Loop cut-off method and device based on active detection and computer equipment

Abstract

The invention relates to a loop cut-off method and device based on active detection and computer equipment. The method comprises the following steps: acquiring loop electromagnetic monitoring data and loop electromagnetic setting data of the automobile power architecture; solving a circuit physical driving equation according to the loop electromagnetic setting data to obtain loop electromagnetic expected data; performing cross validation on the physical parameters according to the loop electromagnetic expected data and the loop electromagnetic monitoring data to obtain abnormal parameter identification data; according to the operation environment parameters, correcting the abnormal parameter identification data to obtain abnormal parameter correction data; according to the abnormal parameter correction data, analyzing a loop abnormal trend, and determining loop abnormal equipment parameter information and a loop abnormal equipment level; and generating a loop cut-off instruction according to the loop abnormal equipment parameter information and the loop abnormal equipment level. By adopting the method, the reliability of loop cut-off of the automobile power architecture can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent automobile technology, and in particular to a circuit cutting method, device and computer equipment based on active detection. Background Art

[0002] With the development of automobile technology, the circuit disconnection methods of automobile power architecture mainly include various technical means such as mechanical fuses, circuit breakers and electronic control switches. Mechanical fuses cut off the circuit to protect the electrical system by melting the internal fuse when overcurrent occurs; circuit breakers use a resettable switch mechanism to automatically disconnect the circuit when overload or short circuit is detected, which is convenient for maintenance and reuse. In recent years, electronic control switches such as solid-state relays and intelligent circuit breakers have gradually been applied to automobile power systems. These devices can achieve faster and more accurate circuit disconnection and are intelligently managed through the vehicle's central control unit.

[0003] However, mechanical fuses are disposable components that need to be replaced after overload, increasing maintenance costs and time. Although circuit breakers can be reset, their mechanical parts are prone to wear, have a limited service life, and may degrade under high-frequency operation. Although electronic control switches have a fast response speed, they are usually more expensive, more complex, and more sensitive to electromagnetic interference and ambient temperature, which may affect their reliability. Therefore, traditional technologies for circuit cutting of automotive power architectures still have the problem of insufficient reliability. Summary of the invention

[0004] Based on this, it is necessary to provide a circuit cutting method, device, computer equipment, computer-readable storage medium and computer program product based on active detection, which can effectively improve the reliability of circuit cutting of the automobile power architecture in order to solve the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a loop disconnection method based on active detection, comprising:

[0006] Obtain loop electromagnetic monitoring data and loop electromagnetic setting data of the vehicle power structure;

[0007] Solving the circuit physical driving equation of the vehicle power structure according to the loop electromagnetic setting data to obtain loop electromagnetic expected data;

[0008] Cross-validating various physical parameters of the vehicle power structure according to the loop electromagnetic expected data and the loop electromagnetic monitoring data to obtain abnormal parameter identification data;

[0009] Correcting the abnormal parameter identification data according to the operating environment parameters of the vehicle power architecture to obtain abnormal parameter correction data;

[0010] Analyzing the abnormal trend of the circuit of the vehicle power structure according to the abnormal parameter correction data, and determining the abnormal circuit equipment parameter information and the abnormal circuit equipment level;

[0011] A loop cut-off instruction for the vehicle power architecture is generated based on the loop abnormal device parameter information and the loop abnormal device level.

[0012] In a second aspect, the present application also provides a circuit cutting device based on active detection, comprising:

[0013] A loop data acquisition module, used to acquire loop electromagnetic monitoring data and loop electromagnetic setting data of the vehicle power structure;

[0014] A driving equation solving module, used to solve the circuit physical driving equation of the vehicle power structure according to the loop electromagnetic setting data to obtain loop electromagnetic expected data;

[0015] an abnormal parameter identification module, used to cross-validate various physical parameters of the vehicle power architecture according to the loop electromagnetic expected data and the loop electromagnetic monitoring data, and obtain abnormal parameter identification data;

[0016] an abnormal parameter correction module, used to correct the abnormal parameter identification data according to the operating environment parameters of the vehicle power architecture to obtain abnormal parameter correction data;

[0017] A loop abnormality analysis module, used to analyze the loop abnormality trend of the vehicle power architecture according to the abnormal parameter correction data, and determine loop abnormality equipment parameter information and loop abnormality equipment level;

[0018] A cut-off instruction generation module is used to generate a circuit cut-off instruction for the vehicle power architecture according to the circuit abnormality device parameter information and the circuit abnormality device level.

[0019] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements any step of a loop disconnection method based on active detection when executing the computer program.

[0020] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the computer program implements any step of a loop disconnection method based on active detection.

[0021] In a fifth aspect, the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements any step of a loop disconnection method based on active detection.

[0022] The above-mentioned loop disconnection method, device, computer equipment, storage medium and computer program product based on active detection obtains loop electromagnetic monitoring data and loop electromagnetic setting data of the automobile power structure; solves the circuit physical driving equation of the automobile power structure according to the loop electromagnetic setting data to obtain loop electromagnetic expected data; cross-validates various physical parameters of the automobile power structure according to the loop electromagnetic expected data and the loop electromagnetic monitoring data to obtain abnormal parameter identification data; corrects the abnormal parameter identification data according to the operating environment parameters of the automobile power structure to obtain abnormal parameter correction data; analyzes the loop abnormality trend of the automobile power structure according to the abnormal parameter correction data, determines the loop abnormal device parameter information and the loop abnormal device level; generates a loop disconnection instruction for the automobile power structure according to the loop abnormal device parameter information and the loop abnormal device level.

[0023] By comprehensively analyzing the loop electromagnetic monitoring data and loop electromagnetic setting data in the automotive power architecture, and using the expected loop electromagnetic data calculated by the circuit physical driving equation, the various physical parameters of the system are cross-validated, and potential abnormalities in the circuit can be accurately identified. On this basis, the abnormal identification data is corrected in combination with the vehicle operating environment parameters, effectively eliminating the interference of environmental factors and ensuring the accuracy of abnormal parameters. Further analysis of the correction data of abnormal parameters can accurately determine the equipment with loop abnormalities and their fault levels, so as to generate loop cut-off instructions in time to prevent the abnormality from expanding into major faults or damage. It not only improves the monitoring accuracy and system stability of the power architecture, effectively improves the reliability of the loop cut-off of the automotive power architecture and optimizes the efficiency of fault diagnosis and prevention, but also enhances the safety and operational reliability of the vehicle, reduces maintenance costs and downtime, and increases the service life of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 is an application environment diagram of a loop cutting method based on active detection in one embodiment;

[0026] Figure 2 is a flow chart of a loop disconnection method based on active detection in one embodiment;

[0027] Figure 3A schematic diagram of a flow chart of a method for obtaining expected loop electromagnetic data in an embodiment;

[0028] Figure 4 It is a flowchart of a method for obtaining implicit electromagnetic expected segmented data in one embodiment;

[0029] Figure 5 A schematic diagram of a flow chart of a method for obtaining abnormal parameter identification data in one embodiment;

[0030] Figure 6 A schematic flow chart of a method for obtaining abnormal parameter identification data in another embodiment;

[0031] Figure 7 It is a flowchart of a method for determining loop abnormal device parameter information and loop abnormal device level in one embodiment;

[0032] Figure 8 A schematic diagram of a flow chart of a method for generating a loop maintenance program flowchart in one embodiment;

[0033] Fig. 9 is a structural block diagram of a loop disconnection device based on active detection in one embodiment;

[0034] Fig.10 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0036] The present application provides a method for disconnecting a circuit based on active detection, which can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Among them, the server 104 can be implemented with an independent server or a server cluster composed of multiple servers.

[0037] In an exemplary embodiment, Figure 2 As shown, a loop cutting method based on active detection is provided, and the method is applied to Figure 1 The server in the example is used to illustrate, including the following steps 202 to 212. Among them:

[0038] Step 202, obtaining loop electromagnetic monitoring data and loop electromagnetic setting data of the vehicle power structure.

[0039] Among them, the automotive power architecture can be a collection of various system components in the vehicle that are responsible for transmitting power, controlling electrical equipment and regulating energy, including batteries, engines, powertrains, motors, control units, etc. It includes functions such as electric drive, energy management, and energy conversion, supporting the overall power performance of the vehicle and the normal operation of electrical equipment.

