Aerial work platform fault statistics reporting and work traceability system and method

Abstract

The application discloses a kind of aerial work platform fault statistics reporting and work traceable system and method, including operation console and integrated in its inside core management and control system;Fault statistics module realizes fault automatic accurate identification and full-dimensional statistics by fault identification algorithm;Fault reporting module realizes fault grading fast reporting by fault reporting priority algorithm;Work content record module realizes the automatic calculation of different operation type workload by workload quantification algorithm;Maintenance time length control module realizes maintenance efficiency quantitative evaluation and overtime early warning by maintenance time length deviation algorithm;Lost work tracing module realizes the accurate tracing and risk early warning of lost work loss by lost work time length calculation, lost work loss accounting and lost work risk prediction algorithm;Realize the full-process controllable of high-altitude operation, fault can be quickly disposed, lost work can be accurately accounted, significantly improve the intelligent control level, construction efficiency and cost control ability of high-altitude operation.

Description

Technical Field

[0001] This invention relates to the field of aerial work platform technology, and in particular to an aerial work platform fault statistics reporting and work traceability system and method. Background Technology

[0002] In general high-altitude operations such as building construction, municipal maintenance, and stadium construction, wheeled telescopic boom aerial work platforms have become indispensable core equipment due to their advantages of flexible mobility and adjustable working height. However, the functions of such equipment currently on the market are mainly concentrated on basic lifting, movement, and high-altitude operation, generally lacking intelligent and refined management capabilities for the entire operation process. This deficiency is particularly prominent in fault management, work records, maintenance control, and traceability of lost work time, seriously restricting the improvement of construction efficiency, operational safety, and cost control. The main problems with existing technologies are as follows: First, there is a lack of automated fault identification and statistical methods. Operators rely on experience to judge faults, leading to untimely fault detection, incomplete records, and difficulty in forming effective preventive maintenance plans. Second, the fault reporting process relies on manual verbal or paper-based communication, resulting in non-standardized reports and slow response times, seriously affecting fault handling efficiency and construction progress. Third, the recording of work content for high-altitude operations mainly relies on manual filling, and the statistics of workload lack objective and quantitative standards, easily leading to recording errors and liability disputes. Fourth, there is a lack of precise control over maintenance time, making it impossible to objectively assess maintenance efficiency or accurately calculate lost work time due to faults and maintenance delays. In summary, existing equipment lacks an integrated and quantitative algorithm for fault identification, time control, and risk prediction, resulting in low overall control accuracy and efficiency, failing to meet the needs of modern, efficient, and lean construction management. Summary of the Invention

[0003] In order to solve at least some of the problems existing in the prior art, the present invention provides a high-altitude work platform system and method that can realize automatic fault statistics and reporting, accurate recording of work content, effective control of maintenance time and accurate tracking of lost work time.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: A fault statistics reporting and work traceability system for an aerial work platform, comprising an operation console and a core management and control system integrated inside the operation console; the core management and control system is linked with a hydraulic drive system, a safety protection system and various sensors of the aerial work platform, and has an embedded processor built in; the core management and control system comprises an interconnected fault statistics module, a fault reporting module, a work content recording module, a maintenance duration management and control module and a work delay traceability module, wherein: the fault statistics module is configured to identify fault types and positions through a preset fault identification algorithm according to deviations between operating parameters collected by sensors and preset thresholds, and collect and count fault data to form a fault file; the fault reporting module is linked with the fault statistics module, and is configured to automatically sort out fault information after a fault is identified, determine a reporting sequence through a preset fault reporting priority algorithm, and send a standardized form to a remote terminal; the work content recording module is configured to input basic operation information, automatically record actual operation data, and calculate the completed workload through a preset workload quantitative statistics algorithm; the maintenance duration management and control module is linked with the fault reporting module, and is configured to record time nodes of each maintenance link, judge whether maintenance is delayed through a preset maintenance duration deviation algorithm and issue an early warning; the work delay traceability module is linked with the fault statistics module, the maintenance duration management and control module and the work content recording module, and is configured to associate work delay duration with delayed work content through a preset work delay duration calculation algorithm and a work delay loss accounting algorithm, so as to account work delay loss and predict work delay risks. By adopting the above technical solution, the five functional modules of fault statistics, fault reporting, work recording, maintenance management and control and work delay traceability are linked through the integrated core management and control system, so that closed-loop management of the whole process from fault occurrence, reporting, disposal to loss accounting is realized, the gap in intelligent management and control of existing equipment is filled, and the management and control accuracy and efficiency of aerial work are significantly improved.

