177 results about "Maintenance planning" patented technology

The invention relates to the technical field of intelligent traffic engineering, and provides a road guardrail intelligent inspection and maintenance planning method based on machine learning, which breaks through the limitation of a traditional single mode in a detection link, fuses laser radar three-dimensional geometrical characteristics and visual texture information, realizes high-precision identification of defects such as microcracks and hidden corrosion, and improves the inspection efficiency. The detection problem in a complex environment is solved, and the problem that a traditional method is high in omission ratio is solved. In the aspect of dynamic planning, a multi-dimensional state space is constructed based on real-time traffic, weather and defect levels, polling path dynamic optimization and multi-vehicle collaborative operation are achieved through a reinforcement learning algorithm, and the current situations that manual planning is low in efficiency and poor in flexibility are changed. In the aspect of maintenance decision, a time sequence data analysis model is used for accurately predicting guardrail maintenance requirements, a scientific maintenance plan is made in combination with an intelligent scheduling algorithm, and efficient utilization of resources is achieved. According to the method, a complete intelligent operation and maintenance closed loop is constructed, and road guardrail operation and maintenance are promoted to be converted from passive low efficiency to active intelligence.

The invention discloses a property maintenance prediction system based on big data, and the system comprises the steps: monitoring equipment and environment in real time through an intelligent sensor, transmitting the data to a cloud or local storage through a wireless network, and carrying out the historical data processing and cleaning through a distributed architecture; predicting the equipment fault risk through machine learning, establishing a customized prediction model for different equipment, analyzing the life cycle and predicting the service life of the equipment; for old equipment, constructing a life extension model by using historical fault data; the system generates intelligent decision suggestions, pushes equipment health states to property management personnel, automatically formulates maintenance plans and priorities, and dispatches maintenance personnel; during fault prediction, early warning is given out in real time, related personnel are reminded through a short message or an APP, meanwhile, maintenance operation is recorded, historical data are analyzed, and future maintenance plans and budget are optimized; the system dynamically adjusts the life prediction model of the old equipment by accumulating historical data, and ensures the accuracy and efficiency of equipment health management.

Systems and methods are disclosed herein for influencing mission planning, vehicle maintenance, and identifying an aerial vehicle that provide a greatest probability of mission success. In some examples, a dynamic reliability model of dynamic reliability models can be updated based on at least component reliability data indicative of a reliability of a component of the aerial vehicle of aerial vehicles. Each dynamic reliability model can characterize a reliability of one of the aerial vehicles. Each dynamic reliability model can be executed to compute an indication of vehicle reliability for each aerial vehicle. A mission of success probability for each aerial vehicle can be computed based on a respective indication of vehicle reliability. A given aerial vehicle of the aerial vehicles can be identified for implementing the mission based on an evaluation of the mission of success probability for each aerial vehicle of the aerial vehicles.

The invention discloses an airport pavement health monitoring method and system, electronic equipment and a storage medium, and relates to the field of airport pavement monitoring. The method comprises the following steps: acquiring multi-source physical data from a target sensor and a radar, and acquiring an inspection image from an inspection unmanned aerial vehicle; performing space-time alignment on the multi-source physical data and the inspection image, and fusing a vibration signal in the multi-source physical data after space-time fusion with a visual feature in the inspection image to obtain a disease feature vector; predicting the failure probability and the residual life according to the disease feature vector and the environment real-time data based on a pavement digital twinborn model, and determining a dynamic risk map according to the failure probability and the residual life; and generating a hierarchical maintenance strategy according to the dynamic risk map and airport operation data, wherein the airport operation data comprises a flight timetable and a maintenance resource inventory. By implementing the technical scheme provided by the invention, accurate evaluation and intelligent maintenance planning of the health state of the airport pavement are realized, and the monitoring efficiency and decision scientificity are improved.

The invention provides a cost-oriented civil aircraft fleet planned maintenance scheme optimization method, relates to the field of regular maintenance plan and cost optimization of an aircraft fleet, and aims to improve the maintenance efficiency of the aircraft fleet and reduce resource waste in task intervals. According to the method, the optimal maintenance frequency and interval are realized by integrating the regular inspection plans and combining the balanced maintenance strategy. According to the method, a discrete event simulation model is adopted, a fleet maintenance plan is subjected to multi-dimensional simulation and optimization, and an efficient maintenance task allocation scheme is formulated according to constraint conditions such as fleet scale, flight hours, task types and hangar resources. Through optimization of a maintenance task distribution mode and fine adjustment of task intervals, the maintenance cost of a fleet can be effectively reduced, and the airworthiness and the utilization rate of aircrafts are improved. The method provides a scientific basis for an airline company to make a maintenance decision, and has an important application value for optimizing the operation and maintenance cost of an aviation fleet.

