Area domain control power supply distribution method, system and device and storage medium

Abstract

The invention discloses a regional domain control power supply distribution method, system and device and a storage medium, and the method comprises the steps: collecting multi-dimensional operation parameters of a current vehicle through a vehicle-mounted bus, and carrying out the Kalman filtering preprocessing of the multi-dimensional operation parameters; identifying the operation condition of the current vehicle according to the preprocessed multi-dimensional operation parameters; when the operation working condition is a superposition working condition, determining a current working condition through a dynamic weight voting mechanism, and performing period locking on the current working condition; judging whether the parameters in the working condition locking period are smaller than a parameter deviation threshold value or not; and if yes, taking the current working condition as a target working condition, and performing regional domain control power distribution through a preset diagnosis correction sequence based on the target working condition. According to the method, when the superposition working condition is detected, the target working condition can be accurately recognized through a dynamic weight voting mechanism, load distribution disorder caused by working condition misjudgment is effectively avoided, and then regional domain control power supply distribution is achieved through a preset diagnosis correction sequence.

Description

Technical Field

[0001] This invention relates to the field of power distribution technology, and in particular to a regional control power distribution method, system, device and storage medium. Background Technology

[0002] In modern automotive electrical and electronic architecture, the zone controller, as the core unit of vehicle power distribution, undertakes the important task of intelligently managing various electrical loads. With the continuous improvement of automotive electronics, the number of electrical devices inside vehicles has increased significantly, including power systems, steering systems, braking systems, air conditioning systems, and various entertainment devices. These devices place higher demands on the real-time performance, accuracy, and reliability of power distribution.

[0003] In existing technologies, the optimization of regional architecture mainly focuses on static topology planning in the design phase, using pre-set fixed thresholds and static rules for operating condition identification and power allocation.

[0004] However, existing technologies are unable to monitor and adjust the electrical load in response to the dynamic changes in electrical load during actual vehicle operation, resulting in a lack of effective real-time management mechanisms and difficulty in achieving adaptive identification of operating conditions and power distribution.

[0005] Therefore, how to achieve adaptive identification of operating conditions and power distribution has become an urgent problem to be solved.

[0006] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0007] The main objective of this invention is to provide a regional control power distribution method, system, device, and storage medium, aiming to address the technical problem of how to achieve adaptive identification of operating conditions and power distribution.

[0008] To achieve the above objectives, the present invention provides a regional control power allocation method, the regional control power allocation method comprising: The vehicle's multi-dimensional operating parameters are collected via the vehicle bus, and these parameters are preprocessed using Kalman filtering. The current operating condition of the vehicle is identified based on the preprocessed multidimensional operating parameters; When the operating condition is an overlay condition, the current operating condition is determined through a dynamic weighted voting mechanism, and the current operating condition is periodically locked. Determine whether the parameter is less than the parameter deviation threshold within the working condition lock-in period; If so, the current operating condition is taken as the target operating condition, and regional control power distribution is performed based on the target operating condition using a preset diagnostic correction sequence.

[0009] Optionally, identifying the current operating condition of the vehicle based on preprocessed multidimensional operating parameters includes: Perform data validity verification on the preprocessed multidimensional operating parameters; Based on the multi-dimensional operating parameters after validity verification, the current operating condition of the vehicle is identified through a preset operating condition judgment threshold table.

[0010] Optionally, determining the current operating condition through a dynamic weighted voting mechanism includes: Based on the current operating conditions of the vehicle, each candidate operating condition is determined, and the overall matching degree of each candidate operating condition is calculated. The final weight value of each candidate working condition is calculated based on the comprehensive matching value of each candidate working condition and the corresponding working condition type weight. Based on the final weight values ​​of each candidate working condition, the current working condition is selected from multiple candidate working conditions through a dynamic weighted voting mechanism.

[0011] Optionally, before calculating the final weight value of each candidate working condition based on the comprehensive matching value and the corresponding working condition type weight, the method further includes: Assign basic weights to power type operating conditions and driving type operating conditions respectively; The base weights are adjusted based on the ambient temperature to obtain the operating condition type weights.