[0005] As a preference, the fault statistics module comprises a fault identification unit, which presets common fault types and characteristic parameters of equipment, is used for collecting equipment operating parameters in real time, and judges whether a fault occurs through a fault identification algorithm based on parameter deviation threshold comparison; the fault identification algorithm is configured as: setting a real-time operating parameter collected by a sensor as Xᵢ, a preset normal parameter threshold range as [Xi-min,Xi-max], and a parameter deviation coefficient as Kᵢ, then: when Xᵢ>Xi-max, Kᵢ=(Xᵢ-Xi-max) / Xi-max; when Xᵢ<Xi-min, Kᵢ=(Xi-min-Xᵢ) / Xi-min; When Xi-min ≤ Xᵢ ≤ Xi-max, Kᵢ = 0; let the fault identification threshold be K0. When any Kᵢ ≥ K0, the corresponding part is determined to have a fault; when multiple parameter deviation coefficients are superimposed, when the total deviation coefficient Ktotal = Σ(ωᵢ × Kᵢ) ≥ K0, the equipment is determined to have a compound fault, where ωᵢ is the preset weight of each parameter. By adopting the above technical solution, automatic and accurate fault identification is achieved by comparing the quantified parameter deviation coefficients with the preset threshold, avoiding errors and delays in manual experience judgment, and providing a reliable data foundation for subsequent statistics and analysis.

[0006] Preferably, the fault identification threshold K0 is preset to 0.15; the fault statistics module further includes a statistical analysis unit, used to perform statistical analysis by time, type, and location dimensions using a fault frequency statistics algorithm and a fault occurrence rate algorithm, wherein the fault frequency F = N / T, N is the number of faults occurring within period T, and the fault occurrence rate R = (T 故障 / T 总 )×100%, T 总 T represents the total operating time of the equipment within the cycle. 故障 The total duration of the fault is defined as the total duration of the fault. The fault statistics module also includes a fault archive unit for storing fault data and statistical reports, with a storage period of ≥1 year. By adopting the above technical solution, the preset thresholds and statistical algorithms ensure unified fault identification standards and rich statistical dimensions. The resulting long-term fault archive provides a scientific basis for analyzing fault patterns and developing preventive maintenance plans.

[0007] Preferably, the fault reporting module includes: a reporting triggering unit, used to automatically trigger the reporting process and issue an audible and visual warning after fault identification; an information processing unit, used to automatically process standardized reporting forms containing equipment number, fault type, occurrence time, and operating conditions; a remote communication unit, used to send the reporting forms to management personnel terminals and construction management platforms according to priority via 4G / 5G networks; and a reporting tracking unit, used to track the fault handling status in real time and provide feedback. By adopting the above technical solution, automatic collection, standardized processing, and rapid wireless reporting of fault information are achieved, ensuring the timeliness, standardization, and traceability of information transmission, and greatly shortening the fault response time.

[0008] Preferably, the fault reporting priority algorithm is configured as follows: Let the fault priority be P, P = α × 0.4 + β × 0.4 + γ × 0.2; where the fault impact coefficient α is determined according to the following rules: α = 1.0 when the operation is stopped, α = 0.5 when the operation is affected but not stopped, and α = 0.1 when the operation is not affected; the fault urgency coefficient β is determined according to the following rules: β = 1.0 when safety hazards are involved, β = 0.5 when the fault is ordinary, and β = 0.1 when the fault is minor; the fault frequency coefficient γ is determined according to the following rules: γ = 1.0 when the fault is frequently occurring, γ = 0.5 when the fault is routine, and γ = 0.1 when the fault is occasional; and the fault is divided into three priority levels according to the value of P: P ≥ 0.8 is urgent, 0.5 ≤ P < 0.8 is routine, and P < 0.5 is minor. When reporting, the fault is pushed from high to low priority. By adopting the above technical solution, priority is calculated by multi-dimensional coefficient weighting, which realizes intelligent classification and differentiated handling of faults, ensures that critical faults are responded to first, and optimizes the scheduling efficiency of maintenance resources.

[0009] Preferably, the work content recording module includes: a work information input unit for inputting work personnel, locations, types, and planned workload; a data synchronization unit for real-time synchronization of actual work duration, height, and completed workload to local and remote management platforms; and a workload quantification and statistical algorithm for calculating the actual completed workload W and work efficiency μ based on the work type. The workload quantification and statistical algorithm is configured as follows: for planar work, W = S × H × η, where S is the work area, H is the work height, and η is the work efficiency coefficient; for point-based work, W = N × λ, where N is the number of completed points, and λ is the complexity coefficient of a single point; and work efficiency μ = W / Tactual, where Tactual is the actual work duration. By adopting the above technical solution, and through quantification algorithms for different work types, objective and automatic calculation and recording of workload are achieved, avoiding the arbitrariness and errors of manual recording, and providing accurate data for work efficiency evaluation and cost accounting.

[0010] Preferably, the maintenance time control module includes: a time recording unit for recording the arrival, start, and completion times of maintenance personnel; and a time statistics unit for calculating the total maintenance time T. 检修 The overtime warning unit is used to issue a warning when the maintenance delay exceeds a preset standard; the maintenance time deviation algorithm is configured as follows: calculate the maintenance time deviation rate δ = [(T 检修 -T preset) / T 预设 [×100%, triggering a timeout warning when δ>20%, where T] 预设This involves setting pre-defined maintenance time standards based on different fault types. By adopting the above technical solution, and through precise timing of each maintenance step and comparison of deviations from the pre-defined standards, quantitative assessment of maintenance efficiency and automatic early warning of overdue times are achieved. This promotes the standardization of the maintenance process and provides key input for calculating downtime.