The invention discloses an intelligent property management system based on artificial intelligence, and relates to the technical field of intelligent property, and the system comprises a data management center which is in communication connection with a data collection and transmission module, a fault recognition and diagnosis module, a knowledge graph auxiliary decision module, an intelligent repair customer service module and an equipment maintenance management module. And the data acquisition and transmission module is used for acquiring and preprocessing operation data of the facility equipment based on various sensors deployed on the property facility equipment. According to the invention, real-time analysis is carried out on facility equipment operation data through an artificial intelligence algorithm, potential faults can be identified in advance and early warning can be given out, fault expansion is avoided, fault causes are rapidly positioned, clear guidance is provided for maintenance personnel, maintenance time is shortened, a knowledge graph is combined with a fault identification result, a solution strategy and decision support are provided, and maintenance efficiency is improved. Maintenance personnel are helped to quickly make a reasonable maintenance plan, resource allocation is optimized, and the fault processing efficiency is remarkably improved.

The invention discloses a digitized collaborative management and control platform and method for a highway roadbed slope, and belongs to the field of highway engineering. The method comprises the steps that a three-dimensional geological information model of the highway roadbed slope is constructed; sensing and fusing multi-source heterogeneous data; intelligent analysis and early warning are realized; co-processing and closed-loop management; optimization and self-learning of a machine learning algorithm model; and visualizing the slope of the whole highway. According to the invention, through the AI algorithm, the conversion from manual interpretation to intelligent prediction is realized, the early warning time is advanced, and passive response is changed into active prevention and control. According to the invention, information sharing and business collaboration are realized through process driving, and the emergency disposal efficiency is greatly improved. Visual and scientific decision support is provided for slope state evaluation and treatment scheme making based on the digital twinborn model and the multi-source fusion data, and dependence on human experience is reduced. According to the invention, a complete slope electronic health record is established, a data basis is provided for long-term performance evaluation and maintenance planning of the slope, and full-life-cycle management of assets is realized.

The invention discloses a multi-dimensional analysis house building health monitoring and early warning system and method, and relates to the technical field of monitoring and early warning. During operation of the system, various sensors are deployed to monitor the physical states of different materials in a building, collect surrounding environment data and transmit the data to a central processing unit in real time, and through a data fusion algorithm, real-time monitoring and early warning are achieved; the method comprises the following steps: integrating sensor data from different materials into a unified data set, preprocessing and normalizing the data, constructing a multi-material interaction mathematical model, calculating an effective stress sigma eff of a building structure, constructing a building overall health assessment model, and calculating a building health comprehensive index HI; according to the building health comprehensive index HI and the comparison result of the health index of each material and the safety threshold value, the health change of the building is detected in real time, an intelligent decision support system is provided, and building management personnel are helped to carry out long-term maintenance planning and resource optimization distribution.

The invention provides a self-adaptive joint maintenance control method for a structural system under environmental heterogeneous damage. The method comprises the following specific steps: 1, modeling a damage accumulation trajectory of a structural component under an environmental heterogeneous mode; 2, predicting the service life of the part under the environment damage heterogeneous condition; 3, delayed maintenance control optimization of single structural components; 4, quantifying a single structural component-system joint maintenance mapping function; and 5, iterative grouping selection heuristic strategy solution algorithm planning is carried out. The method has the advantages that the dynamic capture of an environmental heterogeneity mode in a structural system damage rule is realized, so that a more accurate residual life prediction activity is carried out, and the sensitivity of a damage feature recognition process is ensured; self-adaptive dynamic updating of system maintenance schemes can be realized according to real-time state evaluation information and historical maintenance records of structural components, correlation in adjacent maintenance schemes can be quantified, the adaptability is high, and the intelligent level is high; the balance between the prolonging of the remaining service life of the structural component and the preparation buffering of the maintenance resources is realized, and the scientificity of the maintenance plan is ensured.