[0040] Among them, the loop electromagnetic monitoring data can be various electrical parameter data about the electromagnetic circuit of the vehicle power structure collected in real time through sensors and monitoring systems, including current, voltage, power, magnetic field strength, etc.

[0041] Among them, the loop electromagnetic setting data can be the electrical parameters pre-set during the automobile design and manufacturing process. These parameters represent the theoretical operating status and standard requirements of the circuit, including voltage, current, resistance, inductance, frequency, power, etc.

[0042] Specifically, the electromagnetic circuit data in the vehicle power structure is collected through the vehicle's sensors and monitoring system, including real-time monitoring values ​​such as current, voltage, and magnetic field. At the same time, the circuit electromagnetic setting data is obtained. These setting data usually include standard electrical parameters, circuit configuration, and electromagnetic characteristic parameters during design, etc., as a theoretical basis for subsequent calculations and analysis.

[0043] Step 204 , solving the circuit physical driving equation of the vehicle power structure according to the loop electromagnetic setting data to obtain the loop electromagnetic expected data.

[0044] The circuit physical driving equation may be a mathematical equation that describes the relationship between various electrical components (such as resistance, capacitance, inductance, etc.) and electrical quantities (such as current, voltage) in the circuit.

[0045] Among them, the expected loop electromagnetic data can be normal data calculated based on the loop electromagnetic setting data and the circuit physical driving equation, reflecting the electrical characteristics such as current, voltage, power, etc. that the circuit should exhibit in the absence of external interference or faults.

[0046] Specifically, the circuit physical driving equations of the vehicle power architecture are constructed using the electromagnetic setting data of the loop (such as resistance, inductance, capacitance, source voltage and other parameters). Specifically, these setting data provide the physical characteristics and behavior models for each component in the circuit (such as power supply, load, inductance and capacitance), and combine the basic laws in circuit theory, such as Kirchhoff's voltage law (KVL) and Kirchhoff's current law (KCL), as well as Faraday's law of electromagnetic induction and electromagnetic field theory, to establish the circuit physical driving equations that describe the loop. The circuit physical driving equations can be used to describe the relationship between physical quantities such as voltage, current, and magnetic field in the circuit. For example, for a simple RL circuit, the relationship between voltage and current can be expressed by a first-order ordinary differential equation. For more complex power architectures, a set of linear or nonlinear equations needs to be solved according to the specific structure of the loop, which may contain multiple power supplies, loads, and interacting electromagnetic components. By using implicit solution algorithms including the backward Euler algorithm, the Crank-Nicholson algorithm, and the implicit Runge-Kutta algorithm to solve the circuit physical driving equations, the expected electromagnetic data of the circuit under normal conditions can be obtained, that is, the normal distribution of physical quantities such as current, voltage, and magnetic field in the absence of any faults or interference. These expected data provide a benchmark for the comparative analysis of subsequent monitoring data and actual values, thereby helping to identify abnormal behavior of the circuit.

[0047] For the circuit physics driving equation, the detailed expression is as follows:

[0048]

[0049] Φ B (t) = μ 0 ·N·I(t)+M·I M (t)+β·B env (t),

[0050]

[0051] ω(t)=f(I(t),Φ B (t),θ(t))

[0052] Where V(t) is the output voltage, R·I(t) is the voltage drop caused by the resistor, is the self-inductance voltage caused by the inductor, is the induced voltage caused by mutual inductance, k e ω(t) is the motor back electromotive force of the vehicle power structure, k e is the back electromotive force constant, obtained from the technical specifications or manuals provided by the motor manufacturer, ω(t) is the speed function of the motor, α·E env(t) is the influence of the environmental electric field on the voltage, α is the electric field coupling coefficient, and the coefficient E is extracted by simulating the influence of the environmental electric field on the circuit using electromagnetic simulation software (such as ANSYS Maxwell). env (t) is the ambient electric field, which is obtained by installing a high-sensitivity electric field sensor (such as an electric field probe) for real-time monitoring, μ 0 ·N·I(t) is the magnetic flux generated by the main current, μ 0 is the vacuum magnetic permeability, M·I M (t) is the magnetic flux generated by the mutual inductance current, β·B env (t) is the effect of the environmental magnetic field on the magnetic flux, β is the magnetic field coupling coefficient, and the coefficient B is extracted by simulating the effect of the environmental electric field on the circuit using electromagnetic simulation software (such as ANSYS Maxwell). env (t) is the ambient magnetic field, which is obtained by installing a high-sensitivity magnetic field sensor (such as a Hall effect magnetometer) for real-time monitoring. is the mutual inductance current based on magnetic flux and main current, L m is the inductance of the mutual inductance circuit, γ·H env (t) is the effect of the environmental magnetic field strength on the mutual inductance current, which is calculated by installing a high-sensitivity magnetic field sensor (such as a Hall effect magnetometer) and combining it with the magnetic permeability. γ is the magnetic field strength coupling coefficient, which is calculated based on electromagnetic theory, combined with the circuit structure and material properties. B env (t) is the environmental magnetic field intensity, f is the speed function, I(t) is the current, θ(t) is the environmental parameter, and the temperature sensor, humidity sensor, pressure sensor and other sensors are installed to monitor the environmental parameters in real time and then the data is fused and processed to obtain the value. B (t) is the magnetic flux.

[0053] Step 206 , cross-validate each physical parameter of the vehicle power structure based on the loop electromagnetic expected data and the loop electromagnetic monitoring data to obtain abnormal parameter identification data.

[0054] Among them, physical parameters can be various key data that describe the electrical characteristics and behaviors in the electromagnetic circuit, such as current, voltage, resistance, inductance, power, magnetic field, etc.

[0055] The abnormal parameter identification data may be obtained by comparing the loop electromagnetic monitoring data with the loop electromagnetic expected data to identify the abnormal physical parameters in the circuit.

[0056] Specifically, since the loop electromagnetic monitoring data is real-time data collected from vehicle sensors and monitoring systems during actual operation, and the loop electromagnetic expected data is an ideal value obtained based on the circuit physical driving equation. The core of cross-validation is to compare the two to see whether the actual data is consistent with the expected data. If there is a large deviation between the actual data and the expected data, it means that an abnormality may have occurred. This deviation may be manifested as voltage, current fluctuations, instability of magnetic field strength or beyond the normal range. In the cross-validation process, some error sources, such as sensor accuracy and environmental interference, must also be considered, so that it can be more accurately judged whether it is a fault in the circuit itself or a system problem. In addition, statistical analysis methods (such as error threshold setting, deviation measurement, etc.) are used to further confirm the severity of the abnormality and the location of the abnormal parameters to form abnormal parameter identification data.

[0057] Step 208, correcting the abnormal parameter identification data according to the operating environment parameters of the vehicle power architecture to obtain abnormal parameter correction data.

[0058] The operating environment parameters may be various factors affecting the circuit performance in the external and internal environments during the actual driving of the vehicle, such as temperature, humidity, vibration, load, vehicle speed, etc.

[0059] Among them, the abnormal parameter correction data can be the result of adjusting and correcting the identified abnormal parameter data after considering the operating environment parameters, by eliminating the impact of environmental factors on circuit performance, ensuring that the abnormal data more accurately reflects the faults or problems in the circuit.

[0060] Specifically, the physical properties of the circuit may change when the car is running under different environmental conditions (such as temperature, humidity, vibration, speed, load, etc.). For example, a high temperature environment may cause the resistance to increase, thereby affecting the normal flow of current; excessive humidity may cause the insulation performance to decrease, and the voltage stability of the circuit may be affected; changes in vibration and load may cause poor contact or current fluctuations. In order to eliminate the influence of these environmental factors, it is necessary to use parameters related to the circuit operating environment (such as temperature, humidity, load changes inside and outside the car, etc.) as correction factors to adjust and correct the monitoring data of the circuit. For example, by establishing an environmental impact model, the relationship between environmental parameters and electromagnetic data can be modeled, and then the monitoring data can be mathematically corrected to obtain abnormal parameter correction data that is closer to the actual fault characteristics. Not only can it eliminate errors caused by environmental changes, but it can also ensure that the correction results of abnormal data are more in line with the fault characteristics of the circuit itself.

