Lifting machine working condition evaluation method and system combining digital model and physical simulation

Abstract

This invention provides a method and system for assessing the operating conditions of a lifting platform by combining digital models and physical simulations. It relates to the field of industrial equipment safety assessment technology. First, a bidirectional transmission mechanism for the operating condition evolution between the digital twin evolutionary model and the physical simulation deduction system is established to achieve bidirectional data transfer. Then, based on this bidirectional transmission mechanism, multiple rounds of interactive evolutionary cycles are executed to generate a full evolutionary dataset. Next, the full evolutionary dataset is analyzed to obtain the trajectory of operating parameters, and safety thresholds are correlated to mine safety risk trigger trajectory features. Finally, operating condition safety assessment results are generated based on the safety risk trigger trajectory features. This invention can comprehensively and accurately assess the operating condition safety of the lifting platform's hydraulic system and identify potential risks in advance.

Description

Technical Field

[0001] This invention relates to the field of industrial equipment safety assessment technology, and more specifically, to a method and system for assessing the operating conditions of a lifting machine that combines digital modeling and physical simulation. Background Technology

[0002] In the application scenarios of lifts, the hydraulic system, as its core power transmission and control component, directly affects the overall operational stability of the lift and the safety of the operators. Traditional methods for assessing the operational safety of lift hydraulic systems mainly rely on theoretical calculations and actual tests.

[0003] Theoretical calculation methods typically rely on simplified assumptions and fixed parameter models, using mathematical formulas to assess the safety of hydraulic systems. However, these methods struggle to fully account for the complex and variable factors present in actual operating conditions, such as changes in ambient temperature, fluctuations in hydraulic oil performance, and wear and tear on components, leading to significant discrepancies between the assessment results and real-world scenarios. While practical testing methods can obtain data closer to real-world conditions, they are costly, requiring substantial time, manpower, and resources, and may involve inherent safety risks. Furthermore, traditional methods often only evaluate hydraulic systems under single or limited operating conditions, failing to comprehensively reflect the safety changes of hydraulic systems during the evolution of different operating conditions, and thus unable to promptly identify potential safety risks, ultimately failing to meet the high safety requirements of modern industrial production for lifting platform hydraulic systems. Summary of the Invention

[0004] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a method for evaluating the operating conditions of a lift by combining digital models and physical simulations, the method comprising:

[0005] A bidirectional transmission mechanism for the working condition evolution of the hydraulic system of the lifting machine is established between the digital twin evolution model and the physical simulation system. Through this bidirectional transmission mechanism, the working condition evolution trend data output by the digital twin evolution model is transmitted to the physical simulation system in real time, and the entity evolution deviation data collected by the physical simulation system is transmitted back to the digital twin evolution model.

[0006] Based on the bidirectional transmission mechanism of working condition evolution, a multi-round interactive evolution cycle is executed between the digital twin evolution model and the physical simulation and deduction system to generate a full evolution dataset;

[0007] Based on the full evolution dataset, the complete trajectory of various operating parameters of the lifting hydraulic system during the working condition evolution process is analyzed, the safety thresholds corresponding to various operating parameters are associated, the triggering conditions and evolution rules of the operating parameter trajectory exceeding the safety threshold are extracted, and the safety risk triggering trajectory characteristics of different evolution stages are explored.

[0008] Based on the safety risk trigger trajectory characteristics obtained from the excavation, the working condition safety assessment results of the lifting machine hydraulic system are generated.

[0009] In another aspect, embodiments of the present invention also provide a lifting machine condition assessment system that combines digital modeling and physical simulation, including a processor and a machine-readable storage medium connected to the processor. The machine-readable storage medium is used to store programs, instructions, or code, and the processor is used to run the programs, instructions, or code in the machine-readable storage medium to implement the above-described method.

[0010] Based on the above, this invention establishes a bidirectional transmission mechanism between the digital twin evolutionary model of the lifting platform's hydraulic system and the physical simulation and deduction system to achieve deep interaction between the digital and physical worlds. The multi-round interactive evolutionary cycle executed based on this bidirectional transmission mechanism generates a comprehensive evolutionary dataset that fully covers the changes in various operating parameters of the hydraulic system under different operating conditions. Through in-depth analysis of the comprehensive evolutionary dataset, the complete trajectory of various operating parameters during the evolutionary process can be accurately obtained. After associating with safety thresholds, the triggering conditions and evolutionary patterns of operating parameter trajectories exceeding safety thresholds can be accurately extracted, revealing the safety risk triggering trajectory characteristics at different evolutionary stages. The operating condition safety assessment results generated based on the safety risk triggering trajectory characteristics can reflect the safety status of the lifting platform's hydraulic system during different operating condition evolution processes, identify potential safety risks in advance, and effectively ensure the safe and stable operation of the lifting platform's hydraulic system. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of the execution flow of the lifting machine condition evaluation method that combines digital modeling and physical simulation provided in the embodiments of the present invention.

[0012] Figure 2 This is a schematic diagram of the hardware architecture of the lift condition assessment system that combines digital modeling and physical simulation provided in an embodiment of the present invention. Detailed Implementation

[0013] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a method for evaluating the operating conditions of a lift that combines digital models and physical simulation, provided in one embodiment of the present invention. The following is a detailed description of this method for evaluating the operating conditions of a lift that combines digital models and physical simulation.

[0014] Step S110: Establish a two-way transmission mechanism for the working condition evolution between the digital twin evolution model and the physical simulation system of the lifting machine hydraulic system. Through this two-way transmission mechanism, the working condition evolution trend data output by the digital twin evolution model is transmitted to the physical simulation system in real time, and the entity evolution deviation data collected by the physical simulation system is transmitted back to the digital twin evolution model.

[0015] In this embodiment, the hydraulic system of a certain type of car lift is used as the application scenario. This hydraulic system mainly consists of components such as a hydraulic pump, hydraulic valve group, hydraulic cylinder, hydraulic pipeline, and hydraulic oil. When establishing a two-way transmission mechanism for working condition evolution, it is first necessary to ensure that the digital twin evolution model and the physical simulation and deduction system can perform efficient and accurate data interaction.

[0016] Step S111: Select an industrial transmission link that meets the preset delay requirements to connect the operating carrier of the digital twin evolution model of the lifting hydraulic system with the control carrier of the physical simulation and deduction system. Set the data packet structure of the link transmission according to the transmission requirements of the operating parameters of the lifting hydraulic system, and determine the evolution parameter type, evolution stage identifier and time stamp information contained in the data packet.

[0017] In step S111 above, the preset delay requirement of the industrial transmission link needs to be determined based on the real-time requirements of the lifting hydraulic system's operating condition evolution. Generally, the data transmission delay should not affect the normal evolution and simulation of the system. For example, industrial Ethernet, which has high bandwidth and low latency characteristics, can be selected as the transmission link. The data packet structure setting needs to comprehensively consider various operating parameters of the lifting hydraulic system. Evolution parameter types include hydraulic oil pressure, flow rate, temperature, component displacement, contact stress, etc.; evolution stage identifiers are used to distinguish different evolution stages, such as the lifting stage, descent stage, and stationary stage; time stamp information accurately records the time point when the data is generated for subsequent data alignment and analysis.

[0018] Step S112: Set up an evolution trend data output node on the running carrier of the digital twin evolution model, and collect the working condition evolution trend data generated during the operation of the digital twin evolution model at preset time intervals. The working condition evolution trend data includes evolution path node parameters, parameter evolution rate and evolution stage transition conditions.

[0019] For step S112, the evolution trend data output node can be a specific software module on the operating platform. The selection of the preset time interval needs to balance the accuracy of data acquisition and the operational burden of the system; for example, it can be set to collect data once every certain time interval. Evolution path node parameters refer to the parameter values ​​of each key node on the evolution path, such as the hydraulic oil pressure values ​​corresponding to different height positions in the lifting stage; parameter evolution rate indicates how fast the parameter changes with time, such as the rate of change of hydraulic oil flow; evolution stage transition conditions specify the conditions that must be met to transition from one evolution stage to another, such as transitioning from the lifting stage to the stationary stage when the lifting height reaches the preset maximum value.

[0020] Step S113: Set up entity evolution data acquisition nodes on the control carrier of the physical simulation and deduction system, deploy acquisition devices adapted to the operating status of entity components, and collect operating parameter data, component deformation data and evolution process data of entity components during the evolution test to form entity evolution data.

[0021] In step S113, the entity evolution data acquisition node is also a specific functional module on the control carrier. The selection of acquisition devices must be adapted to the operating state of the entity component. For example, a pressure sensor is installed at the hydraulic pump outlet to collect hydraulic oil pressure, a displacement sensor is installed on the hydraulic cylinder piston rod to collect motion displacement, and strain gauges are installed on the surface of key components to collect component deformation data. The operating parameter data includes hydraulic oil pressure, flow rate, temperature, etc., corresponding to those in the digital twin evolution model; the component deformation data reflects the structural changes of the component during the evolution process; and the evolution process data records information such as the current evolution stage and progress of the entity component.

[0022] Step S114: Align the evolution trend data output node of the digital twin evolution model with the entity evolution data acquisition node of the physical simulation and deduction system according to a preset time interval, so that the time when the digital twin evolution model outputs the working condition evolution trend data is consistent with the time when the physical simulation and deduction system acquires the entity evolution data.

[0023] In step S114, aligning the preset time intervals ensures that the data output by the digital twin evolutionary model corresponds one-to-one with the data collected by the physical simulation system in the time dimension, facilitating subsequent data comparison and deviation analysis. For example, if the preset time intervals of the evolutionary trend data output nodes and the physical evolution data acquisition nodes are both set to the same time unit, alignment can be achieved through a unified time reference. In practice, time consistency can be achieved by synchronizing the clocks of the two nodes or by adding precise timestamps during data transmission.

[0024] Step S115: Compare the working condition evolution trend data transmitted by the digital twin evolution model with the entity evolution data collected by the physical simulation and deduction system. Extract the data differences under the same evolution stage and the same parameter dimension to generate entity evolution deviation data. The entity evolution deviation data includes the name of the difference parameter, the difference value, and the evolution stage in which the difference occurred.

[0025] The specific implementation process of step S115 is as follows: First, the received working condition evolution trend data and entity evolution data are classified and organized according to evolution stage and parameter type. Then, within the same evolution stage, data comparison is performed on the same parameter dimension. For example, in the lifting stage, the working condition evolution trend data and entity evolution data of hydraulic oil pressure are compared. The difference value is calculated by the difference between the two. The name of the difference parameter clearly indicates which parameter caused the difference, and the evolution stage in which the difference occurred indicates which evolution process the difference appeared in.

[0026] Step S116: Set the trigger condition for the reverse transmission of entity evolution deviation data. When the difference value in the entity evolution deviation data reaches the preset transmission threshold, start the reverse transmission process of entity evolution deviation data to the digital twin evolution model.

[0027] For step S116, the preset transmission threshold needs to be determined based on the safety and stability requirements of the lifting hydraulic system. Different parameters may correspond to different preset transmission thresholds. For example, the preset transmission threshold for the difference in hydraulic oil pressure may be set to a specific range. When the actual calculated difference value exceeds this range, the reverse transmission trigger condition is met, the reverse transmission process is started, and the entity evolution deviation data is transmitted to the digital twin evolution model.

[0028] Step S117: Plan the link occupancy priority of forward transmission of operating condition evolution trend data and reverse transmission of entity evolution deviation data, so that the link occupancy priority of entity evolution deviation data transmission is higher than that of operating condition evolution trend data transmission.

[0029] In step S117, since entity evolution deviation data is crucial for the correction and optimization of the digital twin evolutionary model and can reflect the actual situation of the physical simulation system in a timely manner, it needs to be given a higher link occupancy priority. When both forward and reverse transmission link requirements occur simultaneously, priority should be given to ensuring the transmission of entity evolution deviation data to ensure that the digital twin evolutionary model can be adjusted in a timely manner according to the actual deviation.