The present invention discloses a method and system for operation and maintenance management of the status of 5G communication base station equipment, which relates to the technical field of data processing. The method includes: preprocessing real-time operation status data; constructing an equipment degradation model based on historical maintenance data to determine the real-time degradation status of each unit in the 5G communication base station equipment; identifying prone-to-failure points based on the historical maintenance data combined with the real-time operation status data, and identifying potential failure points based on their real-time degradation status using an abductive reasoning network; monitoring the status of the prone-to-failure points and target potential failure points; judging whether equipment maintenance is required based on the status monitoring results. If so, a target maintenance plan is formulated based on the operation and maintenance model combined with equipment load analysis; equipment maintenance is carried out on the 5G communication base station equipment based on the target maintenance plan, and the equipment maintenance information is encrypted and uploaded to the cloud platform. The present invention enables the operation and maintenance management of 5G communication base station equipment to achieve a more ideal effect.

A system and method for strategic track and maintenance planning (STAMP) that can provide an organizational and adaptive infrastructure configured to facilitate railroad asset management and capital planning is presented. The system can provide a railroad asset inspector with relevant data for an assigned inspection segment. Location-based functionality can acquire a device's precise location and provide only the data necessary for the inspection of a particular railroad asset by providing step-by-step input prompting based upon the device location. The system provides adaptive thresholding of asset-related criteria to determine whether and when asset maintenance should occur. The system can acquire inspection data for one or more railroad assets, apply a data optimization algorithm to the inspection data, analyze railroad asset-related data (including historical data), and generate an optimized capital plan with a schedule for railroad asset maintenance and replacement.

The invention discloses a composite fault diagnosis method for motor overload and pressure abnormity of a wind power water cooling system. The method comprises the following steps: S1, data acquisition; the collected original signal data is preprocessed; s2, constructing a lightweight system performance model, and calculating a pressure residual error; s3, extracting time domain and frequency domain features from the original signal data and the residual signals to form feature vectors; s4, performing fault diagnosis based on a machine learning model, and inputting the feature vector into a trained model; the model outputs a diagnosis result and gives a fault confidence coefficient; and S5, outputting a result, and performing early warning. Predictive maintenance is achieved, early warning can be given out at the early stage of a fault when shutdown or secondary damage is not caused, a maintenance plan can be made in advance, passive first-aid repair is changed into active planning, the non-planned shutdown time is shortened, and the availability of a wind field is improved; the unnecessary on-site inspection frequency is reduced, the excessive dependence on the qualification operation and maintenance experts is reduced, and the troubleshooting time is shortened.

The invention relates to the technical field of resource scheduling, in particular to a road maintenance allocation system and method based on data analysis, and the system comprises a material aging evaluation module, a road wear evaluation module, a traffic load influence analysis module, a budget allocation module and a maintenance adjustment module. According to the method, by collecting and analyzing the initial components of the highway pavement material and monitoring changes in different time periods, the attenuation rate and the mass loss of the material can be accurately calculated, the recognition precision of the highway maintenance requirement is improved through data driving, it is ensured that resources can be more effectively distributed to the area where maintenance is most needed, and the maintenance efficiency is improved. Not only can the actual loss degree be responded, but also the future wear trend and damage expansion can be predicted, the utilization and budget distribution of maintenance resources can be optimized, the specific influence on the pavement wear can be analyzed in combination with traffic flow and vehicle load distribution data, and the fund and maintenance plan can be further accurately adjusted. And the maintenance work is more prospective and targeted.

The invention relates to an intelligent sensing and automatic adapting electric pole maintenance prediction system, and aims to improve the electric pole maintenance efficiency and prediction accuracy. The system comprises a sensing unit installed on an electric pole and used for collecting physical state data and environmental factor data of the electric pole. And the prediction unit calculates the residual life and the potential fault risk of the electric pole through a pre-trained neural network prediction model by using the data. The decision-making unit generates specific maintenance suggestions and optimized maintenance schemes according to the prediction result, and then the suggestions and schemes are provided for a maintenance team to be executed. The system further comprises a self-adaptive unit which receives feedback information from a maintenance team and adjusts parameters of a neural network prediction model according to the feedback information, so that the accuracy and responsiveness of prediction are continuously optimized to cope with complex environmental changes and the actual state of the electric pole. The system can carry out efficient and accurate electric pole maintenance planning on the real-time basis, so that the timeliness and effectiveness of maintenance operation are greatly improved.

A system and method for automated post-mission logging of unmanned vehicle operations using generative AI. Flight telemetry, operator notes, and maintenance data are aggregated and normalized into a standardized dataset. A large language model interprets the dataset to generate user-readable debriefs, compliance records, and maintenance logs. The system ensures each post-mission report conforms to regulatory standards and automatically flags anomalies or compliance deviations. The generative AI module may be fine-tuned on domain-specific datasets to produce logs that are structurally consistent with predefined templates and compliance forms. The resulting reports can be stored, retrieved, and shared via secure cloud services, streamlining flight record-keeping and maintenance planning.

