ARM chip-based intelligent terminal for power protection of generator car and use method of ARM chip-based intelligent terminal for power protection of generator car

Abstract

The invention provides an ARM chip-based power generation vehicle power protection smart terminal and a use method thereof, relates to the technical field of power generation vehicle power protection, and aims to solve the technical problem of insufficient accuracy of a power generation vehicle running state monitoring technology in related technologies. The method comprises the following steps: acquiring a real-time operation parameter group of the generator car; acquiring a parameter coupling factor based on the real-time voltage, the real-time current and the real-time temperature; obtaining a state index value based on the real-time operation parameter group, the parameter coupling factor, a preset reference oil pressure and a preset coupling weight; recording the obtained state index values to form a latest N-time state index value sequence; based on the accumulated operation time of the generator car, obtaining an aging compensation coefficient corresponding to the preset aging curve; based on the aging compensation coefficient, a preset initial threshold value, a preset trend factor and the latest N-time state index value sequence, obtaining a dynamic threshold value; and comparing the state index value with a dynamic threshold value, and outputting a fault identifier or a normal identifier corresponding to a comparison result.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of generator power protection, and in particular to a power protection intelligent terminal for a power generation vehicle based on an ARM chip and its usage method. Background Art

[0002] As an important mobile emergency power source, reliable monitoring of the operating status of a power generation vehicle is crucial for ensuring power supply. Traditional monitoring methods for power generation vehicles usually rely on setting fixed thresholds for single parameters such as voltage, current, oil pressure, and temperature to make judgments.

[0003] However, the operating status of a generator set is a manifestation of the complex coupling of multiple parameters, and the performance of the equipment will deteriorate with the passage of operating time. At the same time, the operating parameters themselves often show trend changes. The traditional method based on fixed thresholds is difficult to capture the internal relationship between parameters and early abnormal coupling signals, cannot adapt to the performance changes caused by equipment aging, and fails to effectively utilize the trend information of parameters. This leads to problems such as high false alarm rates, high missed alarm rates, and untimely early fault warnings in complex working conditions and the later stage of equipment operation, affecting the power protection reliability of the power generation vehicle.

[0004] Although some existing improved methods attempt multi-parameter synthesis, they often lack in-depth analysis of parameter physical coupling and are still lacking in dealing with equipment aging and parameter trend changes. Summary of the Invention

[0005] The embodiments of the present application provide a power protection intelligent terminal for a power generation vehicle based on an ARM chip and its usage method, which are used to improve the technical problem of insufficient accuracy in monitoring the operating status of a power generation vehicle in related technologies.

[0006] To achieve the above object, the embodiments of the present application adopt the following technical solutions: In a first aspect, the present application provides a usage method for a power protection intelligent terminal for a power generation vehicle based on an ARM chip, including: obtaining a real-time operating parameter group of the power generation vehicle, where the parameter group includes real-time voltage, real-time current, real-time frequency, real-time oil pressure, and real-time temperature; obtaining a parameter coupling factor based on the real-time voltage, the real-time current, and the real-time temperature; obtaining a status index value based on the real-time operating parameter group, the parameter coupling factor, a preset reference oil pressure, and a preset coupling weight; recording the obtained status index value to form a sequence of the most recent N status index values, where N is a preset integer; obtaining an aging compensation coefficient corresponding to a preset aging curve based on the cumulative operating time of the power generation vehicle; obtaining a dynamic threshold based on the aging compensation coefficient, a preset initial threshold, a preset trend factor, and the sequence of the most recent N status index values; comparing the status index value with the dynamic threshold, and outputting a fault flag or a normal flag corresponding to the comparison result.

[0007] In a possible implementation of the first aspect, obtaining the parameter coupling factor based on the real-time voltage, the real-time current, and the real-time temperature includes: obtaining the real-time power output based on the real-time voltage and the real-time current; obtaining a heat-related term based on a preset heat-work conversion coefficient and the real-time temperature; and obtaining the absolute value of the difference between the real-time power output and the heat-related term to obtain the parameter coupling factor.

