A method and system for managing the full life cycle cost of a shaker

Abstract

The present application relates to asset supervision and prediction data processing technical field, disclose a kind of oscillometer full life cycle cost management method and system, comprising: characteristic matrix computing unit analyzes the zero-point drift of acceleration sequence data and baseline noise variance and is converted into operating state deviation degree parameter, according to this, time-varying correction threshold is constructed to input asset scalar reverse scale correction;Logic topology mapping module calculates the asset group correlation degree index of historical operation and maintenance history asset matrix to construct asset topology tensor, utilize tensor norm to adjust asset value degradation rate parameter;Predictive control unit determines prediction cost curve and identifies nonlinear inflection point;State machine arbitration unit constructs budget scheduling boundary and distributes scheduling instruction, the present application eliminates the convergence distortion of prediction model, accurately captures acceleration fatigue inflection point, realizes feedforward resource scheduling.

Description

Technical Field

[0001] This invention relates to a method and system for full lifecycle cost management of oscillators, belonging to the field of asset monitoring and predictive data processing technology. Background Technology

[0002] The current common practice is to determine the residual value of assets and plan capital allocation by mapping the number of hours the equipment operates to a static depreciation curve. The operating mechanism of this data processing flow is based on the statistical assumption that the asset value decay and service time have a quasi-linear relationship. The physical wear and tear is converted into fixed indicators in the balance sheet through discrete accounting formulas, providing a data monitoring means for continuous operation. However, when the management system monitors the core excitation components in an alternating load environment, the internal cumulative nonlinear strain of core equipment such as oscillators produces non-smooth wear abrupt changes, resulting in a mismatch in the spatiotemporal scale between the conventional low-frequency value accounting cycle and the evolution of the component damage state. The system produces information lag and monitoring blind spots, and cannot accurately identify the nonlinear inflection point corresponding to the accelerated fatigue period. This often makes it difficult to form an effective feedforward response in the industrial field before the sudden failure of the components, causing the system to reserve a large amount of reserve quota for a long time to hedge against unknown risks and reduce the efficiency of capital processing.

[0003] To eliminate data distortion under varying loads, conventional approaches typically attempt to increase inspection frequency or add peripheral sensing hardware to collect physical features. However, these improvement paths, relying on external sensing hardware, face limitations due to physical wear and aging at the hardware level. Furthermore, the control methods suffer from insufficient predictive mechanisms. For example, Chinese invention patent application CN113657693A discloses a predictive maintenance system and method for intelligent manufacturing equipment. Based on a stable and noise-free ideal environment with baseline data, it constructs a degradation model by overshooting fixed thresholds. In high-frequency alternating load industrial environments, the sensing... The link is prone to baseline zero-point drift due to environmental degradation. Due to the lack of uncertainty dissipation hedging mechanism, zero-drift noise pollution directly leads to frequent false triggering of threshold judgment and data divergence, resulting in distorted lifetime prediction. Its algorithm is limited to physical lifetime prediction and fails to build a logical transmission path between high-frequency transient physical degradation and low-frequency financial accounting model. This results in a lack of adaptive control closed loop for resource scheduling under concurrent degradation of multiple devices. However, such linear improvement does not build causal coupling in the logic of translating physical loss into financial value. On the contrary, the aging of the sensor link and baseline zero-point drift introduce computational interference, causing data divergence in the input space of the algorithm.

[0004] Therefore, the technical problem to be solved by this invention is how to use the time window slice feature to reverse reconstruct the asset degradation state space tensor and combine it with the pre-dynamic compensation mechanism to eliminate sensor link aging interference, so as to capture the inflection point of nonlinear degradation of the equipment and drive the adaptive scheduling of budget quota identifiers. Summary of the Invention

[0005] To address the problems in the background art, the technical solution of the present invention is as follows: A method for managing the full life cycle cost of an oscillator, comprising the following steps:

[0006] Step S101: Obtain the acceleration sequence data and historical operation and maintenance record asset matrix of the target oscillator;

[0007] Step S102: The feature matrix calculation unit analyzes the zero-point drift and baseline noise variance of the acceleration sequence data and converts them into operating state deviation parameters. Based on the operating state deviation parameters, a time-varying correction threshold is constructed, and the input asset scalar is reverse-scaled and corrected to generate corrected scalar data.

[0008] Step S103: The logical topology mapping module calculates the asset group correlation index of the historical operation and maintenance history asset matrix based on the multi-dimensional asset association control matrix, calibrates the network weight coefficient of the asset topology network based on the asset group correlation index, constructs the asset topology tensor, and uses the tensor norm to adjust the asset value degradation rate parameter.

[0009] Step S104: The prediction control unit determines the prediction cost curve based on the asset value degradation rate parameter, identifies the nonlinear inflection point on the prediction cost curve that transitions from the stable operation period to the accelerated fatigue period, and locks in high-risk nodes.

[0010] Step S105: When the predicted cost curve touches the cost risk threshold and is in a state of budget space overrun, the state machine arbitration unit uses the heuristic arbitration allocation unit to multiply the real-time output value weight index of each running node with the asset value degradation rate parameter to construct the budget scheduling boundary, and distributes scheduling instructions with budget quota identifiers.

[0011] Preferably, step S103 includes the following sub-steps: Step S1031, when the confidence level of the historical operation and maintenance history asset matrix is ​​lower than the preset low confidence level threshold, retrieve the design intrinsic degradation parameters of the same type of component and apply them to the initial boundary conditions of the asset topology tensor; Step S1032, use the asset group correlation index calculated by the multidimensional asset correlation control matrix as input to calibrate the network weight coefficient of the asset topology network.

[0012] Preferably, step S102 includes the following sub-steps: step S1021, the feature matrix calculation unit continuously acquires acceleration sequence data and analyzes the zero drift and baseline noise variance; step S1022, the zero drift and baseline noise variance are converted into operating state deviation parameters; step S1023, a time-varying correction threshold is constructed based on the operating state deviation parameters, the input asset scalar is reverse scaled and corrected, and corrected scalar data is generated.

[0013] Preferably, step S104 includes the following sub-steps: step S1041, the prediction control unit determines the prediction cost curve based on the asset value degradation rate parameter; step S1042, captures the nonlinear inflection point on the prediction cost curve that transitions from the stable operation period to the accelerated fatigue period and locks in high-risk nodes.

[0014] Preferably, step S105 includes the following sub-steps: Step S1051, when the predicted cost curve touches the cost risk threshold and is in a state of budget space overrun, the heuristic arbitration allocation unit obtains the real-time output value weight index of the running node; Step S1052, the real-time output value weight index is multiplied by the corresponding asset value degradation rate parameter to construct the budget scheduling boundary, and scheduling instructions with budget quota identifiers are distributed.