[0030] Step S118: Set up a data buffer node in the transmission link to store the forward transmission of working condition evolution trend data and the reverse transmission of entity evolution deviation data.

[0031] A data buffer node can be an independent storage device or storage area. During data transmission, when the link is busy or the receiving end is temporarily unable to process data, the data buffer node can temporarily store the data, and then transmit the data again when the link becomes idle or the receiving end is ready. This avoids data loss and transmission interruption, ensuring the stability and integrity of data transmission.

[0032] Step S119: Record the data transmission status in the transmission link according to a preset period to generate a data transmission status sequence. The data transmission status sequence includes the start time, completion time and data integrity information of each data transmission.

[0033] The preset cycle can be set according to the system's operating conditions and the frequency of data transmission. Within each preset cycle, the start and end times of each data transmission are recorded to assess the time consumed during data transmission; at the same time, the integrity of the data during transmission is checked, such as whether there is any data loss or damage, and the above information is recorded to form a data transmission status sequence.

[0034] Step S119: Record the data transmission status in the transmission link according to a preset period to generate a data transmission status sequence. The data transmission status sequence includes the start time, completion time and data integrity information of each data transmission.

[0035] Step S1191: Set the recording period for the data transmission status, which is consistent with the preset time interval of the evolution trend data output node.

[0036] In this embodiment, the recording period for the data transmission status is set to be the same as the preset time interval for the evolution trend data output nodes. For example, if the preset time interval for the evolution trend data output nodes is T, then the recording period for the data transmission status is also T. This setting ensures that the recording of the data transmission status is synchronized with the data acquisition rhythm, facilitating subsequent correlation analysis between the data transmission status and the corresponding evolution data.

[0037] Step S1192: Set up data transmission monitoring nodes in the forward and reverse transmission links of the bidirectional transmission mechanism for operating condition evolution, and establish data connection between the data transmission monitoring nodes and the data buffer nodes.

[0038] Data transmission monitoring nodes can be specialized hardware devices or software modules deployed in the transmission link, capable of monitoring the flow of data in the forward and reverse transmission links in real time. By establishing a data connection with the data buffer node, the data transmission monitoring node can obtain relevant information about all forward and reverse transmission data passing through the buffer node, such as data identifier, size, and transmission direction.

[0039] Step S1193: Within the recording period of each data transmission status, the start time and completion time of each working condition evolution trend data transmission in the forward transmission link are collected by the data transmission monitoring node, and the difference between the start time and completion time is calculated to obtain the forward transmission time.

[0040] Within each recording period T, the data transmission monitoring node tracks the data transmission trend of each operating condition evolution occurring in the forward transmission link. When a data transmission is detected to begin, its start time is recorded; when the data transmission is detected to be completed, its completion time is recorded. Then, the completion time is subtracted from the start time to obtain the forward transmission time for that data transmission. For example, if the start time of a data transmission is t1 and the completion time is t2, then the forward transmission time is t2-t1.

[0041] Step S1194: Collect the start time and completion time of each entity evolution deviation data transmission in the reverse transmission link through the data transmission monitoring node, and calculate the difference between the start time and completion time to obtain the reverse transmission time.

[0042] Similar to the acquisition of forward propagation time, within each recording period T, the data propagation monitoring node also monitors the propagation process of each entity evolution deviation data in the reverse propagation link. The start and end times of each propagation are recorded, and the difference between the two is calculated to obtain the reverse propagation time. For example, if the start time of a certain entity evolution deviation data propagation is t3 and the end time is t4, then the reverse propagation time is t4 - t3.

[0043] Step S1195: Extract the working condition evolution trend data and entity evolution data of each transmission from the data buffer node, compare the extracted data volume with the preset transmission data volume, determine whether the data is complete, and generate a data integrity identifier.

[0044] The data buffer node stores forward-transmitted operational condition evolution trend data and reverse-transmitted entity evolution data. Within each recording period T, the data transmission monitoring node extracts data for each transmission from the data buffer node. The preset transmission data volume is the amount of data that should be included in each transmission, determined before data transmission. The extracted actual data volume is compared with the preset transmission data volume. If they are equal, the data is complete; if the actual data volume is less than the preset transmission data volume, data is missing.

[0045] Step S1196: When the amount of data extracted is consistent with the preset amount of data to be transmitted, a data integrity identifier is generated; when the amount of data extracted is less than the preset amount of data to be transmitted, a data missing identifier is generated, and the parameter type of the missing data is recorded.

[0046] Based on the comparison results of step S1195, a corresponding data integrity identifier is generated. If the data is complete, a data integrity identifier is generated, such as a specific character or code; if the data is missing, a data missing identifier is generated, and the missing data portion is further analyzed to determine the parameter type corresponding to the missing data, such as missing hydraulic oil pressure parameters, missing flow parameters, etc., and the above information is recorded.

[0047] Step S1197: Associate and bind the forward transmission time, reverse transmission time, data integrity identifier, and parameter type of missing data within the recording period of each data transmission state to form a single-cycle data transmission state record. Continuously concatenate multiple single-cycle data transmission state records according to the chronological order of the recording periods of the data transmission states to generate a data transmission state sequence. Add corresponding evolution round identifiers and evolution stage identifiers to the data transmission state sequence so that each single-cycle data transmission state record can correspond to a specific evolution process.

[0048] At the end of each recording period T, the forward transmission time, reverse transmission time, data integrity identifier, and parameter types of missing data collected within that period are associated and bound to form a single-period data transmission status record. Then, multiple single-period data transmission status records are sequentially connected according to the chronological order of the recording periods to form a data transmission status sequence. For ease of traceability, a corresponding evolutionary cycle identifier and evolutionary stage identifier are added to each single-period data transmission status record in the data transmission status sequence, such as the current evolutionary cycle number and whether it is in the rise or fall phase.

[0049] Step S1198: Store the generated data transmission state sequence in a dedicated data storage area, and establish a data connection between the dedicated data storage area and the running carrier of the digital twin evolution model.

[0050] The dedicated data storage area can be an independent database or file system with high storage capacity and fast data read / write speed. The generated data transmission state sequence is stored in this area for subsequent querying and analysis. Simultaneously, because the dedicated data storage area establishes a data connection with the runtime environment of the digital twin evolutionary model, the digital twin evolutionary model can access and acquire the data transmission state sequence at any time, allowing for adjustments to the preset time intervals between evolutionary trend data output nodes and entity evolution data acquisition nodes.

[0051] Step S1110: Adjust the preset time interval between the evolution trend data output node and the entity evolution data acquisition node based on the data transmission state sequence, so that the time matching degree of data transmission is adapted to the link transmission capacity.

[0052] By analyzing the data transmission status sequence, we can understand the time consumption and integrity of data transmission. If the data transmission time is long or the data loss is severe, it may be necessary to appropriately increase the preset time interval to reduce the amount of data transmitted and reduce the link load. If the data transmission is good, the preset time interval can be appropriately reduced to increase the frequency of data acquisition and transmission, thereby improving the system's perception accuracy of the evolution of operating conditions.

[0053] Step S1110-1: Extract the forward transmission time and reverse transmission time of the recording cycle of multiple data transmission states from the data transmission state sequence, and calculate the average value of the forward transmission time and the average value of the reverse transmission time.

[0054] Specifically, multiple consecutive recording periods are selected from the data transmission state sequence, and the forward transmission time and reverse transmission time data are extracted for each period. Then, the forward transmission times are added together and divided by the number of recording periods to obtain the average forward transmission time; the average reverse transmission time is calculated using the same method.

[0055] Step S1110-2: Analyze the relationship between the average forward transmission time and the average reverse transmission time and the preset time interval of the current evolution trend data output node, and determine whether the preset time interval of the current evolution trend data output node causes the transmission time to be too long.

[0056] The calculated average forward and reverse transmission times are compared with preset time thresholds. If either of these averages exceeds the preset time threshold, it indicates that the current preset time interval may be too short, causing data transmission to be too frequent and thus resulting in excessive transmission time.

[0057] Step S1110-3: Extract data integrity identifiers from the data transmission status sequence, count the frequency of occurrence of statistical missing identifiers, and calculate the data missing rate.

[0058] Data integrity flags are used to indicate whether each data transfer is complete. For example, a specific flag value can represent complete data, while another flag value can represent missing data. The data missing rate is obtained by counting the number of times the missing data flag appears in the data transfer state sequence and then dividing by the total number of data transfers.

[0059] Step S1110-4: When the data missing rate exceeds the preset ratio, analyze the transmission link and evolution stage corresponding to the missing data, and determine whether the link load is too high due to the preset time interval of the evolution trend data output node being too short.

[0060] The preset ratio is determined based on the system's requirements for data integrity. When the data loss rate exceeds this preset ratio, a detailed analysis is needed to determine which transmission link (forward or reverse) the missing data occurred on and its corresponding evolution stage. If the missing data is found to be mainly concentrated in time periods with high data transmission frequency, it is likely due to an excessively short preset time interval, leading to excessive link load and an inability to process and transmit all data in a timely manner, thus causing data loss.

[0061] Step S1110-5: When the average time taken for forward transmission or the average time taken for reverse transmission exceeds the preset time threshold, extend the preset time interval between the evolution trend data output node and the entity evolution data acquisition node to reduce the data transmission frequency.

[0062] In the above situation, extending the preset time interval can reduce the number of data transmissions per unit time, thereby reducing the load on the link, making data transmission smoother, and reducing transmission time. For example, if the original preset time interval is a certain value, when the average transmission time exceeds the threshold, it can be adjusted to a larger time interval.

[0063] Step S1110-6: If there is room for optimization in the preset time interval of the current evolution trend data output node, that is, the average transmission time is less than the preset time threshold and the data missing rate is less than the preset ratio, shorten the preset time interval between the evolution trend data output node and the entity evolution data acquisition node by the preset ratio to improve the time accuracy of data transmission.

[0064] When transmission time and data loss are within acceptable limits, it indicates that the current preset time interval has room for optimization. Shortening the time interval by a preset ratio allows for more frequent data collection and transmission, thereby improving the time accuracy of the data and more accurately reflecting the real-time situation of operational changes.

[0065] Step S1110-7: After adjusting the preset time interval between the evolution trend data output node and the entity evolution data acquisition node, record the new preset time interval value and generate a time interval adjustment record. This time interval adjustment record includes the preset time interval before adjustment, the preset time interval after adjustment, and the reason for adjustment.

[0066] Recording adjustments to time intervals helps in tracing and analyzing system operations. The reasons for adjustments need to be explained in detail, such as adjustments made due to excessive transmission time or optimizations made due to excessively low data loss rates.

[0067] Step S1110-8: Apply the adjusted preset time interval to the evolution trend data output node and the entity evolution data acquisition node to start a new round of data transmission status recording.

[0068] Configure the new preset time interval parameter into the evolution trend data output node and the entity evolution data acquisition node, so that they collect and output data according to the new time interval. Then, restart recording the data transmission status to evaluate the effect of the adjustment.

[0069] Step S1110-9: Collect the adjusted new data transmission status sequence, and calculate the adjusted average transmission time and data missing rate.

[0070] Following the same method as before, the adjusted new data transmission state sequence was analyzed to calculate the average transmission time and data missing rate in order to determine whether the adjusted preset time interval achieved the expected effect.

[0071] Step S1110-10: Compare the average transmission time and data loss rate before and after the adjustment, and determine whether the adjusted preset time interval makes the data transmission time matching degree compatible with the link transmission capacity. If it is not compatible, repeat the adjustment steps until it is compatible.

[0072] Compare the adjusted average transmission time and data loss rate with the data before adjustment. If the adjusted transmission time is reduced and the data loss rate is lower, it indicates that the adjusted preset time interval is more suitable for the link transmission capacity; if the expected effect is not achieved, the above adjustment steps need to be repeated until the data transmission time matches the link transmission capacity.

[0073] Step S120: Based on the bidirectional transmission mechanism of working condition evolution, execute multiple rounds of interactive evolution loops between the digital twin evolution model and the physical simulation deduction system to generate a full evolution dataset.

