Three-dimensional parking lot operation and maintenance management method, device and equipment based on life detection

Abstract

The invention provides a three-dimensional parking lot operation and maintenance management method, device and equipment based on life detection, and belongs to the technical field of parking lot management, and the method comprises the steps: for each piece of equipment in a three-dimensional parking lot, fusing target operation data, accumulated operation duration, historical fault data of the equipment and historical data inspection data, and predicting the residual life of the equipment, and performing equipment early warning management according to the residual service life of the equipment. According to the invention, efficient and automatic management of the three-dimensional parking lot equipment can be realized.

Description

Technical Field

[0001] This application belongs to the field of parking management technology, and more specifically, it relates to a method, device, and equipment for the operation and maintenance management of multi-level parking lots based on life detection. Background Technology

[0002] Multi-level parking garages are parking facilities that use mechanical structures to move vehicles vertically or horizontally. Their core equipment includes key components such as drive motors, vehicle carriers, main frames, and information sensors. The stable operation of this equipment directly determines the continuity and safety of the parking garage's operations. With the increasing demand for urban parking, multi-level parking garages are expanding in scale, with more numerous and complex types of equipment, posing significant challenges to equipment management. Current technologies for managing multi-level parking garage equipment often employ fixed inspection cycles, which can lead to over-inspection of healthy equipment, wasting manpower, or insufficient inspection of high-risk equipment, causing sudden malfunctions. Predictions of remaining equipment lifespan often rely on the theoretical lifespan of the equipment at the time of manufacture, resulting in low accuracy due to a lack of data support and an inability to provide early warnings of equipment failure risks. Therefore, a scientific and efficient method for managing multi-level parking garage equipment is urgently needed to address the shortcomings of existing technologies. Summary of the Invention

[0003] The purpose of this application is to provide a method, device, and equipment for the operation and maintenance management of multi-level parking garages based on lifespan detection, so as to achieve efficient and automated management of multi-level parking garage equipment.

[0004] A first aspect of this application provides a method for the operation and maintenance management of a multi-level parking garage based on lifespan detection, including: For each piece of equipment in the multi-level parking garage, obtain the target operating data and cumulative operating time of the equipment; select historical fault data from the historical fault database of the equipment based on the equipment type of the equipment, and select historical inspection data from the historical inspection database of the equipment based on the equipment type of the equipment; predict the remaining lifespan of the equipment based on the target operating data, cumulative operating time, historical fault data, and historical inspection data. Early warning management of equipment in multi-level parking garages is implemented based on the remaining lifespan of all equipment.

[0005] A second aspect of this application provides a lifespan detection-based operation and maintenance management device for automated parking systems, comprising: The equipment life prediction module is used to acquire the target operating data and cumulative operating time of each piece of equipment in the automated parking system; select historical fault data from the historical fault database based on the equipment type of the equipment; select historical inspection data from the historical inspection database based on the equipment type of the equipment; and predict the remaining life of the equipment based on the target operating data, cumulative operating time, historical fault data, and historical inspection data. The early warning management module is used to perform early warning management of the equipment in the multi-level parking garage based on the remaining lifespan of all equipment.

[0006] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the above-described lifespan detection-based operation and maintenance management method for multi-level parking lots.

[0007] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described lifespan detection-based operation and maintenance management method for multi-level parking lots.

[0008] The beneficial effects of the life-cycle-based automated parking system operation and maintenance management method, apparatus, and equipment provided in this application are as follows: This application's embodiments improve the accuracy of equipment remaining life prediction and the effectiveness of early warning. These embodiments comprehensively analyze actual equipment operating data, cumulative operating time, historical faults, and inspection records to predict lifespan. This data directly reflects the aging and usage status of individual equipment, resulting in predictions that are more realistic. Combining remaining lifespan with early warning management allows for the prediction of equipment failure risks in advance, giving managers sufficient time to prepare spare parts replacements and plan maintenance, thus preventing parking lot downtime due to equipment failure. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A flowchart illustrating an embodiment of the operation and maintenance management method for an automated parking system based on lifespan detection provided in this application; Figure 2 A structural block diagram of an operation and maintenance management device for an automated parking garage based on lifespan detection, provided in an embodiment of this application; Figure 3 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0011] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0012] It is understood that in the embodiments of this application, data such as user information are involved. When the embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with relevant laws, regulations and standards.

[0013] It should be noted that the terms "first," "second," etc., used in the specification, claims, and drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in sequences other than those illustrated or described herein.