[0015] Preferably, step S101 includes the following sub-steps: step S1011, reading acceleration sequence data generated in continuous operation and high-frequency alternating load environment from the sensor access interface; step S1012, constructing a historical operation and maintenance history asset matrix based on the ledger of the target oscillator.

[0016] Preferably, step S1023 includes the following sub-steps: step S10231, calculating the parasitic interference loss value of the sensing link based on the operating state deviation parameter; step S10232, correcting the amplitude of the input asset scalar by reverse scaling based on the parasitic interference loss value.

[0017] Preferably, step S1052 includes the following sub-steps: step S10521, multiplying the real-time output value weight index with the corresponding asset value degradation rate parameter to obtain a comprehensive risk assessment value; step S10522, comparing the comprehensive risk assessment value with the risk safety threshold and constructing a budget scheduling boundary.

[0018] Preferably, step S1042 includes the following sub-steps: step S10421, analyzing the rate of change of the first and second derivatives of the predicted cost curve; step S10422, when the rate of change of the first and second derivatives simultaneously exceeds the transition threshold, locking the nonlinear inflection point and locking the high-risk node.

[0019] An oscillator lifecycle cost management system, used to implement an oscillator lifecycle cost management method, includes:

[0020] The feature matrix calculation unit is used to acquire the acceleration sequence data and historical operation and maintenance history asset matrix of the target oscillator, analyze the zero drift and baseline noise variance of the acceleration sequence data and convert them into operating status deviation parameters, construct time-varying correction thresholds based on operating status deviation parameters, reverse scale correction of input asset scalars and generate corrected scalar data.

[0021] The logical topology mapping module is used to calculate the asset group correlation index of the historical operation and maintenance history asset matrix based on the multi-dimensional asset association control matrix, calibrate the network weight coefficient of the asset topology network based on the asset group correlation index, construct the asset topology tensor, and adjust the asset value degradation rate parameter using the tensor norm.

[0022] The predictive control unit is used to determine the predicted cost curve based on the asset value degradation rate parameter, identify the nonlinear inflection point on the predicted cost curve that transitions from the stable operation period to the accelerated fatigue period, and lock in high-risk nodes.

[0023] The state machine arbitration unit is used to construct the budget scheduling boundary by multiplying the real-time output value weight index of each running node with the asset value degradation rate parameter through the heuristic arbitration allocation unit when the predicted cost curve reaches the cost risk threshold and is in a state of budget space over-limit. Then, it distributes scheduling instructions with budget quota identifiers for unified allocation.

[0024] Compared with the prior art, the beneficial effects of the present invention are:

[0025] 1. In the full life cycle cost management of oscillators, the feature matrix calculation unit uses time window slicing and first-order difference operation to process multi-dimensional time series feature data to establish a scalar representing transient physical loss. The logical topology mapping module introduces this scalar into a spatial tensor composed of fatigue strain accumulation dimension, performance degradation entropy change dimension, and environmental condition disturbance dimension. The state machine arbitration unit calculates the asset value degradation rate parameter based on the trajectory position of this scalar in the spatial tensor, so that the prediction control unit continuously integrates on the time axis to output the prediction cost curve. This method overcomes the limitation of traditional methods that rely on the number of operating hours to implement linear amortization, establishes a causal transmission path for the translation of physical degradation into financial data within the accrual period, and determines the inflection point of state change.

[0026] 2. When the confidence level of the historical maintenance history asset matrix is ​​lower than a preset threshold, the uncertainty dissipation hedging module activates the virtual benchmark alignment operator to retrieve the intrinsic degradation parameters of the same type of component to match the initial boundary conditions of the spatial tensor. This works in conjunction with the logical topology mapping module to calculate the asset group correlation index of the history asset matrix through a single-source propagation control matrix, jointly calibrating the connection weights of the spatial topology. This series adjustment method eliminates the risk of calculation divergence of the spatial tensor norm caused by missing historical data, ensuring the convergence of the calculation of the asset value degradation rate parameter under non-ideal data conditions, and providing a solid data verification foundation for determining the predicted cost curve.

[0027] 3. The uncertainty dissipation hedging module continuously monitors the zero-point drift and baseline noise variance of the acceleration sequence at the front end of the feature matrix calculation unit, converting them into a logical rack bounce parameter characterizing the signal degradation degree. Based on this, a time-varying discrete gradient transmission resistance gating is constructed to complete the reverse scaling correction of the input scalar. The corrected scalar replaces the original scalar and is input into the subsequent logical topology mapping process. This pre-processing dynamic compensation mechanism isolates the parasitic interference loss of the sensor link caused by the harsh operating environment, blocks the implicit deviation of the data link caused by hardware aging, and maintains the deterministic input relationship of the asset value degradation rate parameter in the continuous operating cycle. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating the overall steps of a method for managing the full lifecycle cost of an oscillator according to the present invention.

[0029] Figure 2 This is a decision-making diagram for a method of managing the full life cycle cost of an oscillator according to the present invention.

[0030] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0031] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0032] A method for managing the lifecycle cost of an oscillator includes the following steps:

[0033] Step S101: Obtain the acceleration sequence data and historical operation and maintenance record asset matrix of the target oscillator;

[0034] Step S102: The feature matrix calculation unit analyzes the zero-point drift and baseline noise variance of the acceleration sequence data and converts them into operating state deviation parameters. Based on the operating state deviation parameters, a time-varying correction threshold is constructed, and the input asset scalar is reverse-scaled and corrected to generate corrected scalar data.

[0035] Step S103: The logical topology mapping module calculates the asset group correlation index of the historical operation and maintenance history asset matrix based on the multi-dimensional asset association control matrix, calibrates the network weight coefficient of the asset topology network based on the asset group correlation index, constructs the asset topology tensor, and uses the tensor norm to adjust the asset value degradation rate parameter.

[0036] Step S104: The prediction control unit determines the prediction cost curve based on the asset value degradation rate parameter, identifies the nonlinear inflection point on the prediction cost curve that transitions from the stable operation period to the accelerated fatigue period, and locks in high-risk nodes.

[0037] Step S105: When the predicted cost curve touches the cost risk threshold and is in a state of budget space overrun, the state machine arbitration unit uses the heuristic arbitration allocation unit to multiply the real-time output value weight index of each running node with the asset value degradation rate parameter to construct the budget scheduling boundary, and distributes scheduling instructions with budget quota identifiers.

[0038] Preferably, step S103 includes the following sub-steps: Step S1031, when the confidence level of the historical operation and maintenance history asset matrix is ​​lower than the preset low confidence level threshold, retrieve the design intrinsic degradation parameters of the same type of component and apply them to the initial boundary conditions of the asset topology tensor; Step S1032, use the asset group correlation index calculated by the multidimensional asset correlation control matrix as input to calibrate the network weight coefficient of the asset topology network.