[0074] In the aforementioned scenario of a car lift hydraulic system, multi-round interactive evolutionary cycles are the key process for generating a full evolutionary dataset. Through repeated interactions and data feedback between the digital twin evolutionary model and the physical simulation system, the evolutionary path is continuously optimized, thereby obtaining rich and accurate evolutionary data.

[0075] Step S121: Based on the bidirectional transmission mechanism of working condition evolution, initiate the collaborative evolution simulation of the digital twin evolution model and the physical simulation deduction system. The digital twin evolution model generates the initial working condition evolution path according to the initial structural parameters of the lifting hydraulic system. The initial working condition evolution path is then transmitted to the physical simulation deduction system to drive the physical components in the physical simulation deduction system to perform synchronous evolution tests.

[0076] In step S121, the initial structural parameters form the basis for the digital twin evolutionary model to generate the initial operating condition evolution path. In the hydraulic system of a car lift, these parameters encompass the inherent properties of each core component. For example, the rated working pressure and displacement of the hydraulic pump determine the hydraulic power it can provide during evolution; the diameter and opening pressure of various valves in the hydraulic valve group affect the flow characteristics of the hydraulic oil; the cylinder bore and piston rod diameter of the hydraulic cylinder relate to the lifting force and movement speed; the length, inner diameter, and material of the hydraulic pipeline affect the pressure loss and flow rate of the hydraulic oil; and the viscosity and density of the hydraulic oil also affect the system's performance as operating conditions change. After receiving these initial structural parameters, the digital twin evolutionary model combines the working principle of the lift with the preset evolution goals, such as lifting a certain load to a specific height, to generate the initial operating condition evolution path. This initial operating condition evolution path will plan in detail the target values ​​of various operating parameters at different time points, such as the pressure and flow rate curves of the hydraulic oil over time, and the displacement and speed of the hydraulic cylinder. Then, the initial operating condition evolution path is transmitted to the physical simulation system through the bidirectional transmission mechanism of operating condition evolution, so that the physical components in the physical simulation system can undergo synchronous evolution tests according to the requirements of the path.

[0077] Step S1211: Input the initial structural parameters of the lifting hydraulic system into the digital twin evolution model. These initial structural parameters include hydraulic pump structural parameters, hydraulic valve structural parameters, hydraulic cylinder structural parameters, hydraulic pipeline structural parameters, and hydraulic oil property parameters.

[0078] When inputting initial structural parameters into a digital twin evolutionary model, it is essential to ensure the accuracy and completeness of these parameters. For hydraulic pump structural parameters, in addition to rated working pressure and displacement, parameters include the pump's speed range and efficiency characteristics; hydraulic valve structural parameters also involve the valve's adjustment range and response time; hydraulic cylinder structural parameters include stroke length and sealing performance; hydraulic pipeline structural parameters must consider the pipeline's bending radius and connection method; and hydraulic oil property parameters encompass viscosity index and anti-wear properties. These parameters need to be obtained through precise measurement and experimentation and input into the digital twin evolutionary model in a specific data format so that the model can accurately simulate the initial state of the hydraulic system.

[0079] Step S1212: Set the working condition evolution boundary conditions of the digital twin evolution model. The working condition evolution boundary conditions include the working condition types covered by the evolution, the time span of the evolution, and the initial change range of various operating parameters.

[0080] The boundary conditions for the evolution of operating conditions provide constraints for the evolutionary deduction of the digital twin evolutionary model. The types of operating conditions covered in the evolution of the hydraulic system of a car lift mainly include typical conditions such as no-load lifting, full-load lifting, no-load descent, full-load descent, and emergency stop. The time span of the evolution is determined according to actual application requirements, such as simulating multiple work cycles of the lift within a day. The initial variation range of various operating parameters needs to be set according to the design specifications and safety requirements of the lift. For example, the initial variation range of hydraulic oil pressure cannot exceed the rated working pressure of the hydraulic pump, and the initial variation range of component displacement cannot exceed the stroke limit of the hydraulic cylinder.

[0081] Step S1213: Based on the initial structural parameters and working condition evolution boundary conditions, the digital twin evolution model deduces the target values ​​of operating parameters, target values ​​of component motion states, and working condition type conversion nodes of the lifting hydraulic system at different time nodes, forming the initial working condition evolution path.

[0082] After receiving initial structural parameters and boundary conditions for operational evolution, the digital twin evolutionary model employs specialized hydraulic system simulation algorithms for deduction. During the deduction process, the model determines target values ​​for operating parameters at different time points based on the performance curves of the hydraulic pump, the flow characteristics of the hydraulic valves, the force analysis of the hydraulic cylinders, and the fluid dynamics calculations of the pipelines. For example, during the lifting phase, the hydraulic oil pressure gradually increases over time to provide sufficient lifting force, while the flow rate is adjusted according to the lifting speed requirements. Target values ​​for component motion states include the extension speed and position of the hydraulic cylinder piston rod. Operational condition transition nodes are determined based on preset conditions. For instance, when the lifting height reaches a set value, the system transitions from a lifting condition to a stationary condition. At this point, the target values ​​for operating parameters change accordingly, such as maintaining stable hydraulic oil pressure to sustain the lifting height.

[0083] Step S1214: Divide the initial working condition evolution path into multiple evolution stage data blocks according to the preset data format. Each evolution stage data block contains the time range, target values ​​of operating parameters, and component motion state requirements for the corresponding evolution stage.

[0084] The preset data format needs to ensure data clarity and readability, facilitating parsing and execution by the physical simulation system. For example, the data format can be defined as structured data containing fields such as timestamps, parameter types, and parameter values. The initial working condition evolution path is divided into evolution stages, with each stage corresponding to a data block. For instance, the data block for the lifting stage includes the start and end times of that stage, the target value sequence of operating parameters such as hydraulic oil pressure and flow rate within that time period, and the component motion state requirements such as the movement speed and displacement of the hydraulic cylinder. This division allows the physical simulation system to receive and execute evolution tasks in stages, improving the system's operational efficiency and controllability.

[0085] Step S1215: Through the forward transmission link of the bidirectional transmission mechanism of working condition evolution, the split evolution stage data blocks are sequentially transmitted to the control carrier of the physical simulation and deduction system.

[0086] During data transmission, the forward propagation link encapsulates and verifies the data to ensure its integrity and accuracy. Upon receiving the evolutionary stage data blocks, the control vehicle performs preliminary parsing, checking for correct data format and content compliance. If issues are found, a feedback mechanism notifies the digital twin evolutionary model to resend the data; if the data is normal, subsequent drive control operations are prepared.

[0087] Step S1216: After receiving the evolution stage data blocks, the control carrier of the physical simulation system analyzes the target values ​​of the operating parameters and the motion state requirements of the components in each evolution stage data block, and generates the drive control signals for the physical components.

[0088] The control unit contains a dedicated analysis module capable of in-depth analysis of data blocks from different evolutionary stages. For example, for the target values ​​of the hydraulic pump's operating parameters, the analysis module converts them into electrical signals that control the speed of the hydraulic pump motor; for the control requirements of the hydraulic valve, it generates corresponding electromagnetic coil control signals to adjust the valve opening. These drive control signals need to accurately reflect the target values ​​of the operating parameters and the motion state requirements of the components. Parameters such as signal amplitude, frequency, and duty cycle need to be precisely calculated and adjusted based on the target values.

[0089] Step S1217: The drive control signal is transmitted to the actuator in the physical simulation system. The actuator drives the physical components to adjust their operating status according to the requirements in the evolution stage data block and executes the synchronous evolution test.

[0090] The actuators include the drive motor of the hydraulic pump, the solenoids of the hydraulic valves, and the control devices of the hydraulic cylinders. When an actuator receives a drive control signal, it will act according to the signal's instructions. For example, the hydraulic pump drive motor adjusts its speed according to the control signal, thereby changing the output flow and pressure of the hydraulic oil; the solenoids of the hydraulic valves control the opening and closing of the valves according to the signal's energization or de-energization, regulating the flow direction and flow rate of the hydraulic oil; and the hydraulic cylinders, under the action of the hydraulic oil, perform extension and retraction movements according to the component's motion requirements, achieving lifting or lowering actions. Through the coordinated work of the actuators, the physical components can accurately perform synchronous evolution tests according to the requirements of the data blocks in the evolution stage.

[0091] Step S1218: When the synchronous evolution experiment is started, record the start time of the initial evolution stage, and simultaneously turn on the acquisition device in the physical simulation and deduction system to acquire the actual values ​​of the operating parameters of the physical components and the actual values ​​of the motion state of the components at the preset frequency.

[0092] The start time of the initial evolution phase is recorded using a high-precision clock module for comparison with subsequent time nodes in the evolution path. The data acquisition device begins operation the instant the synchronous evolution experiment starts, acquiring data at a preset frequency. The selection of the preset frequency takes into account the rate of change of operating parameters and component motion states. For rapidly changing parameters, such as the dynamic changes in hydraulic oil pressure, a higher acquisition frequency is required to capture the transient characteristics of the parameters; for slowly changing parameters, the acquisition frequency can be appropriately reduced to decrease the amount of data and the system load. The actual values ​​of the acquired operating parameters and component motion states are stored in real-time in the local storage unit of the physical simulation system.

[0093] Step S1219: Compare the actual values ​​of the collected initial evolution stage operating parameters with the corresponding target values ​​of the parameters in the initial working condition evolution path in real time, and extract the parameter differences in the initial stage.

[0094] During real-time comparison, the system compares the actual values ​​of operating parameters at each data acquisition point with the target values ​​of the parameters at the corresponding time points in the initial operating condition evolution path. For example, at a certain time point during the lifting phase, the difference between the actual value of the collected hydraulic oil pressure and the target pressure value at that time point is calculated to obtain the parameter difference. The above parameter difference reflects the deviation between the actual operating state of the physical component and the expected state of the digital twin evolution model. The above parameter differences are organized and stored according to parameter type and time order.

[0095] Step S12110: According to the working condition type conversion node in the initial working condition evolution path, adjust the operation mode of the physical component through the actuator, enter the synchronous evolution test of the next evolution stage, continuously collect the physical operation data of each stage and compare it with the parameter target value of the corresponding stage.

[0096] Step S12110-1: Extract the time information and corresponding operation mode adjustment requirements of all working condition type conversion nodes from the initial working condition evolution path, and establish a working condition conversion node list.

[0097] The initial operating condition evolution path contains detailed information on the transitions between various operating conditions. The system traverses the entire path, extracting the time information for each transition node, such as the specific time point from the lifting stage to the stationary stage; it also obtains the corresponding operating mode adjustment requirements, such as needing to shut down the hydraulic pump and maintain the current state of the hydraulic valves when transitioning to the stationary stage. This information is then compiled into a list of operating condition transition nodes, where each entry corresponds to a transition node and includes time information and adjustment requirements.

[0098] Step S12110-2: During the synchronous evolution test, compare the current evolution time with the conversion time information in the list of working condition conversion nodes in real time to determine whether the current evolution time has reached the working condition type conversion node.

[0099] The system tracks the current evolution time in real time through its internal timing module and continuously compares it with the transition time information in the list of operating condition transition nodes. When the current evolution time approaches the time of a certain transition node, the system will prepare in advance; when the current evolution time reaches the time of the transition node, the operating condition transition process will be triggered immediately.

[0100] Step S12110-3: When the current evolution time reaches the conversion time of any working condition type conversion node, pause the physical component drive of the current evolution stage and record the actual values ​​of the current physical component's operating parameters and motion state.

[0101] When pausing the drive of a physical component, the system gradually reduces the strength of the drive control signal, allowing the component to stop smoothly and avoiding impact and vibration. Simultaneously, it accurately records the actual values ​​of various operating parameters of the physical component at this time, such as hydraulic oil pressure, flow rate, temperature, component position, speed, and actual motion status.

[0102] Step S12110-4: Adjust the drive parameters of the actuator according to the operating mode adjustment requirements corresponding to the switching node in the working condition switching node list. The adjustment of the drive parameters of the actuator includes the adjustment of the drive voltage, drive frequency and drive stroke.

