Full-life-cycle operation and maintenance management system of fluid measurement and control equipment based on multi-source data

Abstract

The invention relates to the technical field of equipment asset management and operation and maintenance decision, and discloses a fluid measurement and control equipment full-life-cycle operation and maintenance management system based on multi-source data, and the system comprises the steps: distributing an operation and maintenance depreciation right initial limit for equipment; the method comprises the following steps of: acquiring the multi-source data reflecting the physical loss, the business value, the real-time operation efficiency and the operation stability of the equipment, determining the product of a plurality of factors based on the multi-source data, dynamically calculating the consumption rate of the ODR, and further driving the operation and maintenance decision according to the residual limit. The consumption cost of the value budget management process is adjusted by the business value, the operation efficiency and the stability risk in real time, and a quantitative bridge for connecting the physical world and the business decision is established, so that the operation and maintenance decision can be carried out based on a clear economic basis instead of a single physical threshold value; therefore, the asset management level and the decision-making quality are improved.

Description

Technical Field

[0001] This invention relates to a full lifecycle operation and maintenance management system for fluid measurement and control equipment based on multi-source data, belonging to the field of equipment asset management and operation and maintenance decision-making technology. Background Technology

[0002] Currently, especially in the full life cycle management of fluid measurement and control equipment, management systems based on condition monitoring or predictive maintenance are commonly used. These systems acquire various physical parameters during equipment operation, such as vibration, temperature, and pressure data, to assess the real-time health status of the equipment and trigger maintenance warnings or intervention actions when the indicators exceed preset thresholds. Compared with traditional periodic maintenance or post-failure maintenance, this approach improves the reliability and availability of the equipment.

[0003] However, when such management systems are applied to high-value, continuously operating industrial processes, their design relies on the assumption that physical wear and tear on equipment is an objective, homogeneous process with limitations under specific conditions. The operating basis of existing technological systems is the belief that at any point in time and under any business context, a unit of physical wear and tear, such as a 1% decrease in equipment health index, has an equal management weight. However, in actual production and operation, the actual economic impact of a small physical wear and tear is highly correlated with the business context in which it occurs. A physical wear and tear that occurs during normal daily production has a different potential economic risk to the entire production plan than a physical wear and tear of the same degree that occurs in preparation for delivering high-value orders or major maintenance windows.

[0004] To address this issue, some intuitive improvement paths, such as simply increasing monitoring accuracy to capture earlier physical degradation or attaching static business importance tags to equipment, cannot solve the problem. The former will disrupt normal production rhythm by generating a large number of physically correct but business-insignificant alarms; the latter, due to its static and qualitative nature, cannot dynamically quantify the cost of equipment wear and tear at different business value cycles. Managers still cannot clearly weigh the true economic cost between continuing operation to complete the current task and shutting down to preserve the long-term value of the equipment. Specifically, existing technologies have the following shortcomings: 1. The lack of an operational mechanism that quantitatively correlates the physical wear and tear of equipment with dynamically changing business value leads to a lack of effective quantitative correlation between the information required for operation and maintenance decisions and the information of enterprise business objectives; 2. The assessment of equipment health status remains at the physical dimension and cannot measure its value status at a specific business cycle, making risk management unable to cover the economic risks caused by changes in business context. Therefore, the technical problem to be solved by this invention is how to build an operation and maintenance management system that can not only monitor the physical health of equipment, but also establish an asset value accounting and budget management mechanism that links physical losses with dynamic accounting of business context, so that operation and maintenance decisions can be based on clear quantitative economic value rather than a single physical threshold. Summary of the Invention

[0005] This invention provides a full lifecycle operation and maintenance management system for fluid measurement and control equipment based on multi-source data. Its main purpose is to solve the problem in the prior art that the assessment of physical wear and tear of equipment is disconnected from the economic value of its business process, resulting in a lack of quantitative economic basis for operation and maintenance decisions.

[0006] To achieve the above objectives, the present invention provides a full lifecycle operation and maintenance management system for fluid measurement and control equipment based on multi-source data, the system comprising:

[0007] An ODR (Operational Loss Reduction) quota allocation module is configured to allocate an initial total ODR quota to a fluid measurement and control device at the beginning of a management cycle.

[0008] An ODR consumption dynamic accounting module is configured to acquire operational data reflecting the physical loss of equipment to determine the basic physical loss rate, acquire management data reflecting the business process context of equipment to determine the business context multiplier, acquire the real-time actual power consumption and theoretical power consumption of equipment to determine the process efficiency correction factor, and acquire high-frequency state data of equipment and calculate the information entropy of the high-frequency state data within a time window to determine the state fluctuation risk multiplier; the ODR consumption dynamic accounting module is also configured to calculate the real-time ODR consumption rate of equipment based on the product of the basic physical loss rate, business context multiplier, process efficiency correction factor and state fluctuation risk multiplier.

[0009] An ODR budget management module is configured to continuously update the remaining ODR quota of the device based on the initial total ODR quota and the real-time ODR consumption rate;

[0010] A decision-driven module is configured to trigger one or more predefined operation and maintenance management actions when the remaining ODR quota meets one or more pre-set decision conditions.

[0011] Preferably, the system also includes an ODR supplement module, which is configured to: after performing maintenance operations on the fluid measurement and control equipment, determine an ODR supplement amount corresponding to the maintenance operation based on the type of maintenance operation and a predetermined effect verification procedure, and use it to increase the remaining ODR quota of the equipment.

[0012] Preferably, the ODR consumption dynamic accounting module is further configured to: acquire real-time energy meter readings of the equipment as actual power consumption; and establish a benchmark model that maps the current operating condition to theoretical power consumption based on the equipment's design performance curve data or historical optimal operating condition data, and determine the theoretical power consumption by the benchmark model; wherein, the process efficiency correction factor is determined in the following way. ,in This represents the actual power consumption. This represents the theoretical power consumption.

[0013] Preferably, the determination of the state fluctuation risk multiplier in the ODR consumption dynamic accounting module is achieved as follows: acquiring a data sequence of high-frequency state data from the vibration sensor or acoustic sensor of the fluid measurement and control equipment; using the sample entropy algorithm to calculate the complexity of the data sequence within the time window, and normalizing the calculation result to generate a scalar value that quantifies the uncertainty of the equipment's operating state, which serves as the state fluctuation risk multiplier; wherein the length of the time window and the calculation step size are preset.

[0014] Preferably, the system also includes a fluctuation pattern analysis module, which is configured to: perform fast Fourier transform on the data sequence of high-frequency state data in parallel to extract its frequency domain energy distribution characteristics; perform pattern matching calculations between the frequency domain energy distribution characteristics and a preset prototype library that stores multiple typical fluctuation prototypes; and when the decision-driven module triggers operation and maintenance management actions, it is also configured to output the classification annotations of the matched typical fluctuation prototypes (VEYI) as decision reference information.

[0015] Preferably, the ODR consumption dynamic accounting module is further configured to: determine the value of the business context multiplier based on a preset rule that maps management data to the business context multiplier; the management data includes production plan information, order value information, and the criticality level information of fluid measurement and control equipment in the process flow.