[0039] Preferably, step S102 includes the following sub-steps: step S1021, the feature matrix calculation unit continuously acquires acceleration sequence data and analyzes the zero drift and baseline noise variance; step S1022, the zero drift and baseline noise variance are converted into operating state deviation parameters; step S1023, a time-varying correction threshold is constructed based on the operating state deviation parameters, the input asset scalar is reverse scaled and corrected, and corrected scalar data is generated.

[0040] Preferably, step S104 includes the following sub-steps: step S1041, the prediction control unit determines the prediction cost curve based on the asset value degradation rate parameter; step S1042, captures the nonlinear inflection point on the prediction cost curve that transitions from the stable operation period to the accelerated fatigue period and locks in high-risk nodes.

[0041] Preferably, step S105 includes the following sub-steps: Step S1051, when the predicted cost curve touches the cost risk threshold and is in a state of budget space overrun, the heuristic arbitration allocation unit obtains the real-time output value weight index of the running node; Step S1052, the real-time output value weight index is multiplied by the corresponding asset value degradation rate parameter to construct the budget scheduling boundary, and scheduling instructions with budget quota identifiers are distributed.

[0042] Preferably, step S101 includes the following sub-steps: step S1011, reading acceleration sequence data generated in continuous operation and high-frequency alternating load environment from the sensor access interface; step S1012, constructing a historical operation and maintenance history asset matrix based on the ledger of the target oscillator.

[0043] Preferably, step S1023 includes the following sub-steps: step S10231, calculating the parasitic interference loss value of the sensing link based on the operating state deviation parameter; step S10232, correcting the amplitude of the input asset scalar by reverse scaling based on the parasitic interference loss value.

[0044] Preferably, step S1052 includes the following sub-steps: step S10521, multiplying the real-time output value weight index with the corresponding asset value degradation rate parameter to obtain a comprehensive risk assessment value; step S10522, comparing the comprehensive risk assessment value with the risk safety threshold and constructing a budget scheduling boundary.

[0045] Preferably, step S1042 includes the following sub-steps: step S10421, analyzing the rate of change of the first and second derivatives of the predicted cost curve; step S10422, when the rate of change of the first and second derivatives simultaneously exceeds the transition threshold, locking the nonlinear inflection point and locking the high-risk node.

[0046] An oscillator lifecycle cost management system, used to implement an oscillator lifecycle cost management method, includes:

[0047] The feature matrix calculation unit is used to acquire the acceleration sequence data and historical operation and maintenance history asset matrix of the target oscillator, analyze the zero drift and baseline noise variance of the acceleration sequence data and convert them into operating status deviation parameters, construct time-varying correction thresholds based on operating status deviation parameters, reverse scale correction of input asset scalars and generate corrected scalar data.

[0048] The logical topology mapping module is used to calculate the asset group correlation index of the historical operation and maintenance history asset matrix based on the multi-dimensional asset association control matrix, calibrate the network weight coefficient of the asset topology network based on the asset group correlation index, construct the asset topology tensor, and adjust the asset value degradation rate parameter using the tensor norm.

[0049] The predictive control unit is used to determine the predicted cost curve based on the asset value degradation rate parameter, identify the nonlinear inflection point on the predicted cost curve that transitions from the stable operation period to the accelerated fatigue period, and lock in high-risk nodes.

[0050] The state machine arbitration unit is used to construct the budget scheduling boundary by multiplying the real-time output value weight index of each running node with the asset value degradation rate parameter through the heuristic arbitration allocation unit when the predicted cost curve reaches the cost risk threshold and is in a state of budget space over-limit. Then, it distributes scheduling instructions with budget quota identifiers for unified allocation.

[0051] Example 1: This invention claims a method and system for full life-cycle cost management of oscillators, applicable to operating conditions requiring high-frequency vibration and temperature cycling. In a high-intensity continuous operation and high-frequency alternating load application scenario, the asset full life-cycle cost management system faces the objective problem of multiple target oscillators experiencing nonlinear physical wear and tear simultaneously, and the sensor links operating in harsh field environments for extended periods. In this case, the data acquisition interface set up in the production site reads the basic operating status parameters of each target oscillator within its operating cycle and inputs them as multi-dimensional time-series feature data into the feature matrix calculation unit. The multi-dimensional time-series feature data includes high-frequency vibration acceleration sequences and real-time bearing temperature rise rate vectors. If the system directly applies general linear financial depreciation allocation rules or triggers periodic maintenance budget predictions based on fixed physical operating hours, the low-frequency financial accounting model lacks a transmission path with high-frequency transient physical degradation parameters, resulting in a lag in the identification of sudden accelerated fatigue inflection points. Furthermore, parasitic interference losses and baseline zero-point drift caused by environmental degradation in the sensor links introduce computational interference, leading to data divergence in the algorithm's input space and distorting the asset value degradation rate parameter due to input noise pollution.

[0052] To address the challenges of zero-drift divergence in multi-source sensor data and the lag in identifying acceleration fatigue inflection points, the asset management data processing system activates the feature matrix calculation unit and the logical topology mapping module. Since acceleration sequence data in multi-dimensional time-series feature data is easily contaminated by on-site noise, the feature matrix calculation unit performs time window slicing and first-order difference operations on the multi-dimensional time-series feature data to remove transient measurement noise and extract a transient physical loss scalar characterizing the physical degradation trend. Building upon this, to mitigate parasitic interference losses in the sensor link caused by environmental degradation, the system incorporates an uncertainty dissipation hedging module at the front end of the feature matrix calculation unit to perform a dynamic data loss compensation process. This module continuously monitors the zero-point drift and baseline noise variance of the acceleration sequence in multi-dimensional time-series feature data. It analyzes the zero-point drift and baseline noise variance of the acceleration sequence data and converts them into a running state deviation parameter characterizing the degree of signal degradation. Based on this, a time-varying discrete gradient transmission resistance gating is constructed. This gating is then used to perform inverse scaling correction on the input original physical loss characterizing scalar, obtaining the corrected scalar. Then replace the original scalar input in subsequent steps.

[0053] It should be noted that when the uncertainty dissipation hedging module performs inverse scaling correction, the corrected scalar it calculates... Completely governed by a deterministic closed-loop mathematical mapping, the modified scalar satisfies the following horizontal linear relationship: ,in, This is the corrected scalar. As a scalar characterizing transient physical losses, This is a logic rack bounce parameter, which serves as an operational state deviation parameter to quantify the signal degradation degree. The preset rack stiffness scaling constant is fixed at 1050.0. Both ends of the calculation formula are dimensionless pure numbers, satisfying the dimension homogeneity constraint. Thus, the amplitude of the input transient physical loss characterization scalar is reversed by gating, blocking the implicit deviation of the data link caused by hardware aging, and maintaining the deterministic input relationship of the asset value degradation rate parameter in the continuous operation cycle. The underlying physical bridging mechanism of using the logic rack bounce parameter to reverse scale the transient physical loss characterization scalar is that the zero-point drift of the sensing link caused by long-term alternating load is essentially manifested as the overall low-frequency rise of the data acquisition level signal, which leads to the falsely high financial loss scalar value translated later.