[0103] The adjustment of the actuator's drive parameters must be strictly performed according to the operating mode adjustment requirements. For example, when switching from the lifting stage to the lowering stage, it is necessary to adjust the drive voltage of the hydraulic valve and change the valve's opening direction and degree to achieve reverse flow of hydraulic oil; the drive frequency of the hydraulic pump may need to be adjusted to decrease or increase according to the requirements of the lowering speed; the adjustment of the drive stroke involves the movement range limitation of the hydraulic cylinder piston rod, etc. The adjustment of the above parameters needs to be achieved through precise calculation and control to ensure a smooth transition of operating modes.

[0104] Step S12110-5: The adjusted drive parameters are transmitted to the actuator, which drives the physical component to start running in the new operating mode, completing the change of working condition type and entering the next evolution stage.

[0105] After receiving the adjusted drive parameters, the actuator will operate according to the new parameter values, smoothly transitioning the physical components from the current state to the new operating mode. During the transition, the system monitors the operating status of the physical components in real time to ensure the safety and stability of the transition process. For example, when the lift transitions from the stationary stage to the lowering stage, the actuator will control the hydraulic valve to open slowly, causing the hydraulic cylinder to descend gradually, avoiding the danger of a sudden fall.

[0106] Step S12110-6: Record the completion time of the working condition type conversion, compare the completion time of the working condition type conversion with the preset time of the conversion node in the initial working condition evolution path, and generate a conversion time difference value.

[0107] The completion time of the operating condition type transition is accurately recorded by the timing module. The difference between this time and the preset time of the transition node in the initial operating condition evolution path is calculated to obtain the transition time difference value. This transition time difference value reflects the deviation between the actual operating condition transition time and the expected time, and is an important indicator for evaluating the system's evolution synchronization. If the difference value is large, it may indicate a delay or anomaly in the system during the operating condition transition process.

[0108] Step S12110-7: Start the acquisition device for the next evolution stage, and acquire the actual values ​​of the operating parameters and motion states of the physical components in this evolution stage at a preset frequency to generate the initial fragment of physical operating data for the next evolution stage.

[0109] Once the next evolution phase begins, the data acquisition devices immediately start working, collecting data at a preset frequency. The initial data acquisition helps the system quickly understand the initial operating status of physical components in the new evolution phase and promptly detect any anomalies. For example, in the initial segment of the descent phase, if the collected hydraulic oil pressure rises abnormally, it may indicate that the hydraulic valve is blocked or stuck.

[0110] Step S12110-8: Compare the initial fragment of entity operation data in the next evolution stage with the parameter target value of the corresponding stage in the initial working condition evolution path, and extract the initial parameter differences.

[0111] By comparing the actual data of the initial segment with the target value, potential deviations can be detected early in the evolutionary process. For example, in the initial segment of the descent phase, the difference between the actual descent velocity and the target velocity represents the initial parameter difference.

[0112] Step S12110-9: According to the requirements of the initial working condition evolution path, continuously drive the physical component to run in this evolution stage until the end time of this evolution stage is reached, collect physical operation data throughout the process and form stage physical operation data.

[0113] Throughout the entire evolution phase, the actuators continuously drive the physical components according to the requirements of the initial operating condition evolution path. The data acquisition devices also continuously collect data to form complete stage-specific physical operation data. This data records the entire operation of the physical components during this evolution phase, including dynamic changes in parameters and the stability of the motion state.

[0114] Step S12110-10: Associate and bind the complete entity operation data of the evolution stage with the conversion time difference value and the initial parameter difference, store it in the local storage unit of the physical simulation and deduction system, and at the same time transfer the associated and bound data to the data buffer node.

[0115] By associating and binding data, the operational data of entities during the evolutionary stage can be linked to the time differences and initial parameter differences during the condition transition process, facilitating subsequent comprehensive analysis and data mining. Local storage units are used to store this data long-term, while data buffer nodes provide temporary storage space for real-time data transmission and sharing, enabling the digital twin evolutionary model to promptly acquire this data for analysis and adjustment.

[0116] Step S122: The physical simulation and deduction system collects physical evolution data during the physical component evolution test, compares the physical evolution data with the target data in the initial working condition evolution path, extracts the data differences to generate physical evolution deviation data, and transmits the physical evolution deviation data back to the digital twin evolution model through the working condition evolution bidirectional transmission mechanism.

[0117] During the physical component evolution test, the physical simulation system uses various acquisition devices to collect physical evolution data in real time. For example, pressure sensors collect hydraulic oil pressure data at different locations, flow sensors collect hydraulic oil flow data, and displacement sensors collect hydraulic cylinder displacement data. This physical evolution data is then compared point-by-point and time-by-time with the corresponding target data in the initial operating condition evolution path. During the comparison, the differences between the actual and target data are calculated for each parameter and each time point, such as pressure difference, flow difference, and displacement difference. These differences are then organized and summarized according to certain rules to generate physical evolution deviation data. This deviation data clearly indicates the name of the difference parameter, the magnitude of the difference, and the specific evolution stage and time point at which the difference occurred. Subsequently, the physical evolution deviation data is transmitted back to the digital twin evolution model through the reverse transmission link of the operating condition evolution bidirectional transmission mechanism. During the reverse transmission process, the previously set link occupancy priority is followed to ensure that the physical evolution deviation data is received by the digital twin evolution model preferentially and promptly.

[0118] Step S123: After receiving the entity evolution deviation data, the digital twin evolution model adjusts its own evolution inference rules based on the entity evolution deviation data, generates a corrected working condition evolution path, and transmits the corrected working condition evolution path to the physical simulation inference system to drive a new round of entity evolution test.

[0119] The digital twin evolutionary model incorporates a dedicated rule adjustment module. Upon receiving deviation data from the entity's evolution, this module performs in-depth analysis to identify the causes of the deviation. For example, if the actual hydraulic oil pressure consistently falls below the target value, it might be due to decreased hydraulic pump efficiency or pipeline leaks. Based on the analysis, the evolutionary rules are adjusted, such as revising the hydraulic pump's performance parameter model and considering the impact of pipeline leaks on pressure. Then, based on the adjusted evolutionary rules, a new operating condition evolution path is generated—the revised operating condition evolution path. This revised path adjusts the previous target data to better reflect the actual operating conditions of the physical components. Subsequently, through a bidirectional operating condition evolution transmission mechanism, the revised operating condition evolution path is transmitted back to the physical simulation system, initiating a new round of entity evolution experiments.

[0120] Step S124: Repeat the steps of generating the modified working condition evolution path, executing the entity evolution test, collecting entity evolution deviation data, and adjusting the evolution inference rules to form a multi-cycle working condition evolution transmission closed loop. Collect all evolution path data and entity evolution data in the multi-cycle process to generate a full evolution dataset.

[0121] In multiple iterations, each iteration adjusts the digital twin evolutionary model based on the entity evolution deviation data from the previous iteration, generating a new, corrected operating condition evolution path and driving the physical simulation system to execute new entity evolution experiments. As the number of iterations increases, the evolutionary rules of the digital twin evolutionary model become increasingly closer to the actual operating characteristics of the physical components, and the generated operating condition evolution paths become more accurate. In each iteration, evolution path data and entity evolution data are recorded, including the initial operating condition evolution path, the corrected operating condition evolution paths for each iteration, and the corresponding entity evolution data and entity evolution deviation data. This data is collected, organized, and stored to form a full evolution dataset. This full evolution dataset contains a large amount of operational data from the lifting platform's hydraulic system at different evolution stages and under different operating conditions.

[0122] Step S130: Based on the full evolution dataset, analyze the complete trajectory of various operating parameters of the lifting hydraulic system during the working condition evolution process, associate the safety thresholds corresponding to various operating parameters, extract the triggering conditions and evolution rules of the operating parameter trajectory exceeding the safety threshold, and explore the safety risk trigger trajectory features of different evolution stages.

[0123] The full evolution dataset contains rich information on operating parameters. In-depth analysis of this data allows for a clear understanding of the changing trajectories of various operating parameters throughout the entire operating condition evolution process. In the hydraulic system of a car lift, the safety thresholds of operating parameters are crucial indicators for ensuring safe system operation, such as the maximum allowable pressure and temperature of the hydraulic oil, and the maximum allowable displacement and maximum allowable contact stress of components. Correlating the complete trajectory of operating parameters with the corresponding safety thresholds allows for intuitive identification of when the parameter trajectory exceeds the safety threshold. Further analysis of the various conditions that cause the safety threshold to be exceeded, such as the status of other operating parameters at that time, the type of operating condition, and the evolution stage, allows for the summarization of triggering conditions and evolutionary patterns. Due to differences in operating conditions and requirements at different evolution stages, the characteristics of the safety risk trigger trajectory will also differ. For example, during the lifting stage, the hydraulic oil pressure is prone to exceeding the safety threshold, and its trigger trajectory characteristics may manifest as a rapid increase in pressure exceeding the threshold; while during the descent stage, the contact stress of components may exceed the safety threshold due to excessive speed, and its trigger trajectory characteristics may manifest as a sharp increase in contact stress with increasing speed. By thoroughly mining the full evolutionary dataset, we can accurately extract the trajectory features of safety risks at different evolutionary stages.

[0124] For example, step S131: Separate the multi-round evolution path data generated by the digital twin evolution model and the multi-round entity evolution data collected by the physical simulation and deduction system from the full evolution dataset, and classify and collect the two types of data according to the evolution round and evolution stage.

[0125] The full evolution dataset is a collection containing multiple types of data, requiring initial data separation and classification. Evolutionary path data is generated by the digital twin evolutionary model in each cycle, including the initial operating condition evolutionary path and the revised operating condition evolutionary paths after each iteration. Entity evolution data consists of actual operational data collected by the physical simulation system during each round of entity evolution experiments. These two types of data are categorized and grouped according to the evolutionary cycle (e.g., first cycle, second cycle, etc.) and the different evolutionary stages within each cycle (e.g., lifting stage, descent stage, etc.). For example, the evolutionary path data and entity evolution data for the lifting stage in the first cycle can be grouped together, while the data for the descent stage in the second cycle can be grouped into another category. This makes the data more organized and facilitates subsequent analysis for specific cycles and stages.

[0126] Step S132: Extract various operating parameter data of the lifting hydraulic system from the classified and aggregated evolution path data and entity evolution data to form a complete data sequence of various operating parameters, including hydraulic oil pressure, hydraulic oil flow, component movement displacement, component contact stress and component temperature.

[0127] Based on the classification and aggregation, various operational parameters are extracted for each data category. For example, target values ​​for hydraulic oil pressure and flow rate at each time point are extracted from the evolution path data; actual values ​​for hydraulic oil pressure and flow rate at each time point are extracted from the entity evolution data. All extracted data for the same operational parameter are arranged in chronological order to form a complete data sequence for that parameter. For example, the hydraulic oil pressure data sequence will include pressure values ​​at different time points throughout the entire evolution process, whether target or actual values.

[0128] Step S133: Collect safety threshold data corresponding to various operating parameters of the lifting hydraulic system, establish a one-to-one correspondence between operating parameters and safety thresholds, and form a safety threshold comparison table for operating parameters.

[0129] Safety threshold data can be obtained from the design manuals of the lifting platform's hydraulic system, as well as relevant industry standards and specifications. For each type of operating parameter, there is a corresponding safety threshold, such as the safety threshold for hydraulic oil pressure and the safety threshold for hydraulic oil flow. A safety threshold lookup table is created by mapping the names of the operating parameters to their corresponding safety thresholds. This table can also include descriptions of the threshold type, such as upper limit thresholds and lower limit thresholds, as well as information on the applicable operating conditions and evolution stages of the thresholds.

[0130] Step S134: Connect the complete data sequences of various operating parameters in chronological order to generate complete trajectory curves of various operating parameters during the evolution of operating conditions. These complete trajectory curves contain the changing trends of parameter values ​​over time.