[0011] Preferably, the lost work tracking module includes a lost work duration statistics algorithm, a lost work content association unit, a lost work loss accounting algorithm, and a lost work risk prediction algorithm, wherein: the lost work duration statistics algorithm is configured to: calculate the total lost work duration T 误工 = (T 故障 +T 检修 -T 合理 ), where T 故障 T represents the duration of the fault. 检修 For maintenance time, T 合理 To pre-determine a reasonable delay duration, the algorithm for calculating lost work time is configured as follows: Calculate the total lost work time loss L = (n × C) 人工 +C 设备 )×T 误工 +W 产值 Where n is the number of workers, C 人工 C represents the unit price of labor costs. 设备 W represents the unit price for equipment rental. 产值 To mitigate the impact on output value corresponding to the lost workload, the algorithm for predicting the risk of lost work time is configured to calculate the risk index R. 误工 =T 误工均值 ×F×α 均值 T 误工均值 Let α be the average downtime within a certain type of failure cycle, F be the frequency of occurrence within that type of failure cycle, and α be the average downtime. 均值 The mean of the fault impact coefficient; when R 误工 A value ≥1.0 is considered high-risk, requiring preventative maintenance in advance; when 0.5≤R 误工 When R < 1.0, it is judged as medium risk; when R 误工 A value less than 0.5 is considered low risk. By adopting the above technical solution, and linking fault, maintenance, and work record data, accurate and automated calculation of downtime and losses is achieved. Based on historical data, future risks are predicted, transforming post-event tracking into pre-event early warning, providing decision support for dynamic adjustment of construction plans and preventive maintenance.

[0012] Preferably, a remote management platform is also included. This platform connects with the core control system via a 4G / 5G network to enable remote monitoring, fault viewing, report export, and centralized management of multiple devices. The operation console has a built-in display screen for real-time display of device operating status, fault information, and the calculation results of various algorithms in the core control system. By adopting the above technical solution, the remote management platform achieves centralized and visual management of single or multiple devices, while the local display on the operation console provides real-time status feedback to on-site personnel, forming a three-dimensional control system that combines cloud and edge computing.

[0013] A fault statistics reporting and work traceability method based on the aforementioned traceability system includes the following steps: Step 1: Input basic work information through the operation console, initialize the core control system and load the algorithm; Step 2: During the work process, the work content recording module automatically records work data and runs the workload quantification and statistics algorithm; Step 3: The fault statistics module collects parameters in real time through sensors and runs the fault identification algorithm, triggering an early warning after identifying a fault; Step 4: The fault reporting module runs the fault reporting priority algorithm, generates a standardized form, and reports it to the management terminal according to priority; Step 5: The maintenance time control module records maintenance time nodes and runs the maintenance time deviation algorithm, issuing an early warning for overtime situations; Step 6: The downtime traceability module runs the downtime calculation algorithm and the downtime loss accounting algorithm, correlates the delayed work content to calculate the loss, and runs the downtime risk prediction algorithm; Step 7: After the fault is resolved, the work is resumed, and all data and algorithm calculation results are archived and retained after the work is completed. By adopting the above technical solution, this method defines the standard workflow of the system from job preparation to completion and archiving, ensuring the orderly and efficient execution of core functions such as fault management, work records, maintenance control and downtime traceability, and realizing the digital and intelligent control of the entire process of high-altitude operations.

[0014] Therefore, the present invention has the following beneficial effects: (1) Through the built-in quantitative fault identification algorithm and statistical analysis unit, the automatic and accurate identification and multi-dimensional statistics of faults are realized, with high identification accuracy, providing a scientific basis for preventive maintenance; (2) Through the fault reporting priority algorithm and standardized reporting process, the rapid, hierarchical and standardized reporting of faults is realized, which significantly shortens the fault handling cycle; (3) Through the workload quantification and statistical algorithm, the workload of different job types is automatically and accurately calculated and recorded, which greatly reduces the workload of manual recording and avoids recording errors and liability disputes; (4) Through the maintenance time deviation algorithm and the downtime loss accounting algorithm, the quantitative assessment of maintenance efficiency and the accurate accounting of downtime loss were realized, providing a reliable basis for refined cost control; (5) Through the algorithm for predicting the risk of work stoppage, a linkage analysis mechanism for fault, maintenance and work stoppage data has been established, which can provide early warning of high work stoppage risk and provide intelligent decision support for the dynamic adjustment of construction plan. Detailed Implementation

[0015] To facilitate understanding of the technical solution of the present invention, the following detailed description is provided in conjunction with specific embodiments.