[0054] This invention introduces a logic rack bounce parameter to lock in the implicit voltage increment caused by mechanical structural impact deformation and sensor thermal drift at the surface physical level in real time, incorporating it as a component of the denominator correction factor. When zero-point drift and baseline noise increase, leading to an increase in logic rack bounce, the denominator term increases proportionally, thereby performing a reverse contraction suppression on the amplitude of the original physical loss scalar characterization, which is too high due to hardware aging. This mechanism constructs an uncertainty hedging path at the front end of physical signal acquisition, normalizing and dissipating the distortion of the underlying transient physical electrical signal before translating it into the overall financial provision indicator, ensuring the purity of the final input accounting data. After obtaining high-quality corrected scalar data, the logic topology mapping module maps the corrected scalar... Forced mapping to a pre-defined high-dimensional asset degradation state space tensor within the system In the high-dimensional asset degradation state space tensor, it is composed of three mutually orthogonal logical bases: the fatigue strain accumulation dimension vector, the performance degradation entropy change dimension vector, and the environmental condition disturbance dimension vector.

[0055] The system dynamically updates the high-dimensional asset degradation state space tensor by reading a pre-stored historical operation and maintenance history asset matrix. When the confidence level of the historical maintenance history asset matrix is ​​lower than the preset low confidence threshold of 0.75, the uncertainty dissipation hedging module automatically activates a virtual benchmark alignment operator to retrieve the known design intrinsic degradation parameters of components of the same model and apply them to the high-dimensional asset degradation state space tensor. Forced balancing is performed in the initial boundary conditions to coordinate with the asset group correlation index calculated by the logical topology mapping module for the historical asset matrix. This eliminates the risk of spatial tensor norm divergence caused by missing historical data. When calibrating the asset topology network weight coefficients, the asset group correlation index calculated by the multidimensional asset correlation control matrix is ​​input into the difference square convergence calculation loop. The network iterative weight values ​​are compared with the target values ​​to generate the root mean square error variation rate. When the error variation rate is greater than 0.01, the connection matrix coefficients of each node are updated with a fixed step size of 0.005 along the negative gradient direction of the error until the error variation rate is no greater than 0.01 within 50 consecutive iterations. The calibrated weight coefficients are then output.

[0056] Furthermore, the logical topology mapping module converts the corrected scalar data into the aforementioned three-dimensional vectors using the following mapping rules: For the fatigue strain accumulation dimension, the system extracts the cumulative increment of the corrected scalar within 10 consecutive sampling periods and normalizes it using the standard tangent activation function to generate a one-dimensional feature vector that characterizes the degree of surface damage accumulation; For the performance degradation entropy change dimension, the system calculates the discrete probability distribution of the corrected scalar within the current slice window and uses the information entropy calculation rule to solve for the entropy value of the current state. This entropy value is then mapped to a one-dimensional feature vector characterizing the evolution of system disorder after comparison with a preset baseline state difference; For the environmental condition disturbance dimension, the system directly extracts the environmental condition change amplitude output by the uncertainty dissipation hedging module and converts it into a one-dimensional feature vector after power consumption weighting calibration. Finally, the logical topology mapping module performs tensor outer product operations on the above three mutually orthogonal one-dimensional feature vectors to reconstruct an asset topology tensor with high-dimensional feature expression capabilities in the outer product space, thus fully realizing the causal mapping from low-dimensional observation scalars to high-dimensional state space.

[0057] Based on this, the state machine arbitration unit captures the scalar tensor in the high-dimensional asset degradation state space in real time. The latest trajectory evolution position within the system adaptively calculates the asset value degradation rate parameter. Asset value degradation rate parameter It is not a monotonic function of time, but rather satisfies the following defined horizontal linear calculation rules: ,in, This is a parameter representing the rate of asset value degradation. The transient adjustment factor, used to capture the instantaneous change characteristics of operating conditions, has a fixed value range between 0.12 and 0.35. The transient physical loss characterizes the first derivative of the scalar with respect to time. This is the global spatial contraction coefficient, and its value is fixed between 0.05 and 0.18. Let Frobenius norm be the tensor of the degenerate state space of a high-dimensional asset. The final dimensions on both sides of the equation are the reciprocals of time, thus maintaining dimensional conservation.

[0058] In the parameter configuration procedure of this invention, the value range and setting basis of the aforementioned key technical parameters are determined by performing multiple rounds of full-element gradient verification tests on a simulated working condition test bench. Specifically, the frame stiffness scaling constant is fixed at 1050.0, which is the engineering equivalent value after dimensionless processing of the maximum eigenvalue of the reference stiffness impedance matrix of the target oscillator spindle at a standard fundamental frequency of 3000 revolutions per minute. If its value is lower than 200.0, it will cause the reverse scaling correction to produce an overcompensation effect, causing the correction scalar to frequently touch the saturation region during the steady period, reducing the detection sensitivity of the system. If its value is higher than 5000.0, the fractional... When the term approaches 0, the correction threshold loses its regulatory polarity. In addition, the transient adjustment factor is fixed between 0.12 and 0.35. If it is below 0.12, the system will not be able to capture sudden mechanical strain caused by load alternation in time. If it is above 0.35, the measurement noise in the input space will be excessively amplified, causing high-frequency chaotic oscillations in the asset value degradation rate. The global space contraction coefficient is fixed between 0.05 and 0.18. This boundary is determined by the convergence extremum condition of the asset topology space tensor. If it is below 0.05, the constraint ability of the space norm is insufficient and cannot effectively hedge against the risk of tensor divergence caused by the lack of historical data.

[0059] If the value is higher than 0.18, the degradation rate curve will be over-smoothed, thus delaying the locking of the nonlinear inflection point. The predictive control unit will then adjust the asset value degradation rate parameter. As a feature input variable, continuous integration is performed on the time axis to calculate the predicted cost curve of the oscillator's entire life cycle. The predictive control unit analyzes the rate of change of the first and second derivatives of the predicted cost curve. When the rate of change of the first and second derivatives simultaneously exceeds the preset transition threshold, the nonlinear inflection point and high-risk node are locked, accurately capturing the abrupt change point when the equipment enters the accelerated fatigue period from the stable period. When multiple oscillators experience simultaneous performance mutations that trigger the system's preset operational risk penalty threshold and cause budget space squeeze, the heuristic arbitration allocation unit abandons the lengthy matrix optimization action of finding the global optimum, obtains the real-time output value weight index of each operating node, and compares the index with the corresponding node's asset value degradation rate parameter. The comprehensive risk assessment value is obtained by multiplying the values. The comprehensive risk assessment value is compared with the risk safety threshold to construct the budget scheduling boundary. The adaptive maintenance scheduling instructions to be allocated are screened in a step-by-step manner. The financial budget quota is prioritized to be allocated to the running node with the highest product value and that has passed the critical jump point. The remaining nodes that are in stable operation are given temporary suspension of maintenance sequence.