[0012] Optionally, after determining whether the parameter is less than the parameter deviation threshold within the working condition locking cycle, the method further includes: If not, the working condition switching mechanism is triggered to determine the target working condition based on the parameters within the working condition locking period. Within a preset cache transition period, the weight of the operating condition and the weight of the target operating condition are linearly adjusted. Based on the adjusted operating condition weights, the current operating condition of the vehicle is switched to the target operating condition.

[0013] Optionally, the step of performing regional control power allocation based on the target operating condition using a preset diagnostic correction sequence includes: Based on the target operating condition, the power matrix corresponding to the current driving demand mode is invoked; Real-time collection of status parameters of each component, and determination of whether the status parameters of each part are overloaded; If so, then based on the power matrix, the upper limit threshold of the power of each component is lowered according to power priority; If the overload is detected but not resolved, regional control power distribution is performed using a preset diagnostic correction sequence.

[0014] Optionally, after identifying the current operating condition of the vehicle through a preset operating condition determination threshold table, the method further includes: When the operating condition is a single operating condition, the operating condition is periodically locked. Determine whether the parameter is less than the parameter deviation threshold within the working condition lock-in period; If so, the operating condition is taken as the target operating condition, and the corresponding power allocation strategy is matched according to the power allocation mapping table based on the target operating condition. Regional control power allocation is performed based on the power allocation strategy.

[0015] Furthermore, to achieve the above objectives, the present invention also proposes a regional control power distribution system, the regional control power distribution system comprising: The processing module is used to collect multi-dimensional operating parameters of the current vehicle through the vehicle bus and perform Kalman filtering preprocessing on the multi-dimensional operating parameters; The identification module is used to identify the current operating condition of the vehicle based on preprocessed multi-dimensional operating parameters; The identification module is further configured to determine the current operating condition through a dynamic weighted voting mechanism when the operating condition is an overlay operating condition, and to periodically lock the current operating condition. The judgment module is used to determine whether the parameter is less than the parameter deviation threshold within the working condition locking cycle; The allocation module is used to, if so, take the current operating condition as the target operating condition and perform regional control power allocation based on the target operating condition using a preset diagnostic correction sequence.

[0016] Furthermore, to achieve the above objectives, the present invention also proposes a regional control power distribution device, the device comprising: a memory, a processor, and a regional control power distribution program stored in the memory and executable on the processor, the regional control power distribution program being configured to implement the steps of the regional control power distribution method described above.

[0017] In addition, to achieve the above objectives, the present invention also proposes a storage medium storing a regional control power allocation program, wherein when the regional control power allocation program is executed by a processor, it implements the steps of the regional control power allocation method as described above.

[0018] This invention first collects multi-dimensional operating parameters of the current vehicle via the vehicle bus and preprocesses these parameters using Kalman filtering. Then, based on the preprocessed parameters, it identifies the current vehicle's operating condition. When the operating condition is a superimposed condition, a dynamic weighted voting mechanism determines the current condition and locks it periodically. It then checks whether the parameters within the locking period are below a parameter deviation threshold. If so, the current condition is designated as the target condition, and regional power allocation is performed based on this target condition using a pre-set diagnostic correction sequence. This invention accurately identifies the target condition through a dynamic weighted voting mechanism when a superimposed condition is detected, effectively avoiding load distribution disorder caused by misjudgment of the operating condition. Finally, regional power allocation is achieved through a pre-set diagnostic correction sequence. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the structure of a regional control power distribution device in the hardware operating environment involved in the embodiments of the present invention; Figure 2 This is a flowchart illustrating the first embodiment of the regional control power allocation method of the present invention; Figure 3 This is a structural block diagram of the first embodiment of the regional control power distribution system of the present invention.

[0020] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0022] Reference Figure 1 , Figure 1 This is a schematic diagram of the regional control power distribution device structure of the hardware operating environment involved in the embodiments of the present invention.

[0023] like Figure 1As shown, the regional control power distribution device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be high-speed random access memory (RAM) or stable non-volatile memory (NVM), such as a disk drive. Optionally, the memory 1005 may also be a storage system independent of the aforementioned processor 1001.