[0008] In a possible implementation of the first aspect, the preset heat-work conversion coefficient is obtained based on a preset rated power, a preset rated temperature, and a preset power generation efficiency constant.

[0009] In a possible implementation of the first aspect, obtaining the state index value based on the real-time operation parameter group, the parameter coupling factor, a preset reference oil pressure, and a preset coupling weight includes: obtaining an energy conversion metric value based on the real-time voltage, the real-time current, the real-time temperature, and a preset reference temperature; obtaining a mechanical efficiency correction factor from the ratio of the real-time oil pressure to the preset reference oil pressure; obtaining a basic state value based on the energy conversion metric value and the mechanical efficiency correction factor; obtaining an abnormal coupling metric value based on the preset coupling weight and the parameter coupling factor; and superimposing the basic state value and the abnormal coupling metric value to obtain the state index value.

[0010] In a possible implementation of the first aspect, the preset coupling weight is obtained based on the real-time oil pressure, and the preset coupling weight has a logarithmic relationship with the real-time oil pressure.

[0011] In a possible implementation of the first aspect, obtaining the aging compensation coefficient corresponding to the preset aging curve based on the cumulative operation time of the device includes: Referring to a preset mapping table that establishes a mapping relationship between different ranges of the cumulative operation time and the corresponding aging compensation coefficient, and obtaining the aging compensation coefficient according to the cumulative operation time.

[0012] In a possible implementation of the first aspect, obtaining the dynamic threshold based on the aging compensation coefficient, a preset initial threshold, a preset trend factor, and the sequence of the most recent N state index values includes: Based on the sequence of the most recent N state index values, obtaining the sum of the differences between adjacent state index values in the sequence; Obtaining the change trend of the state index value from the sum of the differences and the preset integer N; Obtaining a trend correction term based on the preset trend factor and the change trend of the state index value; Superimpose a preset initial threshold and the trend correction term to obtain a reference correction value; and obtain the dynamic threshold based on the aging compensation coefficient and the reference correction value.

[0013] In a possible implementation of the first aspect, the preset trend factor is obtained based on the preset integer N, and the preset trend factor has a preset association relationship with the preset integer N.

[0014] In a second aspect, the present application further provides a power generation vehicle power protection intelligent terminal based on an ARM chip, including: a multi-source sensor array configured to collect real-time voltage, real-time current, real-time frequency, real-time oil pressure, and real-time temperature signals of the power generation vehicle; a processor configured to execute the usage method described in the first aspect; a memory configured to store a preset aging curve mapping table, a preset reference oil pressure, a preset heat-work conversion coefficient, a preset initial threshold, a preset trend factor, and a preset integer N.

[0015] In a possible implementation of the second aspect, the memory is further configured to store the sequence of the most recent N state index values. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of the intelligent terminal provided for some embodiments of the present application; Figure 2 A flowchart of the method of the intelligent terminal provided for some embodiments of the present application. DETAILED DESCRIPTION

[0017] Next, the technical solutions in the embodiments of the present application will be described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments.

[0018] Hereinafter, terms such as "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second", etc. may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0019] In addition, in the present application, orientation terms such as "upper", "lower", "left", "right", etc. may include but are not limited to being defined relative to the schematic placement of components in the drawings. It should be understood that these directional terms may be relative concepts, which are used for relative description and clarification, and they may change accordingly with the change of the orientation of the components in the drawings.

[0020] In this application, unless otherwise clearly specified and defined, the term "connection" shall be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral one; it can be directly connected or indirectly connected through an intermediate medium. In addition, the term "electrical connection" can be a way of achieving electrical connection for signal transmission.

[0021] As used herein, "about", "substantially" or "approximately" includes the stated value and reference values within an acceptable deviation range of the specific value, characterized in that the acceptable deviation range is determined by those of ordinary skill in the art considering the measurements being discussed and the errors associated with the measurement of a particular quantity (i.e., the limitations of the measurement method).