[0061] Step 210 , analyzing the loop abnormality trend of the vehicle power architecture according to the abnormal parameter correction data, and determining loop abnormality equipment parameter information and loop abnormality equipment level.

[0062] Among them, the loop abnormality trend can be the pattern of abnormal phenomena in the circuit observed over time or other factors when analyzing the corrected abnormal parameter data. These trends help predict the evolution of the fault, determine whether the fault will continue to worsen, and provide important predictive information on the overall health of the circuit.

[0063] Among them, the loop abnormal device parameter information can be used to identify the affected electrical equipment and its related fault parameters when analyzing the loop abnormality trend, such as abnormal values ​​such as voltage, power, current, etc. of a certain device.

[0064] The loop abnormality equipment level can be a process of classifying and evaluating the equipment according to the fault severity and impact range of the loop abnormality equipment, which is usually divided into mild fault, moderate fault and severe fault.

[0065] Specifically, by performing time series analysis on the abnormal parameter correction data or inputting the abnormal parameter correction data into the trend prediction model, observing the fluctuation trend of physical quantities such as current, voltage, and magnetic field, it is determined whether these fluctuations determine whether the fault is expanding or whether there will be potential major problems in the circuit in the future, such as whether there are signs of deterioration or periodic changes or revealing potential fault progress. Then, using statistical methods and fault diagnosis models, the specific affected devices or components in the circuit are identified, and the parameter information of the abnormal circuit device is further determined in combination with its position and function in the circuit. For example, if the voltage continues to deviate from the expected value and is accompanied by current fluctuations, it can be inferred that there may be a problem with the power module or capacitor. In addition, according to the manifestation and impact range of the fault, different fault levels (such as mild, moderate, and severe) can be set for each abnormal device as the abnormal circuit device level, which is usually based on the preset threshold and the tolerance of the device. If the parameters of a device have exceeded the normal working range and cannot be self-repaired, it may be classified as a severe fault and immediate measures need to be taken.

[0066] Step 212, generating a circuit disconnection instruction for the vehicle power architecture according to the circuit abnormality device parameter information and the circuit abnormality device level.

[0067] Among them, the loop cut-off instruction can be an execution instruction automatically issued according to the parameter information and fault level of the abnormal loop equipment, which is intended to cut off the faulty circuit part or equipment after triggering a certain data in the instruction to prevent the fault from spreading or causing more serious damage.

[0068] Specifically, according to the loop abnormal device parameter information and loop abnormal device level obtained from the previous step of analysis, the corresponding loop cut-off instruction is generated, and the loop cut-off instruction is calculated and executed to cut off the loop at the appropriate time. For example, if a device in the loop has reached a certain fault threshold and there is a safety hazard, the cut-off function of the cut-off instruction will be triggered to prevent more serious faults from occurring.

[0069] In the above-mentioned loop cutting method based on active detection, the loop electromagnetic monitoring data and loop electromagnetic setting data of the vehicle power structure are obtained; according to the loop electromagnetic setting data, the circuit physical driving equation of the vehicle power structure is solved to obtain the loop electromagnetic expected data; according to the loop electromagnetic expected data and the loop electromagnetic monitoring data, the various physical parameters of the vehicle power structure are cross-validated to obtain abnormal parameter identification data; according to the operating environment parameters of the vehicle power structure, the abnormal parameter identification data is corrected to obtain abnormal parameter correction data; according to the abnormal parameter correction data, the loop abnormality trend of the vehicle power structure is analyzed to determine the loop abnormal equipment parameter information and the loop abnormal equipment level; according to the loop abnormal equipment parameter information and the loop abnormal equipment level, a loop cutting instruction of the vehicle power structure is generated.

[0070] By comprehensively analyzing the loop electromagnetic monitoring data and loop electromagnetic setting data in the automotive power architecture, and using the expected loop electromagnetic data calculated by the circuit physical driving equation, the various physical parameters of the system are cross-validated, and potential abnormalities in the circuit can be accurately identified. On this basis, the abnormal identification data is corrected in combination with the vehicle operating environment parameters, effectively eliminating the interference of environmental factors and ensuring the accuracy of abnormal parameters. Further analysis of the correction data of abnormal parameters can accurately determine the equipment with loop abnormalities and their fault levels, so as to generate loop cut-off instructions in time to prevent the abnormality from expanding into major faults or damage. It not only improves the monitoring accuracy and system stability of the power architecture, effectively improves the reliability of the loop cut-off of the automotive power architecture and optimizes the efficiency of fault diagnosis and prevention, but also enhances the safety and operational reliability of the vehicle, reduces maintenance costs and downtime, and increases the service life of the vehicle.

[0071] In an exemplary embodiment, Figure 3 As shown, according to the loop electromagnetic setting data, the circuit physical driving equation of the vehicle power structure is solved to obtain the loop electromagnetic expected data, including steps 302 to 304. Among them:

[0072] Step 302 , according to the loop electromagnetic setting data, use several implicit solution algorithms to solve the circuit physical driving equations of the vehicle power structure to obtain each implicit electromagnetic expected segment data.

[0073] Among them, the implicit solution algorithm can be used when solving differential equations. When calculating the solution of the current time step, it needs to rely on the value of the next time step, so it needs to obtain the solution by solving the system of equations. Unlike the explicit solution algorithm, the implicit algorithm does not directly calculate the solution of the next step through the current known data, but considers the influence of future data, so it has better stability and convergence when dealing with rigid problems (such as high-frequency oscillations or large fluctuations in circuits).

[0074] The implicit electromagnetic expected segmented data may be the electromagnetic expected data of each time period or each stage obtained by gradually solving the circuit physical driving equations using an implicit solution algorithm.

[0075] Specifically, the specific parameters of the circuit physical driving equation are set according to the loop electromagnetic setting data (such as the resistance, inductance, capacitance and other parameters of the circuit elements), for example, the initial parameters of each element in the circuit physical driving equation, such as resistance, inductance and capacitance, are set. Assuming the circuit physical driving equation of a simple RC circuit, it is necessary to set the resistance value of the resistor (R), such as 10Ω, and the capacitance value of the capacitor (C), such as 100μF. In addition, if the circuit contains an inductor element, it is also necessary to set the inductance value (L), such as 1H. If in the circuit physical driving equation of a complex circuit, in addition to the basic resistance (R), inductance (L) and capacitance (C), more parameters need to be set to fully describe the behavior of the circuit. For example, the type and characteristics of the power supply, such as the voltage value (V) of the DC power supply or the frequency and amplitude of the AC power supply; the nonlinear characteristics of each circuit element, such as the volt-ampere characteristics of the diode, the gain of the transistor, etc.; and the mutual inductance (M) that may exist in the circuit, that is, the mutual coupling effect between multiple inductor elements. In addition, the topology of the circuit needs to be considered to define the node voltage, branch current and connection relationship between different components; for the feedback loop in a complex system, the feedback gain, phase and other parameters need to be set. In multi-layer circuits or those containing multiple subsystems, the coupling effect between systems, power conversion efficiency, the influence of temperature on component characteristics (such as temperature coefficient), and possible environmental factors (such as electromagnetic interference) need to be considered.

[0076] On the basis of these basic parameters, the power supply voltage (V) also needs to be set as one of the data. In order to solve the circuit physical driving equation, several implicit solution algorithms are used. Among them, the implicit algorithm has good stability when dealing with rigid systems, so it is suitable for high-frequency oscillations and large fluctuations that may occur in electromagnetic circuits. The specific steps are: first use the Backward Euler method, which provides a stable solution under the initial conditions. When the initial conditions are unstable and the solution is numerically unstable, the values ​​of the electrical parameters in the circuit can be solved by the method of step-by-step approximation, and the electromagnetic expected data for each time step in the initial stage can be output; then use the Crank-Nicolson method, which is a second-order accurate implicit algorithm. When the state tends to be stable or changes slowly, it has good time stability and computational efficiency, and can further accurately calculate the behavior of each electrical component in the circuit over time, and improve the accuracy of the numerical solution; finally use the Implicit Runge-Kutta method, which is a multi-order accurate solution method. When the system encounters high rigidity or rapid changes, it can solve the equation efficiently and stably to ensure the stability of the numerical solution. Through the step-by-step solution of these algorithms, the implicit electromagnetic expected segmented data of each algorithm is obtained.