[0024] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the regional control power distribution equipment, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0025] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network access module, a user interface module, and a regional control power distribution program.

[0026] exist Figure 1 In the illustrated regional control power distribution device, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and memory 1005 in the regional control power distribution device of the present invention can be set in the regional control power distribution device, and the regional control power distribution device calls the regional control power distribution program stored in the memory 1005 through the processor 1001 and executes the regional control power distribution method provided in the embodiment of the present invention.

[0027] This invention provides a regional control power distribution method, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the regional control power allocation method of the present invention.

[0028] In this embodiment, the regional control power allocation method includes the following steps: S1: Collect multi-dimensional operating parameters of the current vehicle through the vehicle bus, and perform Kalman filtering preprocessing on the multi-dimensional operating parameters.

[0029] It is easy to understand that the executing entity of this embodiment can be a regional control power distribution system with functions such as data processing, network communication and program execution, or other computer devices with similar functions. This embodiment does not limit it.

[0030] In the specific implementation, the multi-dimensional operating parameters of the current vehicle are collected by combining the CANFD bus (core parameters first) with the ordinary CAN bus, namely 4 types of core parameters. The specific parameter specifications are as follows: vehicle speed (sampling frequency 10Hz, accuracy ±0.1km / h, range 0-200km / h), accelerator pedal opening (sampling frequency 20Hz, accuracy ±1%, range 0-100%), brake pedal travel (sampling frequency 20Hz, accuracy ±0.5mm, range 0-150mm), and gradient (sampling frequency 10Hz, accuracy ±0.5°, range -15°~30°). Vehicle speed, accelerator pedal opening, and brake pedal travel are acquired with high priority via the CANFD bus, while gradient is acquired via a regular CAN bus. The original acquired signals are used for state estimation and noise suppression based on the Extended Kalman Filter (EKF) algorithm. This preprocessing is used to eliminate signal jitter caused by electromagnetic interference and improve the reliability of the input data on which subsequent operating condition identification depends. Its input is the original multidimensional operating parameter sequence, and its output is a smooth parameter sequence updated by state prediction and observation, which serves as the sole input source for the subsequent operating condition identification module.

[0031] In this embodiment, based on the extended Kalman filter's two-step mechanism of state prediction and observation update, time-series parameters such as vehicle speed, pedal opening, and gradient are jointly modeled and high-frequency noise is suppressed. Based on the preset system matrix A, observation matrix C, process noise covariance Q, and observation noise covariance R, combined with the real-time sampling time interval dt, the prediction and update steps are iteratively executed to obtain converged parameter estimates. Based on the load-voltage characteristic polynomial coefficients or lookup table interpolation, the filtered voltage observations are mapped to equivalent load power estimates, and then the consistency of parameters in each branch is verified in reverse. Stable, low-jitter, and high-confidence multidimensional operating parameters are obtained using any of the above methods, supporting the accuracy and robustness of subsequent operating condition identification.

[0032] In the specific implementation, after the vehicle is powered on, the main controller calls the initialization boot sequence, sets the EKF initial state—the initial total load is 0.5kW, the error covariance matrix P is a preset diagonal matrix, the system matrix is ​​cleared, the initial Kalman gain K is 0, the sampling time interval dt=0.1s, and the convergence flag is_initialized=false; the basic parameters loaded include the rated bus voltage 14V, the maximum total load 8kW, the load-voltage characteristic polynomial coefficients, and the initial ambient temperature 25℃; then it enters the operation phase, continuously collecting the bus voltage, the total bus current, and the current of four key branches (motor auxiliary, air conditioning, steering, and accessories) at a period of 0.1s, and simultaneously collecting the accelerator pedal opening, brake pedal travel, vehicle speed, and gradient; the EKF prediction step is performed on the above parameters: the state transition matrix A is updated, the prior state estimate is calculated, and the error covariance matrix P=A×P×A is updated. +Q; Then execute the update step: construct the observation matrix C, calculate the innovation (measured values) (Observe the predicted values), update the Kalman gain K and state variables, and finally output the filtered multidimensional operating parameters for subsequent working condition identification.