The invention discloses an underground cable fault acquisition system and a use method thereof, and particularly relates to the technical field of cable fault detection, and the underground cable fault acquisition system comprises a multi-frequency pulse signal transmitting module, a reflected wave detection array module, a time-frequency joint analysis module, a three-dimensional positioning engine module, an augmented reality terminal and a fault identification module. The multi-frequency pulse signal transmitting module generates a specific excitation signal and injects the signal into a cable. The reflected wave detection array module collects a reflected signal. The time-frequency combined analysis module processes the signal to obtain a characteristic. The three-dimensional positioning engine module calculates a fault point coordinate. The augmented reality terminal maps the fault point coordinate and generates a maintenance path. And the fault identification module judges the fault category. Through cooperative work of multiple modules, accurate positioning, comprehensive analysis and efficient maintenance planning of underground cable faults are achieved, and the system has the advantages of being high in positioning accuracy, accurate in fault diagnosis and high in maintenance efficiency.

To provide a repair plan creation apparatus, a repair plan support system, a repair plan creation method, and a repair plan creation program capable of creating a repair plan that takes into account the impact of construction on an asset on the surrounding area and other assets located near an asset under construction.SOLUTION: A repair plan creation apparatus 2 comprises: a communication unit 20 for acquiring construction information of a road system asset and construction information of an occupied property; and a processing unit for extracting a construction region overlapping area where the construction regions of the road system asset and the occupied property overlap based on the construction information of the road system asset and the construction information of the occupied property acquired by the communication unit 20, and calculating a construction period of the road system asset and the occupied property that belong to the construction region overlapping area.SELECTED DRAWING: Figure 1

The invention relates to a dynamic early warning and maintenance strategy adjusting method based on residual life, and belongs to the technical field of equipment management in the oil refining and chemical engineering field. The method comprises the following steps: acquiring multi-source operation data of equipment through an online acquisition module, and constructing a structured equipment state parameter set; based on the parameter set, establishing a residual life admission judgment model and generating a residual life accounting result of the current equipment; further adopting a corrosion rate-residual life two-factor coupling model to generate an equipment risk influence rate curve, and constructing a dynamic risk prevention and control boundary; and according to the risk level of boundary division, a dynamic maintenance strategy adjustment module is linked to perform early warning response, strategy execution, response recording and self-learning optimization are realized through a closed-loop management module, real-time evolution of a fortification value and a maintenance plan along with an equipment state is realized, and the accuracy of risk response and the intelligent level of equipment maintenance are improved.

The invention discloses a remote operation and maintenance diagnosis and predictive maintenance system for heterogeneous equipment, and relates to the technical field of equipment operation and maintenance. The system comprises a data acquisition and access module, a data processing module, a diagnostic analysis module, a prediction maintenance module, a man-machine interaction module and a storage module. The data acquisition and access module supports multiple communication protocols and acquires operation data of heterogeneous equipment; the data processing module preprocesses the collected data and completes multi-source data fusion; the diagnosis analysis module realizes equipment real-time fault detection, fault positioning and fault type association through a two-layer detection mechanism, and generates a fault diagnosis report; and the prediction maintenance module outputs the residual service life and the potential fault risk of the equipment based on the prediction model, and generates a targeted maintenance strategy and plan. According to the system, the problems of difficult access of heterogeneous equipment, poor fault detection real-time performance and insufficient maintenance planning are solved, remote intelligent operation and maintenance are realized, and the equipment shutdown risk and the maintenance cost are reduced.

The invention discloses an operation and maintenance planning method and device for power equipment, equipment, a storage medium and a product. The method comprises the following steps: acquiring operation parameters of power equipment in real time; according to the operation parameters, using a trained fault prediction model to identify a fault state of the power equipment and generating an early warning signal; collecting operation data before and after a fault according to the early warning signal, and determining a fault diagnosis result according to the operation data; and generating an operation and maintenance strategy and an operation and maintenance resource allocation scheme according to the fault diagnosis result and the current operation and maintenance resource state, and scheduling operation and maintenance resources to process faults according to the operation and maintenance strategy and the operation and maintenance resource allocation scheme. According to the technical scheme provided by the invention, the operation and maintenance efficiency and the operation and maintenance quality are improved through an automatic fault early warning mechanism and a fault response mechanism.