[0077] Step 304, concatenate the implicit electromagnetic expected segmented data to obtain loop electromagnetic expected data.

[0078] Specifically, since each implicit solution algorithm processes different time periods or different solution stages of the circuit, the electromagnetic expected data obtained is usually segmented data with time or stage as the variable. These data are continuous on the time axis, but the calculation accuracy and time step of each algorithm may be different. Therefore, the splicing process needs to ensure a smooth transition between data to avoid discontinuity or inconsistency caused by algorithm differences. The calculation results of each algorithm in its time period are connected through the splicing method to obtain the complete circuit electromagnetic expected data.

[0079] In this embodiment, by using several implicit solution algorithms (such as the backward Euler algorithm, the Crank-Nicholson algorithm, and the implicit Runge-Kutta algorithm) to solve the circuit physical driving equations of the vehicle power architecture, the loop electromagnetic expected data can be accurately obtained. These algorithms can provide high-precision numerical solutions in different solution scenarios, especially when dealing with complex circuit systems, and can effectively avoid numerical instability and ensure accurate prediction of electromagnetic expected data. After splicing the implicit electromagnetic expected segmented data, the complete loop electromagnetic expected data obtained provides a reliable basis for subsequent fault detection, parameter analysis, and performance optimization, which helps to achieve more accurate circuit state prediction and abnormality detection, thereby improving the reliability and safety of the system and optimizing maintenance and operation decisions.

[0080] In an exemplary embodiment, Figure 4 As shown, the implicit electromagnetic expected segmented data includes non-stationary electromagnetic expected segmented data and stationary electromagnetic expected segmented data; according to the loop electromagnetic setting data, several implicit solution algorithms are used to solve the circuit physical driving equations of the vehicle power structure to obtain various implicit electromagnetic expected segmented data, including steps 402 to 408. Among them:

[0081] Step 402, according to the loop electromagnetic setting data, use the backward Euler algorithm to solve the circuit physical driving equation of the vehicle power structure until the output data reaches a stable state, and obtain the non-stationary electromagnetic expected segmented data.

[0082] The stable state may be a state in which, after a certain period of time, the electromagnetic parameters of the circuit, such as current, voltage, and power, become stable and no longer change significantly.

[0083] The non-stationary electromagnetic expected segmented data may be electromagnetic expected data when the circuit has not yet reached a steady state when an implicit solution algorithm such as the backward Euler method is used to solve the circuit physical driving equation.

[0084] Specifically, the initial parameters of the circuit physical driving equation are set using the loop electromagnetic setting data (such as resistance, inductance, capacitance and other parameters), and then the circuit physical driving equation is solved using the backward Euler algorithm. The backward Euler algorithm calculates by linking the state variables such as current and voltage of each component in the circuit with the state of the next time step. Since the circuit state at the beginning is non-stationary, that is, the physical quantities such as current and voltage change over time and are not stable, the circuit equation is solved by continuously iteratively until the output data tends to be stable (that is, the changes in current and voltage gradually decrease and reach the preset threshold), and the non-stationary electromagnetic expected segmented data is obtained.

[0085] Step 404, according to the loop electromagnetic setting data, using the Crank-Nicholson algorithm, solves the circuit physical driving equation of the vehicle power structure until the output data reaches a non-stationary state, and obtains the stable electromagnetic expected segmented data.

[0086] Among them, the non-stationary state can be a stage in which the circuit changes, fluctuates or oscillates significantly over time. At this time, the physical quantities such as current and voltage of the circuit do not reach a stable value, but continue to change over time. The circuit behavior may be unstable and cannot be maintained at a constant value due to power supply changes, load fluctuations or changes in component characteristics. Generally, the circuit is in a state of high rigidity or rapid change.

[0087] Among them, the stable electromagnetic expected segmented data can be the electromagnetic expected data obtained when the circuit reaches a stable working state after a period of transition, reflecting the values ​​of physical quantities such as current and voltage of the circuit in a stable state, and no longer showing significant time changes.

[0088] Specifically, after the output data is stable, the Crank-Nicholson algorithm is further used to solve the circuit physical driving equation. Since the Crank-Nicholson algorithm has good accuracy and stability, it is particularly suitable for the numerical solution of dynamic systems. The circuit behavior is gradually calculated by inputting the loop electromagnetic setting data into the Crank-Nicholson algorithm, and the algorithm is run to accurately calculate the current, voltage and other state variables of each circuit component, and maintain numerical stability until the output data of the circuit reappears in a non-stable state, that is, the state of the circuit begins to tend to a high rigidity or rapidly changing state. Finally, the stable electromagnetic expected segmented data obtained by the Crank-Nicholson algorithm includes stable current, voltage, power and other values.

[0089] Step 406, based on the loop electromagnetic setting data, using the implicit Runge-Kutta algorithm, returns to the step of solving the circuit physical driving equations of the vehicle power structure until the output data reaches a stable state, thereby obtaining the non-stationary electromagnetic expected segmented data.

[0090] Specifically, when the circuit tends to be highly rigid or rapidly changing, the implicit Runge-Kutta algorithm is used to solve the physical driving equations of the circuit. The implicit Runge-Kutta algorithm approximates the solution of state quantities such as current and voltage in the circuit through a multi-step prediction and correction method. According to the electromagnetic setting data of the circuit, the implicit Runge-Kutta algorithm is applied to the circuit solution process until the output data of the circuit tends to be stable again and reaches a stable state, generating stable non-stationary electromagnetic expected segmented data.

[0091] Step 408 , when solving the preset time length in the electromagnetic setting data of the time length trigger loop, each non-stationary electromagnetic expected segmented data and each stationary electromagnetic expected segmented data are used as each implicit electromagnetic expected segmented data.

[0092] The solution time may be the total calculation time required when the implicit solution algorithm is used to perform numerical calculations on the circuit physical driving equations.

[0093] The preset duration can be a time parameter pre-set during the circuit modeling and simulation process, indicating that the calculation and solution are completed within this time range. The preset duration is usually used to control the time limit of the simulation process. After reaching this time limit, the solution process will be terminated or a specific operation will be triggered.

[0094] Specifically, in the process of continuously switching between non-equilibrium and equilibrium solutions, according to the preset time limit or requirement, when the solution time reaches the preset time, the solution is stopped and the electromagnetic expected data obtained by each implicit solution algorithm is integrated. Specifically, each non-stationary electromagnetic expected segmented data and each stationary electromagnetic expected segmented data obtained when using the backward Euler algorithm, the Crank-Nicholson algorithm and the implicit Runge-Kutta algorithm are combined in time order to form a complete implicit electromagnetic expected segmented data.

[0095] In this embodiment, by using the backward Euler algorithm, the Crank-Nicholson algorithm and the implicit Runge-Kutta algorithm in sequence to solve the circuit physical driving equations of the vehicle power architecture respectively, the electromagnetic characteristics of the circuit can be effectively modeled accurately, and electromagnetic expected data at different stages can be provided. These implicit solution algorithms have strong stability and are particularly suitable for dealing with nonlinear and unstable problems in complex circuit systems, ensuring that even in the face of large time steps or complex models during the solution process, numerical instability can be avoided and accurate electromagnetic expected results can be obtained. By splicing together non-stationary and stationary electromagnetic expected segmented data, the system can obtain a complete electromagnetic behavior prediction, providing accurate prediction data for subsequent anomaly detection, performance optimization and fault warning, which can help engineers monitor circuit status in real time, identify potential problems and take preventive measures, thereby improving the safety, reliability and operation efficiency of the system.

[0096] In an exemplary embodiment, Figure 5 As shown, the physical parameters of the vehicle power structure are cross-validated according to the loop electromagnetic expected data and the loop electromagnetic monitoring data to obtain abnormal parameter identification data, including steps 502 to 504. Among them:

[0097] Step 502, calculating the difference between the loop electromagnetic expected data and the loop electromagnetic monitoring data to obtain the electromagnetic data deviation calculation result.

[0098] The calculation result of the electromagnetic data deviation may be a quantitative index obtained by comparing the difference between the expected electromagnetic data of the loop and the actual monitoring data.