[0033] S2, Identify the current operating condition of the vehicle based on the pre-processed multi-dimensional operating parameters.

[0034] Furthermore, the preprocessed multidimensional operating parameters are validated for data validity; based on the validated multidimensional operating parameters, the current operating condition of the vehicle is identified through a preset operating condition judgment threshold table (see Table 1, which is the preset operating condition judgment threshold table).

[0035] Data validity check: If no valid input data is collected for 3 consecutive cycles (e.g., voltage / current signal interruption), the average of the previous 5 cycles is used as a substitute; if no valid data is collected after more than 200ms of substitution, it is determined to be a sensor fault, triggering a degraded output (default output is the load reference value under constant speed driving conditions), and the fault status is fed back synchronously for manual handling.

[0036] Table 1:

[0037] S3, when the operating condition is a superimposed operating condition, the current operating condition is determined through a dynamic weighted voting mechanism, and the current operating condition is periodically locked. It should be noted that if the number of identified operating conditions is greater than or equal to 2, the operating condition is determined to be an overlay operating condition.

[0038] Furthermore, the processing method for determining the current operating condition through the dynamic weighted voting mechanism is as follows: the current operating condition of the vehicle is taken as a candidate operating condition, and the comprehensive matching degree of each candidate operating condition is calculated; the final weight value of each candidate operating condition is calculated based on the comprehensive matching degree of each candidate operating condition and the corresponding operating condition type weight; based on the final weight value of each candidate operating condition, the current operating condition is selected from multiple candidate operating conditions through the dynamic weighted voting mechanism.

[0039] For each core decision parameter under a given working condition, the single-parameter matching degree is calculated using the following formula:

[0040] In the formula, The matching degree of the i-th parameter (0≤ ≤1); These are the measured values ​​of the parameters after Kalman filtering (e.g., vehicle speed 60km / h, accelerator pedal opening 40%). This is the center value of the threshold for this parameter under this working condition (e.g., the vehicle speed threshold for constant speed working condition is 50-70km / h, with a center value of 60km / h). This is the threshold half distance of the parameter (e.g., the threshold half distance of the vehicle speed under constant speed condition = (70-50) / 2 = 10km / h).

[0041] It should also be noted that, if Completely equal to : =1 (complete match); if The parameters for this operating condition are outside the threshold range: =0 (complete mismatch); if the calculation result is... <0: Force correction to 0 to avoid negative matching degree affecting weight calculation.

[0042] For all core judgment parameters of a certain working condition, the overall matching degree of the working condition is calculated by weighting the parameters according to their importance. :

[0043] In the formula, The weight coefficient (priority) of the i-th parameter is uniformly set as follows: vehicle speed P=0.4, accelerator pedal opening P=0.3, gradient P=0.2, brake pedal travel P=0.1; n: the number of core judgment parameters for this working condition (e.g., the core parameters for the climbing working condition are vehicle speed, accelerator pedal opening, and gradient, n=3).

[0044] Furthermore, it is necessary to assign basic weights to power type operating conditions and driving type operating conditions respectively; and to correct the basic weights according to the ambient temperature to obtain the operating condition type weights.

[0045] It should be noted that the power type operating conditions include rapid acceleration and hill climbing, and the basic weight can be set to 0.6; the driving type operating conditions include constant speed and deceleration, and the basic weight can be set to 0.4.

[0046] In the specific implementation, based on the real-time temperature collected by the vehicle ambient temperature sensor (sampling period 1s, accuracy ±1℃), refer to Table 2, which is the weight correction rule table. The basic weights of the power-related operating conditions are corrected through the weight correction rule table: Table 2

[0047] Calculate the final weight value for each candidate working condition:

[0048] In the actual implementation, the candidate working conditions are sorted by weight, and the one with the highest weight is selected as the current working condition.

[0049] S4, determine whether the parameter is less than the parameter deviation threshold within the working condition locking cycle; S5, if so, take the current operating condition as the target operating condition, and perform regional control power distribution based on the target operating condition through a preset diagnostic correction sequence.