[0131] By concatenating the complete data sequences of various operating parameters along a time axis, a complete trajectory curve of the parameter's change over time can be obtained. For example, the trajectory curve of hydraulic oil pressure will show how the pressure rises, falls, or remains stable over time from its initial value throughout the entire operating condition evolution, as well as the changes at different stages of evolution. Through the trajectory curve, the changing trend of parameter values ​​can be observed intuitively, such as the trend of change, the amplitude of fluctuation, and the rate of change.

[0132] Step S135: Overlay and compare the complete trajectory curves of various operating parameters with the corresponding safety thresholds in the operating parameter safety threshold comparison table, identify the time nodes in the complete trajectory curves where the parameter values ​​exceed the safety thresholds, and mark them as safety threshold breach nodes.

[0133] The complete trajectory curves of the operating parameters are overlaid with the corresponding safety thresholds on the same coordinate system. By visual observation or automatic computer comparison, the time points in the trajectory curves where parameter values ​​exceed the safety thresholds are identified. For example, when the trajectory curve of hydraulic oil pressure rises and exceeds the upper limit of the safety threshold, this time point is the safety threshold breach point. This comparison and identification is performed on the trajectory curves of all operating parameters to mark all safety threshold breach points, and the specific time and corresponding parameter value of each breach point are recorded.

[0134] Step S136: Extract the trajectory segments of the operating parameters within a preset time range before and after the safety threshold breakthrough node, analyze the rate of change, magnitude of change, and cooperative relationship between parameters within the trajectory segments of the operating parameters, and generate trajectory features before the breakthrough.

[0135] The size of the preset time range can be determined based on the characteristics of parameter changes and the needs of analysis. For example, it can be set as the time range from a period before the breakthrough point to the breakthrough point itself. After extracting the trajectory segments of the operating parameters within this time range, they are analyzed in detail. The rate of change refers to the amount of change of the parameter value per unit time, which is obtained by calculating the ratio of the difference in parameter values ​​between adjacent time points in the trajectory segment to the time difference; the magnitude of change refers to the maximum amount of change of the parameter value within this time range; the cooperative relationship between parameters is to analyze whether there is a correlation between the changes of multiple related parameters within this time period, such as whether the increase in hydraulic oil pressure is accompanied by an increase in flow rate. Combining these analysis results, a pre-breakthrough trajectory feature is generated, which can describe the change pattern and state of the parameters before breaking the safety threshold.

[0136] Step S137: Analyze the trajectory segment of the running parameters within a preset time range after the safety threshold is breached, extract the decay or increase trend of the parameter values, the time interval of parameter fluctuations, and the influence trend on other related parameters within the trajectory segment of the running parameters, and generate the trajectory features after the breach.

[0137] Similar to the extraction of pre-breakthrough trajectory features, the first step is to determine a preset time range after the breakthrough point. Within this range, the decay or increase trend of parameter values ​​is analyzed; that is, whether the parameter value gradually decreases back to a safe range after breaking the threshold, or continues to increase. The time interval of parameter fluctuation refers to the time difference between adjacent peaks or troughs during the fluctuation process. The impact on other related parameters is analyzed to determine how the parameter breaking the safe threshold affects the changes in other related parameters in the system, such as whether the increase in hydraulic oil temperature leads to a decrease in viscosity. Based on these analytical results, post-breakthrough trajectory features are generated to describe the changes and impacts of the parameter after breaking the safe threshold.

[0138] Step S138: Classify the pre-breakthrough trajectory features and post-breakthrough trajectory features corresponding to all safety threshold breakthrough nodes according to the evolution stage, and count the frequency of occurrence of each type of trajectory feature in different evolution stages.

[0139] All safety threshold breach nodes are categorized according to their evolutionary stage; for example, breach nodes in the lift stage are grouped into one category, and those in the descent stage into another. Then, for each evolutionary stage, the frequency of occurrence of various pre-breakthrough and post-breakthrough trajectory features is counted. For example, in the lift stage, a specific pre-breakthrough trajectory feature appears a certain number of times, and a certain post-breakthrough trajectory feature appears a certain number of times. By statistically analyzing the frequency, we can understand which trajectory features are more likely to appear in different evolutionary stages.

[0140] Step S139: Analyze the correlation between different trajectory features within the same evolutionary stage, identify the trajectory feature combinations that can jointly lead to the breach of the safety threshold, and summarize the evolutionary conditions corresponding to the trajectory feature combinations.

[0141] Within the same evolutionary stage, different trajectory features may exhibit certain correlations. For example, the trajectory features preceding a hydraulic oil pressure exceedance may simultaneously appear with those preceding a flow rate exceedance, and their combination can lead to a pressure exceeding the safety threshold. Data analysis methods, such as association rule mining and cluster analysis, can be used to identify these trajectory feature combinations. Then, the evolutionary conditions corresponding to these trajectory feature combinations are summarized, including the operating conditions, initial parameter states, and changes in other parameters under which this trajectory feature combination will occur and lead to a safety threshold exceedance.

[0142] Step S1310: Extract unique trajectory features and combinations of trajectory features for different evolution stages, and combine them with the working condition type and initial state of operating parameters for the corresponding evolution stage to generate safety risk triggering trajectory features for different evolution stages.

[0143] For example, step S1310-1: Summarize the trajectory features before and after the breakthrough corresponding to the safety threshold breakthrough node in each evolution stage to form a trajectory feature set for each evolution stage.

[0144] For each evolutionary stage, the pre-breakthrough and post-breakthrough trajectory features of all safety threshold breach nodes in that stage are collected together to form a set containing all possible trajectory features for that stage. For example, the trajectory feature set for the lifting stage includes all pre-breakthrough and post-breakthrough trajectory features that appear in that stage.

[0145] Step S1310-2: Compare the trajectory feature sets of different evolutionary stages, extract the trajectory features that appear only in one evolutionary stage but not in other evolutionary stages, and mark them as unique trajectory features of that evolutionary stage.

[0146] By comparing the trajectory feature sets of different evolutionary stages, we can identify those trajectory features that appear only in a certain evolutionary stage and never appear in other evolutionary stages. For example, if a certain trajectory feature after a hydraulic oil temperature exceeds a certain threshold only appears in the lifting stage and not in the descent or stationary stages, it can be marked as a unique trajectory feature of the lifting stage.

[0147] Step S1310-3: Analyze the combination relationship between unique trajectory features within the same evolutionary stage, identify unique trajectory feature combinations that can jointly lead to the breach of the safety threshold, and mark them as unique trajectory feature combinations of that evolutionary stage.

[0148] After identifying unique trajectory features, the combinations of these unique trajectory features within the same evolutionary stage are further analyzed. The unique trajectory feature combinations that can work together to cause the safety threshold to be exceeded are identified. For example, during the lifting stage, when a certain unique pressure trajectory feature and a certain unique flow trajectory feature appear simultaneously, they will jointly cause the hydraulic oil pressure to exceed the safety threshold. Therefore, the combination of these two features is marked as the unique trajectory feature combination for the lifting stage.

[0149] Step S1310-4: Extract the operating condition type information corresponding to each evolution stage from the full evolution dataset, and determine the operating mode and load characteristics of each evolution stage.

[0150] Operating condition type information is clearly recorded in the full evolution dataset; for example, the identification information for each evolution stage includes the operating condition type. Based on this information, the operating mode of each evolution stage can be determined, such as lifting mode, lowering mode, no-load mode, full-load mode, etc.; as well as load characteristics, such as load size and load variation.

[0151] Step S1310-5: Extract the initial state data of the operating parameters corresponding to each evolution stage. The initial state data of the operating parameters includes the initial values ​​of various operating parameters when the evolution stage starts.

[0152] The initial state data of the operating parameters are the specific values ​​of various operating parameters at the start of the evolution phase, such as the initial pressure and temperature of the hydraulic oil and the initial position of the hydraulic cylinder at the start of the lifting phase. The above data can be extracted from the evolution phase data blocks or entity operating data in the full evolution dataset.

[0153] Step S1310-6: Associate the unique trajectory features of any evolution stage with the working condition type information and initial state data of the operating parameters of that evolution stage, and analyze the influence of working condition type and initial state on the formation of unique trajectory features.

[0154] This involves comprehensively analyzing the unique trajectory characteristics of a specific evolutionary stage in conjunction with information on the operating conditions and initial state data of the parameters for that stage. For example, analyzing how a particular unique trajectory characteristic is formed under lifting conditions, full load characteristics, and high initial pressure. This analysis reveals the degree and manner in which operating conditions and initial states influence unique trajectory characteristics; for instance, certain operating conditions may more readily induce specific unique trajectory characteristics, and certain ranges of initial parameter values ​​may increase the probability of unique trajectory characteristics appearing.

[0155] Step S1310-7: Associate the unique trajectory feature combination of the evolution stage with the corresponding working condition type information and initial state data of the operating parameters to determine the formation conditions of the unique trajectory feature combination under the combined effect of working condition type and initial state.

[0156] Similar to the correlation analysis of unique trajectory features, this study correlates unique trajectory feature combinations with operating condition information and initial state data of operating parameters. It delves into how unique trajectory feature combinations are formed under different operating condition types and initial state combinations. For example, under descent conditions and no-load / load characteristics, what are the conditions for the formation of a certain unique trajectory feature combination when the initial speed is low?

[0157] Step S1310-8: Based on the correlation analysis results, supplement the background information for the formation of unique trajectory features and combinations of unique trajectory features. This background information includes working condition type parameters and initial state parameters.

[0158] Based on the results of the correlation analysis, background information is added to each unique trajectory feature and combination of unique trajectory features. This information describes in detail the operating conditions (such as operating mode, load size, etc.) and initial state parameters (such as initial pressure, initial temperature, etc.) under which the feature or combination was formed. This background information helps to more comprehensively understand the environment and causes of the safety risk-triggered trajectory features.

[0159] Step S1310-9: Standardize the description of the unique trajectory features and combinations of unique trajectory features after supplementing the background information, and determine the range of parameter changes, rate of change, and influence relationship of related parameters.

[0160] Standardized descriptions aim to provide a unified way to express different unique trajectory features and combinations, facilitating communication, comparison, and application. Standardized descriptions need to clearly define the range of parameter variation for the feature or combination (i.e., the interval within which parameter values ​​change); the rate of change (i.e., how quickly parameter values ​​change); and the relationships between related parameters (i.e., what kind of influence the feature or combination has on other relevant parameters, and the direction and extent of that influence).

[0161] Step S1310-10: Combine the standardized and described unique trajectory features to generate the safety risk triggering trajectory features for this evolution stage, and form a multi-stage safety risk triggering trajectory feature set according to the evolution stage sequence.

[0162] The standardized, unique trajectory features and combinations thereof are integrated, and redundant and repetitive information is removed to form the final safety risk triggering trajectory features for this evolutionary stage. Then, according to the chronological order of the evolutionary stages, the safety risk triggering trajectory features of each evolutionary stage are combined to form a multi-stage safety risk triggering trajectory feature set. This multi-stage safety risk triggering trajectory feature set comprehensively reflects the safety risk triggering trajectory features that may occur in the hydraulic system of the vehicle lift at different evolutionary stages.

[0163] Step S140: Based on the safety risk trigger trajectory characteristics obtained from the excavation, generate the working condition safety assessment results of the lifting machine hydraulic system.

[0164] After obtaining the safety risk trigger trajectory characteristics at different evolution stages, the operational safety of the lifting platform's hydraulic system can be assessed based on this. By constructing an operational condition evolution safety risk correlation model, the evolution trajectory of operating parameters is linked to the safety risk trigger state. Then, specialized evolution verification experiments are conducted, collecting entity evolution data to optimize and verify the model. Finally, combining the model's risk assessment results with the full evolution dataset, the system's safety performance under different operating conditions is comprehensively evaluated, generating a comprehensive and accurate operational safety assessment result. The assessment result will include the main safety risk points of the system, the probability of risk occurrence, the severity of the risk, and corresponding prevention and control recommendations.