[0099] Specifically, the expected loop electromagnetic data obtained by the implicit solution algorithm is compared with the actual loop electromagnetic monitoring data collected by the monitoring equipment. Specifically, the deviation between the data can be quantified by calculating the difference between the two at the same time point, or by calculating indicators such as absolute error, relative error or root mean square error (RMSE) to evaluate the difference between the two sets of data in physical quantities such as current, voltage, and power, and obtain the electromagnetic data deviation calculation result, which reflects the degree of deviation between the actual behavior in the circuit and the theoretical prediction. If the deviation is large, it may indicate that the circuit is abnormal or deviates from the expected fault behavior.

[0100] Step 504, identifying abnormal parameter identification data in various physical parameters of the vehicle power structure based on the electromagnetic data deviation calculation result.

[0101] Specifically, based on the calculated electromagnetic data deviation results, the next step is to identify abnormal parameters within a larger deviation range through certain threshold judgments or statistical analysis methods. These deviation data are usually compared with a predetermined tolerance range or standard value. If the deviation exceeds the allowable range, it can be considered that the physical parameter is abnormal. For example, the monitoring data of a current or voltage value deviates significantly from the expected value, which may be caused by a failure of a component in the circuit (such as resistance change, inductance failure, poor contact, etc.). In practical applications, machine learning algorithms (such as anomaly detection algorithms) or rule-based judgment systems can be used to further analyze the data to automatically identify abnormal physical parameters. These identified abnormal parameters constitute abnormal parameter identification data.

[0102] In this embodiment, by calculating the difference between the expected loop electromagnetic data and the loop electromagnetic monitoring data, the electromagnetic data deviation calculation result can effectively reveal potential abnormal behaviors in the circuit system. This difference analysis helps identify electromagnetic characteristics that are inconsistent with expectations, thereby providing a basis for abnormality detection. Further, based on the electromagnetic data deviation calculation results, the system can accurately identify abnormal data in various physical parameters, such as abnormal fluctuations in indicators such as current, voltage, power or temperature. This abnormal parameter identification can promptly detect potential problems in the circuit, such as component failure, poor connection or the impact of external environmental changes on the system, and then provide maintenance personnel with accurate fault diagnosis information, help quickly locate problems and take necessary repair measures, thereby improving the reliability and operational safety of the system.

[0103] In an exemplary embodiment, Figure 6As shown, according to the calculation result of electromagnetic data deviation, the abnormal parameter identification data in each physical parameter of the vehicle power structure is identified, including steps 602 to 604. Among them:

[0104] Step 602: Perform data cross-correlation analysis on any two physical parameters to obtain correlation analysis data of each parameter.

[0105] Among them, cross-correlation analysis can be used to measure the similarity or dependency between two time series data. By calculating the correlation coefficient between one series and another series at different time lags, cross-correlation analysis can reveal the correlation or synchronization between the two series.

[0106] The parameter correlation analysis data may be the result obtained by statistically analyzing the relationship between any two physical parameters (such as current, voltage, magnetic field, power, temperature, etc.).

[0107] Specifically, data cross-correlation analysis is performed on any two physical parameters (such as current, voltage, magnetic field, power, temperature, etc.) in the circuit to determine the relationship between them. Specifically, in the process of cross-correlation analysis, two physical parameters are first randomly selected for comparison, such as current and voltage. Assume that there is a time series data, and each physical quantity has a corresponding value at different time points; and the basic idea of ​​cross-correlation analysis is to calculate the similarity or correlation between the two physical parameters, and calculate their correlation under different time lags by moving one sequence to compare with the other sequence. Specifically, the time series of one physical parameter (such as current) is compared with the time series of another physical parameter (such as voltage) by sliding, and the correlation coefficient at each time point is calculated (for example, using the Pearson correlation coefficient). If the two physical parameters have a high correlation coefficient at a certain time lag, it means that there is a strong linear relationship between them, which may indicate that the two parameters are interdependent. If the correlation coefficient does not change much during the entire analysis process, it indicates that they should have consistent changes under certain working conditions; if the correlation coefficient suddenly changes drastically or deviates from the expected range, it may indicate that a component in the circuit is faulty or other abnormal conditions. By performing such cross-correlation analysis on multiple physical parameters, it is possible to further identify which parameters show abnormal correlation at certain moments, and obtain correlation analysis data of each parameter.

[0108] Step 604, determining abnormal parameter identification data from current, voltage, magnetic field, power and temperature according to the relevant analysis data of each parameter and the preset boundary conditions of each physical parameter.

[0109] Specifically, according to the previously obtained parameter-related analysis data, combined with the preset boundary conditions of each physical parameter (such as the normal working range of current, voltage, magnetic field, power and temperature), it can be further determined which physical parameters are abnormal. The preset boundary conditions are usually set based on the design specifications or empirical data of the circuit, such as the current should fluctuate within a certain range, the voltage should be maintained at a certain value, and the temperature should not be too high. If the parameter-related analysis data finds that the change of a physical parameter exceeds the preset boundary conditions, or the expected correlation with other parameters has significantly deviated, it can be determined that the physical parameter is abnormal. For example, if the change of current and voltage does not change in the expected proportion, it may indicate that a component is faulty. In this way, abnormal parameter identification data is determined from physical parameters such as current, voltage, magnetic field, power and temperature.

[0110] In this embodiment, by performing data cross-correlation analysis on any two physical parameters (such as current, voltage, magnetic field, power and temperature), the mutual relationship and potential correlation between these parameters can be revealed. Through this analysis, the coordination between the physical parameters that should be followed under normal operation and their deviation under abnormal conditions can be identified. It can help identify abnormal parameters that do not meet the preset boundary conditions. Through these abnormal parameter identification data, the system can accurately locate potential faults or abnormal behaviors in the circuit. For example, if the relationship between current and voltage deviates from the normal range, it may indicate the presence of a short circuit or open circuit fault. Through the cross-correlation analysis of parameters, the system can detect problems earlier, reduce the risk of faults, improve the accuracy of fault diagnosis, and provide an effective basis for subsequent maintenance and preventive measures.

[0111] In an exemplary embodiment, Figure 7 As shown, according to the abnormal parameter correction data, the abnormal trend of the circuit of the vehicle power structure is analyzed to determine the abnormal circuit equipment parameter information and the abnormal circuit equipment level, including steps 702 to 708. Among them:

[0112] Step 702, analyzing the abnormal parameter correction data according to the time series to obtain the loop long-term abnormal trend data and the loop short-term abnormal fluctuation data.

[0113] Among them, the long-term abnormal trend data of the loop can be the abnormal change trend of the circuit system over a long period of time, which is usually related to factors such as equipment aging, changes in environmental factors or long-term load imbalance.

[0114] Among them, the short-term abnormal fluctuation data of the circuit can be the sudden changes or unstable fluctuations that occur in the circuit system in a short period of time, which are usually related to factors such as instantaneous faults, load changes, and switching operations.

[0115] Specifically, the abnormal parameter correction data is analyzed according to the time series, with the purpose of dividing the abnormal performance of the circuit system into long-term abnormal trends and short-term abnormal fluctuations. Therefore, the time series analysis method is adopted, specifically combining sliding window, Fourier transform, wavelet transform and other technologies. Among them, the sliding window method divides the entire time series into multiple windows of fixed size, and analyzes the change characteristics of the data window by window, so as to capture the short-term fluctuations in each time period (such as instantaneous changes in voltage and current). In each window, by calculating the mean, standard deviation and other statistics in the time period, it is identified whether there are obvious abnormal fluctuations. For long-term abnormal trend data, by performing trend fitting or smoothing on the data in a longer time period, using methods such as weighted moving average or linear regression, long-term change trends are extracted, such as the gradual decrease of current and long-term fluctuations of voltage. These trends are usually related to factors such as equipment aging and environmental changes. The data obtained after sliding window processing can be further used to identify periodic fluctuations using Fourier transform, which can convert time domain signals into frequency domain, helping us to discover periodic faults or regular fluctuations in the circuit; and the use of wavelet transform is aimed at multi-scale fluctuations in the data, which can capture sudden anomalies in the short term and reveal long-term gradual changes. It is especially used for circuit data with nonlinear and non-stationary characteristics, and ultimately obtains long-term abnormal trend data of the circuit and short-term abnormal fluctuation data of the circuit.