The invention discloses a dynamic intelligent maintenance path planning method and system for a generator set, relates to the technical field of intelligent maintenance of power generation equipment, and solves the problems of insufficient dynamic quantification of potential safety hazards and low response efficiency of sudden working conditions in conventional maintenance planning of the generator set. The method comprises the steps that a constrained directed graph model is constructed based on a three-dimensional topological relation of equipment, nodes represent a to-be-overhauled part, and edges represent a feasible moving path; a temperature risk sensing mechanism is introduced, and the safety coefficient in the path edge weight is dynamically adjusted; and designing a hybrid optimization algorithm to solve the maintenance sequence with the lowest total cost. Project verification shows that the high-temperature operation risk can be effectively reduced, the construction period is shortened, and the operation and maintenance intelligence level of the generator set is remarkably improved.

The invention provides a civil aviation fleet-oriented maintenance plan and work package integrated optimization method, which comprises the following steps of: firstly, taking minimization of total maintenance cost as a target function; constructing a mathematical planning model for integrated optimization of the fleet maintenance plan and the work package by taking a maintenance item number constraint, a maintenance item and maintenance work package corresponding relation constraint, a maintenance resource constraint, a maintenance interval constraint, a variable relation constraint, a variable value constraint and the like as constraint conditions; then, auxiliary variables are introduced to carry out linearization processing on the established mathematical programming model; and finally, solving the linearized mathematical programming model by adopting a differential evolution-adaptive large-scale neighborhood search two-stage heuristic algorithm, namely, continuously iteratively solving through a designed two-stage interaction strategy, so as to achieve the purpose of integrated optimization. According to the method, the dual goals of reducing the total maintenance cost and improving the maintenance scheme making efficiency are achieved, and a scientific solution is provided for the complex optimization problem in civil aviation maintenance management.

The invention relates to an equipment maintenance plan dynamic adjustment method based on machine learning. The method comprises the following steps: acquiring real-time operation data and historical operation data of specified equipment, maintenance record information and equipment inherent attribute information; performing data preprocessing on the real-time operation data and the historical operation data respectively, and determining key index parameters of specified equipment and a state definition threshold range of each parameter in combination with maintenance record information and inherent attribute information of the equipment; inputting the preprocessed real-time operation data corresponding to the key index parameters into a pre-trained machine learning model to obtain a fault prediction probability; judging whether the preprocessed real-time operation data corresponding to the key index parameters is in a state definition threshold range or not, and obtaining a judgment result; and according to the judgment result and the fault prediction probability, obtaining a maintenance time node of the specified equipment and updating a maintenance plan. The service life of the equipment is prolonged, and the maintenance resource configuration is optimized.

The invention relates to the technical field of maintenance planning, and particularly discloses a landscape greening maintenance planning method and system based on big data, and the method comprises the steps: constructing a maintenance region model, and updating the maintenance region model based on a shot image; rasterizing the maintenance area model, and determining greening parameters in each grid area; and clustering the grid areas based on the greening parameters, synchronously determining the demand precision of each type of grid areas, obtaining the real-time information of each grid area, feeding back the real-time information to the management end, and receiving a maintenance scheme which is fed back by the management end and contains a maintenance target. According to the method, the landscape model is constructed based on the BIM model, the landscape model is updated based on a large amount of image data, the real-time performance of the landscape model is extremely high, software analysis is performed on the landscape model, a maintenance target can be determined, a more targeted maintenance task can be generated, and the labor workload is greatly reduced under the condition that the maintenance effect is almost unchanged.

The invention relates to the technical field of big data analysis and application operation and maintenance, and discloses a multi-data fusion-based application operation and maintenance planning method, which comprises the following steps of: combining multi-source sensor data into a multi-dimensional state vector representing a local space-time state, and comparing the multi-dimensional state vector with a baseline vector representing a normal operation mode to generate a drift vector; according to the method, the stability of the correlation mode in the multi-dimensional data is analyzed, and then the stability of the modulus length and the direction of the drift vector in the time sequence is analyzed, so that potential risks which cannot be perceived by a traditional threshold value mode can be recognized. The focus point of risk recognition is transferred from isolated value out-of-limit to analysis of the stability of the correlation mode in the multi-dimensional data; the early gradual change risk formed by the synergistic effect of a plurality of physical quantities can be effectively identified, and the risk source can be traced based on the analysis process, so that the predictability and decision reliability of operation and maintenance planning are remarkably improved.