[0022] In the prior art, the monitoring of the operating state of a power generation vehicle usually adopts a method based on a preset threshold. By collecting key operating parameters such as voltage, current, frequency, oil pressure, temperature, etc., and comparing the real-time measured values of these parameters with their respective set fixed upper and lower limits. When any parameter exceeds its preset safety range, the system triggers an alarm or shutdown protection. The advantage of this method is its simple implementation and relatively low cost.

[0023] However, with the increasing complexity of the application scenarios of power generation vehicles and the improvement of users' requirements for reliability, the traditional monitoring method based on fixed thresholds has exposed significant limitations. First of all, a generator set is a complex electromechanical coupling system, and there are close physical correlations and mutual influences among the operating parameters. Merely monitoring each parameter in isolation and setting independent thresholds is difficult to comprehensively and accurately reflect the true working state inside the equipment. In particular, the abnormal coupling relationship among parameters is often a subtle signal of early faults, and the traditional method is powerless in this regard. For example, under different loads and environmental conditions, the normal parameter values will change dynamically, and it is difficult for fixed thresholds to adapt, resulting in a relatively high false alarm rate and missed alarm rate.

[0024] Secondly, any mechanical equipment, including a power generation vehicle, will experience wear and aging as the operating time accumulates. The performance of the equipment will gradually decline, and its tolerance to the same external disturbances or internal parameter deviations will decrease. A parameter fluctuation that was considered to be within the normal range at the initial stage of the equipment may already indicate a potential failure risk in the later stage of the equipment operation. The prior art generally lacks consideration of the influence of the actual operating life and aging degree of the equipment on fault judgment, and is unable to dynamically adjust the early warning strategy according to the aging state of the equipment, resulting in untimely early fault warning for aging equipment or false alarms due to the inability to distinguish normal aging from abnormal deterioration.

[0025] Furthermore, the operating parameters of power generation equipment are not completely stable; they often exhibit both transient fluctuations and long-term trends driven by factors such as load changes and ambient temperature fluctuations. Focusing solely on the relationship between instantaneous parameter values and thresholds can easily overlook information about the trend of parameter changes over time. A persistently deteriorating trend can indicate an impending failure, even if the instantaneous value has not yet exceeded a fixed threshold. Existing technologies lack the ability to analyze and apply operating parameter trends, limiting predictive maintenance capabilities.

[0026] Some improved monitoring methods attempt to combine multiple parameters for analysis, such as using a simple weighted average to calculate a comprehensive health index. However, these methods often lack a solid physical model foundation and fail to fully understand the inherent connections between parameters. This is particularly true in terms of their ability to assess equipment status under abnormal operating conditions. Furthermore, most improved solutions still lack effective solutions to address the impact of threshold drift caused by equipment aging and parameter trends on warning sensitivity.

[0027] Therefore, how to build an intelligent monitoring method and terminal that can deeply explore the physical coupling relationship between the multi-source operating parameters of power generation vehicles, dynamically adjust the warning sensitivity according to the actual cumulative operating time of the equipment to adapt to equipment aging, and further optimize the accuracy and timeliness of fault judgment based on the short-term change trend of parameters, is a technical problem that needs to be urgently solved in the current power supply protection field of power generation vehicles.

[0028] This application provides an ARM chip-based smart power protection terminal for power generation vehicles and its usage method. By collecting multiple operating parameters of the power generation vehicle and applying an analysis method that combines physical coupling, equipment aging and operating trends, it can achieve real-time assessment of the health status of the power generation equipment and early fault warning.

[0029] like Figure 1 As shown, the ARM chip-based power generation vehicle power protection smart terminal provided in this application includes a multi-source sensor array 110, a processor 120, a memory 130 and a communication unit 140. The multi-source sensor array 110 is configured to collect the real-time voltage V, real-time current I, real-time frequency F, real-time oil pressure O and real-time temperature T signals of the power generation vehicle. The processor 120 is configured for use, and the processor is preferably an ARM Cortex-R5 processor. The memory 130 is configured to store a preset aging curve mapping table, a preset reference oil pressure O and a preset reference temperature T. base , a preset heat-to-work conversion coefficient k1, a preset initial threshold S0, a preset trend factor γ, and a preset integer N. The memory is preferably a FRAM non-volatile memory. The memory 130 is also configured to store the most recent N state indicator value sequences. The communication unit 140 is configured for external communication of the smart terminal and is preferably a narrowband Internet of Things communication unit.