[0050] The operating condition lockout period can be customized by the user, for example, 500ms.

[0051] In practice, regional control power allocation for the target operating condition can be directly performed through a preset diagnostic correction sequence.

[0052] In one embodiment, after determining a certain operating condition, a 500ms locking cycle is initiated. If the parameters (multi-dimensional operating parameters after real-time filtering and preprocessing) deviate slightly (≤10% of the threshold) within the cycle, a re-determination is not triggered. The current operating condition is taken as the target operating condition. Then, the power matrix corresponding to the current driving demand mode can be called based on the target operating condition. The status parameters of each component are collected in real time, and it is determined whether the status parameters of each part are overloaded. If so, the power upper limit threshold of each component is lowered according to the power priority based on the power matrix. If the overload is detected and still not resolved, regional domain control power allocation is performed through a preset diagnostic correction sequence. If not, regional domain control power allocation is performed based on the power matrix.

[0053] In the specific implementation, the battery SOC, total vehicle load rate, ambient temperature, current operating conditions, and driver acceleration intention are obtained. Based on the battery SOC, total vehicle load rate, ambient temperature, current operating conditions, and driver acceleration intention, the driving mode is determined. The driving modes are normal, heavy load, and energy saving.

[0054] Heavy load mode trigger conditions: Climbing conditions, acceleration conditions (heavy acceleration), battery SOC ≥ 60%, total load ≥ 70%, extreme ambient temperature (< -5℃ or > 35℃), meeting any two of the above conditions determines heavy load mode; Energy saving mode trigger conditions: Battery SOC ≤ 30%, operating conditions are constant speed, downhill, deceleration, total load ≤ 50%, meeting any one of the above conditions determines energy saving mode; Normal mode trigger conditions: not meeting heavy load or energy saving conditions, normal driving, idling, medium and low speed constant speed, default is normal.

[0055] Once the mode is selected, lock it for 1-2 seconds to prevent frequent switching due to small fluctuations in pedal / load. Power Matrix: Normal Mode - Start-up Acceleration: Motor 95% (rated power, time limit 10s), Brake Assist 100%, Steering 100%, Air Conditioning 50%, Entertainment 30%; Normal Mode - Constant Speed ​​Driving: Motor 60-70%, Steering 100%, Air Conditioning 80%, Entertainment 100%, Brake Assist 50%; Normal Mode - Climbing: Motor 100% (rated power, time limit 5min, automatically drops to 90% after timeout), Steering 100%, Brake Assist 100%, Air Conditioning 0%, Entertainment 0%; Normal Mode - Idling Stop: Motor 0%, Air Conditioning 30-80% (dynamically adjusted according to set temperature), Steering 50%, Brake Assist 50%, Entertainment 80%; Heavy Load Mode increases the upper limit of motor power by 5-10% compared to Normal Mode, and Energy Saving Mode reduces the upper limit of auxiliary component power by 10-20%.

[0056] The operation to determine whether the status parameters (power, temperature, battery SOC) of each part are overloaded is as follows: Different overload thresholds are set based on component type: core components (motor, steering, braking): power exceeds the rated value by 95% and lasts for ≥200ms; auxiliary components (air conditioning): power exceeds the rated value by 100% and lasts for ≥300ms; entertainment system: power exceeds the rated value by 110% and lasts for ≥500ms; at the same time, the temperature parameter is combined for secondary judgment. When the temperature of the core component exceeds 105℃, the overload protection is directly triggered.

[0057] The operation of adjusting the upper limit threshold of each component based on the power matrix and according to power priority is as follows: On the basis of the power matrix, the upper limit threshold of each component is adjusted in order from low to high according to the four power priority levels: Step 1: Lower the threshold of Level 4 components (entertainment system, high-power accessories); Targets for adjustment: in-car entertainment systems, audio systems, in-car refrigerators, and external high-power devices; Adjustment method: The upper limit of power is directly reduced by 30% to 50%; Objective: To quickly release high-power redundancy.