[0014] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a lifespan detection-based operation and maintenance management method for an automated parking system according to an embodiment of this application. The method can be executed by an electronic device, and specifically, the method may include steps S101 to S102.

[0015] S101: For each device in the multi-level parking garage, obtain the target operating data and cumulative operating time of the device; select historical fault data from the historical fault database of the device based on the device type, and select historical inspection data from the historical inspection database of the device based on the device type; predict the remaining lifespan of the device based on the target operating data, cumulative operating time, historical fault data, and historical inspection data.

[0016] In this embodiment, historical fault data is selected from the historical fault database of the device based on the device type, including: The time span and amount of fault data to be acquired are determined based on the equipment type of the device. Using the current time as the end time, fault data is selected from the historical fault database of the device based on the fault data acquisition time span. If the amount of fault data is less than the amount of fault data acquired, the earliest time corresponding to the fault data is used as the end time, and fault data is selected from the historical fault database of the device until the amount of all acquired fault data is not less than the amount of fault data acquired. All acquired fault data is then used as historical fault data.

[0017] In this embodiment, historical inspection data is selected from the historical inspection database of the device based on the device type, including: The time span and amount of inspection data to be acquired are determined based on the equipment type of the equipment. Using the current time as the end time, inspection data is selected from the historical inspection database of the device based on the time span of the inspection data acquisition. If the amount of inspection data does not reach the inspection data acquisition limit, the earliest time corresponding to the inspection data is used as the end time, and inspection data is selected from the historical inspection database of the device until the amount of all acquired inspection data reaches the inspection data acquisition limit. All acquired inspection data is then used as historical inspection data.

[0018] In this embodiment, the equipment refers to the components that perform operational functions in a multi-level parking garage. The equipment / components in the multi-level parking garage include: vehicle guide wheels, guide mirrors, motor chains, spherical ball bearings with raised bosses (hanging plate bearings), circulating chain plates with cylindrical roller bearings, shock-absorbing pads, motor sprockets, motor brakes, motors, main frames, roofs, access control door opening buttons, pedestrian door magnetic locks, front vehicle detection photoelectric sensors, left-side overwidth detection photoelectric sensors, vehicle detection photoelectric sensors, right-side overwidth detection photoelectric sensors, warning lights, license plate recognition cameras, rear vehicle detection photoelectric sensors, overheight detection photoelectric sensors, and touchscreens, etc. Inspection items include pedestrian door magnetic locks, entrance / exit photoelectric detectors, warning lights, license plate recognition, and operation screens, etc.

[0019] In this embodiment, the target operating data refers to the parameters generated during the real-time operation of the equipment. Taking the equipment as a traversing device as an example, the target operating data may include real-time parameters such as the load of the drive motor during operation. The cumulative running time is the total operating time of the equipment from its commissioning to the current moment. The equipment type is the specific category of the equipment. The historical fault database is a database storing past fault records of the equipment, which may include the fault type, fault severity, number of faults, or fault frequency of the equipment. The historical inspection database is a database storing past inspection records of the equipment, which may include inspection anomaly records of the equipment, such as abnormal noise from the drive motor. The remaining lifespan of the equipment is the remaining time that the equipment is expected to be able to operate normally. The fault data acquisition time span is the time range for selecting fault data. The fault data acquisition quantity is the number of fault data entries to be selected. The inspection data acquisition time span is the time range for selecting inspection data. The inspection data acquisition quantity is the number of inspection data entries to be selected. The current time is the moment when the data selection operation is performed. The termination time is the time cutoff point for data selection.

[0020] The core objective of this embodiment is to improve the accuracy of equipment remaining life prediction through precise and targeted data selection. Considering the differences in aging time cycles among different equipment types, the corresponding effective data time ranges differ. For equipment operating at high frequencies and with rapid aging rates, recent data better reflects the current state, requiring a shorter time span; for equipment operating at low frequencies and with slow aging rates, a longer data span is needed to reflect aging trends. Therefore, this embodiment determines data acquisition rules according to equipment type, ensuring the adaptability of the data time dimension, avoiding data invalidation due to inappropriate time ranges, and improving prediction accuracy.

[0021] To avoid prediction errors due to insufficient data and to prevent data redundancy from reducing efficiency, this embodiment sets the data acquisition time span and acquisition volume. Prioritizing recent data with the current time as the end time is because recent data better reflects the current aging state of the equipment; if the data volume is insufficient, data is traced back to ensure sufficiency and provide reliable basic data support for subsequent lifespan prediction.