[0165] Step S141: Based on the safety risk triggering trajectory characteristics obtained from the mining, construct a working condition evolution safety risk association model. This working condition evolution safety risk association model takes the evolution trajectory of operating parameters as input and the safety risk triggering state as output.

[0166] The construction of a safety risk correlation model based on operational condition evolution can employ methods such as machine learning and statistical analysis. First, the mined safety risk triggering trajectory features are used as training samples for the model. The model's input is the evolution trajectory of operating parameters, i.e., the sequence data of various operating parameters changing over time; the output is the safety risk triggering state, such as whether a safety risk will be triggered and what type of safety risk will be triggered. During model construction, appropriate model structures and algorithms need to be selected, such as neural network models or support vector machine models. The model is then trained using training samples to determine its parameters and weights, enabling the model to accurately predict the safety risk triggering state based on the evolution trajectory of the input operating parameters.

[0167] Step S142: Drive the physical simulation and deduction system to perform a special evolution verification test on the safety risk trigger trajectory features obtained by mining through the bidirectional transmission mechanism of working condition evolution, and collect entity evolution data during the special evolution verification test.

[0168] Step S1421: Organize the safety risk trigger trajectory features obtained from different evolution stages, and determine the type of operating parameters, parameter change trend and evolution stage corresponding to each safety risk trigger trajectory feature.

[0169] The safety risk trigger trajectory features obtained from the mining are systematically organized to clarify the type of operating parameters involved in each feature, such as hydraulic oil pressure and flow rate; the trend of parameter changes, such as whether the parameter rises rapidly, falls slowly, or fluctuates sharply; and the evolution stage to which the feature belongs, such as the lifting stage or the descent stage.

[0170] Step S1422: Based on the parameter change trend of each safety risk trigger trajectory feature, set the operating condition parameter range for the special evolution verification test. This operating condition parameter range covers the parameter change interval corresponding to the safety risk trigger trajectory feature.

[0171] Based on the parameter change trends of the safety risk trigger trajectory characteristics, determine the range of operating parameters that need to be focused on in the special evolution verification test. For example, if a certain safety risk trigger trajectory characteristic is that the hydraulic oil pressure rises rapidly and exceeds the threshold during the lifting phase, then the range of operating parameters in the special test should cover the interval of rapid pressure rise, including the range of changes in parameters such as the initial pressure value, the rate of rise, and the time to reach the threshold. This ensures that the test can fully simulate the conditions under which the safety risk trigger trajectory characteristics occur.

[0172] Step S1423: Determine the evolution time length of the special evolution verification test. The evolution time length includes the preset time range before the breakthrough, the breakthrough node time, and the preset time range after the breakthrough corresponding to the safety risk trigger trajectory characteristics.

[0173] Determining the evolution timeframe needs to cover the entire process from the generation to the development of the safety risk trigger trajectory characteristics. The pre-breakthrough preset time range refers to the time from when the parameters begin to change abnormally until the safety threshold is exceeded; the breakthrough node time is the point in time when the parameters just exceed the safety threshold; the post-breakthrough preset time range refers to a period of time after the threshold is exceeded, to observe subsequent changes in the parameters and the system's response. Combining these three time components constitutes the evolution timeframe of the specialized evolution verification experiment.

[0174] Step S1424: Generate the evolution path of the special evolution verification test according to the set operating condition parameter range and evolution time length. The evolution path of the special evolution verification test includes the target value of the operating parameters and the component motion state requirements corresponding to each time node.

[0175] Referring to the method for generating the initial operating condition evolution path, an evolution path for a specific evolution verification test is generated based on the set range of operating condition parameters and the length of the evolution time. This path specifies in detail the target values ​​of operating parameters at each time point, such as the hydraulic oil pressure gradually increasing at a specific rate within a preset time range before the breakthrough; reaching a threshold at the breakthrough point; and the pressure maintaining or changing within a preset time range after the breakthrough. Simultaneously, the requirements for component motion states are also clearly defined, such as the target values ​​for the hydraulic cylinder's speed and position at each time point.

[0176] Step S1425: Through the forward transmission link of the bidirectional transmission mechanism of working condition evolution, the evolution path of the special evolution verification test is transmitted to the control carrier of the physical simulation and deduction system.

[0177] Similar to transmitting the initial operating condition evolution path, the evolution path of the special evolution verification test is transmitted to the control carrier of the physical simulation and deduction system through a forward transmission link. During the transmission process, the integrity and accuracy of the path data are ensured. After receiving the data, the control carrier parses and verifies it to prepare for the execution of the special evolution verification test.

[0178] Step S1426: The control carrier of the physical simulation and deduction system analyzes the evolution path of the special evolution verification test and generates special drive control signals for the physical components. The parameter adjustment accuracy of the special drive control signals meets the accuracy requirements of the special evolution verification test.

[0179] When analyzing the evolution path of a specialized evolution verification test, the control platform pays greater attention to the details and precision of the parameters. The generated specialized drive control signals have higher parameter adjustment precision than those of ordinary evolution tests to ensure accurate reproduction of the safety risk trigger trajectory characteristics. For example, the control signal for a hydraulic valve requires higher opening precision to accurately control changes in hydraulic oil flow and pressure.

[0180] Step S1427: Transmit the special drive control signal to the actuator of the physical simulation and deduction system to drive the physical component to perform the evolution test according to the evolution path of the special evolution verification test.

[0181] After receiving the specific drive control signal, the actuator strictly follows the signal's instructions to drive the physical components. During operation, it more precisely controls the motion state and parameter changes of the components to simulate the process of generating safety risk trigger trajectory characteristics. For example, in simulating the process of hydraulic oil pressure exceeding the safety threshold, the actuator precisely controls the output of the hydraulic pump and the opening of the hydraulic valves to make the pressure change according to the requirements of the specific evolution path.

[0182] Step S1428: During the special evolution verification test, increase the data acquisition frequency of the acquisition device, focus on acquiring key operating parameter data corresponding to the safety risk trigger trajectory characteristics, and generate high-resolution entity evolution data sequences.

[0183] To capture subtle changes in the characteristics of safety risk triggering trajectories in greater detail, the data acquisition frequency of the acquisition devices will be appropriately increased during the specialized evolution verification experiment. Simultaneously, key operating parameters corresponding to the safety risk triggering trajectory characteristics, such as the main pressure and flow parameters that lead to risk triggering, will be collected with particular focus. This will generate high-resolution entity evolution data sequences containing more detailed information, facilitating in-depth analysis and model validation of the safety risk triggering trajectory characteristics.

[0184] Step S1429: According to the evolution stage of the special evolution verification test, record the difference between the high-resolution entity evolution data sequence of each evolution stage and the corresponding parameter target value in the evolution path of the special evolution verification test, and generate special evolution deviation data fragments.

[0185] Specialized evolutionary verification experiments are also divided according to set evolutionary stages, such as the pre-breakthrough stage, the breakthrough stage, and the post-breakthrough stage. After each evolutionary stage, the high-resolution entity evolutionary data sequence of that stage is compared with the parameter target values ​​of the corresponding stage in the specialized evolutionary path, the difference value is calculated, and specialized evolutionary deviation data segments are generated. The deviation data segments record in detail the differences of each parameter in that stage.

[0186] Step S14210: After the completion of the special evolution verification test, the high-resolution entity evolution data sequence and special evolution deviation data fragments of each stage are summarized to form a special evolution verification test data set.

[0187] Step S14210-1: Extract high-resolution entity evolution data sequences for each evolution stage from the data acquisition device storage unit of the physical simulation and deduction system. These high-resolution entity evolution data sequences contain the operating parameter values ​​and component motion state data at each acquisition time point.

[0188] The data acquisition device's storage unit stores all high-resolution entity evolution data sequences collected during the specialized evolution verification experiment. After the experiment, the data sequences are extracted from this storage unit sequentially according to the evolution stages. Each data sequence contains detailed operating parameter values ​​for each acquisition time point within the corresponding evolution stage, such as hydraulic oil pressure, flow rate, and temperature, as well as component motion state data, such as hydraulic cylinder displacement, velocity, and acceleration.

[0189] Step S14210-2: According to the evolutionary stage sequence of the special evolution verification test, the high-resolution entity evolution data sequence of each stage is spliced ​​together to form a continuous full-cycle high-resolution entity evolution data sequence.

[0190] The extracted high-resolution entity evolution data sequences for each evolution stage are stitched together according to the order of the experimental evolution stages, making the data sequences continuous in time and forming a full-cycle high-resolution entity evolution data sequence covering the entire cycle of the special evolution verification experiment. This can completely reflect the operating status and parameter changes of the entity components during the experiment.

[0191] Step S14210-3: Extract the special evolution deviation data segments of each stage from the data buffer node. The special evolution deviation data segments contain the difference data between the entity evolution data of the corresponding stage and the target value of the evolution path parameter of the special evolution verification test.

[0192] The data buffer nodes store data fragments of specific evolutionary deviations generated during the specific evolutionary verification experiment. These data fragments are extracted; each fragment contains information about the differences between the entity evolution data and the target data at the corresponding evolutionary stage, such as parameter difference values ​​and the time points at which the differences occurred.

[0193] Step S14210-4: Align the specific evolutionary deviation data segments of each stage with the full-cycle high-resolution entity evolution data sequence according to the evolutionary stage order, so that each specific evolutionary deviation data segment corresponds to a specific evolutionary stage of the full-cycle high-resolution entity evolution data sequence.

[0194] By using time-stamped information, specific evolutionary deviation data segments are precisely aligned with the full-cycle high-resolution entity evolution data sequence. This ensures that each deviation data segment accurately corresponds to the relevant evolutionary stage in the full-cycle data sequence, facilitating correlation analysis between the deviation data and the entity evolution data.

[0195] Step S14210-5: Extract the evolution path information of the special evolution verification test, including the time range, target parameter values ​​and working condition type of each stage, and associate the evolution path information of the special evolution verification test with the full-cycle high-resolution entity evolution data sequence.

[0196] The evolution path information of the special evolution verification experiment serves as the target reference for the experiment. By associating it with the full-cycle high-resolution entity evolution data sequence, the target data can be compared at any time when analyzing entity evolution data to understand the deviation between the actual data and the target data, as well as the reasons for the deviation.

[0197] Step S14210-6: Record the start time, end time, and transition time of each stage of the special evolution verification experiment, generate the experiment timeline information, and add the experiment timeline information to the full-cycle high-resolution entity evolution data sequence.

[0198] The experimental timeline information provides a temporal reference framework for the full-cycle high-resolution entity evolution data sequence. The start and end times mark the overall duration of the experiment, while the transition times for each stage clearly define the boundaries between different evolutionary phases. Adding this time information to the data sequence facilitates more convenient temporal analysis and processing of the data.

[0199] Step S14210-7: Identify anomalous data points in the full-cycle high-resolution entity evolution data sequence. These anomalous data points are data that exceed the normal parameter variation range. Extract the acquisition time and parameter type corresponding to the anomalous data points.

[0200] Each data point in the full-cycle high-resolution entity evolution data sequence is checked by using preset normal parameter variation ranges. If the parameter value of a data point exceeds the normal range, it is marked as an abnormal data point. The acquisition time and corresponding parameter type of the abnormal data point are also recorded, such as an abnormal increase in hydraulic oil temperature at a certain time point.

[0201] Step S14210-8: Associate the identified abnormal data points with the corresponding special evolutionary deviation data segments to analyze the causes of the abnormal data.

[0202] The information from abnormal data points is correlated with the deviation data at the corresponding time points in the specific evolutionary deviation data segment. For example, if an abnormal data point with abnormally high pressure appears at a certain time point, and the specific evolutionary deviation data at that time point shows a large difference between the pressure and the target value, it can be analyzed whether the abnormal data is caused by abnormal drive control signals, physical component failures, or other external factors.

[0203] Step S14210-9: Integrate the full-cycle high-resolution entity evolution data sequence, the special evolution deviation data fragment, the evolution path information of the special evolution verification experiment, the experiment time axis information, and the results of the abnormal data cause analysis.