[0030] The usage method described in this application is executed by the processor 120. As Figure 2 shown, the method includes the following steps: Step S1: Obtain the real-time operation parameter set of the power generation vehicle.

[0031] The multi-source sensor array 110 collects multiple key parameters during the operation of the power generation vehicle in real time. The parameter set includes the real-time voltage V of the power generation output, the real-time current I, the real-time frequency F, the real-time oil pressure O of the engine lubrication system, and the real-time temperature T of the key components of the equipment (such as near the engine block, generator winding, or exhaust port). The sensor array converts the collected signals into digital form and transmits them to the processor 120. The acquisition of parameters can be set to be performed at fixed intervals.

[0032] Exemplarily, in a certain acquisition cycle, the obtained parameter set is: real-time voltage V = 220V, real-time current I = 150A, real-time frequency F = 50Hz, real-time oil pressure O = 0.25MPa, real-time temperature T = 353K (80°C).

[0033] Step S2: Based on the real-time voltage, the real-time current, and the real-time temperature, obtain the parameter coupling factor.

[0034] The processor 120 is used to execute this step, aiming to quantify the potential deviation between the electric power output and the thermal state of the power generation vehicle to obtain the parameter coupling factor.

[0035] The obtaining of the parameter coupling factor includes: Based on the real-time voltage V and the real-time current I, obtain the real-time power output P rt . Exemplarily, the real-time power output can be obtained from the real-time voltage value and the real-time current value: P rt = V × I.

[0036] Based on the preset thermo-mechanical conversion coefficient k1 and the real-time temperature T, obtain the heat-related term H term . The preset thermo-mechanical conversion coefficient k1 is a preset constant stored in the memory 130. The heat-related term H term can be obtained by multiplying the preset thermo-mechanical conversion coefficient k1 by the real-time temperature T: H term = k1 × T. The preset thermo-mechanical conversion coefficient k1 is obtained based on the preset rated power P rated , the preset rated temperature T rated and the preset power generation efficiency constant η. For example, k1 = P rated / (η × T rated ). The P rated , T ratedBoth \(P\) and \(\eta\) are preset parameters stored in the memory 130.

[0037] Obtain the real-time power output \(P\) rt and the heat-related term \(H\) term Take the absolute value of the difference between them to obtain the parameter coupling factor \(\delta\). The calculation method is \(\delta = |P\) rt - H| = |(V×I)-(k1×T)|. The parameter coupling factor \(\delta\) reflects the degree of deviation from the association between the actual energy conversion state and the heat state estimated based on the preset heat characteristics. term | = |(V×I)-(k1×T)|. The parameter coupling factor \(\delta\) reflects the degree of deviation from the association between the actual energy conversion state and the heat state estimated based on the preset heat characteristics.

[0038] Exemplarily, assume the preset parameter \(P\) rated = 50000W, \(T\) rated = 373K, \(\eta = 0.9\). Then \(k1 = 50000 / (0.9×373)\approx149.2W / K\).

[0039] Use the example parameters of step S1: \(V = 220V\), \(I = 150A\), \(T = 353K\).

[0040] The real-time power output \(V×I = 220V×150A = 33000W\).

[0041] The heat-related term \(k1×T = 149.2W / K×353K\approx52739.6W\).

[0042] The parameter coupling factor \(\delta = |33000 - 52739.6|\approx19739.6\).

[0043] Step S3: Based on the real-time operating parameter group, the parameter coupling factor, the preset reference oil pressure, and the preset coupling weight, obtain the state index value.

[0044] The processor 120 is used to execute this step, thereby constructing the state index value \(S\) that comprehensively reflects the device operating state. The state index value \(S\) is an index that comprehensively considers multi-dimensional parameters, and its form is designed to effectively reflect the comprehensive health of the power generation vehicle operating state.