[0060] Ultimately, the predictive control unit and the state machine arbitration unit form a closed-loop data chain. As the operating conditions of the oscillator dynamically evolve, the system automatically generates adaptive maintenance scheduling instructions with specific financial budget quota identifiers and issues them to the asset management terminal, realizing the dynamic and precise allocation of administrative funds. The budget quota identifiers and joint maintenance control parameters contained in the adaptive maintenance scheduling instructions are synchronously input to the physical control core of the target oscillator. When the comprehensive risk assessment value of the operating node exceeds the risk safety threshold and the budget space is exceeded, the physical control core disconnects the load power supply relay to stop the current node from operating, or directly adjusts the output frequency of the oscillator spindle drive inverter, reducing the spindle speed from 3000 rpm to 1500 rpm, so that the excitation components avoid the nonlinear fatigue strain zone under high-frequency alternating load. During the continuous prediction period of up to 36 months, the root mean square error of the prediction residual of the full life cycle maintenance cost is stably controlled within 4.3%, eliminating the lack of transparency in financial accounting caused by the mismatch of time and space scales in the traditional value management system, and completing the resource optimization and precise prevention of on-site production downtime risks under the concurrent degradation state of multiple devices.

[0061] Example 2: This invention claims a method and system for full lifecycle cost management of oscillators. In an application scenario involving high-intensity continuous operation and high-frequency alternating loads, a high-level physical verification test system is established to verify the practical effectiveness of the data processing scheme in asset management data prediction and administrative scheduling. The test system includes multiple target oscillators for generating high-frequency mechanical excitation, a data acquisition interface connected to the target oscillators via standard cable data lines, and a microprocessor for data stream scheduling and conversion. The functional specifications of the data acquisition interface are limited to: a signal sampling rate of not less than 10kHz, an analog-to-digital conversion resolution of not less than 16 bits, and a time synchronization error between measurement channels of not more than 1μm. To achieve distortion-free capture of high-frequency transient physical loss signals at the hardware performance boundary, the test system was subjected to temperature fluctuations caused by the periodic start and stop of the rooftop air conditioner during continuous operation. In addition, the high-power power supply in the workshop would induce 50Hz power frequency interference harmonics on the sensing link during switching. The above environmental parameter changes constituted typical interference noise sources when the sensing link operated in a harsh field environment for a long time. In order to verify the system's anti-interference and fault tolerance capability against the above complex environmental factors, Gaussian white noise with a signal-to-noise ratio of 20dB was actively superimposed at the input end of the data acquisition interface by a signal generator in the experiment to simulate non-ideal working conditions in a harsh industrial environment.

[0062] When designing the data acquisition procedure, the feature matrix calculation unit extracts the time window slice length as a key parameter. The main technical factors affecting this parameter's value include the rotational fundamental frequency of the oscillator's spindle, the highest harmonic frequency of the sensing signal, and the real-time computational load of the system processor. The essence of this technical trade-off lies in the following: if the time window slice length is too long, the time-domain averaging effect will smooth out the transient fatigue characteristics caused by sudden changes in fatigue strain, reducing the real-time performance of data acquisition and the sensitivity of detecting nonlinear inflection points; if the time window slice length is too short, the signal period contained within a single time window is insufficient, leading to drastic fluctuations in the statistical variance after first-order differencing, increasing the system's data processing load. To achieve an optimal balance between these two factors, the system constructs a decision model, whose decision... The decision rule is as follows: when the highest effective spectral bandwidth of the monitored signal increases, in order to avoid signal aliasing under the Nyquist sampling theorem, the sampling frequency is increased. Correspondingly, the time window slice length is directly proportional to the reciprocal of the highest effective spectral bandwidth. Under the typical working conditions of this application scenario, the spindle speed of the target oscillator is set to 3000 rpm, and its corresponding rotational fundamental frequency is 50 Hz. According to the above decision rule, the application logic is used to solve the problem, and finally a definite and non-restrictive preferred time window slice length of 20 ms is locked. This parameter covers the complete single fundamental frequency mechanical vibration cycle, and at a data acquisition sampling rate of 10 kHz, each slice time window can carry exactly 200 discrete sampling data points, realizing the alignment of feature extraction accuracy and computational cost.

[0063] In the core workflow of this verification experiment, the feature matrix calculation unit directly reads 200 raw voltage signal readings accumulated by the data acquisition interface within the preferred time window slice length. At this time, the unprocessed raw time-series signal exhibits a chaotic baseline and random amplitude jumps due to the superposition of Gaussian white noise and power frequency interference. Its signal variance is 1.45, which cannot show a definite physical degradation trajectory. The feature matrix calculation unit starts the time window slice and first-order difference operation, and activates the uncertainty dissipation hedging module at the front end. The uncertainty dissipation hedging module analyzes the low-frequency envelope trend of the discrete signal in the current slice time window, calculates the zero-point drift caused by power frequency harmonics and sensor heating as 0.12V, and calculates the baseline noise variance as 0.38. These two quantitative feature data are substituted into the front-end operating state deviation conversion operator to convert into a logic rack bounce parameter characterizing the signal degradation degree. Next, the system utilizes the logic rack bounce parameter. The time-varying correction threshold is used to characterize the calculated transient physical loss scalar. Applying inverse scaling correction, when the current zero-point drift and baseline noise variance are input into the horizontal linear relationship formula for closed-loop verification, the output is a corrected scalar. 92.5% of parasitic noise interference was successfully eliminated, and the variance of the data stream converged from 1.45 to 0.04. This resulted in the processed degradation curve exhibiting a smooth physical degradation trend that increases monotonically and steadily with the machine's operating cycle. Thus, the data reflects the counterbalancing effect of the pre-dynamic compensation mechanism on the aging interference of the sensor link.

[0064] To demonstrate the inventiveness of the complete technical solution of this invention and to provide empirical evidence for the parameter boundaries defined by this invention, a multi-dimensional control system was simultaneously introduced during the experimental phase for comprehensive gradient verification. This multi-dimensional control system consisted of a control group with the uncertainty dissipation hedging module removed, an out-of-range control group with parameters set outside the defined range, and an experimental group using the complete solution. Simultaneously, three problem intensity gradients were set in the experiment. By adjusting the eccentric alternating load of the target oscillator, the severity of physical nonlinear losses was systematically set into three gradient levels: low fatigue, medium fatigue, and high fatigue. Under the low fatigue gradient, due to the smaller deviation of the sensing link, the asset value degradation rate parameter output by the control group was... The experimental group output a parameter of 0.015 / s, while the experimental group output a parameter of 0.012 / s. The two data are quite close. However, as the experiment progresses towards a high fatigue gradient, the cumulative nonlinear strain caused by the eccentric load intensifies, and the zero-point drift of the sensor increases to 0.45V. At this point, due to the lack of reverse scale correction, the input space of the algorithm of the control group diverges, and the output prediction cost curve exhibits high-frequency chaotic oscillations due to noise pollution. This results in a 42.5-minute recognition lag error in the nonlinear inflection point time axis position calculated by the prediction control unit.