[0016] Preferably, the decision-driven module is also configured to provide a human-computer interaction interface, which responds to the simulation request input by the user, which includes the future business scenario and runtime, and calls the ODR consumption dynamic accounting module to predict the future consumption trajectory of the device's remaining ODR quota under the simulation request.

[0017] Preferably, the system also includes an asset behavior profile analysis module, which is configured to: extract a set of management characteristic indicators characterizing the economic behavior of the equipment based on historical ODR consumption data and ODR replenishment data of the fluid monitoring and control equipment within one or more historical management cycles; use an unsupervised clustering algorithm to automatically group the equipment according to the management characteristic indicators and determine a behavior profile classification for each equipment; wherein, the ODR quota allocation module applies a set of differentiated allocation rules based on the equipment's behavior profile classification to determine the initial total ODR quota allocated to it in the next management cycle.

[0018] Preferably, the system further includes: an asset relationship definition module configured to determine that there is a functional substitutability relationship between the first fluid measurement and control equipment and the second fluid measurement and control equipment; and an ODR budget financing module configured to, when the financing condition of the remaining ODR of the first fluid measurement and control equipment being lower than a first preset threshold and the remaining ODR of the second fluid measurement and control equipment being higher than a second preset threshold is met, transfer part of the ODR of the second fluid measurement and control equipment to the ODR of the first fluid measurement and control equipment according to a financing cost coefficient preset in the range of 1.05 to 1.3.

[0019] Preferably, the system further includes: a strategic ODR issuance module, configured to respond to an authorization instruction to add a temporary ODR credit line to the fluid measurement and control equipment when a strategic business event that meets the conditions is identified; and a maintenance liability management module, configured to generate a future maintenance liability associated with the credit line when the ODR credit line is increased, and to trigger a compensatory maintenance work order based on the future maintenance liability during a future business window.

[0020] Compared with the prior art, the beneficial effects of the present invention are:

[0021] 1. Through the collaborative work of the ODR (Operation and Maintenance Loss) quota allocation module, the dynamic accounting module, and the budget management module, an asset operation and maintenance method has been established that binds the physical loss of equipment with the business process context. At the beginning of the management cycle, the system allocates a total ODR quota to the equipment, which defines the total amount of loss that it can withstand within that cycle. During operation, the ODR consumption dynamic accounting module obtains the physical loss data of the equipment and, in combination with the price multiplier determined by the management data reflecting the value of the business process, continuously calculates the consumption rate of the ODR. Finally, the ODR budget management module updates the quota balance, and the decision-driven module triggers operation and maintenance actions based on the quota status. This operating mode transforms the physical loss process of the equipment from an objective physical process determined only by time or intensity into a management process in which its consumption cost is adjusted in real time by business value, providing operation and maintenance decisions with a judgment basis that directly reflects the economic efficiency of the business, independent of the physical state.

[0022] 2. The ODR consumption dynamic accounting module also acquires process efficiency data reflecting the real-time operating efficiency of the equipment and high-frequency state data reflecting the stability of the equipment's operating status. It determines the process efficiency correction factor and the state fluctuation risk multiplier, and uses these to correct the ODR consumption rate. This mechanism integrates management data, operating efficiency data, and state stability data into the ODR consumption accounting model. This ensures that the ODR cost required for a single physical loss also takes into account the business value at the time of the loss, the energy conversion efficiency that caused the loss, and the equipment's operating stability risk at that time. This avoids treating the same physical loss generated under different efficiencies and risks as equivalent, making the ODR budget consumption more realistically reflect the comprehensive losses currently borne by the equipment.

[0023] 3. This system further incorporates an asset behavior profile analysis module and an ODR budget allocation module. The former, based on the ODR consumption and replenishment data of devices within historical management cycles, defines a behavior profile category for each device. The ODR quota allocation module will then allocate quotas differentiatedly based on this category in the next management cycle. The latter, among devices with pre-defined functional substitutability, allows for internal transfer of quotas based on the condition that one device has a low ODR quota while another has a sufficient quota. The introduction of these two modules expands the ODR management system from independent management focused on a single device and a single cycle to adaptive resource allocation across cycles for device clusters. As a result, the system possesses the ability to self-optimize budget allocation strategies based on historical economic behavior data and dynamically balance and adjust ODR resources within the system, enhancing the resource allocation flexibility of the entire device group when dealing with localized high-intensity business impacts. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the core functional modules and closed-loop data flow of the system of the present invention;

[0025] Figure 2 This is a schematic diagram of the multi-layered deployment architecture required for the implementation of the system of this invention;

[0026] Figure 3 This is an interactive timing diagram of the dynamic adjustment of the device health quota in this invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] The present invention provides a full lifecycle operation and maintenance management system for fluid measurement and control equipment based on multi-source data. Its overall architecture mainly includes an operation and maintenance loss right. Quota allocation module, one Consumption dynamic accounting module, one The module includes a budget management module and a decision-driven module; The quota allocation module is responsible for setting an initial quota for the fluid monitoring and control equipment managed by the system at the beginning of a management cycle. The total quota, which is then handed over to The budget management module tracks and updates the entire lifecycle of the equipment; during equipment operation, The dynamic consumption calculation module continuously obtains information from multiple data sources and dynamically calculates the device's real-time consumption. Consumption rate, and pass that rate data to The budget management module is used to deduct the remaining credit limit; ultimately, the decision-driven module bases its decisions on... The budget management module provides the credit limit status, which triggers corresponding operation and maintenance management actions when preset conditions are met, thus forming a closed-loop management process.

[0029] In a specific application scenario, such as the core process management of a large-scale refining and chemical enterprise, the safe and stable operation of fluid monitoring and control equipment is directly related to the economic benefits of the entire production unit. A common challenge of existing operation and maintenance management methods is that the assessment of equipment physical wear and tear is disconnected from the business environment in which it occurs, resulting in a lack of clear economic quantification for management decisions. To address this challenge, the system of this invention sets an Operational Depreciation Right. The quota allocation module transforms the physical health of equipment from a passively monitored status indicator into a proactively manageable, economically-oriented, and consumable budget. The quota allocation module allocates an initial quota to a fluid monitoring and control device at the beginning of a management cycle, such as the start of a fiscal year or a production quarter. The total amount is determined as follows: First, input the design life characteristics of the equipment, such as the mean time between failures (MTBF) data provided by the equipment manufacturer, and combine it with the historical performance degradation curve data of the equipment within the enterprise; Second, input the enterprise's core business objectives for this management cycle, such as the annual total output target or the overall equipment efficiency (OEE) target. The quota allocation module has a built-in benchmark model that transforms the above input into a standardized dimensionless value. Total amount, for example, setting an initial annual quota of 10,000 standard loss units for the feedstock supply pump group of a core cracking unit ( Thus, an uncertain and continuous physical decay process is transformed into a budget management problem with a fixed total amount and auditability, laying the foundation for subsequent value-based operation and maintenance management; furthermore, to address the issue that the economic impact of physical losses is highly correlated with the business context in which they occur, the system is configured with... The dynamic accounting module consumes resources. Its core task is to dynamically calculate the product of multiple factors determined from multi-source data. The consumption rate; its complete calculation logic is determined as follows: ,in, Based on the basic physical loss rate For business context multipliers, This is a process efficiency correction factor. This is the risk multiplier for state fluctuations.