[0045] The obtaining of the state index value includes: Obtain the energy conversion metric value \(E\) from the real-time power, the real-time temperature \(T\), and the preset reference temperature \(T\) base That is, \(E\) measure . That is, \(E\) measure = (V×I)×(T / T base ). The preset reference temperature \(T\) base is a preset constant stored in the memory 130 and is used to normalize the temperature. This metric value increases with the increase of power and temperature and reflects the stress related to power and temperature.

[0046] From the ratio of the preset reference oil pressure O base to the real-time oil pressure O, obtain the mechanical efficiency correction factor M factor . The preset reference oil pressure O base is a preset constant stored in the memory 130. That is, M factor =O base / O.

[0047] Based on the energy conversion metric value E measure and the mechanical efficiency correction factor M factor , obtain the basic state value S base . It can be obtained through the energy conversion metric value and the mechanical efficiency correction factor: S base =E measure ×M factor =(V×I)×(T / Tbase)×(O base / O).

[0048] Meanwhile, based on the preset coupling weight λ and the parameter coupling factor δ, obtain the abnormal coupling metric value A measure =λ×δ. The preset coupling weight λ is obtained based on the real-time oil pressure O, and the preset coupling weight λ has a logarithmic relationship with the real-time oil pressure O. An exemplary relationship is λ = 0.2×ln(O + 1), where 0.2 is a preset constant and ln is the natural logarithm. This metric value reflects the impact of parameter coupling anomalies (especially the deviation of electro-thermal correlation) on the device state. The determination method of λ is stored in the memory 130.

[0049] Superimpose the basic state value S base and the abnormal coupling metric value A measure to obtain the state index value S. The state index value S is an index that comprehensively considers multi-dimensional parameters, and its form is designed to effectively reflect the comprehensive health of the power generation vehicle's operating state. Exemplarily, it can be obtained by adding the basic state value and the abnormal coupling metric value: S = S base +A measure =(V×I)×(T / Tbase)×(O base / O)+λ×δ.

[0050] The state index value S is an index that comprehensively quantifies the health of the current operating state of the power generation vehicle. The magnitude of its value reflects the comprehensive stress borne by the device or the potential risk of deterioration. Generally, the higher the value of the state index value S, the more deviated the operating state of the device is from normal, the greater the stress it bears, and the higher the risk of failure. By continuously monitoring the state index value S and comparing it with the dynamic threshold, it can be determined whether the device is in a healthy state or whether there is a potential risk of failure.

[0051] Exemplarily, using the example parameters and results of steps S1 and S2, and assuming a preset reference oil pressure O base = 0.3 MPa, a preset reference temperature Tbase = 300 K, V = 220 V, I = 150 A, T = 353 K, O = 0.25 MPa, δ ≈ 19739.6 W.

[0052] The real-time power output (V × I) = (220 × 150) = 33000 W.

[0053] Energy conversion metric: E measur = (V × I) × (T / T base base) = 33000 W × (353 K / 300 K) ≈ 33000 W × 1.177 ≈ 38841 W.

[0054] Mechanical efficiency correction factor O base ref / O = 0.3 MPa / 0.25 MPa = 1.2.

[0055] Basic state value S base ≈ 38841 W × 1.2 ≈ 46609.2.

[0056] Preset coupling weight λ = 0.2 × ln(O + 1) = 0.2 × ln(0.25 + 1) = 0.2 × ln(1.25) ≈ 0.2 × 0.223 ≈ 0.0446.

[0057] Abnormal coupling metric A measure = λ × δ ≈ 0.0446 × 19739.6 ≈ 880.4.

[0058] State index value S = S base base + A measure ≈ 46609.2 W + 880.4 W ≈ 47489.6.

[0059] Step S4: Record the obtained state index value S to form a sequence of the most recent N state index values.