[0204] The above data and analysis results are integrated to form a complete dataset for a specialized evolutionary verification experiment. During the integration process, the correlation and consistency between various data types are ensured to facilitate subsequent data retrieval, analysis, and application.

[0205] Step S14210-10: Encapsulate all the integrated data according to the preset data format to generate a special evolution verification test data set, which contains full verification data corresponding to the safety risk trigger trajectory characteristics.

[0206] The preset data format must meet the requirements of data storage and transmission, and have good compatibility and readability. The integrated data is then encapsulated according to this format, and necessary metadata information, such as the test number, test date, and safety risk trigger trajectory feature identifiers, is added to ultimately generate a specialized evolutionary verification test dataset. This dataset contains all verification data related to the safety risk trigger trajectory features.

[0207] Step S143: Input the entity evolution data collected from the special evolution verification test into the working condition evolution safety risk association model, adjust the association weight between the operating parameter trajectory and the safety risk triggering state in the working condition evolution safety risk association model, optimize the model inference accuracy, and combine the risk judgment results output by the optimized working condition evolution safety risk association model with the full evolution dataset generated by the multi-round working condition evolution transmission closed loop to generate the working condition safety assessment results of the lifting machine hydraulic system.

[0208] The entity evolution data collected from the specialized evolution verification test is input into the operational condition evolution safety risk correlation model. The model adjusts its internal correlation weights based on this new data. These correlation weights reflect the degree of influence of different operating parameter trajectories on the safety risk triggering state. By adjusting the weights, the model can better fit the actual safety risk triggering situation, improving the model's inference accuracy. After model optimization, the model is used to determine the risk of the operating parameter trajectories in the full evolution dataset, obtaining the risk determination results. Then, the model's risk determination results are comprehensively analyzed with the full evolution dataset. Combining the system's design requirements, safety standards, and actual operating conditions, the operational condition safety of the lifting platform's hydraulic system is evaluated from multiple dimensions, ultimately generating a comprehensive and detailed operational condition safety assessment result.

[0209] Figure 2 The following is a schematic diagram of the hardware structure of a lift condition assessment system 100 combining digital modeling and physical simulation, provided by an embodiment of the present invention, for implementing the above-described lift condition assessment method combining digital modeling and physical simulation. Figure 2 As shown, the lift condition assessment system 100, which combines digital modeling and physical simulation, may include a processor 110, a machine-readable storage medium 120, a bus 130, and a communication unit 140.

[0210] Machine-readable storage medium 120 can store data and / or instructions. In some embodiments, machine-readable storage medium 120 can store data acquired from an external terminal. In some embodiments, machine-readable storage medium 120 can store data and / or instructions used by the lift condition assessment system 100 combining digital models and physical simulations to perform or use in order to complete the exemplary methods described in this invention. In a specific implementation, one or more processors 110 execute the computer-executable instructions stored in machine-readable storage medium 120, causing processor 110 to perform the lift condition assessment method combining digital models and physical simulations as described in the above method embodiments. Processor 110, machine-readable storage medium 120, and communication unit 140 are connected via bus 130, and processor 110 can be used to control the transmission and reception actions of communication unit 140. The specific implementation process of processor 110 can be found in the various method embodiments executed by the lift condition assessment system 100 combining digital models and physical simulations described above. The implementation principles and technical effects are similar, and will not be repeated here.

[0211] Furthermore, this embodiment of the invention also provides a readable storage medium containing computer-executable instructions. When the processor executes the computer-executable instructions, the above-described method for evaluating the operating conditions of a lift, which combines digital models and physical simulation, is implemented.

[0212] It should be noted that, in order to simplify the description of this invention and thus aid in the understanding of one or more embodiments, the foregoing description of the embodiments of this invention sometimes combines multiple features into a single embodiment, drawing, or description thereof. Similarly, it should be noted that, in order to simplify the description of this invention and thus aid in the understanding of one or more embodiments, the foregoing description of the embodiments of this invention sometimes combines multiple features into a single embodiment, drawing, or description thereof.

Claims

1. A method for evaluating the operating conditions of a lift by combining digital modeling and physical simulation, characterized in that, The method includes: A bidirectional transmission mechanism for the working condition evolution of the hydraulic system of the lifting machine is established between the digital twin evolution model and the physical simulation system. Through this bidirectional transmission mechanism, the working condition evolution trend data output by the digital twin evolution model is transmitted to the physical simulation system in real time, and the entity evolution deviation data collected by the physical simulation system is transmitted back to the digital twin evolution model. Based on the bidirectional transmission mechanism of working condition evolution, a multi-round interactive evolution cycle is executed between the digital twin evolution model and the physical simulation and deduction system to generate a full evolution dataset; Based on the full evolution dataset, the complete trajectory of various operating parameters of the lifting hydraulic system during the working condition evolution process is analyzed, the safety thresholds corresponding to various operating parameters are associated, the triggering conditions and evolution rules of the operating parameter trajectory exceeding the safety threshold are extracted, and the safety risk triggering trajectory characteristics of different evolution stages are explored. Based on the safety risk trigger trajectory characteristics obtained from the excavation, the working condition safety assessment results of the lifting hydraulic system are generated; The bidirectional transmission mechanism for the evolution of working conditions between the digital twin evolutionary model and the physical simulation deduction system of the lifting machine hydraulic system includes: An industrial transmission link that meets the preset delay requirements is selected to connect the operating carrier of the digital twin evolution model of the lifting hydraulic system with the control carrier of the physical simulation and deduction system. The data packet structure of the link transmission is set according to the transmission requirements of the operating parameters of the lifting hydraulic system, and the evolution parameter types, evolution stage identifiers and time stamp information contained in the data packets are determined. An evolution trend data output node is set on the running carrier of the digital twin evolution model. The working condition evolution trend data generated during the operation of the digital twin evolution model is collected at a preset time interval. The working condition evolution trend data includes evolution path node parameters, parameter evolution rate and evolution stage transition conditions. On the control carrier of the physical simulation and deduction system, a physical evolution data acquisition node is set up, and acquisition devices adapted to the operating status of the physical components are deployed to collect the operating parameter data, component deformation data and evolution process data of the physical components during the evolution test, forming physical evolution data; Align the evolution trend data output nodes of the digital twin evolution model with the entity evolution data acquisition nodes of the physical simulation and deduction system at preset time intervals, so that the time when the digital twin evolution model outputs the working condition evolution trend data is consistent with the time when the physical simulation and deduction system acquires the entity evolution data. By comparing the working condition evolution trend data transmitted by the digital twin evolution model with the entity evolution data collected by the physical simulation and deduction system, the data differences are extracted under the same evolution stage and the same parameter dimension to generate entity evolution deviation data. The entity evolution deviation data includes the name of the difference parameter, the difference value, and the evolution stage in which the difference occurred. Set the trigger condition for the reverse transmission of entity evolution deviation data. When the difference value in the entity evolution deviation data reaches the preset transmission threshold, the reverse transmission process of entity evolution deviation data to digital twin evolution model is initiated. The link occupancy priority of the forward transmission of planned working condition evolution trend data and the reverse transmission of entity evolution deviation data is set so that the link occupancy priority of entity evolution deviation data is higher than that of working condition evolution trend data. Set up data buffer nodes in the transmission link to store the forward transmission of working condition evolution trend data and the reverse transmission of entity evolution deviation data; The data transmission status in the transmission link is recorded according to a preset period to generate a data transmission status sequence. This data transmission status sequence includes the start time, completion time, and data integrity information of each data transmission. The preset time interval between the evolution trend data output node and the entity evolution data acquisition node is adjusted based on the data transmission state sequence to make the time matching degree of data transmission compatible with the link transmission capacity.

2. The method for evaluating the operating conditions of a lift combining digital modeling and physical simulation as described in claim 1, characterized in that, The aforementioned bidirectional transmission mechanism based on working condition evolution executes multiple rounds of interactive evolutionary cycles between the digital twin evolutionary model and the physical simulation and deduction system, generating a full evolutionary dataset, including: Based on the bidirectional transmission mechanism of working condition evolution, the collaborative evolution simulation of the digital twin evolution model and the physical simulation deduction system is initiated. The digital twin evolution model generates the initial working condition evolution path based on the initial structural parameters of the lifting hydraulic system. The initial working condition evolution path is then transmitted to the physical simulation deduction system, driving the physical components within the physical simulation deduction system to perform synchronous evolution tests. The physical simulation and deduction system collects physical evolution data during the physical component evolution test, compares the physical evolution data with the target data in the initial working condition evolution path, extracts the data differences to generate physical evolution deviation data, and transmits the physical evolution deviation data back to the digital twin evolution model through the working condition evolution bidirectional transmission mechanism. After receiving entity evolution deviation data, the digital twin evolution model adjusts its own evolution inference rules based on the entity evolution deviation data, generates a corrected working condition evolution path, and then transmits the corrected working condition evolution path to the physical simulation inference system to drive a new round of entity evolution test. Repeatedly execute the steps of generating the modified working condition evolution path, executing the entity evolution test, collecting entity evolution deviation data, and adjusting the evolution inference rules to form a multi-cycle working condition evolution transmission closed loop. Collect all evolution path data and entity evolution data in the multi-cycle process to generate a full evolution dataset. Based on the safety risk trigger trajectory characteristics obtained from the excavation, the operational safety assessment results of the lifting platform's hydraulic system are generated, including: Based on the safety risk trigger trajectory characteristics obtained from the mining, a working condition evolution safety risk correlation model is constructed. This working condition evolution safety risk correlation model takes the evolution trajectory of operating parameters as input and the safety risk trigger state as output. The physical simulation and deduction system is driven by the bidirectional transmission mechanism of working condition evolution to perform special evolution verification tests on the safety risk trigger trajectory characteristics obtained by mining, and to collect entity evolution data during the special evolution verification test process; The entity evolution data collected from the special evolution verification test is input into the working condition evolution safety risk association model. The association weights between the operating parameter trajectory and the safety risk triggering state in the working condition evolution safety risk association model are adjusted to optimize the model inference accuracy. The risk judgment results output by the optimized working condition evolution safety risk association model are combined with the full evolution dataset generated by the multi-round working condition evolution transmission closed loop to generate the working condition safety assessment results of the lifting machine hydraulic system.

3. The method for evaluating the operating conditions of a lift combining digital modeling and physical simulation as described in claim 2, characterized in that, The collaborative evolution simulation based on the bidirectional transmission mechanism of working condition evolution initiates the digital twin evolution model and the physical simulation system. The digital twin evolution model generates an initial working condition evolution path based on the initial structural parameters of the lifting machine's hydraulic system. This initial working condition evolution path is then transmitted to the physical simulation system, driving the physical components within the physical simulation system to perform synchronous evolution tests, including: Input the initial structural parameters of the lifting hydraulic system into the digital twin evolution model. These initial structural parameters include hydraulic pump structural parameters, hydraulic valve structural parameters, hydraulic cylinder structural parameters, hydraulic pipeline structural parameters, and hydraulic oil property parameters. Define the working condition evolution boundary conditions of the digital twin evolution model. These working condition evolution boundary conditions include the types of working conditions covered by the evolution, the time span of the evolution, and the initial variation range of various operating parameters. The digital twin evolution model, based on initial structural parameters and working condition evolution boundary conditions, extrapolates the target values ​​of operating parameters, target values ​​of component motion states, and working condition type transition nodes of the lifting hydraulic system at different time points, forming the initial working condition evolution path; The initial working condition evolution path is divided into multiple evolution stage data blocks according to a preset data format. Each evolution stage data block contains the time range, target values ​​of operating parameters, and component motion state requirements for the corresponding evolution stage. Through the forward transmission link of the bidirectional transmission mechanism of working condition evolution, the data blocks of the split evolution stage are sequentially transmitted to the control carrier of the physical simulation and deduction system. After receiving the evolution stage data blocks, the control carrier of the physical simulation and deduction system analyzes the target values ​​of the operating parameters and the motion state requirements of the components in each evolution stage data block, and generates the driving control signals of the physical components. The drive control signal is transmitted to the actuator in the physical simulation system, and the actuator drives the physical components to adjust their operating status according to the requirements in the evolution stage data block to perform synchronous evolution test; When the synchronous evolution experiment is started, the start time of the initial evolution stage is recorded, and the acquisition devices in the physical simulation and deduction system are turned on simultaneously to acquire the actual values ​​of the operating parameters of the physical components and the actual values ​​of the motion state of the components at a preset frequency. The actual values ​​of the collected operating parameters in the initial evolution stage are compared with the corresponding target values ​​of the parameters in the initial working condition evolution path in real time to extract the parameter differences in the initial stage. According to the working condition type conversion node in the initial working condition evolution path, the operating mode of the physical components is adjusted by the actuator, and the synchronous evolution test enters the next evolution stage. The physical operation data of each stage is continuously collected and compared with the parameter target value of the corresponding stage.