[0030] The determination of the basic physical wear rate (BDR) involves acquiring operational data reflecting the physical wear of the equipment, such as cumulative operating hours, rotational speed, or stroke count. A basic BDR rate is then derived based on a static lookup table or empirical formula derived from historical equipment data. The procedure for establishing the lookup table or model used to determine the BDR for this fluid control equipment includes the following steps: First, retrieve historical operating data and corresponding maintenance records for at least one complete overhaul cycle of the equipment or similar equipment. These maintenance records contain status assessments or replacement information for core vulnerable components such as bearings and seals. Second, define each core component replacement event that restores equipment performance to more than 95% of the factory design standard as the zero-point anchor for physical wear. The cumulative operating parameters between two anchor points, such as cumulative operating hours or equivalent revolutions, are used as independent variables for loss evolution. Next, based on the wear quantification data or performance degradation curves of vulnerable parts under historical replacement, a Weibull distribution or exponential function is used to nonlinearly fit the relationship between the loss degree and the independent variables, resulting in a loss accumulation function. Finally, the first derivative of this loss accumulation function is calculated to obtain the loss rate function, which is then discretized according to the cumulative operating parameters to generate a lookup table with the cumulative operating parameters as input and the basic physical loss rate as output. 100% of the total loss degree is calibrated as corresponding to an initial total ODR (Operational Defect Rate). For example, for the aforementioned pump set, each hour of stable operation under rated conditions is determined by the model to generate [loss rate]. indivual The basic loss, i.e. , The determination of this is intended to provide a real-time valuation of the business value of physical losses. The dynamic consumption accounting module is configured to acquire management data reflecting the context of equipment business processes. This management data is obtained through standardized data interfaces with Enterprise Resource Planning (ERP) or Manufacturing Execution System (MES). The interaction path prioritizes encrypted data exchange via the enterprise's intranet. In backup cases of network outages, authorized personnel can import the data offline using encrypted storage media. The management data includes order value information associated with the production plan information of the current production batch, or the criticality level information of the equipment in the current process flow. The module is internally configured with a mapping rule engine that can be set by administrators to map different management data states to specific... Numerical values, for example, when executing a regular production plan, Set as During the sprint production phase to ensure a high-value export order, the rules engine automatically... Upgraded to This move means that the same amount of physical waste incurred during critical business periods will consume ten times more energy. Health budget.

[0031] The purpose of determining this is to correct the calculation of physical losses through process efficiency, avoiding treating the same physical losses caused by efficient operation and inefficient idling as equivalent; to achieve this purpose, The dynamic power consumption calculation module is configured to obtain the device's real-time actual power consumption. ) and theoretical power consumption To determine the process efficiency correction factor ;in, The data is obtained by using the existing digital energy meter data used for electricity billing in the equipment's power distribution cabinet, and reading it in real time through a standard industrial bus protocol (such as Modbus). This utilizes existing equipment without adding new hardware. The determination of the power consumption is established in the early stage of system deployment through an offline modeling procedure. This procedure uses the factory performance curve data of the equipment or the flow-pressure-power consumption corresponding data under the historical best operating conditions to establish a benchmark model that maps the current operating conditions (such as real-time flow and pressure) to the theoretical power consumption. The method of determination is as follows When equipment operates inefficiently due to cavitation or abnormal medium viscosity, its It will be higher than that under the same working conditions. ,For example and ,but This makes The rate of consumption is penalized, thus reflecting the additional physical stress that the equipment suffers due to inefficient operation.

[0032] The determination is intended to provide The pricing system incorporates a risk premium arising from the inherent uncertainty itself, to quantify the risk of drastic fluctuations in the equipment's condition that may occur before critical failure; therefore, The dynamic consumption calculation module is also configured to acquire high-frequency status data of the device and calculate the information entropy of the high-frequency status data within a time window to determine the status fluctuation risk multiplier. The calculation of the risk multiplier for state fluctuation (CVE) is based on a band-limited linear gain function calibrated using historical data. The specific procedure is as follows: First, select high-frequency state data segments from the historical dataset under multiple verified, fault-free, and stable operating conditions. Calculate the sample entropy of these data segments, and determine the statistical average of the obtained sample entropy as the baseline entropy value for the equipment. The second step is to determine the real-time value of CVE using the following function: ,in, It is the sample entropy value calculated in real time within the current time window. This is a risk multiplier upper limit set based on the criticality of the equipment and the risk tolerance, for example, 5.0, while the sensitivity coefficient... The value is determined by replaying and testing a complete historical fault evolution dataset, from normal to near failure, and adjusting the parameters accordingly. The CVE variation curve is determined when it can meet the preset warning time window while avoiding frequent false alarms caused by instantaneous operating condition fluctuations; in specific implementation, the system utilizes the equipment's existing vibration sensors or acoustic sensors, at a speed not lower than... The sampling rate is used to obtain high-frequency state data sequences; and the sample entropy algorithm is used to process the data sequences within a length of... The calculation step size is seconds. The complexity is calculated within a sliding time window of seconds, and the result is normalized to a scalar value. When the equipment is running smoothly, the signal is regular, and the calculated... Value close to However, when the equipment generates irregular impact signals due to an impending bearing failure, the signal complexity increases dramatically, potentially leading to... The value jumped to Or higher, thus making it The consumption rate is multiplied by a risk factor.

[0033] Taking the aforementioned pump set as an example, let's perform a complete numerical simulation: at a certain moment, the pump set is producing for a high-value order ( Its basic loss rate, as determined by referring to the table, is: Meanwhile, due to slight clogging of the inlet filter, its operating efficiency decreased, and its actual power consumption was measured. The theoretical power consumption model shows that the current operating condition should be... Therefore Furthermore, vibration sensor data showed a slight abnormal noise inside, and the risk multiplier for its state fluctuation was calculated after calculating the sample entropy. Then the real-time data at that moment... Consumption rate was The consumption dynamic accounting module calculates as follows: Where h represents hours; this result is compared to its performance under normal operating conditions. The consumption rate was amplified 18 times, thus precisely quantifying the comprehensive losses caused by operational efficiency and stability risks in the equipment's health budget consumption; subsequently, The budget management module is based on the initial allocation. indivual Total amount and by Real-time consumption calculated by the dynamic accounting module Consumption rate, at a rate not lower than The frequency of continuous updates to the remaining device The remaining credit limit; the decision-driven module then monitors the remaining credit limit in real time; when the remaining credit limit... If the credit limit is lower than a preset warning threshold, for example... When the remaining budget falls below a certain threshold, the decision-driven module is configured to trigger a budget shortfall warning and operations management action, pushing a warning message to the management platform; when the remaining budget further falls below a certain threshold... When the action threshold is reached, a higher-priority operation and maintenance management action is triggered, such as automatically generating an electronic work order requiring inspection or maintenance. Furthermore, to enhance the forward-looking nature of decision-making, the decision-driven module also provides a human-computer interaction interface that responds to user input containing future business scenarios (which determine...). ) and the estimated runtime of the simulation request, and call Consume dynamic accounting modules to predict the remaining device resources under this simulated request. The future consumption trajectory of the quota provides data support for managers to schedule production.

