore, the surplus capacity can be analyzed in real time, and the analysis efficiency is higher.

The invention discloses a 5G power production management system based on AI driving, and relates to the technical field of intelligent power grids and industrial Internet of Things. The system is mainly characterized in that a data acquisition module is connected with an Internet of Things sensor through a 5G network to acquire power, current, voltage, insulation, load and safety state data of equipment in each link of power generation, power transmission, power transformation and power distribution in real time; the AI processing module carries out fusion analysis on multi-source data through a mixed deep learning model, thereby realizing fault early warning, accurate state evaluation and operation trend prediction. The control management module issues instructions of power regulation, circuit breaker operation, load switching and the like to the power equipment based on an AI result and a network slicing technology, a maintenance management sub-module is arranged in the control management module, planned maintenance is carried out on fault equipment according to a maintenance complexity score, and optimization of maintenance resources is ensured; and multi-terminal visual interaction is supported, and the intelligent level and the operation and maintenance efficiency of power production are improved.

The invention discloses a fuselage maintenance task pool generation method and device, equipment, a medium and a program product, and belongs to the technical field of aviation maintenance engineering. The method comprises the following steps: acquiring planned maintenance requirement data of a target fleet, and analyzing the planned maintenance requirement data to obtain an analysis result; determining a fuselage maintenance task based on the analysis result, and obtaining an MRS number, an applicability definition and an interval definition of the fuselage maintenance task; obtaining a target work card number according to the mapping relation table and the MRS number; performing matching according to the applicability definition to obtain a target aircraft, and obtaining an aircraft sampling type and a tail number of the target aircraft; generating a task interval type based on the historical task processing state, the aircraft sampling type and the interval definition; calculating a task expiration date of the body maintenance task according to the task interval type; and constructing a fuselage maintenance task pool. According to the embodiment of the invention, the accuracy and efficiency of calculating the expiration date can be improved, so that the efficiency of generating the fuselage maintenance task pool is improved.

The present invention discloses a method, computer device, and storage medium for controlling the disconnection of a power battery switch in an electric vehicle. By determining the disconnection moment based on an operating current drop curve and a life curve, and disconnecting the switch device at that moment, the present invention can control the operating current of the electric vehicle to decrease at a matching rate based on the severity of the fault in the event of a fault in the electric vehicle, thereby reducing the impact of sudden current changes on the switch device. This helps to ensure the power demand of the electric vehicle while avoiding greater dangers caused by the fault. Furthermore, the change in the operating current flowing through the switch device can be matched with the change in the remaining life of the switch device, making it easier to plan maintenance or replacement of the switch device, thereby reducing consumption caused by improper use of the switch device while ensuring the safety of the electric vehicle. The present invention has wide application in the field of electric vehicle technology.

The invention provides a rail vehicle direct maintenance cost target allocation method, which comprises the following steps of: 1, determining influence factors, and identifying seven types of core influence factors, namely complexity, technical maturity, environmental factors, utilization rate, severity, accessibility and repair material cost, based on a plan maintenance cost model and a non-plan maintenance cost model of a DMC; 2, determining an assignment range and a standard of each influence factor; step 3, according to the range and the standard of the step 2, assigning each influence factor of each system; 4, calculating a DMC comprehensive score of each system; 5, calculating a DMC proportion and a DMC distribution value of each system, judging whether the distribution value needs to be adjusted, if so, returning to the step 3, and if not, executing the step 6; and step 6, outputting a final DMC distribution result. According to the method, seven types of core influence factors are identified through the planned maintenance cost model and the non-planned maintenance cost model based on the DMC, and the accuracy and rationality of DMC distribution are remarkably improved.

This invention discloses a time-axis-based method and device for visualizing the risks of optical transmission systems, relating to the field of risk visualization technology. This invention constructs a mapping between the time axis and the maintenance link by acquiring planned maintenance information, combines link disconnection simulation to locate target services affected at different times, and then assesses the risk level of the service links and maps them into a topology visualization graphic. This achieves a closed-loop process for handling affected services in scenarios with simultaneous maintenance of multiple transmission paths or nodes, moving from inefficient manual analysis to automated system identification, quantitative assessment, and intuitive presentation. This significantly improves the efficiency and accuracy of service impact analysis and overcomes the decision-making lag problem caused by cumbersome manual derivation in complex maintenance scenarios using traditional methods.