4. The method for evaluating the operating conditions of a lift combining digital modeling and physical simulation according to claim 3, characterized in that, The working condition type conversion node in the initial working condition evolution path adjusts the operating mode of the physical components through the actuator, and enters the synchronous evolution test of the next evolution stage. It continuously collects physical operating data at each stage and compares it with the corresponding stage's parameter target values, including: Extract the time information and corresponding operation mode adjustment requirements of all operating condition type conversion nodes from the initial operating condition evolution path, and establish a list of operating condition conversion nodes; During the synchronous evolution test, the current evolution time is compared with the conversion time information in the list of working condition conversion nodes in real time to determine whether the current evolution time has reached the working condition type conversion node; When the current evolution time reaches the transition time of any working condition type transition node, pause the driving of the physical component in the current evolution stage and record the actual values ​​of the operating parameters and motion state of the current physical component. According to the operating mode adjustment requirements corresponding to the switching node in the list of operating condition switching nodes, adjust the drive parameters of the actuator. The adjustment of the drive parameters of the actuator includes the adjustment of drive voltage, drive frequency and drive stroke. The adjusted drive parameters are transmitted to the actuator, which then drives the physical components to start operating in the new operating mode, completing the change of operating condition type and entering the next evolution stage; Record the completion time of the working condition type conversion, compare the completion time of the working condition type conversion with the preset time of the conversion node in the initial working condition evolution path, and generate a conversion time difference value; The acquisition device for the next evolution stage is activated, and the actual values ​​of the operating parameters and motion states of the physical components in this evolution stage are acquired at a preset frequency to generate the initial fragment of physical operating data for the next evolution stage. The initial fragment of entity operation data in the next evolution stage is compared with the target value of the parameter in the corresponding stage of the initial working condition evolution path, and the difference of the initial parameter is extracted. According to the requirements of the initial working condition evolution path, the physical components are continuously driven to operate in this evolution stage until the end time of this evolution stage is reached, and physical operation data is collected throughout the process to form stage physical operation data; The complete entity operation data of this evolution stage is associated and bound with the conversion time difference value and the initial parameter difference value, and stored in the local storage unit of the physical simulation and deduction system. At the same time, the associated and bound data is transferred to the data buffer node.

5. The method for evaluating the operating conditions of a lift combining digital modeling and physical simulation according to claim 2, characterized in that, The physical simulation and deduction system, driven by a bidirectional transmission mechanism of operational condition evolution, performs a special evolutionary verification test on the safety risk trigger trajectory characteristics obtained from the mining process, and collects entity evolution data during the special evolutionary verification test, including: The characteristics of safety risk triggering trajectories at different evolutionary stages were sorted out and mined, and the types of operating parameters, parameter change trends and evolutionary stages corresponding to each safety risk triggering trajectory characteristic were determined. Based on the parameter change trend of each safety risk trigger trajectory feature, the operating condition parameter range of the special evolution verification test is set, which covers the parameter change interval corresponding to the safety risk trigger trajectory feature; The evolution time length of the special evolution verification test is determined. This evolution time length includes the preset time range before the breakthrough, the breakthrough node time, and the preset time range after the breakthrough, corresponding to the safety risk trigger trajectory characteristics. Based on the set operating condition parameter range and evolution time length, an evolution path for a special evolution verification test is generated. This evolution path for a special evolution verification test includes the target values ​​of operating parameters and component motion state requirements corresponding to each time node. Through the forward transmission link of the bidirectional transmission mechanism of working condition evolution, the evolution path of the special evolution verification test is transmitted to the control carrier of the physical simulation and deduction system. The control carrier of the physical simulation and deduction system analyzes the evolution path of the special evolution verification test and generates special drive control signals for physical components. The parameter adjustment accuracy of the special drive control signals meets the accuracy requirements of the special evolution verification test. The special drive control signal is transmitted to the actuator of the physical simulation and deduction system, which drives the physical components to perform evolution tests according to the evolution path of the special evolution verification test; During the special evolution verification test, the data acquisition frequency of the acquisition device was increased, and the key operating parameter data corresponding to the safety risk trigger trajectory characteristics were collected to generate high-resolution entity evolution data sequences. According to the evolution stages of the special evolution verification test, the difference between the high-resolution entity evolution data sequence of each evolution stage and the corresponding parameter target value in the evolution path of the special evolution verification test is recorded to generate special evolution deviation data fragments. After the completion of the special evolution verification test, the high-resolution entity evolution data sequences and special evolution deviation data fragments of each stage are compiled to form a special evolution verification test data set.

6. The method for evaluating the operating conditions of a lift combining digital modeling and physical simulation according to claim 5, characterized in that, After the specific evolution verification experiment is completed, the high-resolution entity evolution data sequences and specific evolution deviation data fragments from each stage are compiled to form a specific evolution verification experiment dataset, including: High-resolution entity evolution data sequences for each evolution stage are extracted from the storage unit of the acquisition device of the physical simulation and deduction system. These high-resolution entity evolution data sequences contain the values ​​of operating parameters and component motion state data at each acquisition time point. Following the evolutionary stages of the special evolutionary verification experiment, the high-resolution entity evolution data sequences of each stage are sequentially spliced ​​together to form a continuous full-cycle high-resolution entity evolution data sequence. Extract specific evolutionary deviation data segments from each stage from the data buffer nodes. These specific evolutionary deviation data segments contain the difference data between the entity evolution data of the corresponding stage and the target values ​​of the evolutionary path parameters of the specific evolutionary verification experiment. Align the specific evolutionary deviation data segments of each stage with the full-cycle high-resolution entity evolution data sequence according to the evolutionary stage order, so that each specific evolutionary deviation data segment corresponds to a specific evolutionary stage of the full-cycle high-resolution entity evolution data sequence. Extract the evolution path information of the special evolution verification test, including the time range, target parameter values ​​and working condition type of each stage, and associate the evolution path information of the special evolution verification test with the full-cycle high-resolution entity evolution data sequence; Record the start time, end time, and transition time of each stage of the special evolution verification experiment, generate the experiment timeline information, and add the experiment timeline information to the full-cycle high-resolution entity evolution data sequence; Identify anomalous data points in the full-cycle high-resolution entity evolution data sequence. These anomalous data points are data that exceed the normal parameter variation range. Extract the acquisition time and parameter type corresponding to the anomalous data points. The identified abnormal data points are correlated with the corresponding special evolutionary deviation data segments to analyze the causes of the abnormal data. The data sequence of high-resolution entity evolution throughout the entire cycle, the data fragments of specific evolution deviations, the evolution path information of specific evolution verification experiments, the experimental time axis information, and the results of the analysis of the causes of abnormal data are integrated. All integrated data are encapsulated according to a preset data format to generate a special evolution verification test data set, which contains full verification data corresponding to the characteristics of safety risk triggering trajectories.

7. The method for evaluating the operating conditions of a lift combining digital modeling and physical simulation according to claim 1, characterized in that, The process of recording the data transmission status in the transmission link at a preset period to generate a data transmission status sequence includes: Set the recording period for the data transmission status, which is consistent with the preset time interval of the evolution trend data output node; In the forward and reverse transmission links of the bidirectional transmission mechanism for operating condition evolution, a data transmission monitoring node is set up, and the data transmission monitoring node establishes a data connection with the data buffer node. Within each data transmission status recording cycle, the start time and completion time of each working condition evolution trend data transmission in the forward transmission link are collected by the data transmission monitoring node, and the difference between the start time and completion time is calculated to obtain the forward transmission time. The start and end times of each entity evolution deviation data transmission in the reverse transmission link are collected by the data transmission monitoring node, and the difference between the start and end times is calculated to obtain the reverse transmission time. Extract the working condition evolution trend data and entity evolution deviation data of each transmission from the data buffer node, compare the extracted data volume with the preset transmission data volume, determine whether the data is complete, and generate a data integrity identifier; When the amount of data extracted is consistent with the preset amount of data to be transmitted, a data integrity flag is generated; when the amount of data extracted is less than the preset amount of data to be transmitted, a data missing flag is generated, and the parameter type of the missing data is recorded. The forward transmission time, reverse transmission time, data integrity identifier, and parameter type of missing data within the recording period of each data transmission state are associated and bound to form a single-cycle data transmission state record. Multiple single-cycle data transmission state records are sequentially linked according to the chronological order of the recording periods of the data transmission states to generate a data transmission state sequence. Corresponding evolution round identifiers and evolution stage identifiers are added to the data transmission state sequence so that each single-cycle data transmission state record can correspond to a specific evolution process. The generated data transmission state sequence is stored in a dedicated data storage area, which establishes a data connection with the running carrier of the digital twin evolution model.

8. The method for evaluating the operating conditions of a lift combining digital modeling and physical simulation according to claim 1, characterized in that, The preset time interval between the evolution trend data output node and the entity evolution data acquisition node, adjusted based on the data transmission state sequence, to ensure that the time matching degree of data transmission is adapted to the link transmission capacity, includes: Extract the forward and reverse transmission times of the recording cycles of multiple data transmission states from the data transmission state sequence, and calculate the average forward transmission time and the average reverse transmission time. Analyze the relationship between the average forward transmission time and the average reverse transmission time and the preset time interval of the current evolution trend data output node, and determine whether the preset time interval of the current evolution trend data output node leads to excessive transmission time. Extract data integrity identifiers from the data transmission status sequence, count the frequency of occurrence of statistical missing identifiers, and calculate the data missing rate; When the data missing rate exceeds the preset ratio, analyze the transmission link and evolution stage corresponding to the missing data to determine whether the link load is too high due to the preset time interval of the evolution trend data output node being too short. When the average time taken for forward transmission or the average time taken for reverse transmission exceeds the preset time threshold, the preset time interval between the evolution trend data output node and the entity evolution data acquisition node is extended to reduce the data transmission frequency. If there is room for optimization in the preset time interval of the current evolution trend data output node, that is, when the average transmission time is less than the preset time threshold and the data missing rate is less than the preset ratio, the preset time interval between the evolution trend data output node and the entity evolution data acquisition node is shortened by the preset ratio to improve the time accuracy of data transmission. After adjusting the preset time interval between the evolution trend data output node and the entity evolution data acquisition node, record the new preset time interval value and generate a time interval adjustment record. This time interval adjustment record includes the preset time interval before adjustment, the preset time interval after adjustment, and the reason for adjustment. The adjusted preset time interval is applied to the evolution trend data output node and the entity evolution data acquisition node to start a new round of data transmission status recording; Collect the adjusted new data transmission status sequence, and calculate the adjusted average transmission time and data missing rate; Compare the average transmission time and data loss rate before and after the adjustment to determine whether the adjusted preset time interval makes the data transmission time match the link transmission capacity. If it does not match, repeat the adjustment steps until it matches.

9. A lifting machine condition assessment system combining digital modeling and physical simulation, characterized in that, The system includes a processor and a memory, the memory being connected to the processor. The memory is used to store programs, instructions, or code, and the processor is used to run the programs, instructions, or code in the memory to implement the lifting machine condition evaluation method combining digital modeling and physical simulation as described in any one of claims 1-8.

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