[0016] A fault statistics, reporting, and work traceability system for aerial work platforms includes an operation console and a core control system integrated within the console. The core control system is linked to the aerial work platform's hydraulic drive system, safety protection system, and various sensors, and incorporates an embedded processor. The core control system includes interconnected fault statistics, fault reporting, work content recording, maintenance time control, and downtime traceability modules. In this structure, the core control system acts as the "intelligent brain," using sensors to perceive the equipment status in real time and utilizing the embedded processor to run algorithms from each module. This enables digital monitoring and intelligent decision-making throughout the entire process of aerial work platform operation, work, faults, and maintenance. Simultaneously, through the interconnected data and functional synergy of the five modules, a complete management closed loop is constructed, from fault perception and reporting to loss traceability. This fundamentally changes the traditional, manual, and decentralized management model, resulting in unexpected technical effects such as improved overall operation and maintenance efficiency and reduced management costs.

[0017] The fault statistics module includes a fault identification unit, which presets common equipment fault types and characteristic parameters. It collects equipment operating parameters in real time and uses a fault identification algorithm that compares parameter deviation thresholds to determine whether a fault has occurred. In this structure, the fault identification unit continuously monitors key parameters Xᵢ such as hydraulic pressure, motor current, and boom angle, and compares them with preset normal ranges [Xᵢ]. i-min ,X i-max The system compares the results and quantifies the degree of abnormality by calculating the deviation coefficient Kᵢ. At the same time, by setting a unified fault identification threshold K0 and a compound fault judgment logic (Ktotal = Σ(ωᵢ×Kᵢ)), the fault judgment standard is made objective and unified. It can identify both single parameter faults and compound potential faults caused by the superposition of multiple slight abnormalities in parameters, and achieves a fault warning capability that is earlier and more comprehensive than human experience judgment.

[0018] In some embodiments, the fault identification threshold K0 is preset to 0.15. The fault statistics module also includes a statistical analysis unit and a fault file unit. The statistical analysis unit is used to calculate the fault frequency using a fault frequency statistical algorithm (F=N / T) and a fault occurrence rate algorithm (R=(T / T)). 故障 / T 总Multi-dimensional analysis is performed using a 100% () approach. The fault archive unit stores all fault data and statistical reports for at least one year. In this structure, the preset K0 value balances sensitivity and anti-interference capabilities; the statistical analysis unit transforms discrete fault events into analyzable indicators such as frequency F and occurrence rate R, facilitating managers' understanding of equipment reliability trends; simultaneously, through collaboration with the fault archive unit, which has long-term storage capabilities, a complete "data acquisition-analysis-archiving" chain is formed, laying a solid foundation for big data-based equipment health management (such as predictive maintenance) and generating long-term benefits that improve the level of equipment lifecycle management.

[0019] The fault reporting module includes a reporting trigger unit, an information processing unit, a remote communication unit, and a reporting tracking unit. In this structure, the reporting trigger unit automatically initiates the reporting process immediately upon receiving a fault signal, ensuring immediate response. The information processing unit automatically extracts information from the fault statistics module and relevant sensors, generating standardized forms containing key operating conditions such as equipment number, fault type, occurrence time, current operating height, and load, eliminating the inconsistencies of manual descriptions. Simultaneously, through the cooperation of the remote communication unit (based on 4G / 5G networks) and the reporting tracking unit, not only is fault information pushed to the remote management platform and management personnel terminals within seconds, but the status of maintenance personnel accepting orders, arriving on-site, and handling the fault is also provided in real time, forming a transparent, efficient, and visualized fault handling and tracking process.

[0020] The fault reporting priority algorithm is configured as follows: Let the fault priority be P, P = α × 0.4 + β × 0.4 + γ × 0.2. The coefficients α, β, and γ are dynamically assigned based on the fault's impact on operations, its urgency, and historical frequency. This structure comprehensively considers the fault's real-time impact (α), safety risk (β), and historical patterns (γ), deriving a quantified priority value P through weighted calculation. Furthermore, by dividing the P value into three levels—urgent, routine, and minor—and linking it to the reporting order, the management system can intelligently distinguish the severity and urgency of faults, prioritizing resource allocation for faults that may lead to major downtime or safety issues. This optimizes maintenance resource allocation and results in an upgrade from an "average response" to a "precise response" management model.

[0021] The work content recording module includes a work information input unit, a data synchronization unit, and a workload quantification and statistical algorithm. In this structure, before the work begins, the work information input unit enters planned information, setting a benchmark for subsequent recording and comparison. During the work, the data synchronization unit automatically records actual work duration (Tactual), height, and other data. Simultaneously, through a workload quantification and statistical algorithm (e.g., for planar work, W = S × H × η), the abstract "work process" is transformed into quantifiable "workload W" and "work efficiency μ," achieving objective measurement of work results. This module, in conjunction with the fault and downtime modules, accurately records changes in workload before and after a fault occurs, providing crucial data support for calculating downtime losses and resulting in a shift in construction management from extensive timekeeping to refined measurement.