[0034] To address the attribution challenge in operations and maintenance management, namely, only knowing that the risk is high ( In cases where the value surges but the nature of the risk is unknown, this system also includes a volatility pattern analysis module; this module, along with... The computation operates in parallel, utilizing a data sequence of the same high-frequency state data and performing a Fast Fourier Transform (FFT) to extract its frequency domain energy distribution characteristics. The system has a pre-built prototype library storing various typical wave prototypes, such as steady-state switching noise prototypes (characterized by wide bandwidth and transient energy) and typical mechanical imbalance vibration prototypes (characterized by energy concentrated at specific low-frequency harmonics). The wave pattern analysis module performs pattern matching calculations between the real-time extracted frequency domain features and the prototype library, and the classification annotations of the matched typical wave prototypes are output by the decision-driven module as both decision reference information and early warning information. Thus, the information received by managers is no longer isolated risk values, but annotations with business implications, such as... The consumption rate increases instantaneously due to high state fluctuation entropy. The fluctuation mode prototype, namely the steady-state operating condition switching noise, provides a basis for determining whether the event requires immediate intervention. In the technical solution of this invention, a maintenance operation is considered as a supplement to the equipment health budget. To this end, the system also includes a... The supplementary module is configured to: after performing maintenance operations on the fluid measurement and control equipment, determine a corresponding function based on the type of maintenance operation and a predetermined effect verification procedure. Replenishment amount, and used to increase the remaining capacity of the equipment. Credit; for example, for a major overhaul involving the replacement of bearings and seals, supplementary credit can be provided according to regulations. indivual of Credit limit; To enable the system's resource allocation to have adaptive optimization capabilities across cycles, the system further includes an asset behavior profiling analysis module; this module analyzes the assets within a management cycle at the end of that cycle. Consume historical data and Supplement historical data and extract a set of management characteristic indicators that characterize the economic behavior of equipment, such as high... Under working conditions Consumption percentage or maintenance replenishment The utility cycle; subsequently, the asset behavior profiling module uses unsupervised clustering algorithms (such as K-means clustering) to automatically group devices based on these characteristic indicators, determining a behavior profile classification for each device, such as strategic cornerstone type or hidden drainer type; at the beginning of the next management cycle, The quota allocation module will classify devices based on their behavioral profiles and apply a set of differentiated allocation rules, such as increasing the quota for strategic cornerstone devices. initial The total amount will be reduced for devices that cause hidden wear and tear. This allows for the optimization of budget allocation strategies.

[0035] Meanwhile, to enhance the resource allocation flexibility of equipment groups when dealing with localized high-intensity business impacts, the system also introduces a cross-equipment resource integration mechanism; the system includes an asset relationship definition module, used by engineers during the system configuration phase to determine whether there are functional substitutability relationships between equipment based on the process flow diagram. For example, two pumps, A and B, which serve as backups for each other, are defined as a first-level fully substitutable cluster; the system also includes a The budget allocation module is configured to perform internal transfers of funds when an allocation condition is met. The allocation condition is: when the remaining balance of the first device in the cluster (e.g., primary pump A) is... The credit limit is lower than the first preset threshold (e.g.) ), and the remaining second device in the cluster (such as standby pump B) The credit limit is higher than the second preset threshold (e.g.) At this time, the system allows the use of a preset value. to Financing cost coefficient within the range (e.g.) ), part of the second device The credit limit is transferred to the first device; for example, deducted from Pump B's account. indivual Add to Pump A's account indivual This avoids disrupting critical production tasks due to the depletion of a single equipment budget; finally, to enhance the system's ability to cope with the impact of nonlinear strategic opportunities, the system also includes a strategic... The system includes an issuance module and a maintenance liability management module; when a strategic business event occurs in the upper-level management system such as ERP, meeting specific conditions, such as a short-term surge in order volume for a certain product exceeding historical peaks, [the system will take action]. At that time, strategic The issuance module responds to an authorization command issued by a user with the highest administrative privileges (such as the production director) and adds a temporary [application name] to the relevant device. Credit lines were allocated to meet the needs of overcapacity production; meanwhile, the debt management module was configured to maintain... When the credit limit is increased, a future maintenance liability equal to that credit limit is automatically generated in the management system. );Should This represents a corporate commitment to repay the overdraft on equipment lifespan potential during a future business window by performing compensatory, higher-standard maintenance; the maintenance liability management module will, based on this, address planned downtime or off-season production. An automatic compensatory maintenance work order is triggered, thereby ensuring a balance between short-term strategic overdraft and long-term value maintenance.

[0036] Example 1: This example is a specific operational instance of the technical solution in a particular industrial management scenario. All technical features and parameter settings are subject to the disclosure in the specific implementation method. In the production management of a large-scale integrated refining and chemical enterprise, its core hydrocracking unit is scheduled for a week-long annual shutdown overhaul next month. This overhaul is a critical production and operation activity, aiming to ensure that the entire unit can maintain stable production with high overall efficiency and a long operating cycle after the overhaul. Against this backdrop, a core circulating hydrogen compressor unit that supplies high-pressure hydrogen-rich gas to the cracking unit is subject to maintenance loss at the beginning of the year. Based on its design life and this year's production targets, the quota allocation module was allocated 12,000 standard depreciation units. The initial The total amount, one week before this major overhaul, for the compressor unit The budget management module displays its The remaining credit limit is 4500 The situation is stable.

[0037] On Monday morning of that week, a minor leak occurred in an auxiliary pipeline of the compressor unit, causing a slight fluctuation in the unit's outlet pressure. This change in physical condition would not trigger an alarm threshold in a conventional condition-monitoring system; however, in the system of this invention, The dynamic accounting module acquired high-frequency pressure sensor data from the unit and used a sample entropy algorithm to calculate that the complexity of the pressure signal increased compared to the stable operating state, resulting in a higher risk multiplier for state fluctuations. From the benchmark value Rise to Meanwhile, to maintain the constant pressure required for the process flow, the unit's control system automatically increased the operating load, resulting in an increase in the actual power consumption measured by the electricity meter. Slightly increased, while its theoretical power consumption The process efficiency correction factor remained unchanged under this operating condition, thus affecting the efficiency of the process. Depend on Rise to The changes in these two aspects reflect the synergistic effect of the system mechanism; that is, a tiny physical fault source is simultaneously quantified in terms of both stability and efficiency. The multiplicative amplification factor of the consumption rate; at this point, the management data in the Enterprise Resource Planning (ERP) system has marked the coming week as a preparation period for major maintenance, and this business process context information is... Consume the dynamic accounting module to obtain the data, and multiply the business context of the compressor unit according to preset rules. Automatically adjust to a high value The underlying management logic behind this approach is that any damage to the equipment's condition at this moment will directly affect its initial health condition when it enters the overhaul window, thus posing a potential economic risk to its long-term operation after the overhaul; therefore, The real-time consumption of the unit calculated by the dynamic accounting module Consumption rate, in its basic physical loss rate If it remains unchanged, because , and The combined effect of the three multipliers increases, leading to The budget management module's remaining credit limit began to be depleted at a rate far exceeding normal levels, prompting the system's decision-driven module to issue a warning on Monday afternoon. Warning of rapidly depleting budget.