[0065] In contrast, the experimental group at the same fatigue gradient, through the synergistic effect of the uncertainty dissipation hedging module and the logical topology mapping module, achieved a higher-dimensional asset degradation state space tensor. The internal fatigue strain accumulation dimension vector and performance degradation entropy change dimension vector undergo nonlinear spatial contraction, resulting in the calculated asset value degradation rate parameter. The data shows a smooth gradient response that is monotonically positively correlated with the severity of physical deterioration. Under the three problem intensity gradients (low, medium, and high), the experimental group calculated the asset value degradation rate parameter. The values ​​stabilized at 0.012 / s, 0.045 / s, and 0.088 / s respectively, while the corresponding nonlinear inflection point identification time residuals were only 0.8 minutes, 1.2 minutes, and 1.5 minutes, respectively, without diverging with increasing problem intensity. This demonstrates the adaptability of the proposed scheme to the dimensionality reduction rule for multidimensional composite features. In the out-of-range control group test, when the frame stiffness scaling constant was manually adjusted... When the lower limit of the technical solution of this invention is set to 200.0, the value of the fractional term in the formula is too large, which causes an overcompensation effect in the reverse scaling correction, resulting in a scalar value after correction. The system frequently reaches the saturation zone during the stable operation period, the data curve tends to flatten, the growth rate slows down, and the system response site is close to saturation, indicating that the out-of-range values ​​cannot produce predictive benefits commensurate with the system's computational load.

[0066] And when When the value is set to 5000.0, which is higher than the upper limit of the technical solution of this invention, the fractional term approaches zero, the correction threshold loses its adjustment polarity, the material and structural degradation characteristics are drowned out by noise, the performance indicators deteriorate, and it is impossible to establish an effective transmission path for the translation of physical degradation into financial data on the time axis. Thus, through the above-mentioned multi-dimensional comparative data including the plateau period and the decline zone, the optimization process of the parameter working window in this invention is empirically confirmed. Based on the objective measurement data obtained from the above-mentioned full-element gradient verification and multi-dimensional comparative experiment, this experiment proves the following technical facts: through the dynamic correction of the uncertainty dissipation hedging module and the tensor norm adjustment of the logical topology mapping module, asset management data The processing system establishes a causal transmission path for translating physical degradation into financial monitoring and forecasting indicators within the accrual period, achieving continuity and logical closure of the data chain along the entire lifecycle axis. The experimental conclusions are completely aligned with the core technical problem addressed by this invention, confirming that the system employing this invention's technical solution can effectively suppress sensor drift in harsh industrial environments. While breaking free from the reliance on the linear amortization of traditional fixed asset depreciation over the years of depreciation, it stably controls the root mean square error of the predicted residual for lifecycle maintenance costs within 4.3%. Accompanying the evolution trajectory of multiple oscillators under concurrent degradation, the heuristic arbitration allocation unit, based on the asset value degradation rate parameter... The constructed budget scheduling boundary accurately executes step-by-step screening based on the real-time output value weight index of the running node when multiple objectives conflict, and precisely issues adaptive maintenance scheduling instructions with specific financial budget quota identifiers to the administrative management terminal, completing a complete value loop from physical signal perception to feedforward allocation of management resources.

[0067] Example 3: This example combines Figures 1 to 2 A description of a method and system for managing the full lifecycle cost of an oscillator, such as... Figure 1As shown, step S101 involves acquiring the acceleration sequence data and historical operation and maintenance history asset matrix of the target oscillator. Then, in step S102, the feature matrix calculation unit analyzes the zero-point drift and baseline noise variance of the acceleration sequence data and converts them into operating state deviation parameters. A time-varying correction threshold is constructed based on the operating state deviation parameters. The input asset scalar is then reverse-scaled and corrected to generate corrected scalar data. Next, step S103 is executed, where the logical topology mapping module calculates the asset group correlation index of the historical operation and maintenance history asset matrix based on the multi-dimensional asset association control matrix. The network weight system of the asset topology network is then calibrated based on the asset group correlation index. The system counts and constructs an asset topology tensor, adjusts the asset value degradation rate parameter using the tensor norm, and then proceeds to step S104. The prediction control unit determines the prediction cost curve based on the asset value degradation rate parameter, identifies the nonlinear inflection point on the prediction cost curve that transitions from the stable operation period to the accelerated fatigue period, and locks high-risk nodes. Finally, it reaches step S105. When the prediction cost curve touches the cost risk threshold and is in a state of budget space overrun, the state machine arbitration unit uses the heuristic arbitration allocation unit to multiply the real-time output weight index of each operating node with the asset value degradation rate parameter to construct the budget scheduling boundary, and distributes scheduling instructions with budget quota identifiers.

[0068] like Figure 2 As shown, the oscillator's full lifecycle cost management decision is divided into two parallel control branches: data confidence verification and cost prediction assessment. In the data confidence verification stage, it is determined that the confidence level of the historical maintenance history asset matrix is ​​lower than a preset threshold. If this determination condition is not met, the network weight is calibrated by calculating the asset group correlation index. If this determination condition is met, the intrinsic degradation parameters of the same type of component are retrieved and balanced. In the cost prediction assessment stage, it is determined that the rate of change of the first and second derivatives simultaneously exceeds the transition threshold. If this determination condition is not met, the system determination unit maintains a stable operation period. If this determination condition is met, the nonlinear inflection point and high-risk node are locked. It is further determined that the predicted cost curve has reached the risk threshold and the space is exceeded. If this subsequent determination condition is not met, the maintenance sequence is temporarily postponed. If this subsequent determination condition is met, the budget scheduling boundary is constructed by multiplication and the scheduling instructions are distributed.

[0069] Example 4: This invention claims a method and system for managing the full lifecycle cost of an oscillator. In applications involving high-intensity continuous operation and high-frequency alternating loads, to prevent nonlinear inflection point capture shifts in the long-term evolution of the high-dimensional asset degradation state space tensor due to accumulated computational errors, a full-link logic constraint transfer scheme is introduced. In the test system, the microprocessor includes a core control unit for executing timeliness assurance and reconstruction logic. This control unit is equipped with a preset data bus interface to receive the raw voltage signal from the oscillator. The system pre-sets a physical logic benchmark: when the spindle speed fluctuation of the oscillator exceeds 150 rpm, it is considered a substantial change in operating conditions. To address the prediction drift and parameter setting issues of the full lifecycle cost curve, this scheme embeds a timeliness assurance and reconstruction operator within the prediction control unit. This operator represents the raw physical loss as a scalar. The processing logic is broken down into several independent action nodes: The vibration acceleration sequence of the sensing link within a 3600s travel time window is read; the variance of this sequence within the window is calculated, which characterizes the surface strain activity of the oscillator; the variance is compared with the historical benchmark variance, and if the rate of change of the ratio exceeds a threshold of 0.15, a reconstruction procedure is triggered to update the regression parameters of the predicted cost curve. During this process, the calculation logic of the prediction control unit satisfies the following first-order linear decay model: ,in, This is the revised cost forecast. For reference to the asset cost benchmark, The real-time output value weight index based on this running node The calibrated model dynamic correction coefficients The time interval since the last reconstruction is defined, and the meaning of each parameter corresponds to its dimension to ensure that the calculation results have the physical meaning of currency.