[0060] The processor 120 records each obtained real-time state index value S and adds it to the storage area for storing the sequence of the most recent N state index values. The sequence of the most recent N state index values [S1, S2,..., S N N] is stored in the memory 130, where S Nis the latest S value obtained at the current moment, while S1 is the oldest value in the sequence. The preset integer N is a preset constant stored in the memory 130, and its value sets the length of the historical data window for trend analysis. To maintain the sequence of the most recent N state metric values, the memory 130 can be configured as a circular buffer or use other data structures to ensure that the latest N S values are always stored. When a new S value is recorded, if the stored quantity has reached N, the oldest S value will be removed to ensure that the sequence always contains the most recent N values.

[0061] Exemplarily, assume the preset integer N = 15. The currently obtained state metric value S = 47489.6. If there are currently 15 values [S1,..., S 15 stored in the memory 130, then S1 is removed, and the current S is added to the end of the sequence to form a new sequence of the most recent 15 state metric values [S2,..., S 15 , 47489.6].

[0062] Step S5: Obtain the cumulative operating time τ of the power generation vehicle.

[0063] The cumulative operating time τ is the total operating hours accumulated by the power generation vehicle since it was first put into use. The processor 120 is responsible for maintaining the record of the cumulative operating time τ. The cumulative operating time τ can be calculated cumulatively based on the internal real-time clock and the working state of the power generation vehicle (for example, by detecting the generator output voltage or engine speed to determine whether it is running) and stored in the memory 130. Since the cumulative operating time is a critical parameter, the non-volatility of the memory 130 (such as FRAM) ensures that the value of τ will not be lost after a power outage.

[0064] Exemplarily, assume that the cumulative operating time τ of the power generation vehicle is currently 2850 hours.

[0065] Step S6: Based on the cumulative operating time τ, obtain the aging compensation coefficient β corresponding to the preset aging curve.

[0066] The processor 120 executes this step to determine a coefficient β for adjusting the fault warning threshold according to the aging degree reflected by the actual cumulative operating time of the power generation vehicle. The aging compensation coefficient β reflects the performance decay of the device due to aging over time.

[0067] The obtaining of the aging compensation coefficient β corresponding to the preset aging curve based on the cumulative operation time τ includes: looking up a preset mapping table. The preset mapping table is stored in the memory 130, and this mapping table establishes a mapping relationship between different ranges of the cumulative operation time τ and the corresponding aging compensation coefficient β. The processor 120 looks up the range where τ is located in this preset mapping table according to the obtained cumulative operation time τ, and obtains the value of the aging compensation coefficient β associated with this range.

[0068] An example structure and values of the preset mapping table are as follows: In this example mapping table, as the cumulative operation time τ increases, the value of the aging compensation coefficient β decreases. A β value less than 1 means that the dynamically calculated threshold in subsequent steps will be correspondingly reduced, enabling a device with a higher degree of aging to trigger an alarm when the state index S value is relatively low, thereby achieving a more sensitive identification of early faults in aging devices.

[0069] Exemplarily, assume that the cumulative operation time τ of the power generation vehicle is 2850 hours. Looking up the above preset mapping table, 2850 hours is within the range of "1000 ≤ τ < 3000", so the obtained aging compensation coefficient β = 0.95.

[0070] Step S7: Obtain a dynamic threshold S based on the aging compensation coefficient β, the preset initial threshold S0, the preset trend factor γ, and the sequence of the most recent N state index values th 。

[0071] The processor 120 executes this step to generate a dynamic fault threshold S that can adapt to the aging degree of the device and the short-term operation trend th 。

[0072] The obtaining of the dynamic threshold S th includes: Based on the sequence of the most recent N state index values [S1, S2,..., S N , obtain the sum of the differences between adjacent state index values in the sequence. This can be achieved by calculating the cumulative sum of the differences between adjacent elements in the sequence. For example 。It can be understood that this value is actually the difference between the last value S N of the sequence and the first value S1, that is, S N - S1.

[0073] From the sum of the differences and the preset integer N, obtain the change trend of the state index value. The obtaining can specifically be obtaining the result of dividing the sum of the differences by the preset integer N. For example, calculating (S N-S1) / N, as a measure of the average rate of change of the state index value S over the most recent N cycles.