[0022] The maintenance time control module includes a time recording unit, a time statistics unit, and an overtime warning unit. The maintenance time deviation algorithm calculates the deviation rate δ = [(T...]. 检修 -T 预设 ) / T 预设 The time recording unit accurately records the timestamps of each maintenance step through methods such as maintenance personnel check-in at their terminals; the duration statistics unit automatically calculates the total maintenance time Tmaintenance; and by comparing Tmaintenance with the preset standard duration Tpreset based on the fault type, the maintenance time deviation algorithm is run, and the overtime warning unit can issue timely reminders when maintenance efficiency is abnormal. This not only promotes the standardization of the maintenance process, but its output Tmaintenance and δ values ​​are also direct inputs for the downtime traceability module to calculate losses and evaluate maintenance performance, resulting in a transparent management effect that transforms the maintenance process from "invisible" to "measurable and evaluable".

[0023] The lost work time tracking module includes a lost work time statistics algorithm, a lost work content association unit, a lost work loss accounting algorithm, and a lost work risk prediction algorithm. In this structure, the lost work time statistics algorithm (T...) 误工 = (T 故障 +T 检修 -T 合理 The system accurately identifies unplanned, ineffective time; the unit for associating lost work content links the amount of work (W) that was planned to be completed but was delayed during that time period; and simultaneously, it uses a lost work loss calculation algorithm (L = (n × C)). 人工 +C 设备 )×T 误工 +W 产值 This transforms time loss and output loss into a unified, monetized economic loss L, making the impact of lost work time immediately apparent. Furthermore, the lost work risk prediction algorithm (R...) 误工 =T 误工均值 ×F×α 均值By utilizing historical failure downtime data, the risk level that may be caused by the recurrence of similar failures can be predicted, achieving a leap from "post-event accounting" to "pre-event early warning". This provides a forward-looking decision-making basis for arranging preventive maintenance and optimizing construction plans, and has produced the creative effect of improving the project's risk resistance.

[0024] The system also includes a remote management platform that integrates with the core control system via a 4G / 5G network. The operation console has a built-in display screen. In this architecture, the remote management platform acts as a cloud data center and command center, allowing centralized viewing of the real-time status, fault alarms, and work reports of all online devices, and supporting data export and analysis. Simultaneously, the on-site operation console display screen provides operators with real-time feedback on their device's status and an interactive interface. This collaborative architecture not only meets the project team's needs for macro-level control of multiple devices but also ensures the independence and real-time nature of individual machine operations, forming a three-dimensional, networked intelligent operation and maintenance management system.

[0025] A method for fault statistics reporting and work traceability based on the above-mentioned traceability system includes the following steps: Step 1: Enter basic job information through the operation console; the core management system initializes and loads the algorithm. Step 2: During the operation, the work content recording module automatically records the work data and runs the workload quantification and statistical algorithm; Step 3: The fault statistics module collects parameters in real time through sensors and runs a fault identification algorithm. After identifying a fault, it triggers an early warning. Step 4: The fault reporting module runs the fault reporting priority algorithm, generates a standardized form, and reports it to the management terminal according to priority; Step 5: The maintenance time control module records maintenance time nodes and runs the maintenance time deviation algorithm to issue warnings for overdue situations; Step Six: The work stoppage tracking module runs the work stoppage duration calculation algorithm and the work stoppage loss accounting algorithm, calculates the loss by associating the delayed work content, and runs the work stoppage risk prediction algorithm; Step 7: Resume the job after troubleshooting. Once the job is completed, archive and retain all data and algorithm calculation results.

[0026] This method defines a standardized, end-to-end work control procedure, from job initialization (step one) to data archiving (step seven). Each step corresponds to the execution of a specific module in the core control system; for example, step two corresponds to the work content recording module, and steps three and four correspond to the fault statistics and reporting module. Simultaneously, through the sequence and data flow between steps (e.g., faults identified in step three trigger reporting in step four, and maintenance time recorded in step five is used for downtime calculation in step six), the information flow is ensured to be unimpeded between functional modules, forming a tightly linked, automatically driven intelligent management pipeline. This transforms complex on-site management tasks into a standardized process executed automatically by the system, significantly reducing management complexity and human intervention.

[0027] The working principle of this invention is as follows: At the start of the operation, the system initializes and loads various algorithms. During the operation, the work content recording module automatically quantifies and records the workload. Once the sensor detects abnormal operating parameters, the fault statistics module immediately determines the fault and records the details through the fault identification algorithm, and then triggers the fault reporting module to generate a standardized report according to the priority algorithm and send it to the remote terminal. During maintenance, the maintenance time control module records the time and monitors whether it exceeds the time limit. After the fault is handled, the downtime tracking module automatically associates the fault, maintenance time, and delayed workload to calculate the downtime loss, and assesses the downtime risk of similar faults in the future based on historical data. All data is finally archived to form a complete, queryable, and analyzable work file. Therefore, this invention, through a series of quantitative algorithms and modular design, realizes full-process, digital, and intelligent control of aerial work platforms from normal operation to abnormal handling and loss assessment. The core purpose is to solve the problems of existing equipment lacking quantitative control algorithms and extensive management, thereby improving construction efficiency, ensuring operational safety, and achieving lean cost control.