[0022] For example, regarding the lift motor in a multi-level parking garage, this embodiment can determine the fault data acquisition time span as one year, with at least three fault data entries, and the inspection data acquisition time span as one year, with at least two inspection data entries, based on the device type "lift motor". Similarly, regarding parking space sensors, this embodiment can determine the fault data acquisition time span as three years, with at least three fault data entries, and the inspection data acquisition time span as three years, with at least three inspection data entries.

[0023] This embodiment can select historical fault data of the lifting motor, using the current time as the end time, to extract fault data from the previous year. If there are fewer than three fault records, it traces back until the data volume reaches the target. This embodiment can also select historical inspection data of the parking space sensor, using the current time as the end time, to extract inspection data from the previous three years. If there are at least three records, the earliest data time is used as the end time to determine the final data. This embodiment can combine the cumulative runtime of the target operating data of the two types of equipment with the selected historical fault and inspection data to predict the remaining lifespan of each type of equipment. The process of predicting and determining the remaining lifespan is detailed in subsequent embodiments.

[0024] S102: Implement early warning management for the equipment in a multi-level parking garage based on the remaining lifespan of all equipment.

[0025] In this embodiment, the early warning management is a management method that classifies risk levels and triggers corresponding control actions based on the remaining lifespan of all equipment. It is used to provide early warning of equipment failure risks, clarify maintenance priorities, and ensure the safe and continuous operation of the multi-level parking garage.

[0026] This embodiment transforms the remaining lifespan of equipment into a practical basis for risk management. Differences in the remaining lifespan of different devices directly reflect the level of failure risk, and tiered early warning systems can avoid reactive responses to faults. This embodiment can prioritize high-risk equipment with short remaining lifespans, rationally allocate maintenance resources, reduce parking lot downtime losses due to sudden failures, and achieve a management upgrade from reactive emergency repairs to proactive prediction.

[0027] For example, this embodiment can set warning level thresholds. These thresholds can be differentiated based on the type of equipment in the automated parking system, specifically using the scope of equipment failure impact, maintenance cycle, and aging rate as core criteria. Equipment risk levels are also categorized based on remaining lifespan; longer remaining lifespans indicate lower risk. This embodiment can aggregate the remaining lifespans of all equipment, compare them with the thresholds to complete risk level labeling, and generate a warning list containing equipment number, type, location, remaining lifespan, and risk level.

[0028] This embodiment can trigger corresponding actions based on different risk levels. High-risk equipment sends an emergency alert to the operations and maintenance manager, simultaneously generating a spare parts procurement request and a special inspection task; medium-risk equipment sends a regular alert, increasing the frequency of inspections; and low-risk equipment is included in routine monitoring. This embodiment can track the progress of alert handling. After the operations and maintenance personnel complete maintenance, they update the equipment status and remaining lifespan data, completing the alert process and ensuring that high-risk equipment is handled promptly.

[0029] This embodiment improves the accuracy of equipment remaining life prediction and the effectiveness of early warning. It integrates actual equipment operating data, cumulative operating time, historical faults, and inspection records for life prediction. This data directly reflects the aging and usage status of individual equipment, resulting in more realistic predictions. Combining remaining life with early warning management allows for anticipating equipment failure risks in advance, giving managers ample time to prepare spare parts replacements and plan maintenance, thus preventing parking lot downtime due to equipment failure.

[0030] Furthermore, step S102 may be followed by S103 and S104, which are not shown in the figure, and are specifically shown below: S103: For each piece of equipment in the multi-level parking garage, obtain the current inspection cycle and the latest inspection data of the equipment, select the latest fault data, the fault type and fault time of each fault data from the historical fault database of the equipment; update the current inspection cycle based on the latest inspection data, the latest fault data and the fault type and fault time of each fault data to obtain the target inspection cycle.

[0031] In this embodiment, the current inspection cycle is updated based on the latest inspection data, the latest fault data, and the fault type and fault time of each fault data to obtain the target inspection cycle, specifically including: Calculate equipment health score based on the latest inspection data and the latest fault data; The failure impact coefficient is determined based on the failure type and failure time of each failure data, and the equipment failure frequency is predicted based on the failure time of all failure data. The current inspection cycle is updated based on the equipment health score, fault impact coefficient, and equipment fault frequency to obtain the target inspection cycle.