[0038] The core of this warning message is no longer whether the physical parameters exceed the limits, but rather the equipment itself. The available capacity is being consumed at an unsustainably high cost; this change shifts the focus of operational decisions from a technical equipment issue to an asset budgeting and risk management issue; managers are no longer faced with a trade-off between immediately shutting down to address a minor leak and accepting production losses, but rather a budgetary decision about whether to allow the equipment's health reserves to continue to be depleted at current high prices; Therefore, management decided to schedule a brief online tightening operation during Tuesday's production lull. After the operation, the pipeline leak was eliminated, and the unit's outlet pressure returned to stable levels; after the operation... The dynamic accounting module for this unit updates the calculation results in real time. and All values ​​reverted to In fact The consumption rate also returned to normal levels. The budget management module interface shows that the credit consumption curve has flattened again, and ultimately, the compressor unit is operating at a healthy level. With budget reserves remaining, the annual overhaul window has begun. By quantifying and coupling physical wear and tear with dynamic business value, operational efficiency, and stability risks, the entire management process can be conducted within a clear economic framework, avoiding potential economic losses caused by the lag or ambiguity of physical indicators.

[0039] Example 2: To objectively verify the effectiveness of the present invention's technical solution in dynamically quantifying equipment wear and tear costs under different business value cycles, this comparative experiment was designed. The purpose of the experiment is to compare the experimental group using the operation and maintenance management system of the present invention with a control group using a traditional condition monitoring and management method based on physical thresholds under the same, reproducible physical wear and tear process and business scenario simulation, in order to quantitatively evaluate the differences in decision-making information provision between the two methods. The experimental platform consists of a 15kW centrifugal fluid pump, a programmable logic controller (PLC), and a corresponding data acquisition system. This platform... The PLC precisely controls the pump's start-up, shutdown, speed, and operating load to simulate different working conditions. The data acquisition system includes a triaxial accelerometer mounted on the pump bearing housing, with its sampling frequency set to 2048Hz to acquire high-frequency vibration data; a digital energy meter with an accuracy class of 0.5S is used to collect real-time power consumption data; and a host computer software system is used to record all data and execute management logic. It should be noted that, in order to simulate a gradual and controllable physical loss process, a small, known-mass unbalanced body is gradually added to the pump impeller in the experiment to simulate the continuous deterioration of mechanical imbalance faults.

[0040] The experiment was divided into two independent, sequential phases, each lasting 100 operating hours, corresponding to the control group and the sample group of the present invention, respectively. The management logic for the control group was as follows: the root mean square (RMS) value of the vibration signal was continuously monitored, and when this value exceeded the alarm threshold set according to the ISO10816-3 standard... When a maintenance alarm is triggered, the management logic of the sample group of this invention is as follows: according to the method disclosed in the specific implementation method, the pump is set... Standard loss unit ( The initial Total amount, and continuously calculate in real time. Consumption rate, when remaining The amount is lower than the preset limit. (Right now When the time condition is reached, an early warning is triggered; during the test of the two sample groups, the same operating conditions and fault evolution sequence are applied through the PLC, as follows: From hour 0 to hour 40, normal production conditions are simulated, at which time the business context multiplier... Set as From hour 41 to hour 60, simulate high-value order production conditions. Set as From hour 61 to hour 100, normal production conditions will be restored. Set again The imbalance fault was introduced at the 20th hour and worsened linearly over time. During the test of the control group, the system continuously recorded vibration data until the 85th hour, when the pump's vibration RMS value reached [value missing]. When the physical alarm threshold is exceeded for the first time, the system triggers a maintenance alarm. Prior to this, although the physical wear and tear of the equipment had accelerated due to the continued development of the fault during the high-value production period from the 41st to the 60th hour, the traditional management method failed to provide any early warning information because the physical quantity had not yet reached the absolute threshold, resulting in decision-makers being unable to perceive the loss process with higher economic risk that occurred during this critical business window.

[0041] During the testing of the sample group of this invention, the system, from hour 0 to hour 40, The consumption rate remained at a low baseline level; when the test entered the 41st hour, although the vibration RMS value at this point was only... It is far below the physical alarm threshold, but because Value by leap to Meanwhile, the escalating faults have led to a multiplier of risk for state fluctuations. Process efficiency correction factor Rise to and , Real-time consumption calculated by the dynamic accounting module The consumption rate increased more than 20 times instantaneously; this change led to The remaining balance in the budget management module began to decline rapidly, and by the 52nd hour, the remaining balance... The first time the amount is lower than The system decision-driven module triggered a budget shortfall warning accordingly; Table 1 compares the status data of the two sample groups at several key time points.

[0042] Table 1: Comparison of Experimental Data

[0043] Key time points (hours) Vibration RMS value (mm / s) System status Control group system action The operation of the sample system of the present invention 40 2.7 Before entering high-value working conditions none none 52 3.1 In high-value working conditions none Triggering ODR budget warning 60 3.3 Exiting high-value operating conditions none Continuous warning 85 4.52 In normal working conditions Trigger physical threshold alarm Continuous warning

[0044] Experimental data, as shown in Table 1, demonstrates that the management system of this invention, by introducing a mechanism that dynamically calculates physical losses in relation to business value, operational efficiency, and stability risks, can provide managers with early warning information about the deterioration of equipment economic health 33 hours earlier (85 hours compared to 52 hours) than the traditional management method of the control group. The triggering time of this warning corresponds to the time window in which the economic cost of equipment losses is amplified due to changes in the business context, thus providing a basis for decision-making to conduct preventive intervention and avoid asset value losses during high-value production. Its technical effectiveness has been objectively verified.

[0045] To further verify from another perspective the decision support advantages of the system of the present invention in dealing with sudden high-load business impacts compared to traditional technologies that only integrate regular maintenance and status monitoring, the following comparative example 1 is designed as a supplement.