[0116] Step 704 , predicting the loop abnormality pattern of the vehicle power structure based on the loop long-term abnormal trend data and the loop short-term abnormal fluctuation data.

[0117] Among them, the loop abnormal pattern can be a collection of typical abnormal behaviors that appear repeatedly in the circuit system, usually composed of a series of similar short-term fluctuations or long-term trends. For example, the current continues to rise for a period of time accompanied by voltage fluctuations, or the voltage is low for a long time and fluctuates periodically.

[0118] Specifically, based on the long-term abnormal trend data and short-term abnormal fluctuation data of the circuit, the abnormal pattern of the circuit is predicted through model analysis and prediction technology (such as time series prediction, machine learning model, etc.). The model analysis and prediction technology is trained and learned using historical data to become a neural network with abnormal patterns and abnormal behaviors that may appear in the future, and identify the abnormal patterns and abnormal behaviors of the circuit that may cause failures. The abnormal patterns include periodic current or voltage fluctuations, long-term performance degradation, periodic failures, etc. For example, if the long-term abnormal trend data shows a trend of gradually decreasing voltage, and the short-term abnormal fluctuations are manifested as frequent current fluctuations, the prediction model may determine that a certain electrical equipment is gradually aging or has a poor contact abnormal pattern.

[0119] Step 706 , identifying data points that match the loop abnormality pattern from the loop short-term abnormal fluctuation data as loop short-term abnormality parameter information and loop short-term abnormality level.

[0120] The short-term abnormal parameter information of the circuit can be key parameter data that is consistent with the abnormal mode of the circuit and is identified in a short period of time. These parameters reflect the abnormal performance of the circuit in an instant or short period of time, such as current surge, voltage drop or temperature fluctuation.

[0121] The short-term loop abnormality level can be an indicator for classifying the severity of the abnormality shown by the short-term loop abnormality parameter information. It reflects the degree of harm of the circuit system abnormality in a short period of time, and is usually divided into several levels, such as slight, medium, severe, etc.

[0122] Specifically, by comparing the short-term abnormal fluctuation data of the loop with the abnormal data range preset by the loop abnormal pattern, it is identified whether the short-term abnormal fluctuation data has data points that fall into the preset abnormal data range. These data points are usually manifested as abnormal current and voltage changes, or power fluctuations that do not meet expectations, etc. By matching with the preset abnormal pattern, the occurrence of short-term abnormalities is identified, and then these data points are classified as loop short-term abnormal parameter information of the loop, and combined with the severity or frequency of the abnormality, the loop short-term abnormality level (such as mild, medium, severe, etc.) is determined. If it is identified that the short-term abnormal fluctuation data does not fall into the data point of the preset abnormal data range, but some short-term fluctuations show characteristics consistent with the loop abnormal pattern and exceed the normal fluctuation range, then these data points will also be classified as loop short-term abnormal parameter information, and the corresponding loop short-term abnormality level will be determined.

[0123] Step 708, when the short-term loop abnormality parameter information and the short-term loop abnormality level are null values, data points that match the loop abnormality pattern are identified from the long-term loop abnormality trend data as the long-term loop abnormality parameter information and the long-term loop abnormality level.

[0124] Among them, the long-term abnormal parameter information of the circuit can be the key parameter data reflecting the abnormal characteristics and trends of the circuit over a long period of time. These parameters usually do not show instantaneous fluctuations, but present a pattern of continuous changes, such as a gradual decrease in voltage and a continuous increase in current.

[0125] Among them, the long-term abnormality level of the circuit can be an indicator for classification according to the degree of abnormality shown by the long-term abnormal parameter information of the circuit, which is usually divided into minor, medium, severe and other levels. It reflects the abnormal trend or severity of the fault in the circuit system over a long period of time. For example, if the voltage of the circuit continues to be low for a long time and gradually becomes serious, the long-term abnormality may be classified as a severe level, while if the slight fluctuation of the current does not change significantly for a long time, it may be regarded as a minor abnormality.

[0126] Specifically, if the short-term abnormal parameter information of the loop and the short-term abnormal level of the loop are null values, it means that no obvious abnormal fluctuations have occurred in the short term or the relevant fault data has not been identified. At this time, it is necessary to turn to the long-term abnormal trend data of the loop for analysis. Similarly, by comparing the long-term abnormal trend data of the loop with the abnormal data range preset by the loop abnormal mode, it is identified whether the long-term abnormal trend data of the loop has data points that fall into the preset abnormal data range (the short-term and long-term abnormal data ranges can be the same or different. Generally, the long-term abnormal data range is smaller than the short-term abnormal data range). These data points reflect the long-term performance of the system, such as the current gradually becoming low, the voltage being unstable for a long time, etc. By matching with the preset abnormal mode and identifying the occurrence of long-term abnormalities, these data points are classified as the long-term abnormal parameter information of the loop, and combined with the severity or frequency of the abnormality, the long-term abnormal level of the loop is determined (such as slight, medium, severe, etc.). If data points are identified that do not fall within the preset abnormal data range for the long-term abnormal trend data of the loop, but certain long-term trends show characteristics consistent with the loop abnormal pattern and are beyond the normal trend range, then these data points will also be classified as long-term abnormal parameter information of the loop, such as gradual aging of equipment, continuous overload and other problems, and the corresponding long-term abnormality level of the loop will be determined.

[0127] In this embodiment, by analyzing the abnormal parameter correction data according to the time series, the long-term abnormal trend and short-term abnormal fluctuation of the circuit can be distinguished. The long-term abnormal trend data reflects the long-term performance changes or gradually worsening faults of the system, while the short-term abnormal fluctuation data reveals the instantaneous disturbances or short-term fluctuations of the system. Based on these data, the system can predict the abnormal mode of the circuit, that is, establish an abnormal behavior model of the circuit based on historical data, so as to identify potential fault trends and the probability of occurrence. Further, by extracting data points that match the abnormal mode from the short-term abnormal fluctuation data, the system can accurately identify short-term abnormal parameter information and the corresponding abnormal level, and provide immediate fault warning; and in the case of insufficient short-term data, the system can also identify data points that match the abnormal mode from the long-term abnormal trend data of the circuit to help determine the long-term abnormal parameter information and its abnormal level. It effectively improves the accuracy and timeliness of fault warning, ensuring that the system can quickly respond to potential problems and optimize maintenance and management decisions.

[0128] In an exemplary embodiment, Figure 8 As shown, after the step of generating a circuit cut-off instruction of the vehicle power architecture according to the circuit abnormality device parameter information and the circuit abnormality device level, the method further includes steps 802 to 808. Among them:

[0129] Step 802, determining loop maintenance suggestion information of the vehicle power architecture according to loop abnormality equipment parameter information and loop abnormality equipment level.

[0130] Among them, loop maintenance suggestion information can be maintenance suggestions given by the system based on the fault diagnosis results and abnormality level of the loop abnormal equipment. It includes solutions for specific faults, such as recommended replacement or repair parts, whether emergency repair or regular inspection is required, and the priority of maintenance operations.

[0131] Specifically, based on the determined loop abnormal equipment parameter information (such as abnormal values ​​such as current, voltage, power, etc.) and loop abnormal equipment level (such as short-term or long-term abnormal level), the nature and severity of the equipment failure are comprehensively evaluated, and then loop maintenance suggestions are generated. These suggestions include parts that may need to be replaced, recommended repair methods, whether emergency repairs are required or can be delayed, etc. The system will combine the abnormality level and give specific treatment plans for different fault categories. For example, emergency repair suggestions are given for high-level faults, and regular inspections or monitoring suggestions are given for low-level faults. The maintenance suggestions and specific treatment plans are combined and digitized to obtain the final loop maintenance suggestion information.

[0132] Step 804 , in response to a confirmation instruction for the loop maintenance suggestion information, calculate loop maintenance material resource information of the vehicle power structure according to the loop maintenance suggestion information.

[0133] The circuit maintenance material resource information may include all the material and equipment resources required for circuit maintenance, including parts, tools, consumables, etc. For example, if a power module in a circuit fails, the system will automatically calculate the required power module, wiring materials, welding equipment, maintenance tools, etc. based on the circuit maintenance suggestion information, and provide a detailed bill of materials for maintenance personnel.