[0032] In one embodiment, the equipment health score can be calculated based on specific deduction items in the latest inspection data and latest fault data. In this embodiment, the deduction values ​​corresponding to each inspection item and each fault item should be preset. For example, if the equipment health score is out of 100, and the latest inspection data of a certain device shows abnormal noise from the device drive motor, deducting 10 points, and the latest fault data shows touchscreen lag, deducting 10 points, with no other deductions, then the equipment health score of this device is 80 points.

[0033] In another embodiment, the device health score can also be calculated based on data from other dimensions. For example, the deduction dimension of the latest inspection data remains unchanged, while the latest fault data contains the fault frequency of the device. Different fault frequencies can correspond to different deduction values, and the specific correspondence between fault frequency and deduction value can be set by the user based on preferences.

[0034] Furthermore, the specific method for determining the fault impact coefficient based on the fault type and fault time of each fault data can include: pre-setting a mapping table, which contains the relationship between fault type, fault time interval, and fault impact coefficient, and then determining the corresponding fault impact coefficient based on the mapping table after determining the fault type and fault time of each fault data.

[0035] In one embodiment, a periodic adjustment coefficient can be determined based on the equipment health score, the fault impact coefficient, and the equipment fault frequency. The product of the periodic adjustment coefficient and the current inspection cycle is then used as the target inspection cycle. In this embodiment, the equipment health score is positively correlated with the periodic adjustment coefficient, the fault impact coefficient is negatively correlated with the periodic adjustment coefficient, and the equipment fault frequency is negatively correlated with the periodic adjustment coefficient. In this embodiment, the calculation method for determining the periodic adjustment coefficient based on the equipment health score, the fault impact coefficient, and the equipment fault frequency can be set independently and is not limited in this embodiment.

[0036] S104: Conduct inspection management of the equipment in the multi-level parking garage based on the target inspection cycle of all equipment and the area where the equipment is located.

[0037] In this embodiment, the equipment in the multi-level parking garage is inspected and managed based on the target inspection cycle of all equipment and the area where the equipment is located. Specifically, this includes: Obtain the equipment type of all devices, and cluster all devices according to equipment type, target inspection cycle and device location to obtain multiple device subsets; For each subset of equipment, a centralized inspection cycle is calculated based on the target inspection cycle corresponding to each of the equipment in that subset; and inspection management is performed on all equipment in that subset based on the centralized inspection cycle.

[0038] In this embodiment, a device subset refers to each group of devices formed after clustering, where devices within a group share similar inspection-related attributes. The centralized inspection cycle refers to a unified inspection cycle calculated based on the target inspection cycles of each device within the subset, used for the centralized inspection scheduling of that subset. The core objective of this embodiment is to balance the safety and efficiency of device inspections. This embodiment groups devices in similar areas, of the same type, and with similar inspection dates into the same subset through clustering, and calculates the centralized inspection cycle. This reduces round-trip inspection costs, increases the coverage of a single inspection, and significantly optimizes the allocation of operational resources without compromising safety standards, thus balancing safety and economy.

[0039] For example, this embodiment can retrieve basic information about all equipment in the parking lot, including the equipment type, target inspection cycle, and location of each equipment, clarifying the core attributes and management parameters of each equipment to ensure data integrity and accuracy. This embodiment can group all equipment according to rules such as consistent equipment type, target inspection cycles differing within a preset range, and the same location. For example, within the same area, equipment of the same type and with similar target inspection cycles can be grouped into a subset, forming multiple regionalized and type-based subsets of equipment, avoiding inspection confusion caused by mixing equipment from different areas and of different types.

[0040] For each subset of equipment, this embodiment can collect the target inspection cycle for all equipment in the subset, determine the centralized inspection cycle through statistical analysis, and prioritize the cycle that is suitable for most equipment in the subset to ensure that the centralized inspection covers the inspection needs of all equipment within the subset, balancing efficiency and comprehensiveness. This embodiment can coordinate the inspection sequence according to the region where the equipment is located, prioritizing the continuous inspection of multiple equipment subsets in the same region, reducing the need for inspection personnel to travel across regions. Inspection personnel conduct inspections on the corresponding equipment subset according to the centralized inspection cycle, focusing on verifying the core functions and operating status of the equipment, recording inspection data and synchronizing it.