[0046] Comparative Example 1: This comparative example aims to simulate a common industrial scenario: a piece of equipment at the end of its maintenance cycle, whose physical condition has not reached the absolute alarm threshold but has potential wear and tear, faces a sudden production task that exceeds the conventional design load. This illustrates the differences in decision support capabilities provided by different operation and maintenance management systems. The test platform and parameter benchmarks used in this comparative example are completely consistent with Example 2, except for the management logic. Specifically, a centrifugal fluid pump with a rated power of 15kW is used, and its physical alarm threshold for the root mean square (RMS) vibration value is set to 4.5mm / s according to the ISO10816-3 standard. The control management method is a common technical path in the field that combines fixed-cycle maintenance with physical threshold alarms: the maintenance cycle of the pump is set to 2000 operating hours. When the operating time is reached or the vibration RMS value exceeds the limit, a maintenance work order is triggered. At the beginning of this comparative example, the pump has accumulated 1800 hours of operation (i.e., it is at the 90% node of its maintenance cycle), and its bearing vibration RMS value is 3.8mm / s, which has not reached the alarm threshold, and the system status is normal.

[0047] The experiment simulated a sudden, highly profitable, but urgent order that had to be completed within 48 hours. This order required the pump to operate continuously at 120% of its rated load. The management logic of the sample group was the same as in Example 2, with an initial total ODR of 1000 standard loss units (SDUs) and a warning threshold of 300 remaining SDUs. At the start of the experiment, the ODR margin of the sample group was 450 SDUs. Due to the extremely high value of the urgent order, the Business Context Multiplier (BCM) of the sample group was set to 15. The experimental process and results are shown in Table 2. Under conventional technical management, because the pump's cumulative operating time was less than 2000 hours and its vibration value did not exceed the static threshold of 4.5 mm / s for most of the task execution time, the system did not provide any warning until the 48th hour, when the pump's bearings failed due to accelerated fatigue caused by overload. In the case of a difficult failure, the vibration value spikes instantly. Although the system triggers a physical threshold alarm at this point, it is already impossible to avoid unplanned downtime and production losses. In contrast, in the first hour of the emergency task, the ODR consumption dynamic accounting module of this invention integrates factors such as high business value (BCM=15), efficiency decline due to overload (PEM rises to 1.3), and increased operational instability (CVE rises to 2.0) to calculate that the real-time ODR consumption rate is rising sharply. Based on this, the system predicts that the ODR quota will be exhausted within 10 hours and immediately issues the highest level of warning to the manager that the asset health budget is about to be overdrawn. This warning provides the manager with a decision window 47 hours in advance to make quantitative decisions between executing high-value orders and accepting the foreseeable risk of equipment failure, and to take risk avoidance measures such as activating backup equipment or negotiating phased delivery with customers.

[0048] Table 2: Comparison of experimental data for Comparative Example 1.

[0049]

[0050] The comparative experimental results objectively demonstrate that conventional technical approaches, due to the lack of a core link in their management mechanisms to quantify and correlate equipment physical wear and tear with dynamically changing business value, operational efficiency, and stability risks, suffer from inherent defects in providing delayed decision-making information and failing to reveal potential economic risks when dealing with such sudden and high-risk production scenarios.

[0051] Example 3: This example combines Figures 1 to 3 This document describes a full lifecycle operation and maintenance management system for fluid measurement and control equipment based on multi-source data, such as... Figure 1As shown, the system uses four external data sources as inputs: an Enterprise Resource Planning (ERP) / Manufacturing Execution System (MES) for management data, a digital energy meter for real-time power consumption data, an equipment operation data source for basic physical loss data, and a vibration / acoustic sensor for high-frequency status data. The quota allocation module allocates initial quotas to devices based on management data. Total amount, The dynamic consumption calculation module integrates information from four types of data sources to calculate real-time consumption. Consumption rate The budget management module is based on the initial Total amount, real-time Consumption rate and by The supplementary module is provided after maintenance. Supplement amount, for the remaining The credit limit is continuously updated; the decision-driven module updates the credit limit based on the remaining amount. The system analyzes credit limit status and, combined with decision references (categorized annotations) generated from high-frequency status data by the fluctuation pattern analysis module, ultimately outputs operational management actions such as alerts and work orders. Additionally, the system includes a human-computer interface for receiving user simulation requests and invoking the decision-driven module for future consumption prediction, as well as an asset behavior profiling module. The budget management module provides historical data. The data generates differentiated allocation rules and feeds them back to... The quota allocation module thus forms a complete closed-loop management and optimization process.

[0052] like Figure 2 As shown, the operation and maintenance management terminal includes a human-machine interface web front-end. Users initiate access and simulation requests to the operation and maintenance management application server through this terminal. This server contains a core processing engine and analysis service module, responsible for executing the system's core calculations and logic. On the one hand, the operation and maintenance management application server communicates with the enterprise application server, which carries the enterprise resource planning / manufacturing execution system, through a business management data interaction interface. On the other hand, it reads and writes data to the data storage system, which includes real-time / time-series databases and relational databases. At the device end, signals collected by sensors / instruments are sent to the data acquisition unit / edge gateway. The data acquisition agent in this unit obtains data from the fluid measurement and control equipment through the industrial bus and sends the processed real-time device data to the operation and maintenance management application server. This constitutes a complete physical deployment from data acquisition, processing and analysis to user interaction.

[0053] like Figure 3 As shown, the asset relationship definition module first sends... The budget coordination module provides equipment substitution relationships. The budget financing module then proceeded to The budget management module queries the remaining credit limits for device A and device B. After receiving feedback that device A has insufficient credit and device B has sufficient credit, if the conditions for credit transfer are met, The budget allocation module applies an allocation cost coefficient to deduct the credit limit from device B and increase the credit limit for device A, while simultaneously calling... The budget management module updates the balances of device B and device A respectively, and after the financing is completed, the decision-driven module sends the financing transaction record to the administrator; if the financing conditions are not met, the decision-driven module suggests other operation and maintenance strategies to the administrator.

[0054] Example 4: This example aims to standardize the offline calibration and construction procedures for the key parameters and internal model of the system, so as to eliminate the uncertainty in specific engineering application deployment and ensure that the decision logic of the entire system has a reproducible and auditable basis. In a specific engineering deployment scenario, when the system of the present invention needs to be applied to a newly installed key raw material delivery pump for a continuous polymerization reactor, the initial state of the pump is that the mechanical installation and electrical connection have been completed, and it has a multi-channel historical dataset with precise timestamps containing at least 1000 hours of its initial trial operation. The data source of this dataset is an integrated data acquisition unit, whose functional specifications are: able to synchronously record the pump's inlet pressure, outlet pressure, instantaneous flow rate, motor input power, and triaxial vibration signal at the bearing housing, wherein the acquisition accuracy of power data is not less than 0.5%, and the sampling frequency of vibration data is not less than 2048Hz. In addition, the dataset is also associated with the business process context label corresponding to each moment through the interface with the Manufacturing Execution System (MES), which can be regular production, product brand switching, or standby.