[0134] Specifically, the system sends the circuit maintenance suggestion information to the terminal held by the maintenance personnel. Once the maintenance personnel confirm the circuit maintenance suggestion information in the terminal and decide to carry out maintenance, the system will calculate the required material resource information according to the maintenance suggestion content, including the parts, tools, consumables, etc. required for maintenance. For example, if it is a circuit board failure, the system will recommend the required circuit board model and other related components according to the fault type, and estimate the number of spare parts required for maintenance.

[0135] Step 806, calculating the loop maintenance human resource information and loop maintenance process information of the vehicle power structure based on the loop maintenance suggestion information and the loop maintenance material resource information.

[0136] Specifically, the system will further calculate the required human resources and the entire maintenance process based on the loop maintenance suggestion information and the required material resources. The human resource information includes the required technical personnel's skill requirements, the required working hours, and the number of operators, while the process information involves the detailed planning of the maintenance steps, such as disassembly, repair, inspection, and testing. The system will automatically calculate the required manpower and operation process based on the maintenance suggestions, match the manpower and process, and check whether there are reasonable human resources under the current process. If there is no conflict between the two, the loop maintenance human resource information and loop maintenance process information will be output.

[0137] Step 808, generating a loop maintenance program flowchart according to the loop maintenance suggestion information, the loop maintenance material resource information, the loop maintenance human resource information and the loop maintenance process information.

[0138] The circuit maintenance procedure flowchart can be used to guide maintenance personnel in various operations of circuit maintenance. The flowchart lists all the steps from fault diagnosis to complete repair, and clarifies the order of each step, the tools and equipment required, the operating standards, precautions and specific data.

[0139] Specifically, the system will integrate loop maintenance suggestion information, material resource information, human resource information and process flow information to generate a comprehensive loop maintenance procedure flowchart. The flowchart shows in detail the sequence of each step in the maintenance process, the required materials and equipment, the task allocation of technicians, the time schedule, etc. In addition, the flowchart will also mark the specific parameters of each operation step (such as temperature, pressure, voltage standards, etc.), providing maintenance personnel with a clear operation guide to ensure that the entire maintenance process is standardized and smoothly executed.

[0140] In this embodiment, by generating loop maintenance suggestion information based on loop abnormal equipment parameter information and abnormal equipment level, it is possible to provide a targeted, timely and effective maintenance plan for the vehicle power architecture. By responding to and confirming the maintenance suggestion information, the system can accurately calculate the required material resources, maintenance tools, technical personnel and specific maintenance processes, thereby ensuring that all resources required in the maintenance process can be reasonably arranged and scheduled. In addition, based on the maintenance suggestion information and resource calculation, the loop maintenance flowchart generated by the system provides the maintenance team with detailed operating guidelines and processes, including each maintenance step, required materials, personnel allocation and operating parameters. Comprehensive maintenance planning can not only improve maintenance efficiency and reduce downtime, but also ensure maintenance quality and safety, thereby optimizing the overall operation and maintenance management of the vehicle power architecture.

[0141] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0142] Based on the same inventive concept, the embodiment of the present application also provides a circuit cutting device based on active detection for implementing the circuit cutting method based on active detection involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the circuit cutting device based on active detection provided below can refer to the limitations of the circuit cutting method based on active detection above, and will not be repeated here.

[0143] In an exemplary embodiment, Fig. 9As shown, a circuit cutting device based on active detection is provided, including: a circuit data acquisition module 902, a driving equation solving module 904, an abnormal parameter identification module 906, an abnormal parameter correction module 908, a circuit abnormality analysis module 910 and a cutting instruction generation module 912, wherein:

[0144] A loop data acquisition module 902 is used to acquire loop electromagnetic monitoring data and loop electromagnetic setting data of the vehicle power structure;

[0145] The driving equation solving module 904 is used to solve the circuit physical driving equation of the vehicle power structure according to the loop electromagnetic setting data to obtain the loop electromagnetic expected data;

[0146] The abnormal parameter identification module 906 is used to cross-validate various physical parameters of the vehicle power structure according to the loop electromagnetic expected data and the loop electromagnetic monitoring data to obtain abnormal parameter identification data;

[0147] The abnormal parameter correction module 908 is used to correct the abnormal parameter identification data according to the operating environment parameters of the vehicle power architecture to obtain abnormal parameter correction data;

[0148] The loop abnormality analysis module 910 is used to analyze the loop abnormality trend of the vehicle power structure according to the abnormal parameter correction data, and determine the loop abnormality equipment parameter information and loop abnormality equipment level;

[0149] The cut-off instruction generating module 912 is used to generate a circuit cut-off instruction of the vehicle power architecture according to the circuit abnormality device parameter information and the circuit abnormality device level.

[0150] In one embodiment, the driving equation solving module 904 is also used to solve the circuit physical driving equations of the vehicle power structure according to the loop electromagnetic setting data using several implicit solving algorithms to obtain various implicit electromagnetic expected segmented data; and splice the various implicit electromagnetic expected segmented data to obtain the loop electromagnetic expected data.

[0151] In one embodiment, the driving equation solving module 904 is also used to solve the circuit physical driving equation of the vehicle power architecture according to the loop electromagnetic setting data, using the backward Euler algorithm, until the output data reaches a stable state, and non-steady electromagnetic expected segmented data is obtained; according to the loop electromagnetic setting data, using the Crank-Nicholson algorithm, the circuit physical driving equation of the vehicle power architecture is solved until the output data reaches a non-steady state, and stable electromagnetic expected segmented data is obtained; according to the loop electromagnetic setting data, using the implicit Runge-Kutta algorithm, return to execute the step of solving the circuit physical driving equation of the vehicle power architecture until the output data reaches a stable state, and non-steady electromagnetic expected segmented data is obtained; when the solution time triggers the preset time in the loop electromagnetic setting data, each non-steady electromagnetic expected segmented data and each stable electromagnetic expected segmented data are used as each implicit electromagnetic expected segmented data.

[0152] In one embodiment, the abnormal parameter identification module 906 is also used to calculate the difference between the loop electromagnetic expected data and the loop electromagnetic monitoring data to obtain the electromagnetic data deviation calculation result; based on the electromagnetic data deviation calculation result, the abnormal parameter identification data in each physical parameter of the vehicle power structure is identified.

[0153] In one embodiment, the abnormal parameter identification module 906 is also used to perform data cross-correlation analysis on any two physical parameters to obtain correlation analysis data of each parameter; the physical parameters include current, voltage, magnetic field, power and temperature; based on the correlation analysis data of each parameter and the preset boundary conditions of each physical parameter, the abnormal parameter identification data is determined from the current, voltage, magnetic field, power and temperature.

[0154] In one embodiment, the loop abnormality analysis module 910 is also used to analyze the abnormal parameter correction data according to the time series to obtain the long-term abnormal trend data of the loop and the short-term abnormal fluctuation data of the loop; predict the loop abnormality mode of the vehicle power structure according to the long-term abnormal trend data of the loop and the short-term abnormal fluctuation data of the loop; identify the data points that are consistent with the loop abnormality mode from the short-term abnormal fluctuation data of the loop as the short-term abnormal parameter information of the loop and the short-term abnormal level of the loop; when the short-term abnormal parameter information of the loop and the short-term abnormal level of the loop are null values, identify the data points that are consistent with the loop abnormality mode from the long-term abnormal trend data of the loop as the long-term abnormal parameter information of the loop and the long-term abnormal level of the loop.

[0155] In one embodiment, the cut-off instruction generation module 912 is also used to determine the loop maintenance suggestion information of the vehicle power architecture based on the loop abnormal equipment parameter information and the loop abnormal equipment level; in response to the confirmation instruction for the loop maintenance suggestion information, calculate the loop maintenance material resource information of the vehicle power architecture based on the loop maintenance suggestion information; calculate the loop maintenance human resource information and loop maintenance process information of the vehicle power architecture based on the loop maintenance suggestion information and the loop maintenance material resource information; generate a loop maintenance program flowchart based on the loop maintenance suggestion information, the loop maintenance material resource information, the loop maintenance human resource information and the loop maintenance process information; the loop maintenance program flowchart is annotated with loop maintenance operation parameters.

[0156] Each module in the above-mentioned active detection-based circuit disconnection device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0157] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.10 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store server data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a loop cutting method based on active detection is implemented.