[0074] Based on a preset trend factor γ and the trend of the state index value, a trend correction term is obtained. The preset trend factor γ is a preset constant stored in the memory 130 and is used to adjust the influence intensity of the trend of the state index value on the dynamic threshold. The obtaining of the trend correction term can be achieved by multiplying the preset trend factor γ by the trend of the state index value: trend correction term = γ × (trend of the state index value). The preset trend factor γ is obtained based on the preset integer N, and there is a preset association relationship between γ and N. For example, when N is small (focusing on short-term trends), γ can be set larger to make the threshold more sensitive to short-term fluctuations; when N is large (focusing on long-term trends), γ can be set smaller to smooth out the fluctuations. This association relationship (such as a function or a look-up table) is stored in the memory 130.

[0075] The preset initial threshold S0 is superimposed on the trend correction term to obtain a reference correction value. The preset initial threshold S0 is a preset constant stored in the memory 130 and represents the basic fault threshold of the device in a brand-new healthy state. The superimposition is achieved by adding the preset initial threshold S0 to the trend correction term: reference correction value = S0 + trend correction term = S0 + γ × (trend of the state index value).

[0076] Based on the aging compensation coefficient β and the reference correction value, the dynamic threshold S is obtained. th The dynamic threshold S th is achieved by multiplying the aging compensation coefficient β by the reference correction value: S th = β × reference correction value = β × (S0 + γ × (trend of the state index value)).

[0077] Exemplarily, assume that the preset initial threshold S0 = 5.0×10 4 , and the preset integer N = 15. The calculated trend of the most recent 15 state index value sequences is +80 (for example, (S 15 -S1) / 15 = +80). The preset trend factor γ is obtained according to the association with N = 15. For example, γ = 1.1. The aging compensation coefficient β obtained from step S6 = 0.95, then: Trend of the state index value = +80.

[0078] Trend correction term = γ × (trend of the state index value) = 1.1 × 80 = 88.

[0079] Reference correction value = S0 + trend correction term = 5.0×10 4 + 88 = 50088.

[0080] Dynamic threshold S th = β × Reference correction value = 0.95 × 50088 ≈ 47583.6.

[0081] Step S8: Compare the status index value S with the dynamic threshold S th and output a fault flag or a normal flag corresponding to the comparison result.

[0082] The processor 120 compares the currently obtained real-time status index value S with the dynamic threshold S calculated in step S7 th for comparison.

[0083] If the status index value S is greater than the dynamic threshold S th (S > S th ), it is determined that the current operating state of the device is abnormal or has reached the warning threshold, and the processor outputs a fault flag.

[0084] If the status index value S is less than or equal to the dynamic threshold S th (S ≤ S th ), it is determined that the current operating state of the device is normal, and the processor outputs a normal flag.

[0085] The output fault flag or normal flag can be sent to the remote monitoring platform through the communication unit 140 for operation and maintenance personnel to view, analyze and take corresponding maintenance measures. The fault flag can be further used to trigger local audible and visual alarms or other forms of prompts.

[0086] Exemplarily, assume that the currently obtained status index value S = 47489.6 and the dynamic threshold S th ≈ 47583.6.

[0087] Compare S with S th : 47489.6 ≤ 47583.6.

[0088] The comparison result is S ≤ S th , so a normal flag is output.

[0089] Through the description of the above embodiments, those skilled in the art can clearly understand that for the convenience and simplicity of description, only the above division of each functional module is used as an example. In actual applications, the above functions can be allocated to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above.

[0090] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be in electrical, mechanical or other forms.

[0091] The units described as separate components may or may not be physically separated. The components displayed as units may be one physical unit or multiple physical units, that is, they can be located in one place, or they can be distributed to multiple different places. The parts or all of the units with their characteristics can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0092] In addition, each functional unit in various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware.