The invention belongs to the technical field of nuclear power DCS health management, and aims to provide a DCS health management platform for nuclear power plant DCS operation and maintenance business application. The invention discloses a DCS (Distributed Control System) health management platform based on agile development and a general-branch architecture, which comprises a display layer, a business application layer, a model and service layer, an operation and maintenance deployment layer and an infrastructure layer, and is characterized in that intelligent operation and maintenance management, software and hardware state remote monitoring and service life prediction of a nuclear power plant DCS are realized through the business application layer; and a dynamic logic deduction and fault tracing mechanism is constructed, full-process digital operation management and control, overall planning of a maintenance plan, authority auditing and intelligent guiding are carried out, and a full-life-cycle data system is established. According to the invention, DCS operation and maintenance service application is provided for the nuclear power plant, the reliability management of the DCS of the nuclear power plant and the risk analysis level of an instrument control system can be improved, and the probability of unexpected unit operation events caused by the DCS is reduced.

The invention discloses an offshore wind turbine generator reliability distribution modeling and maintenance decision-making method under long-life management, and the method employs a multi-source heterogeneous data fusion and meta-transfer learning technology to improve the prediction accuracy and adaptability of the health condition and remaining service life of key parts of an offshore wind turbine generator. Based on the RCM theory and the system analysis component structure and influence factors, uncertainty in fuzzy theory processing evaluation is introduced, predicted health and degradation information is combined, an objective and quantitative reliability index distribution model of the key components is established, and the reasonable reliability index distribution weight of each key component is determined; according to the method, the maximum average reliability of an offshore wind turbine generator system and the minimum full-life-cycle operation and maintenance cost serve as targets, a long-life-period multi-target optimization model considering part degradation, the maintenance effect and multiple constraints is constructed, an intelligent algorithm is adopted for solving, and an optimal preventive maintenance plan is obtained.

The invention discloses an unmanned aerial vehicle inspection maintenance planning method and device based on natural language man-machine interaction, and relates to the technical field of unmanned aerial vehicle multi-task cooperative scheduling. According to the scheme, natural language tasks are understood through semantic analysis, and maintenance procedures and resource requirements are automatically reasoned by using a knowledge graph; then, a multi-objective optimization model fusing inspection time efficiency, cost, preventive benefits and maintenance technician task load balance is constructed; and adopting an adaptive algorithm to solve and generate a candidate maintenance plan set, determining a target maintenance plan based on multi-dimensional evaluation, and finally synchronously outputting a maintenance scheduling scheme and an updated inspection task allocation scheme. According to the scheme, dependence on data entry of artificial experience and scheme planning is reduced through natural language analysis and knowledge graph reasoning, automation of maintenance decision is achieved, meanwhile, maintenance and inspection are uniformly optimized under a multi-target framework, a preventive maintenance mechanism is introduced, conversion from passive response to active prevention is achieved, and the maintenance efficiency is improved. And the overall utilization efficiency of resources is improved.

An automated test equipment (ATE) for testing semiconductor devices, the test equipment comprises a test handler, a spare part, or a contactor socket, and a semiconductor devices tester, The spare part comprises an electronic component for storing and / or processing data regarding the spare part or a portion thereof, The test equipment comprises an operator terminal comprising a display or GUI and a data exchange interface which is connected or connectable to the electronic component within the spare part, for at least displaying data stored therein. The ATE further comprises a data buffer unit for buffering the data, a maintenance planning and control unit for planning and controlling maintenance actions of the test equipment, and a dedicated database residing in a control computer.

The invention discloses an urban rail cross-professional multi-mode production and maintenance plan and scheduling method, which comprises the following steps: a cross-professional production plan dynamic generation step: establishing a unified data interface and a data standard, converging and integrating data in different professional systems, analyzing the production and maintenance requirements of each professional system, and combining the existing plan and real-time construction information to generate a cross-professional production plan; generating a trans-professional preliminary production plan; a cross-professional production scheduling step: based on the preliminary production plan, performing preliminary scheduling on three resources of personnel, time and tools, and generating a globally optimized production plan and resource scheduling scheme based on an improved genetic algorithm; and a multi-mode production plan execution and tracking step: according to the work order type, performing execution management by respectively adopting a precise mode of a curing period and a rough mode of a non-curing period, and establishing a real-time monitoring and dynamic adjustment mechanism. The method is used for solving the problems that professional system data standards are different, sharing is difficult, and cross-professional conjoint analysis and collaborative planning cannot be supported.