[0070] In practice, the specific mapping path from the aforementioned real-time output weight index to the model dynamic correction coefficient is as follows: The system obtains the real-time output weight index of the running node and inputs it into the preset index convergence mapping table; when the real-time output weight index is 0, it indicates that the node is in a standard full-load high-output state. At this time, the model dynamic correction coefficient is directly locked to the benchmark minimum value, i.e., 0.0001 units per second, to maintain the smooth evolution of the cost prediction curve; as the running equipment wears out, causing the real-time output weight index to gradually increase from 0 to the maximum limit value of 5, the system performs linear proportional step amplification processing on the real-time output weight index, so that the model dynamic correction coefficient increases linearly with a slope of 0.002 units per second until it reaches the upper limit extreme value, i.e., 0.0101 units per second. This calibration process establishes a deterministic quantitative mapping relationship between the degree of production benefit loss and the rate of decline in financial prediction by converting the dimensionless weight value into a decay rate parameter with the reciprocal dimension of time.

[0071] To address the parameter calibration black box of heuristic arbitration allocation units, this embodiment discloses a real-time output weight index. Closed-loop calibration procedure: Measure the actual output power of the target oscillator within a unit production cycle. Measure the total output power of all oscillators in the same workshop. Calculate the probability of output value This ratio is a dimensionless pure number; subsequently, it is obtained according to the following formula. : ,in, It is a natural logarithmic function and satisfies Physical constraints, guarantee This calibration procedure utilizes the negative logarithmic function characteristic of output probability; when the actual output efficiency of a certain operating node decreases, its output probability... Reduced, leading to The increase triggers the heuristic arbitration allocation unit to increase the comprehensive risk assessment value of the node, realizing the automated risk weight assignment based on objective benefit output. To ensure the privacy compliance of the system operation, in the data transmission stage, the system will process the raw data stream containing the specific oscillator load characteristics obtained through the data acquisition interface through the local data desensitization chip. The processing flow includes: obtaining the device hardware address identifier of the target oscillator.

[0072] Secondly, the hardware address identifier is de-identified using the SHA-256 hash mapping algorithm to generate an anonymous index value with a fixed length of 64 bytes. Finally, this anonymous index value is used as the primary key for all subsequent maintenance scheduling data streams. After the above processing, the maintenance scheduling instructions finally issued to the administrative management terminal only contain the anonymous index value and the corresponding budget quota identifier, blocking the technical path of directly locating the physical asset entity under the control of a specific natural person from the financial administrative instructions. The closed-loop logic verification of the entire link shows that the calculation drift deviation of the prediction control unit is offset by the timeliness guarantee and the real-time offset by the reconstruction operator. The output value weight index is generated by the objective mapping of the output value probability based on electricity metering. The asset data privacy protection is completed through the technical closed loop of local hash mapping. In the full life cycle prediction of up to 36 months, the root mean square error of the oscillator full life cycle prediction cost curve after being regulated by the closed loop logic is stably controlled within 4.1%, which confirms the logical rigor and prediction accuracy of the above-mentioned engineering rule link in processing high-dimensional asset degradation state data.

[0073] Example 5: In high-intensity continuous operation and high-frequency alternating load application scenarios, the asset lifecycle cost management system is deployed in a continuous operation workshop containing multiple concurrently operating target oscillators. The operating environment of the target oscillators is affected by the temperature alternation disturbance caused by the periodic start and stop of the rooftop air conditioner in the workshop. In addition, the high-power power supply in the workshop will induce 50Hz power frequency interference harmonics on the sensing link when switching. The above-mentioned environmental parameter changes constitute a typical interference noise source when the sensing link operates in a harsh field environment for a long time. Before the system is deployed, in order to offset the parameter degradation deviation caused by the long-term operation of the sensing link, the feature matrix calculation unit preloads a calibration baseline library. The calibration baseline library is constructed by placing the target oscillator on a simulated working condition test bench and collecting 1000 sampling periods of no-load static reference data in a preset standard constant temperature environment of 25.0℃.

[0074] When the system encounters a nonlinear fatigue inflection point caused by physical wear and tear of equipment in the production field, in order to identify and offset the cumulative error caused by sensor link aging to the prediction results in advance, the control unit activates a physical calibration mechanism. This calibration mechanism calculates the reference zero-point deviation of the sensor output signal under no-load conditions. To calibrate the effective measurement baseline of the current link, this physical calibration mechanism includes: when the system detects the end of the work cycle and the oscillator is completely shut down, triggering the sensor sampling module to perform reference zero-point acquisition, obtaining a sampling sequence of 500 data points; calculating the mean of this sampling sequence to obtain the zero-point level value under the current physical environment; and subtracting the calculated zero-point level value from the reference zero-point value constructed by the test bench to obtain the offset. , This reflects the link zero-point drift under the current on-site temperature and electromagnetic interference.

[0075] The system uses offset For the degenerate state space tensor of high-dimensional assets The environmental condition disturbance dimension vector in the data undergoes linear weighted compensation. Specifically, the updated dimension vector satisfies the following mathematical mapping rule: ,in, This is the corrected environmental condition disturbance dimension vector; This is the uncalibrated environmental disturbance vector calculated by the system based on the original data; The preset zero-drift compensation coefficient is fixed at 0.25. This is the baseline zero-point deviation value of the current link. This calculation formula corrects the spatial tensor distortion caused by environmental disturbances through linear hedging logic with dimensional balance. While ensuring that the pre-data input for the maintenance scheduling command is completely anchored to the actual physical state of the equipment, it effectively protects the administrative supervision and privacy security of on-site data by performing SHA-256 hash mapping and de-identification masking on sensitive financial management data. This physical calibration mechanism enables the system to maintain a success rate of over 96.5% in identifying accelerated fatigue inflection points during long-term operation. Moreover, in the event of a sudden squeeze on the administrative budget, it can complete the accurate allocation of maintenance funds with a defined adaptive strategy, maintaining the root mean square error of the predicted residual of the asset's life cycle maintenance cost within a stable state of less than 4.1%.