[0041] As can be seen from the above, this embodiment can solve the problem of unreasonable fixed inspection cycles. This embodiment dynamically updates the target inspection cycle based on the latest equipment inspection data, historical fault types, and fault times, rather than using a uniform fixed model. Equipment in good health can have its inspection interval appropriately extended to avoid wasting manpower due to excessive inspections; equipment with a higher risk of failure will have its inspection cycle shortened to ensure timely identification of potential problems, reduce the occurrence of sudden failures at the source, and balance maintenance costs with equipment safety.

[0042] In one embodiment of this application, predicting the remaining lifespan of the device based on target operating data, cumulative operating time, historical fault data, and historical inspection data includes: The rated remaining lifespan of the equipment is determined based on its cumulative operating time. Determine the equipment health status based on the latest inspection data from target operation data and historical inspection data; The remaining life of the equipment is determined based on its rated remaining life and its health status.

[0043] In this embodiment, the rated remaining lifespan of the equipment can be derived by analyzing the proportion of cumulative operating time to the equipment's lifespan. For example, if the equipment's lifespan is 10 years and the cumulative operating time is 3 years, then the rated remaining lifespan of the equipment may be 7 years.

[0044] In this embodiment, the remaining lifespan of the equipment is obtained based on the rated remaining lifespan and the equipment health status, specifically including: The lifespan impact coefficient is obtained based on the equipment's health status. The remaining life of the equipment is obtained by updating the rated remaining life of the equipment based on the life impact coefficient.

[0045] In this embodiment, the lifespan impact coefficient is a parameter derived from the equipment's health status and is used to quantify the degree to which the current health status adjusts the rated remaining lifespan of the equipment. The better the equipment's health status, the closer the coefficient is to or greater than 1, indicating that the actual remaining lifespan is close to or better than the rated value; the worse the equipment's health status, the smaller the coefficient, indicating that the actual remaining lifespan is shorter than the rated value. It is a key adjustment factor connecting the theoretical lifespan and the actual state.

[0046] The core objective of this embodiment is to overcome the limitations of the rated remaining lifespan of equipment. The rated remaining lifespan is derived solely from cumulative operating time, without considering the individual health differences of each piece of equipment, leading to significant discrepancies between the predicted and actual lifespans. By determining the equipment's health status through target operating data and the latest inspection data, and then converting this into a lifespan impact coefficient, the rated remaining lifespan can be specifically adjusted. This allows equipment in good health to receive a more realistic lifespan assessment, avoiding over-maintenance; and for equipment in poor health, the lifespan expectation can be adjusted downwards in a timely manner, providing early warnings of risks and ensuring accurate and practical predictions.

[0047] For example, taking the traverse device of a multi-level parking garage as an example, this embodiment can obtain the cumulative running time of the traverse device. Combining its equipment lifespan and the average lifespan data of similar equipment in the industry, this embodiment can deduce the rated remaining lifespan of the traverse device by analyzing the proportion of the cumulative running time to the equipment lifespan, which serves as the basic benchmark value for lifespan prediction. This embodiment can obtain the target operating data of the traverse device, including real-time parameters such as the load of the drive motor during operation; at the same time, this embodiment can retrieve the latest inspection data from the historical inspection database. This embodiment can comprehensively analyze the stability of real-time operating parameters and the compliance of health indicators in the latest inspection data to determine the equipment health status level. This embodiment can determine the equipment health status level based on the preset correspondence rules between health status and lifespan influence coefficient, combined with the equipment type characteristics of the traverse device. If its health status is determined to be good, with no abnormal parameters and all key indicators meeting the standards, it corresponds to a higher lifespan influence coefficient; if there are minor abnormalities but they do not affect the core function, it corresponds to a medium lifespan influence coefficient; if there are deviations in multiple indicators or potential hidden dangers, it corresponds to a lower lifespan influence coefficient. This embodiment can integrate and calculate the rated remaining lifespan of the equipment and the lifespan influence coefficient, and adjust the rated value through the coefficient to obtain the final remaining lifespan of the traverse device.

[0048] This embodiment integrates the rated remaining lifespan of equipment with the equipment health status through a lifespan impact coefficient, overcoming the limitations of predictions based solely on cumulative operating time. Equipment health status is determined based on real-time operating data and the latest inspection data; the corrected remaining lifespan more closely reflects actual operating conditions, significantly improving prediction accuracy. This embodiment avoids resource waste caused by over-maintaining healthy equipment and can proactively predict the failure risk of sub-healthy equipment, providing a reliable basis for operation and maintenance decisions and ensuring the stable operation of automated parking systems.