[0058] Step 2: If still overloaded → lower the threshold of the third-level components (air conditioner, PTC heater); Adjusted components: Air conditioning compressor, PTC heater, seat heating / ventilation; Adjustment method: The upper limit of power is reduced by 20% to 40%; Special rules for low / high temperatures: Only lower the threshold to the minimum protection level, do not shut down directly.

[0059] Step 3: If still overloaded → reduce the threshold of secondary components (steering, brake assist); Targets for adjustment: Electric power steering, brake booster pump; Adjustment method: Lower the upper limit of power by 5% to 10%; Safety baseline: No less than 90% of rated power to ensure driving safety.

[0060] Step 4: If still overloaded → limit the current of the core component (motor); Adjustment target: drive motor; Adjustment method: The power limit is restricted to 90% or less of the rated power; Bottom line: No less than 50% of rated power to ensure the vehicle can run.

[0061] In its implementation, when the system detects an overload, it immediately performs automatic adjustment of component thresholds: power upper limit thresholds are progressively lowered from low to high according to a four-level power priority, prioritizing the reduction of non-core load power thresholds. First, the power upper limit thresholds for entertainment systems and high-power accessories are lowered by 30%~50%; if the overload persists, the power upper limit thresholds for air conditioning and PTC heaters are further lowered by 20%~40%; if the overload still persists, the power thresholds for steering and brake assist are lowered by 5%~10%; finally, the drive motor power is limited to within 90% of its rated value. All adjustments are made at a single rate not exceeding 10%, and monitoring continues for 100ms after each adjustment. If the overload condition is still not resolved, an adaptive correction process is initiated (i.e., regional control power allocation is performed using a preset diagnostic correction sequence).

[0062] It should also be noted that the preset diagnostic correction sequence is a power distribution strategy instruction remotely issued by the engineer, which can be manually adjusted.

[0063] In another embodiment, when the parameter is greater than or equal to the parameter deviation threshold within the operating condition locking period, an operating condition switching mechanism is triggered. The target operating condition is determined based on the parameter within the operating condition locking period. Within a preset buffer transition period, the operating condition weights of the current operating condition and the target operating condition are linearly adjusted. Based on the adjusted operating condition weights, the current operating condition of the vehicle is switched to the target operating condition.

[0064] In the specific implementation, if the parameters deviate from the threshold by more than 10% after the locking period ends, it is determined that a switch to a different operating condition is required. The system then initiates a 200ms buffer transition mechanism, gradually completing the switch according to the current operating condition → transition operating condition → target operating condition. During the buffer period, the load is allocated using a weighted linear gradient + power weighted superposition method, specifically divided into three stages: 1) 0ms~50ms (current operating condition dominant): the current operating condition weight is 0.8, the target operating condition weight is 0.2, and the load power is mainly allocated according to the current operating condition; 2) 50ms~150ms (linear balanced transition): the current operating condition weight decreases uniformly from 0.8 to 0.2, and the target operating condition weight increases uniformly from 0.2 to 0.8, achieving a smooth power gradient; 3) 150ms~200ms (target operating condition dominant): the current operating condition weight is 0.2, the target operating condition weight is 0.8, and stability confirmation before the switch is completed.

[0065] The formula for calculating the total load power under transition conditions is: Ptransition = Pcurrent × Wcurrent + Ptarget × Wtarget, where Wcurrent + Wtarget = 1, and the weights change linearly with time to ensure that the load power does not change abruptly or experience any shocks.

[0066] After the buffer period ends, the system officially switches to the target operating condition and executes the operation of calling the corresponding power matrix to perform regional control power distribution. Then, a new round of 500ms operating condition locking is immediately initiated to achieve a smooth connection between operating condition identification and power distribution, thereby improving the stability of the vehicle's electrical system and driving smoothness.