[0076] In the actual data processing flow, the aforementioned conversion interface and normalization logic for correcting the environmental condition disturbance dimension vector through the reference zero-point deviation value are as follows: The reference zero-point deviation value obtained by the system in a completely stopped state, although its original dimension is volts, needs to be translated from analog to digital by a sensitivity transformation operator placed inside the control unit before inputting the voltage offset into algebraic operations. This operator multiplies the absolute value of the zero-point voltage deviation in volts by a preset unit reciprocal volt reference mapping scalar, thereby completely converting the static electrical deviation into a dimensionless environmental impact correction increment. Then, the system performs item-by-item position alignment and product weighting on each discrete component in the environmental condition disturbance dimension vector. Through this conversion interface, the two objects participating in the linear algebraic subtraction operation are normalized to the same dimensionless space in terms of data structure dimension and control logic level, eliminating the physical barrier between static voltage and dynamic condition characteristics, and realizing the quantity closed-loop offsetting of environmental condition disturbance characteristics throughout the entire link.

[0077] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for managing the entire lifecycle cost of an oscillator, characterized in that, Includes the following steps: Step S101: Obtain the acceleration sequence data and historical operation and maintenance record asset matrix of the target oscillator; Step S102: The feature matrix calculation unit analyzes the zero-point drift and baseline noise variance of the acceleration sequence data and converts them into operating state deviation parameters. Based on the operating state deviation parameters, a time-varying correction threshold is constructed, and the input asset scalar is reverse-scaled and corrected to generate corrected scalar data. Step S103: The logical topology mapping module calculates the asset group correlation index of the historical operation and maintenance history asset matrix based on the multi-dimensional asset association control matrix, calibrates the network weight coefficient of the asset topology network based on the asset group correlation index, constructs the asset topology tensor, and uses the tensor norm to adjust the asset value degradation rate parameter. Step S104: The prediction control unit determines the prediction cost curve based on the asset value degradation rate parameter, identifies the nonlinear inflection point on the prediction cost curve that transitions from the stable operation period to the accelerated fatigue period, and locks in high-risk nodes. Step S105: When the predicted cost curve touches the cost risk threshold and is in a state of budget space overrun, the state machine arbitration unit uses the heuristic arbitration allocation unit to multiply the real-time output value weight index of each running node with the asset value degradation rate parameter to construct the budget scheduling boundary, and distributes scheduling instructions with budget quota identifiers.

2. The method for managing the full life cycle cost of an oscillator according to claim 1, characterized in that, Step S103 includes the following sub-steps: Step S1031, when the confidence level of the historical operation and maintenance history asset matrix is ​​lower than the preset low confidence level threshold, retrieve the design intrinsic degradation parameters of the same type of component and apply them to the initial boundary conditions of the asset topology tensor; Step S1032, use the asset group correlation index calculated by the multidimensional asset correlation control matrix as input to calibrate the network weight coefficient of the asset topology network.

3. The method for managing the full life cycle cost of an oscillator according to claim 1, characterized in that, Step S102 includes the following sub-steps: Step S1021, the feature matrix calculation unit continuously acquires acceleration sequence data and analyzes the zero-point drift and baseline noise variance; Step S1022, the zero-point drift and baseline noise variance are converted into operating state deviation parameters; Step S1023, a time-varying correction threshold is constructed based on the operating state deviation parameters, the input asset scalar is reverse-scaled and corrected, and corrected scalar data is generated.

4. The method for managing the full life cycle cost of an oscillator according to claim 1, characterized in that, Step S104 includes the following sub-steps: Step S1041, the prediction control unit determines the prediction cost curve based on the asset value degradation rate parameter; Step S1042, captures the nonlinear inflection point on the prediction cost curve that transitions from the stable operation period to the accelerated fatigue period and locks in high-risk nodes.

5. The method for managing the full life cycle cost of an oscillator according to claim 1, characterized in that, Step S105 includes the following sub-steps: Step S1051, when the predicted cost curve touches the cost risk threshold and is in a state of budget space overrun, the heuristic arbitration allocation unit obtains the real-time output value weight index of the running node; Step S1052, the real-time output value weight index is multiplied by the corresponding asset value degradation rate parameter to construct the budget scheduling boundary, and scheduling instructions with budget quota identifiers are distributed.

6. The method for managing the full life cycle cost of an oscillator according to claim 1, characterized in that, Step S101 includes the following sub-steps: Step S1011, read acceleration sequence data generated in continuous operation and high-frequency alternating load environment from the sensor access interface; Step S1012, construct historical operation and maintenance history asset matrix according to the ledger of the target oscillator.

7. The method for managing the full life cycle cost of an oscillator according to claim 3, characterized in that, Step S1023 includes the following sub-steps: Step S10231, calculate the parasitic interference loss value of the sensing link based on the operating state deviation parameter; Step S10232, correct the amplitude of the input asset scalar by reverse scaling based on the parasitic interference loss value.

8. The method for managing the full life cycle cost of an oscillator according to claim 5, characterized in that, Step S1052 includes the following sub-steps: Step S10521, multiply the real-time output value weight index by the corresponding asset value degradation rate parameter to obtain the comprehensive risk assessment value; Step S10522, compare the comprehensive risk assessment value with the risk safety threshold and construct the budget scheduling boundary.

9. The method for managing the full life cycle cost of an oscillator according to claim 4, characterized in that, Step S1042 includes the following sub-steps: Step S10421, analyze the rate of change of the first and second derivatives of the predicted cost curve; Step S10422, when the rate of change of the first and second derivatives simultaneously exceeds the transition threshold, lock the nonlinear inflection point and lock the high-risk node.

10. An oscillator lifecycle cost management system, used to implement the oscillator lifecycle cost management method of claim 1, characterized in that, include: The feature matrix calculation unit is used to acquire the acceleration sequence data and historical operation and maintenance history asset matrix of the target oscillator, analyze the zero drift and baseline noise variance of the acceleration sequence data and convert them into operating status deviation parameters, construct time-varying correction thresholds based on operating status deviation parameters, reverse scale correction of input asset scalars and generate corrected scalar data. The logical topology mapping module is used to calculate the asset group correlation index of the historical operation and maintenance history asset matrix based on the multi-dimensional asset association control matrix, calibrate the network weight coefficient of the asset topology network based on the asset group correlation index, construct the asset topology tensor, and adjust the asset value degradation rate parameter using the tensor norm. The predictive control unit is used to determine the predicted cost curve based on the asset value degradation rate parameter, identify the nonlinear inflection point on the predicted cost curve that transitions from the stable operation period to the accelerated fatigue period, and lock in high-risk nodes. The state machine arbitration unit is used to construct the budget scheduling boundary by multiplying the real-time output value weight index of each running node with the asset value degradation rate parameter through the heuristic arbitration allocation unit when the predicted cost curve reaches the cost risk threshold and is in a state of budget space over-limit. Then, it distributes scheduling instructions with budget quota identifiers for unified allocation.

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