The invention relates to the technical field of power systems, and provides a system and a method for planning multiple substation maintenance schemes based on power grid load analysis. The information acquisition module acquires information, a power grid load analyzes different maintenance requirements of multiple substations according to data acquired by the information acquisition module, and the maintenance process planning module performs maintenance process planning and maintenance mode judgment according to the different maintenance requirements of the multiple substations to generate a basic maintenance scheme; the maintenance prediction module predicts the power grid load change of other substations when the maintenance substation is maintained according to the basic maintenance scheme, and the maintenance scheme generation module optimizes the maintenance scheme according to the maintenance cost information acquired by the information acquisition module and the prediction result of the maintenance prediction module; by analyzing the power grid load condition and the mutual influence condition between the transformer substations, the calculation of the maintenance requirements of the transformer substations and the prediction of the power grid load during the combined maintenance of the plurality of transformer substations are realized, the rationality of the maintenance plan is ensured, and the combined maintenance risk is reduced.

Method for planning maintenance measures for a railway technical system The invention comprises a method for planning maintenance measures for a railway technical system (BTS), comprising a functional component (FK) that performs a function belonging to railway operation, a monitoring device (UV) that monitors the function of the functional component (FK) and generates monitoring data (UD) describing this function, a diagnostic device (DGN) that receives messages containing monitoring data (UD) generated by the monitoring device (UV) and generates computer-aided diagnostic data (DD) that describe a need for maintenance measures.A computer-aided planning module (PLM) is used, which employs a Large Language Model (LLM) to evaluate messages containing diagnostic data (DD) and to generate analysis data (AD) that describe the diagnostic data (DD) in human language. The evaluation is performed with regard to maintenance requirements and / or maintenance measures. A description of the maintenance requirements and / or maintenance measures is generated in human language. Output data (OD) representing the description of the maintenance requirements and / or maintenance measures is output via a first interface (S1). Furthermore, the invention comprises a railway engineering system, a computer program product, and a computer-readable storage medium. The advantage lies in the fact that various types of documentation describing the railway engineering system can be evaluated automatically.

The invention discloses a garbage incinerator adaptive control method fused with a natural language, and the specific implementation scheme comprises the steps: S1, obtaining manual inspection information and planned maintenance information, inputting the manual inspection information, the planned maintenance information and a control instruction into a working condition description instruction generation model in combination with cues, and generating working condition description information; s2, inputting the working condition description information into a pre-action generation model to generate pre-action generation description; s3, converting the pre-action generation description into a 1024-dimensional integral fusion feature through a vectorization model; and S4, inputting the overall fusion features and field sensor data into a data-driven action model to output a control command of an execution mechanism. The method solves the problems that the model is difficult to train and the working condition adaptability is poor due to the fact that the quantization instruction feature space of the controller is very complex.

The invention discloses a conical connecting structure of a hot mill frame roller and a spline shaft and an operation method of the conical connecting structure, and belongs to the technical field of hot mill frame rollers. The structure mainly comprises a rack roller, a spline shaft and a conical sleeve. One end of the spline shaft is of an external cone structure; the inner conical surface of the conical sleeve is matched with the outer conical surface of the spline shaft, and the outer cylindrical surface of the conical sleeve is in interference fit with a connecting hole in the end part of the rack roller. The spline shaft is provided with a high-pressure oil way with an oil outlet and provided with a fastening screw used for axial fixing. The operation method comprises the assembly process and the disassembly process, disassembly damage is concentrated on low-cost parts through the conical sleeve which is a replaceable protection piece, and the precious rack roller and spline shaft are effectively protected. By means of the synergistic effect of conical surface matching and high-pressure oil, convenient and low-loss disassembly of the spline shaft is achieved, high-cost destructive maintenance is converted into repeatable planned maintenance, and the maintenance cost of equipment in the whole life cycle is reduced.