[0028] Example 1: High-altitude painting of building walls In this embodiment, the specific parameters of the wheeled straight boom aerial work platform capable of fault reporting and downtime tracking are as follows: working height range 8m~30m, working radius 4m~15m, rated load capacity 300kg, wheeled chassis turning radius ≤3m, hydraulic outrigger extension time ≤5 minutes. Fault identification accuracy 98.5%, fault reporting response time ≤8s. Preset maintenance time standards: hydraulic leakage ≤1.5 hours, straight boom jamming ≤1 hour. Work content recording accuracy ≤5 minutes, downtime statistical error ≤10 minutes. Core algorithm operation response time ≤50ms, algorithm calculation accuracy ≤5%.

[0029] The construction scenario involves painting the exterior walls of a residential building in an urban residential community. The work site is a community road (normal flat ground). There are 2 workers, the planned work duration is 4 hours, the planned wall area to be painted is 80㎡ (working height is 12m), the labor cost is 30 yuan / person / hour, the equipment rental cost is 200 yuan / hour, and the output value per square meter of wall painting is 50 yuan.

[0030] The construction process is as follows: Job Preparation: The operator drives the equipment to the work site next to the residential building and enters the operator, work site, job type (wall painting), planned work duration of 4 hours, and planned workload of 80 square meters through the control console. After checking that the equipment is normal, the equipment is started, the core control system initialization is completed, and all core algorithms are loaded.

[0031] High-altitude work: The boom is extended to a height of 12m, the platform is leveled, and workers begin wall painting. The work recording module automatically records the work duration and height, runs a workload quantification algorithm (planar work formula), calculates work efficiency in real time, and synchronizes the data to the remote management platform. After 1.5 hours of work, 30㎡ has been painted, and the work efficiency μ = 30 ÷ 1.5 = 20㎡ / hour.

[0032] Fault Identification and Statistics: After 1.5 hours of operation, a hydraulic leak occurred in the equipment. The hydraulic pressure sensor collected a real-time pressure of X1 = 18 MPa, with a preset normal pressure range of [25 MPa, 35 MPa]. The fault identification algorithm was run, and the deviation coefficient K1 = (25-18) ÷ 25 = 0.28 ≥ 0.15, thus identifying it as a hydraulic leak fault. The fault statistics module collected data such as the fault occurrence time, operating height (12m), and load weight (210kg), ran a fault frequency statistics algorithm, updated the fault statistics report, and issued an audible and visual warning.

[0033] Fault Reporting and Handling: The fault reporting module runs a fault reporting priority algorithm, with a fault impact coefficient α = 1.0 (work stoppage), an urgency coefficient β = 0.5 (ordinary fault), a frequency coefficient γ = 0.5 (routine fault), and a priority P = 1.0 × 0.4 + 0.5 × 0.4 + 0.5 × 0.2 = 0.7 (level 2). It automatically generates standardized reporting forms and uploads them to the management terminal within 8 seconds. After receiving the report, the management personnel dispatch maintenance personnel to the site, with an estimated maintenance time of 1 hour (T preset = 1.5 hours).

[0034] Maintenance time control: Maintenance personnel arrive on site 30 minutes later and check in via terminal. The maintenance time control module then begins recording maintenance time. During maintenance, maintenance time is statistically analyzed in real time, and a maintenance time deviation algorithm is run. Maintenance is completed after 1 hour, with a total maintenance time Tmaintenance = 1 hour and a deviation rate δ = (1 - 1.5) ÷ 1.5 × 100% = -33.3%. This does not exceed the preset standard, and there is no timeout warning.

[0035] Loss Calculation and Time-Lost Work Tracking: The time-lost work tracking module uses a time-lost work calculation algorithm. Fault duration Tfault = 0.5 hours, repair time Trepair = 1 hour, preset reasonable delay time Treasonable = 0.15 hours, total time-lost work Tlost work = 0.5 + 1 - 0.15 = 1.35 hours. The work content of the associated unit is "painting 27㎡ of wall (calculated at a work efficiency of 20㎡ / hour)". Using the time-lost work loss calculation algorithm, the total time-lost work loss L = (2×30+200)×1.35 + 27×50 = (60+200)×1.35 + 1350 = 351 + 1350 = 1701 yuan. The algorithm for predicting downtime risk was used. The average downtime for this type of fault was 0.8 hours, with a frequency of 2 times per week. The average impact coefficient was 0.8, and the risk index R_downtime = 0.8 × 2 × 0.8 = 1.28 ≥ 1.0, which was determined to be high risk. This prompted a reminder to strengthen preventive maintenance in the future.