[0158] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0159] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0160] In one embodiment, a computer-readable storage medium is provided, storing a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0161] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in the above-mentioned method embodiments.

[0162] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0163] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0164] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0165] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A loop cutting method based on active detection, characterized in that: The method comprises: Obtain loop electromagnetic monitoring data and loop electromagnetic setting data of the vehicle power structure; Solving the circuit physical driving equation of the vehicle power structure according to the loop electromagnetic setting data to obtain loop electromagnetic expected data; Cross-validating various physical parameters of the vehicle power structure according to the loop electromagnetic expected data and the loop electromagnetic monitoring data to obtain abnormal parameter identification data; Correcting the abnormal parameter identification data according to the operating environment parameters of the vehicle power architecture to obtain abnormal parameter correction data; Analyzing the abnormal trend of the circuit of the vehicle power structure according to the abnormal parameter correction data, and determining the abnormal circuit equipment parameter information and the abnormal circuit equipment level; A loop cut-off instruction for the vehicle power architecture is generated based on the loop abnormal device parameter information and the loop abnormal device level.

2. The method according to claim 1, characterized in that Solving the circuit physical driving equation of the vehicle power structure according to the loop electromagnetic setting data to obtain the loop electromagnetic expected data includes: According to the loop electromagnetic setting data, using several implicit solution algorithms, solving the circuit physical driving equations of the vehicle power structure to obtain each implicit electromagnetic expected segmented data; The implicit electromagnetic expected segmented data are spliced ​​together to obtain the loop electromagnetic expected data.

3. The method according to claim 2, characterized in that The implicit solution algorithm includes a backward Euler algorithm, a Crank-Nicholson algorithm and an implicit Runge-Kutta algorithm; the implicit electromagnetic expected segmented data includes non-stationary electromagnetic expected segmented data and stationary electromagnetic expected segmented data; according to the loop electromagnetic setting data, using several implicit solution algorithms, solving the circuit physical driving equations of the vehicle power structure, and obtaining various implicit electromagnetic expected segmented data, including: According to the loop electromagnetic setting data, using the backward Euler algorithm, solving the circuit physical driving equation of the vehicle power structure until the output data reaches a stable state, thereby obtaining the non-stationary electromagnetic expected segmented data; Solving the circuit physical driving equation of the vehicle power structure using the Crank-Nicholson algorithm according to the loop electromagnetic setting data until the output data reaches a non-stationary state, thereby obtaining the stable electromagnetic expected segmented data; According to the loop electromagnetic setting data, using the implicit Runge-Kutta algorithm, returning to the step of solving the circuit physical driving equation of the vehicle power structure until the output data reaches a stable state, thereby obtaining the non-stationary electromagnetic expected segmented data; In the case where the solution time triggers the preset time in the loop electromagnetic setting data, each of the non-stationary electromagnetic expected segmented data and each of the stationary electromagnetic expected segmented data are used as each of the implicit electromagnetic expected segmented data.

4. The method according to any one of claims 1 to 3, characterized in that: The circuit physical driving equation is: F B (t)=μ0·N·I(t)+M·I M (t)+β·B emv (t), ω(t)=f(I(t),Φ B (t),θ(t)) Where V(t) is the output voltage, R·I(t) is the voltage drop caused by the resistor, is the self-inductance voltage caused by the inductor, is the induced voltage caused by mutual inductance, k e ω(t) is the motor back electromotive force of the vehicle power structure, k e is the back electromotive force constant, ω(t) is the speed function of the motor, α·E env (t) is the influence of the environmental electric field on the voltage, α is the electric field coupling coefficient, E env (t) is the ambient electric field, μ0·N·I(t) is the magnetic flux generated by the main current, μ0 is the vacuum magnetic permeability, M·IM(t) is the magnetic flux generated by the mutual inductance current, β·B env (t) is the influence of the ambient magnetic field on the magnetic flux, β is the magnetic field coupling coefficient, B env (t) is the ambient magnetic field, is the mutual inductance current based on magnetic flux and main current, L m is the inductance of the mutual inductance circuit, γ·H env (t) is the influence of the environmental magnetic field strength on the mutual inductance current, γ is the magnetic field strength coupling coefficient, B env (t) is the environmental magnetic field strength, f is the speed function, I(t) is the current, θ(t) is the environmental parameter, Φ B (t) is the magnetic flux.

5. The method according to claim 1, characterized in that The method of cross-validating the physical parameters of the vehicle power structure according to the loop electromagnetic expected data and the loop electromagnetic monitoring data to obtain abnormal parameter identification data includes: Calculating the difference between the expected electromagnetic data of the loop and the electromagnetic monitoring data of the loop to obtain an electromagnetic data deviation calculation result; According to the calculation result of the electromagnetic data deviation, the abnormal parameter identification data in each of the physical parameters of the vehicle power architecture is identified.

6. The method according to claim 5, characterized in that The step of identifying the abnormal parameter identification data in each physical parameter of the vehicle power structure according to the electromagnetic data deviation calculation result includes: Performing data cross-correlation analysis on any two of the physical parameters to obtain correlation analysis data of each parameter; the physical parameters include current, voltage, magnetic field, power and temperature; The abnormal parameter identification data is determined from the current, the voltage, the magnetic field, the power and the temperature according to the preset boundary conditions of each of the parameter-related analysis data and each of the physical parameters.

7. The method according to claim 1, characterized in that The loop abnormal device parameter information includes loop short-term abnormal parameter information and loop long-term abnormal parameter information; the loop abnormal device level includes loop short-term abnormal level and loop long-term abnormal level; the loop abnormal trend of the vehicle power architecture is analyzed according to the abnormal parameter correction data to determine the loop abnormal device parameter information and loop abnormal device level, including: Analyze the abnormal parameter correction data according to the time series to obtain the long-term abnormal trend data of the circuit and the short-term abnormal fluctuation data of the circuit; Predicting a circuit abnormality mode of the vehicle power architecture according to the circuit long-term abnormal trend data and the circuit short-term abnormal fluctuation data; Identifying data points that match the loop abnormality pattern from the loop short-term abnormal fluctuation data as the loop short-term abnormality parameter information and the loop short-term abnormality level; When the short-term loop abnormality parameter information and the short-term loop abnormality level are null values, data points that match the loop abnormality pattern are identified from the long-term loop abnormality trend data as the long-term loop abnormality parameter information and the long-term loop abnormality level.

8. The method according to claim 1, characterized in that After the step of generating a circuit cut-off instruction of the vehicle power architecture according to the circuit abnormality device parameter information and the circuit abnormality device level, the method further includes: Determining loop maintenance suggestion information of the vehicle power architecture according to the loop abnormal device parameter information and the loop abnormal device level; In response to a confirmation instruction for the loop maintenance suggestion information, calculating loop maintenance material resource information of the vehicle power architecture according to the loop maintenance suggestion information; Calculating the loop maintenance human resource information and loop maintenance process information of the vehicle power architecture according to the loop maintenance suggestion information and the loop maintenance material resource information; A loop maintenance program flowchart is generated according to the loop maintenance suggestion information, the loop maintenance material resource information, the loop maintenance human resource information and the loop maintenance process information; the loop maintenance program flowchart is annotated with loop maintenance operation parameters.

9. A circuit disconnection device based on active detection, characterized in that: The device comprises: A loop data acquisition module, used to acquire loop electromagnetic monitoring data and loop electromagnetic setting data of the vehicle power structure; A driving equation solving module, used to solve the circuit physical driving equation of the vehicle power structure according to the loop electromagnetic setting data to obtain loop electromagnetic expected data; an abnormal parameter identification module, used to cross-validate various physical parameters of the vehicle power architecture according to the loop electromagnetic expected data and the loop electromagnetic monitoring data, and obtain abnormal parameter identification data; an abnormal parameter correction module, used to correct the abnormal parameter identification data according to the operating environment parameters of the vehicle power architecture to obtain abnormal parameter correction data; A loop abnormality analysis module, used to analyze the loop abnormality trend of the vehicle power architecture according to the abnormal parameter correction data, and determine loop abnormality equipment parameter information and loop abnormality equipment level; A cut-off instruction generation module is used to generate a circuit cut-off instruction for the vehicle power architecture according to the circuit abnormality device parameter information and the circuit abnormality device level.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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