[0055] Given this initial state, establish the theoretical power consumption of the pump. The baseline model follows these procedures: First, select all data segments from the historical dataset that are in a stable operating state, excluding processes involving start-up, shutdown, or drastic changes in operating conditions; second, calculate the hydraulic power-to-electric power conversion efficiency for each set of records in the selected data segments, and select the one with the highest conversion efficiency. The data points are used as the optimal operating condition dataset for the pump; finally, using this optimal operating condition dataset, a multivariate nonlinear regression analysis method is employed to establish a model with real-time flow rate and outlet pressure as inputs and theoretical power consumption as the input. The output mathematical model is embedded in the system and used as an efficiency correction factor in subsequent online computation processes. The baseline; next, the business context multiplier. Calibration is performed, and the calibration procedure is as follows: First, in collaboration with the production management and finance departments, a quantitative assessment is conducted on the economic losses caused by unplanned downtime per unit time for the pump under different business process contexts, denoted as... The calculation yielded the following results regarding the normal production context: The loss is defined as 10,000 yuan / hour; however, during the product brand switching window, downtime will result in the scrapping of an entire batch of raw materials and delays in production plans, and the calculated loss is significant. The first step is to calculate the cost per hour as 10,000 yuan; the second step is to analyze the context of each business process. The value is determined as the ratio of the economic loss in this context to the baseline loss, i.e.: According to this formula, the pump in normal production Value And when switching product brands The value is then labeled as This procedure transforms a management concept into a traceable engineering parameter supported by specific calculation formulas and data sources.

[0056] Finally, to enable the system's fluctuation pattern analysis module to attribute and annotate risks, a fluctuation pattern prototype library needs to be established. Using the aforementioned historical dataset, vibration data segments corresponding to events such as routine production, product brand switching, and known minor blockage of the pump inlet filter are extracted. For each data segment, its frequency domain energy distribution characteristics are extracted using Fast Fourier Transform (FFT), and these feature vectors, along with the corresponding event labels, are stored in the prototype library. The steady-state vibration spectrum characteristics under routine production conditions are marked as the steady-state operation prototype. Through this series of procedures, the core model, key parameters, and knowledge base of the operation and maintenance management system, originally general-purpose software, have all undergone personalized calibration for this specific equipment's data-driven operation. The system now possesses all the initial conditions for online operation, and subsequent... Both calculations and decision-making outputs will be based on this set of objectively calibrated, logically transparent internal criteria.

[0057] Example 5: This example aims to standardize the offline calibration and construction procedures for the key parameters and internal model of the system, so as to eliminate the uncertainty in specific engineering application deployment and ensure that the decision logic of the entire system has a reproducible and auditable basis. In a specific engineering deployment scenario, when the system of the present invention needs to be applied to a newly installed key raw material delivery pump for a continuous polymerization reactor, the initial state of the pump is that the mechanical installation and electrical connection have been completed, and it has a multi-channel historical dataset with precise timestamps containing at least 1000 hours of its initial trial operation. The data source of this dataset is an integrated data acquisition unit, whose functional specifications are: able to synchronously record the pump's inlet pressure, outlet pressure, instantaneous flow rate, motor input power, and triaxial vibration signal at the bearing housing, wherein the acquisition accuracy of power data is not less than 0.5%, and the sampling frequency of vibration data is not less than 2048Hz. In addition, the dataset is also associated with the business process context label corresponding to each moment through the interface with the Manufacturing Execution System (MES), which can be regular production, product brand switching, or standby.

[0058] Given this initial state, establish the theoretical power consumption of the pump. The baseline model follows these procedures: First, select all data segments from the historical dataset that are in a stable operating state, excluding processes involving start-up, shutdown, or drastic changes in operating conditions; second, calculate the hydraulic power-to-electric power conversion efficiency for each set of records in the selected data segments, and select the one with the highest conversion efficiency. The data points are used as the optimal operating condition dataset for the pump; finally, using this optimal operating condition dataset, a multivariate nonlinear regression analysis method is employed to establish a model with real-time flow rate and outlet pressure as inputs and theoretical power consumption as the input. The output mathematical model is embedded in the system and used as an efficiency correction factor in subsequent online computation processes. The baseline; next, the business context multiplier. The calibration procedure is as follows: First, in collaboration with the production management and finance departments, a quantitative assessment of the economic losses caused by unplanned downtime per unit time of the pump under different business process contexts is conducted, denoted as... The calculation yielded the following results regarding the normal production context: The loss is defined as 10,000 yuan / hour; however, during the product brand switching window, downtime will result in the scrapping of an entire batch of raw materials and delays in production plans, and the calculated loss is significant. The first step is to calculate the cost per hour as 10,000 yuan; the second step is to analyze the context of each business process. The value is determined as the ratio of the economic loss in this context to the baseline loss, i.e.: ,in, Representative at the Business context multiplier under a business process context; : Represents the first Economic losses caused by unplanned downtime per unit time in the context of various business processes; This represents the economic loss in the specific context of routine production, and this value is defined as the baseline loss, according to this formula, for the pump during routine production. Value And when switching product brands The value is then labeled as Finally, to enable the system's fluctuation pattern analysis module to attribute and annotate risks, a fluctuation pattern prototype library needs to be established. Using the aforementioned historical dataset, vibration data segments corresponding to events such as routine production, product brand switching, and known minor blockage of the pump inlet filter are extracted. For each data segment, its frequency domain energy distribution characteristics are extracted using Fast Fourier Transform (FFT), and these feature vectors, along with the corresponding event labels, are stored in the prototype library. The steady-state vibration spectrum characteristics under routine production conditions are marked as steady-state operation prototypes. Through this series of procedures, the core model, key parameters, and knowledge base within the system have all been data-driven and personalized calibrated for this specific equipment. The system now possesses all the initial conditions for online operation.

[0059] Example 6: This example aims to provide a standardized annual calibration and optimization procedure for ensuring the timeliness and adaptive reconstruction of the system's internal models and decision thresholds during long-term operation, in order to cope with the natural evolution of equipment performance and dynamic changes in the business environment. In an asset management scenario, after the system of this invention has been running stably on a set of key centrifugal compressors for a complete annual cycle, in order to ensure its management effectiveness in the following year, the system automatically triggers an annual verification and reconstruction process. The initial input of this process is all the timestamped operating data of the compressor set recorded in the past year, the associated business context tags, and all maintenance depreciation rights. Consumption and replenishment of historical flow data; the first step in the process is to analyze the theoretical power consumption. The effectiveness of the benchmark model is verified by automatically calculating the results under all stable operating conditions over the past year. The theoretical power consumption predicted by the model and the actual power consumption recorded by the electricity meter The root mean square error (RMSE) between the values ​​exceeds a preset threshold. If the threshold is reached, it indicates that the compressor's energy efficiency characteristics have drifted, and the system will automatically call [a new feature / method]. The model building procedure utilizes the best operating condition dataset from the most recent year. The model is then refitted and updated.