[0093] The above content is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A usage method of a power protection intelligent terminal for a power generation vehicle based on an ARM chip, characterized in that, Including: Obtaining a real-time operation parameter set of a power generation vehicle, the parameter set including real-time voltage, real-time current, real-time frequency, real-time oil pressure, and real-time temperature; Obtaining a parameter coupling factor based on the real-time voltage, the real-time current, and the real-time temperature; Obtaining a state index value based on the real-time operation parameter set, the parameter coupling factor, a preset reference oil pressure, and a preset coupling weight; Recording the obtained state index value to form a sequence of the most recent N state index values, where N is a preset integer; Obtaining an aging compensation coefficient corresponding to a preset aging curve based on the cumulative operation time of the power generation vehicle; Obtaining a dynamic threshold based on the aging compensation coefficient, a preset initial threshold, a preset trend factor, and the sequence of the most recent N state index values; Comparing the state index value with the dynamic threshold and outputting a fault flag or a normal flag corresponding to the comparison result.

2. The method according to claim 1, characterized in that, The obtaining the parameter coupling factor based on the real-time voltage, the real-time current, and the real-time temperature includes: Obtaining a real-time power output based on the real-time voltage and the real-time current; Obtaining a heat-related term based on a preset heat-work conversion coefficient and the real-time temperature; Obtaining the absolute value of the difference between the real-time power output and the heat-related term to obtain the parameter coupling factor.

3. The method according to claim 2, wherein The preset heat-work conversion coefficient is obtained based on a preset rated power, a preset rated temperature, and a preset power generation efficiency constant.

4. The method according to claim 1, characterized in that The obtaining the state index value based on the real-time operation parameter set, the parameter coupling factor, a preset reference oil pressure, and a preset coupling weight includes: Obtaining an energy conversion metric value based on the real-time voltage, the real-time current, the real-time temperature, and a preset reference temperature; Obtaining a mechanical efficiency correction factor from the real-time oil pressure and the preset reference oil pressure; Obtaining a basic state value based on the energy conversion metric value and the mechanical efficiency correction factor; Obtaining an abnormal coupling metric value based on the preset coupling weight and the parameter coupling factor; and superimposing the basic state value and the abnormal coupling metric value to obtain the state index value.

5. The method according to claim 4, wherein The preset coupling weight is obtained based on the real-time oil pressure, and the preset coupling weight has a logarithmic relationship with the real-time oil pressure.

6. The method according to claim 1, wherein The obtaining the aging compensation coefficient corresponding to a preset aging curve based on the cumulative operation time of the equipment includes: Consulting a preset mapping table, the preset mapping table establishing a mapping relationship between different ranges of the cumulative operation time and the corresponding aging compensation coefficient, and obtaining the aging compensation coefficient according to the cumulative operation time.

7. The method according to claim 1, characterized in that, The obtaining the dynamic threshold based on the aging compensation coefficient, a preset initial threshold, a preset trend factor, and the sequence of the most recent N state index values includes: Obtaining the sum of the differences between adjacent state index values in the sequence based on the sequence of the most recent N state index values; Obtaining a state index value change trend from the sum of the differences and the preset integer N; Obtaining a trend correction term based on a preset trend factor and the state index value change trend; Superimpose a preset initial threshold value and the trend correction term to obtain a reference correction value; and obtain the dynamic threshold value based on the aging compensation coefficient and the reference correction value.

8. The method according to claim 7, wherein The preset trend factor is obtained based on the preset integer N, and the preset trend factor has a preset correlation with the preset integer N.

9. A power protection intelligent terminal for a power generation vehicle based on an ARM chip, characterized in that, Comprising: A multi-source sensor array configured to collect real-time voltage, real-time current, real-time frequency, real-time oil pressure, and real-time temperature signals of a power generation vehicle; A processor configured to execute the usage method described in claim 1; A memory configured to store a preset aging curve mapping table, a preset reference oil pressure, a preset heat-work conversion coefficient, a preset initial threshold value, a preset trend factor, and a preset integer N.

10. The intelligent terminal according to claim 9, characterized in that, The memory is further configured to store the most recent N state index value sequences.

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