[0049] In another embodiment of this application, predicting the remaining lifespan of the device based on target operating data, cumulative operating time, historical fault data, and historical inspection data includes: The rated remaining lifespan of the equipment is determined based on its cumulative operating time. Determine the equipment health status based on the latest inspection data from target operation data and historical inspection data; Determine the lifespan decay rate based on historical fault data and historical inspection data. The remaining life of the equipment is predicted based on the rated remaining life of the equipment, the health status of the equipment, and the life decay rate.

[0050] For example, this embodiment can acquire target operating data, cumulative operating time, historical fault data, and historical inspection data of each device in a multi-level parking garage, providing basic data support for lifespan prediction. By combining the cumulative operating time of the device with its service life, the rated remaining lifespan of the device is calculated, serving as a benchmark value for lifespan prediction. The latest inspection data from the real-time target operating data and historical inspection data of the device is analyzed to comprehensively determine the current health status of the device.

[0051] This embodiment can extract fault trigger indicator data from historical fault data to determine the fault threshold for equipment failure; select multiple core equipment health indicators according to equipment type, extract the trend characteristics of each indicator value with the duration of operation, and calculate the lifespan decay rate by combining the threshold and trend characteristics. This embodiment can integrate the rated remaining lifespan of the equipment, the equipment health status, and the lifespan decay rate, and through multi-dimensional data calibration, ultimately predict the remaining lifespan of the equipment.

[0052] This embodiment extracts fault trigger index data from historical fault data and determines the fault critical threshold, providing a precise failure boundary benchmark for the life decay rate. It selects equipment health indicators according to equipment type to ensure that the indicators match the core influencing factors of equipment aging. Furthermore, it combines the analysis of indicator change trend characteristics to capture the dynamic degradation patterns of the equipment. The integration of these three aspects makes the life decay rate calculation more closely reflect the actual state of the equipment, thereby improving the accuracy of remaining life prediction. This allows for accurate early prediction of equipment failure risks, providing reliable data support for subsequent early warning and inspection management, reducing parking lot downtime caused by sudden failures, optimizing the allocation of operation and maintenance resources, and ensuring the continuous and stable operation of multi-level parking garages.

[0053] Corresponding to the lifespan detection-based operation and maintenance management method for multi-level parking lots in the above embodiments, Figure 2 This is a structural block diagram of a lifespan-based automated parking system operation and maintenance management device according to an embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. References Figure 2 The lifespan detection-based operation and maintenance management device 20 for multi-level parking lots includes: an equipment lifespan prediction module 21 and an early warning management module 22.

[0054] Among them, the equipment life prediction module 21 is used to obtain the target operating data and cumulative operating time of each piece of equipment in the multi-level parking garage; select historical fault data from the historical fault database of the equipment based on the equipment type of the equipment; select historical inspection data from the historical inspection database of the equipment based on the equipment type of the equipment; and predict the remaining life of the equipment based on the target operating data, cumulative operating time, historical fault data and historical inspection data. The early warning management module 22 is used to perform early warning management on the equipment of the multi-level parking garage based on the remaining lifespan of all equipment. Furthermore, such as Figure 2 As shown, the lifespan detection-based multi-level parking garage operation and maintenance management device 20 also includes: an equipment inspection cycle calculation module 23 and an inspection management module 24.

[0055] Among them, the equipment inspection cycle calculation module 23 is used to obtain the current inspection cycle and the latest inspection data of each piece of equipment in the multi-level parking lot, select the latest fault data, the fault type and fault time of each fault data from the historical fault database of the equipment, and update the current inspection cycle based on the latest inspection data, the latest fault data and the fault type and fault time of each fault data to obtain the target inspection cycle; The inspection management module 24 is used to manage the inspection of equipment in the multi-level parking garage based on the target inspection cycle of all equipment and the area where the equipment is located.

[0056] In one embodiment of this application, when the equipment life prediction module 21 selects historical fault data from the historical fault database of the equipment based on the equipment type, it is specifically used for: The time span and amount of fault data to be acquired are determined based on the equipment type of the device. Using the current time as the end time, fault data is selected from the historical fault database of the device based on the fault data acquisition time span. If the amount of fault data is less than the amount of fault data acquired, the earliest time corresponding to the fault data is used as the end time, and fault data is selected from the historical fault database of the device until the amount of all acquired fault data is not less than the amount of fault data acquired. All acquired fault data is then used as historical fault data.