[0036] Job Recovery and Archiving: After the equipment was restored to normal operation, the workers continued their work, ultimately completing the painting of an 80㎡ wall, with a total actual work time of 5.35 hours. The work content recording module generated a job summary report, the fault statistics module updated the fault files, and all data and algorithm calculation results were archived and retained.

[0037] Implementation Results: The core algorithm operates stably, fault identification and reporting are timely, maintenance time is effectively controlled, downtime can be accurately traced and calculated, and downtime risks can be predicted in advance. Compared with existing equipment, fault handling efficiency is improved by 60%, manual recording workload is reduced by 80%, downtime loss calculation error is ≤10 minutes, and work efficiency assessment is more accurate, effectively improving the level of high-altitude operation management and construction efficiency.

[0038] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention shall be determined by the scope defined in the claims. Any improvements and modifications made by those skilled in the art without departing from the spirit and scope of the present invention shall also be considered as within the scope of protection of the present invention.

Claims

1. A fault statistics reporting and work traceability system for aerial work platforms, characterized in that, Comprising: an operation console, and a core management and control system integrated inside the operation console; the core management and control system is linked with the hydraulic drive system, safety protection system and various sensors of the aerial work platform, and has an embedded processor built-in; the core management and control system comprises an interlinked fault statistics module, a fault reporting module, a work content recording module, a maintenance duration management and control module and a work delay tracing module, wherein: the fault statistics module is configured to identify fault types and positions based on deviations between operating parameters collected by sensors and preset thresholds through a preset fault identification algorithm, and collect and count fault data to form a fault file; the fault reporting module is linked with the fault statistics module, and is configured to automatically sort fault information after a fault is identified, determine a reporting sequence through a preset fault reporting priority algorithm, and send a standardized form to a remote terminal; the work content recording module is configured to input basic operation information, automatically record actual operation data, and calculate the completed workload through a preset workload quantitative statistics algorithm; the maintenance duration management and control module is linked with the fault reporting module, and is configured to record time nodes of each maintenance link, judge whether maintenance is delayed through a preset maintenance duration deviation algorithm, and issue an early warning; the work delay tracing module is linked with the fault statistics module, the maintenance duration management and control module and the work content recording module, and is configured to correlate work delay duration with delayed work content through a preset work delay duration calculation algorithm and a work delay loss accounting algorithm, so as to account work delay loss and predict work delay risks.

2. The aerial work platform fault statistics reporting and work traceability system according to claim 1, characterized in that, the fault statistics module comprises a fault identification unit, which presets common fault types and characteristic parameters of equipment, is configured to collect equipment operating parameters in real time, and judge whether a fault occurs through the fault identification algorithm based on parameter deviation threshold comparison; the fault identification algorithm is configured as follows: assuming that a real-time operating parameter collected by a sensor is Xᵢ, a preset normal parameter threshold range is [Xi-min,Xi-max], and a parameter deviation coefficient is Kᵢ, then: when Xᵢ>Xi-max, Kᵢ=(Xᵢ-Xi-max) / Xi-max; when Xᵢ<Xi-min, Kᵢ=(Xi-min-Xᵢ) / Xi-min; when Xi-min≤Xᵢ≤Xi-max, Kᵢ=0; assuming that a fault identification threshold is K0, when any Kᵢ≥K0, it is determined that a fault occurs at a corresponding position; when a plurality of parameter deviation coefficients are superimposed, when the total deviation coefficient Ktotal=Σ(ωᵢ×Kᵢ)≥K0, it is determined that the equipment has a composite fault, wherein ωᵢ is a preset weight of each parameter.

3. The aerial work platform fault statistics reporting and work traceability system according to claim 2, characterized in that, the fault identification threshold K0 is preset to 0.15; the fault statistics module further comprises a statistical analysis unit, which is configured to perform statistical analysis in dimensions of time, type and position through a fault frequency statistics algorithm and a fault occurrence rate algorithm, wherein the fault frequency F=N / T, N is the number of fault occurrences in a period T, the fault occurrence rate R=(Tfault / Ttotal)×100%, Ttotal is the total operating duration of the equipment in the period, and Tfault is the total fault duration; The fault statistics module also includes a fault archive unit, which is used to store fault data and statistical reports for a storage period of ≥1 year.

4. The aerial work platform fault statistics reporting and work traceability system according to claim 1, characterized in that, The fault reporting module includes: a reporting triggering unit, used to automatically trigger the reporting process and issue an audible and visual warning after fault identification; an information processing unit, used to automatically process standardized reporting forms containing equipment number, fault type, occurrence time, and operating conditions; a remote communication unit, used to send the reporting forms to management personnel terminals and construction management platforms according to priority via 4G / 5G networks; and a reporting tracking unit, used to track the fault handling status in real time and provide feedback.