The utility model relates to the technical field of mechanical transmission lubrication, in particular to a monitoring device for thin oil lubrication, which comprises a shell, a plurality of filter plates which work in turn and are used for filtering metal scraps are arranged in the shell, and after the filter plates filter for a set time, the collected metal scraps are moved to the top of a weighing unit to be weighed. The top of the shell is provided with a brush plate in a penetrating and sliding manner, the brush plate is used for cleaning the filter plates, the top of the shell is communicated and fixedly provided with a liquid inlet pipe, and the bottom of the shell is communicated and fixedly provided with a liquid outlet pipe. Collected metal scraps are moved to the weighing unit to be weighed, then the weight of the metal scraps in set time is obtained, whether metal part abrasion is normal or not is judged, data are sent to a control center, and if the data are abnormal, planned maintenance or emergency shutdown is conducted according to program setting, and loss expansion is avoided.

The invention provides an intelligent load transfer auxiliary decision-making method and system, and belongs to the technical field of power grid dispatching automation. In order to solve the problems that an existing intelligent decision-making system cannot be used when data is missing, wrong or delayed due to excessive pursuit of data completeness and algorithm accuracy, and even misleads decision making, a light-weight decision-making design concept oriented to data authenticity is provided. The system actively abandons dependence on non-essential data, strictly limits a data input source as a limited data set which is highly trusted and inevitably used in daily work of a dispatcher, and the limited data set comprises switch displacement, recent steady-state load stored before a fault, an equipment rated parameter table and planned maintenance information.

The invention relates to an automatic control method and system for a concrete mixing plant, and belongs to the technical field of concrete production control, and the method comprises the steps: collecting the operation state data of key equipment in a mixing plant production line in real time; inputting the operation state data into a preset equipment health prediction model to generate predictive maintenance information for the key equipment; when the early warning key equipment meets the condition that the corresponding predictive maintenance information meets a preset early warning condition, a production scheduling plan is dynamically adjusted based on the predicted fault time point corresponding to the early warning key equipment, and a planned maintenance window before the corresponding predicted fault time point is planned for the early warning key equipment; the maintenance personnel are reminded to maintain the early warning key equipment during the planned maintenance window; wherein in the planned maintenance window, other parts except the early warning key equipment normally operate on the production line of the mixing plant. The device has the effect of improving the production efficiency under the abnormal condition of the stirring station equipment.

The present application relates to the technical field of structural health monitoring and artificial intelligence, and discloses a steel structure corrosion prevention evaluation system based on environmental data and AI, which comprises a data sensing module, an environmental index generating module, an impact function generating module, a stress-corrosion coupling effect model constructing module, a steel structure life prediction module, a grading early warning module and a system self-learning module; the present application can quantify the corrosion impact of environmental emergencies and realize advanced early warning; for the first time, real-time stress monitoring data is directly incorporated into a corrosion life evaluation model, and the coupling effect of stress corrosion is accurately depicted; modeling and prediction are carried out for the unique environment and stress state of each specific structure, and the results are more instructive; the present application realizes the transformation from planned maintenance to condition-based maintenance, avoids waste caused by excessive maintenance, prevents risks caused by insufficient maintenance, and significantly reduces the life cycle cost.

The invention relates to a road condition index prediction method and system based on index attenuation. The method comprises the following steps: firstly, obtaining historical and planned maintenance information, road condition data and road surface types of a target road, calculating annual index attenuation values of various road surface types, extracting sample paragraphs of specified road surface types, calculating historical annual interval attenuation values of the sample paragraphs, and further obtaining a latest annual attenuation value through a weighted average algorithm; dividing a target road into basic road units, determining the latest road surface type of each unit by using a time sequence covering method, and matching a corresponding attenuation value; and finally, subtracting the attenuation value from the latest annual road condition index value to obtain the predicted annual road condition index of each unit. According to the method, data-driven attenuation law analysis is adopted, weighted average and time sequence coverage technologies are combined, the accuracy of road surface technical condition index prediction is improved, and maintenance road section selection, scheme making and resource allocation decision making can be effectively guided.