[0057] In one embodiment of this application, when the equipment life prediction module 21 selects historical inspection data from the historical inspection database of the equipment based on the equipment type, it is specifically used for: The time span and amount of inspection data to be acquired are determined based on the equipment type of the equipment. Using the current time as the end time, inspection data is selected from the historical inspection database of the device based on the time span of the inspection data acquisition. If the amount of inspection data does not reach the inspection data acquisition limit, the earliest time corresponding to the inspection data is used as the end time, and inspection data is selected from the historical inspection database of the device until the amount of all acquired inspection data reaches the inspection data acquisition limit. All acquired inspection data is then used as historical inspection data.

[0058] In one embodiment of this application, when the equipment life prediction module 21 predicts the remaining life of the equipment based on target operating data, cumulative operating time, historical fault data, and historical inspection data, it is specifically used for: The rated remaining lifespan of the equipment is determined based on its cumulative operating time. Determine the equipment health status based on the latest inspection data from target operation data and historical inspection data; Determine the lifespan decay rate based on historical fault data and historical inspection data. The remaining life of the equipment is determined based on its rated remaining life and its health status.

[0059] In one embodiment of this application, when the equipment life prediction module 21 obtains the remaining equipment life based on the rated remaining equipment life and the equipment health status, it is specifically used to: obtain a life influence coefficient based on the equipment health status; update the rated remaining equipment life based on the life influence coefficient to obtain the remaining equipment life.

[0060] In one embodiment of this application, when the equipment inspection cycle calculation module 23 updates the current inspection cycle based on the latest inspection data, the latest fault data, and the fault type and fault time of each fault data to obtain the target inspection cycle, it is specifically used for: Calculate equipment health score based on the latest inspection data and the latest fault data; The failure impact coefficient is determined based on the failure type and failure time of each failure data, and the equipment failure frequency is predicted based on the failure time of all failure data. The current inspection cycle is updated based on the equipment health score, fault impact coefficient, and equipment fault frequency to obtain the target inspection cycle.

[0061] In one embodiment of this application, when the inspection management module 24 performs inspection management on the equipment in the multi-level parking garage based on the target inspection cycle of all equipment and the area where the equipment is located, it is specifically used to: obtain the equipment type of all equipment, cluster all equipment according to equipment type, target inspection cycle and area where the equipment is located to obtain multiple equipment subsets; for each equipment subset, calculate the centralized inspection cycle based on the target inspection cycle corresponding to each equipment in the equipment subset; and perform inspection management on all equipment in the equipment subset based on the centralized inspection cycle.

[0062] See Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 3The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of the modules in the aforementioned device embodiments, for example... Figure 2 The functions of the equipment life prediction module 21, early warning management module 22, equipment inspection cycle calculation module 23, and inspection management module 24 are shown.

[0063] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), but it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0064] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.

[0065] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store device type information.

[0066] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation methods described in the embodiments of the life detection-based three-dimensional parking lot operation and maintenance management method provided in the embodiments of this application, or they can execute the implementation methods of the electronic device 300 described in the embodiments of this application, which will not be repeated here.

[0067] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0068] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., provided on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0069] Those skilled in the art will recognize that the modules / units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0070] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0071] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules, units, or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces or modules / units, or it may be an electrical, mechanical, or other form of connection.

[0072] The modules / units described as separate components may or may not be physically separate. Similarly, the components shown as modules / units may or may not be physical modules / units; they may be located in one place or distributed across multiple network modules / units. Some or all of the modules / units can be selected to achieve the purpose of the embodiments in this application, depending on actual needs.

[0073] Furthermore, the functional modules / units in the various embodiments of this application can be integrated into one processing module / unit, or each module / unit can exist physically separately, or two or more modules / units can be integrated into one module / unit. The integrated modules / units described above can be implemented in hardware or as software functional modules / units.

[0074] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for operation and maintenance management of multi-level parking garages based on lifespan detection, characterized in that, include: For each device in the multi-level parking garage, obtain the target operating data and cumulative operating time of that device; Based on the equipment type, historical fault data is selected from the historical fault database of the equipment; based on the equipment type, historical inspection data is selected from the historical inspection database of the equipment. The remaining lifespan of the equipment is predicted based on the target operating data, the cumulative operating time, the historical fault data, and the historical inspection data. The equipment in the multi-level parking garage is managed with early warning based on the remaining lifespan of all devices.