[0060] Secondly, the process multiplier of business context Upon review of the matrix, the system sends a review request to the designated production and finance administrators, which lists all current business context tags and their corresponding economic losses per unit time for unplanned downtime. The administrator, based on the new year's production plan and product pricing system, adjusts the values ​​for each item. The value will be reviewed and updated; after the update is complete, the system will... The calculation formula will automatically use the new one. Value and updated baseline loss Recalculate and overwrite the original Matrix; furthermore, for the decision-driven module The budget warning threshold has been transformed from a static setpoint into a dynamic parameter driven by historical data. The system executes an adaptive threshold optimization procedure that analyzes all events that caused unplanned downtime in the past year and extracts the remaining data from the 72 hours prior to these events. By statistically analyzing the credit limit change trajectory, a mechanism can be calculated that provides at least 48 hours of early warning time in 95% of cases. A percentage of the credit limit, and this percentage, for example The threshold for early warning in the following year is set; the annual process also extracts and solidifies management characteristic indicators used for profile clustering; the system extracts data from each compressor unit. From historical transaction data, a set of specific quantitative indicators are calculated and extracted, including: indicators representing volatility consumption. The Gini coefficient, representing the intensity of undertaking critical tasks, indicates the high level of daily consumption. Operating conditions Consumption percentage, and the unit representing maintenance responsiveness. The supplementary data supports the average number of fault-free operating days; these metrics are used as input to an unsupervised clustering algorithm to generate a new year's asset behavior profile classification that reflects the economic behavior of the equipment in the previous cycle; by executing this complete annual calibration and optimization procedure, the system updates its core data model, management parameter matrix, decision logic thresholds, and analysis feature set without interrupting online monitoring tasks, in order to adapt to the long-term dynamic changes of equipment and business environment.

[0061] 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.

[0062] 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 full lifecycle operation and maintenance management system for fluid measurement and control equipment based on multi-source data, characterized in that, The system includes: An ODR (Operational Loss Reduction) quota allocation module is configured to allocate an initial total ODR quota to a fluid measurement and control device at the beginning of a management cycle. An ODR consumption dynamic accounting module is configured to acquire operational data reflecting the physical loss of equipment to determine the basic physical loss rate, acquire management data reflecting the business process context of equipment to determine the business context multiplier, acquire the real-time actual power consumption and theoretical power consumption of equipment to determine the process efficiency correction factor, and acquire high-frequency state data of equipment and calculate the information entropy of the high-frequency state data within a time window to determine the state fluctuation risk multiplier; the ODR consumption dynamic accounting module is also configured to calculate the real-time ODR consumption rate of equipment based on the product of the basic physical loss rate, business context multiplier, process efficiency correction factor and state fluctuation risk multiplier. An ODR budget management module is configured to continuously update the remaining ODR quota of the device based on the initial total ODR quota and the real-time ODR consumption rate; A decision-driven module is configured to trigger one or more predefined operation and maintenance management actions when the remaining ODR quota meets one or more pre-set decision conditions.

2. The full lifecycle operation and maintenance management system for fluid measurement and control equipment based on multi-source data as described in claim 1, characterized in that, The system also includes an ODR supplement module, which is configured to: after performing maintenance operations on the fluid measurement and control equipment, determine an ODR supplement amount corresponding to the maintenance operation based on the type of maintenance operation and a predetermined effect verification procedure, and use it to increase the remaining ODR quota of the equipment.

3. The full lifecycle operation and maintenance management system for fluid measurement and control equipment based on multi-source data as described in claim 1, characterized in that, The ODR (Optical Discharge Rate) dynamic consumption calculation module is further configured to: acquire real-time energy meter readings of the equipment as actual power consumption; and establish a benchmark model that maps the current operating condition to theoretical power consumption based on the equipment's design performance curve data or historical optimal operating condition data, and determine the theoretical power consumption using the benchmark model; wherein, the process efficiency correction factor is determined in the following way. ,in, This represents the actual power consumption. This represents the theoretical power consumption.

4. The full lifecycle operation and maintenance management system for fluid measurement and control equipment based on multi-source data as described in claim 1, characterized in that, The determination of the state fluctuation risk multiplier in the ODR consumption dynamic accounting module is achieved as follows: A data sequence of high-frequency state data is obtained from the vibration or acoustic sensors of the fluid monitoring and control equipment; the sample entropy algorithm is used to calculate the complexity of the data sequence within a time window, and the calculation result is normalized to generate a scalar value that quantifies the uncertainty of the equipment's operating state, which serves as the state fluctuation risk multiplier; the length of the time window and the calculation step size are preset.

5. The full lifecycle operation and maintenance management system for fluid measurement and control equipment based on multi-source data according to claim 4, characterized in that, The system also includes a wave pattern analysis module, which is configured to perform fast Fourier transform on the data sequence of high-frequency state data in parallel to extract its frequency domain energy distribution characteristics; The frequency domain energy distribution characteristics are matched with a prototype library that stores multiple typical wave prototypes. When the decision-driven module triggers operation and maintenance management actions, it is also configured to output the classification annotations of the matched typical wave prototypes VEYI as decision reference information.

6. The full lifecycle operation and maintenance management system for fluid measurement and control equipment based on multi-source data according to claim 1, characterized in that, The ODR consumption dynamic accounting module is further configured to determine the value of the business context multiplier based on a preset rule that maps management data to the business context multiplier; the management data includes production plan information, order value information, and the criticality level information of fluid measurement and control equipment in the process flow.

7. The full lifecycle operation and maintenance management system for fluid measurement and control equipment based on multi-source data according to claim 1, characterized in that, The decision-driven module is also configured to provide a human-computer interaction interface. The human-computer interaction interface responds to the simulation request input by the user, which includes the future business scenario and runtime, and calls the ODR consumption dynamic accounting module to predict the future consumption trajectory of the device's remaining ODR quota under the simulation request.

8. The full lifecycle operation and maintenance management system for fluid measurement and control equipment based on multi-source data according to claim 1, characterized in that, The system also includes an asset behavior profile analysis module, which is configured to: extract a set of management characteristic indicators characterizing the economic behavior of the equipment based on historical ODR consumption data and ODR supplementation data of fluid monitoring and control equipment within one or more historical management cycles; use an unsupervised clustering algorithm to automatically group the equipment according to the management characteristic indicators and determine a behavior profile classification for each equipment; and apply a set of differentiated allocation rules based on the behavior profile classification of the equipment.

9. The full lifecycle operation and maintenance management system for fluid measurement and control equipment based on multi-source data according to claim 1, characterized in that, The system also includes: an asset relationship definition module configured to determine the functional substitutability between the first fluid measurement and control equipment and the second fluid measurement and control equipment; and an ODR budget financing module configured to, when the financing condition of the remaining ODR of the first fluid measurement and control equipment being lower than a first preset threshold and the remaining ODR of the second fluid measurement and control equipment being higher than a second preset threshold is met, transfer a portion of the ODR of the second fluid measurement and control equipment to the ODR of the first fluid measurement and control equipment based on a financing cost coefficient preset in the range of 1.05 to 1.

3.

10. The full lifecycle operation and maintenance management system for fluid measurement and control equipment based on multi-source data according to claim 1, characterized in that, The system also includes: a strategic ODR issuance module, configured to respond to an authorization instruction to add a temporary ODR credit line to the fluid measurement and control equipment when a strategic business event that meets the conditions is identified; and a maintenance liability management module, configured to generate a future maintenance liability associated with the credit line when the ODR credit line is increased, and to trigger a compensatory maintenance work order based on the future maintenance liability during a future business window.

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