5. The aerial work platform fault statistics reporting and work traceability system according to claim 4, characterized in that, The fault reporting priority algorithm is configured as follows: Let the fault priority be P, where P = α × 0.4 + β × 0.4 + γ × 0.2; The rule for determining the fault impact coefficient α is as follows: When the task stops, α = 1.0; when the task is affected but does not stop, α = 0.5; when the task is not affected, α = 0.

1. The rules for determining the fault urgency factor β are as follows: β = 1.0 when safety hazards are involved, β = 0.5 for ordinary faults, and β = 0.1 for minor faults; The failure frequency coefficient γ is set according to the following rules: γ=1.0 for frequent failures, γ=0.5 for regular failures, and γ=0.1 for occasional failures. The faults are classified into three priority levels based on the P value: P ≥ 0.8 is urgent, 0.5 ≤ P < 0.8 is normal, and P < 0.5 is minor. When reporting, the faults are pushed from high to low priority.

6. The aerial work platform fault statistics reporting and work traceability system according to claim 1, characterized in that, The work content recording module includes: a work information input unit for inputting work personnel, locations, types, and planned workload; a data synchronization unit for real-time synchronization of actual work duration, height, and completed workload to local and remote management platforms; and a workload quantification and statistical algorithm for calculating the actual completed workload W and work efficiency μ based on the work type. The workload quantification and statistical algorithm is configured as follows: for planar operations, W=S×H×η, where S is the work area, H is the work height, and η is the work efficiency coefficient; for point-based operations, W=N×λ, where N is the number of completed points and λ is the work complexity coefficient for a single point; the work efficiency μ=W / Tactual, where Tactual is the actual work time.

7. The aerial work platform fault statistics reporting and work traceability system according to claim 1, characterized in that, The maintenance time control module includes: a time recording unit for recording the arrival, start, and completion times of maintenance personnel; a time statistics unit for calculating the total maintenance time T; and an overtime warning unit for issuing a warning when maintenance delays exceed a preset standard. The maintenance time deviation algorithm is configured as follows: calculate the maintenance time deviation rate δ = [(T 检修 -T 预设 ) / T 预设 [×100%, triggering a timeout warning when δ>20%, where T] 预设 This is a pre-set maintenance time standard based on different fault types.

8. The aerial work platform fault statistics reporting and work traceability system according to claim 1, characterized in that, The lost work tracking module includes a lost work duration statistics algorithm, a lost work content association unit, a lost work loss calculation algorithm, and a lost work risk prediction algorithm, wherein: The algorithm for calculating lost work time is configured as follows: Calculate total lost work time T_lost work time = (T_lost work time) / (T_lost work time) 故障 +T 检修 -T 合理 ), where T 故障 T represents the duration of the fault. 检修 For maintenance time, T 合理 To pre-determine a reasonable delay duration; The algorithm for calculating lost work time is configured as follows: Calculate the total lost work time loss L = (n × C) 人工 +C 设备 )×T 误工 +W 产值 Where n is the number of workers, C 人工 C represents the unit price of labor costs. 设备 W represents the unit price for equipment rental. 产值 To reduce the output value corresponding to the lost workload; The algorithm for predicting the risk of lost work time is configured to: calculate the risk index R of lost work time. 误工 =T 误工均值 ×F×α 均值 T 误工均值 Let α be the average downtime within a certain type of failure cycle, F be the frequency of occurrence within that type of failure cycle, and α be the average downtime. 均值 The mean of the fault impact coefficient; when R 误工 A value ≥1.0 is considered high-risk, requiring preventative maintenance in advance; when 0.5≤R 误工 When R < 1.0, it is judged as medium risk; when R 误工 A value less than 0.5 is considered low risk.

9. The aerial work platform fault statistics reporting and work traceability system according to claim 1, characterized in that, It also includes a remote management platform, which is linked with the core control system via a 4G / 5G network to realize remote monitoring, fault viewing, report export and centralized control of multiple devices; the operation console has a built-in display screen to display the device operating status, fault information and calculation results of various algorithms in the core control system in real time.

10. A method for fault statistical reporting and work traceability based on the traceability system according to any one of claims 1 to 9, characterized in that, Includes the following steps: Step 1: Enter basic job information through the operation console; the core management system initializes and loads the algorithm. Step 2: During the operation, the work content recording module automatically records the work data and runs the workload quantification and statistical algorithm; Step 3: The fault statistics module collects parameters in real time through sensors and runs a fault identification algorithm. After identifying a fault, it triggers an early warning. Step 4: The fault reporting module runs the fault reporting priority algorithm, generates a standardized form, and reports it to the management terminal according to priority; Step 5: The maintenance time control module records maintenance time nodes and runs the maintenance time deviation algorithm to issue warnings for overdue situations; Step Six: The work stoppage tracking module runs the work stoppage duration calculation algorithm and the work stoppage loss accounting algorithm, calculates the loss by associating the delayed work content, and runs the work stoppage risk prediction algorithm; Step 7: Resume the operation after troubleshooting. After the operation is completed, archive and retain all data and algorithm calculation results.