2. The operation and maintenance management method for multi-level parking garages based on lifespan detection as described in claim 1, characterized in that, The method further includes: For each piece of equipment in the multi-level parking garage, obtain the current inspection cycle and the latest inspection data of the equipment, select the latest fault data, the fault type and fault time of each fault data from the historical fault database of the equipment, and update the current inspection cycle based on the latest inspection data, the latest fault data and the fault type and fault time of each fault data to obtain the target inspection cycle; The equipment in the multi-level parking garage is inspected and managed based on the target inspection cycle of all equipment and the area where the equipment is located.

3. The operation and maintenance management method for multi-level parking garages based on lifespan detection as described in claim 1, characterized in that, The selection of historical fault data from the historical fault database of the device based on the device type includes: The time span and amount of fault data to be acquired are determined based on the equipment type of the device. Using the current time as the end time, fault data is selected from the historical fault database of the device based on the fault data acquisition time span. If the amount of fault data is less than the amount of fault data acquired, then the earliest time corresponding to the fault data is used as the end time, and fault data is selected from the historical fault database of the device until the amount of all acquired fault data is not less than the amount of fault data acquired. All acquired fault data is then used as historical fault data.

4. The operation and maintenance management method for multi-level parking garages based on lifespan detection as described in claim 1, characterized in that, The selection of historical inspection data from the historical inspection database based on the device type includes: The time span and amount of inspection data to be acquired are determined based on the equipment type of the equipment. Using the current time as the end time, inspection data is selected from the historical inspection database of the device based on the time span of the inspection data acquisition. If the amount of inspection data does not reach the inspection data acquisition limit, the earliest time corresponding to the inspection data is used as the end time, and inspection data is selected from the historical inspection database of the device until the amount of all acquired inspection data reaches the inspection data acquisition limit. All acquired inspection data is then used as historical inspection data.

5. The operation and maintenance management method for multi-level parking garages based on lifespan detection as described in claim 1, characterized in that, The method of predicting the remaining lifespan of the equipment based on the target operating data, the cumulative operating time, the historical fault data, and the historical inspection data includes: The rated remaining lifespan of the equipment is determined based on its cumulative operating time. The health status of the equipment is determined based on the target operating data and the latest inspection data in the historical inspection data. The remaining life of the equipment is obtained based on the rated remaining life of the equipment and the health status of the equipment.

6. The operation and maintenance management method for multi-level parking garages based on lifespan detection as described in claim 5, characterized in that, The process of determining the remaining lifespan of the equipment based on the rated remaining lifespan and the equipment health status includes: The lifespan impact coefficient is obtained based on the equipment's health status. The remaining lifespan of the rated equipment is updated based on the lifespan impact coefficient to obtain the remaining lifespan of the equipment.

7. The operation and maintenance management method for multi-level parking garages based on lifespan detection as described in claim 2, characterized in that, The process of updating the current inspection cycle based on the latest inspection data, the latest fault data, and the fault type and fault time of each fault data to obtain the target inspection cycle includes: Calculate the equipment health score based on the latest inspection data and the latest fault data; The fault impact coefficient is determined based on the fault type and fault time of each fault data, and the equipment fault frequency is predicted based on the fault time of all fault data. The current inspection cycle is updated based on the equipment health score, the fault impact coefficient, and the equipment fault frequency to obtain the target inspection cycle.

8. The operation and maintenance management method for multi-level parking garages based on lifespan detection as described in claim 2, characterized in that, The inspection management of the equipment in the multi-level parking garage based on the target inspection cycle of all equipment and the area where the equipment is located includes: Obtain the equipment type of all devices, and cluster all devices according to equipment type, target inspection cycle and device location to obtain multiple device subsets; For each subset of equipment, a centralized inspection cycle is calculated based on the target inspection cycle corresponding to each of the equipment in the subset; and inspection management is performed on all equipment in the subset based on the centralized inspection cycle.

9. A three-dimensional parking garage operation and maintenance management device based on lifespan detection, characterized in that, include: The equipment life prediction module is used to obtain the target operating data and cumulative operating time of each piece of equipment in the automated parking system. Based on the equipment type, historical fault data is selected from the historical fault database of the equipment; based on the equipment type, historical inspection data is selected from the historical inspection database of the equipment. The remaining lifespan of the equipment is predicted based on the target operating data, the cumulative operating time, the historical fault data, and the historical inspection data. The early warning management module is used to perform early warning management on the equipment of the multi-level parking garage based on the remaining lifespan of all equipment.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 8.

Images