[0067] In another embodiment, it should also be explained that when the operating condition is a single operating condition, the operating condition is periodically locked; it is determined whether the parameter within the operating condition locking period is less than the parameter deviation threshold; if so, the operating condition is taken as the target operating condition; if not, the operating condition switching mechanism is triggered, the target operating condition is determined according to the parameter within the operating condition locking period, and within a preset buffer transition period, the operating condition weight and the target operating condition weight are linearly adjusted, and the operating condition is switched to the target operating condition based on the adjusted operating condition weight; the corresponding power allocation strategy is matched according to the target operating condition through the power allocation mapping table; and regional control power allocation is performed based on the power allocation strategy. Refer to Table 3, which is the power allocation mapping table: Table 3

[0068] In this embodiment, multi-dimensional operating parameters of the current vehicle are first collected via the vehicle bus and preprocessed using Kalman filtering. Then, the operating condition of the current vehicle is identified based on the preprocessed parameters. When the operating condition is a superimposed condition, a dynamic weighted voting mechanism is used to determine the current operating condition, which is then periodically locked. Next, it is determined whether the parameters within the locking period are less than the parameter deviation threshold. If so, the current operating condition is taken as the target operating condition, and regional control power allocation is performed based on the target operating condition using a preset diagnostic correction sequence. This embodiment accurately identifies the target operating condition through the dynamic weighted voting mechanism when a superimposed operating condition is detected, effectively avoiding load distribution disorder caused by misjudgment of the operating condition. Then, regional control power allocation is achieved through the preset diagnostic correction sequence.

[0069] Reference Figure 3 , Figure 3 This is a structural block diagram of the first embodiment of the regional control power distribution system of the present invention.

[0070] like Figure 3 As shown, the regional control power distribution system proposed in this embodiment of the invention includes: The processing module 3001 is used to collect the multi-dimensional operating parameters of the current vehicle through the vehicle bus and perform Kalman filtering preprocessing on the multi-dimensional operating parameters; The identification module 3002 is used to identify the current operating condition of the vehicle based on preprocessed multi-dimensional operating parameters; The determination module 3003 is used to determine the optimal operating condition through a dynamic weighted voting mechanism when the operating condition is a superimposed operating condition, and to periodically lock the optimal operating condition. The judgment module 3004 is used to determine whether the parameter is less than the parameter deviation threshold within the working condition locking cycle. The allocation module 3005 is used to, if so, take the optimal operating condition as the target operating condition and perform regional control power allocation based on the target operating condition through a preset diagnostic correction sequence.

[0071] Other embodiments or specific implementations of the regional control power distribution system of the present invention can be referred to the above-described method embodiments, and will not be repeated here.

[0072] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0073] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0074] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0075] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A regional control power distribution method, characterized in that, The method includes the following steps: The vehicle's multi-dimensional operating parameters are collected via the vehicle bus, and these parameters are preprocessed using Kalman filtering. The current operating condition of the vehicle is identified based on the preprocessed multidimensional operating parameters; When the operating condition is an overlay condition, the current operating condition is determined through a dynamic weighted voting mechanism, and the current operating condition is periodically locked. Determine whether the parameter is less than the parameter deviation threshold within the working condition lock-in period; If so, the current operating condition is taken as the target operating condition, and regional control power distribution is performed based on the target operating condition using a preset diagnostic correction sequence.

2. The method as described in claim 1, characterized in that, The step of identifying the current operating condition of the vehicle based on preprocessed multidimensional operating parameters includes: Perform data validity verification on the preprocessed multidimensional operating parameters; Based on the multi-dimensional operating parameters after validity verification, the current operating condition of the vehicle is identified through a preset operating condition judgment threshold table.

3. The method as described in claim 1, characterized in that, The determination of the current operating condition through a dynamic weighted voting mechanism includes: Based on the current operating conditions of the vehicle, each candidate operating condition is determined, and the overall matching degree of each candidate operating condition is calculated. The final weight value of each candidate working condition is calculated based on the comprehensive matching value of each candidate working condition and the corresponding working condition type weight. Based on the final weight values ​​of each candidate working condition, the current working condition is selected from multiple candidate working conditions through a dynamic weighted voting mechanism.

4. The method as described in claim 3, characterized in that, Before calculating the final weight value of each candidate working condition based on the comprehensive matching value and the corresponding working condition type weight, the method further includes: Assign basic weights to power type operating conditions and driving type operating conditions respectively; The base weights are adjusted based on the ambient temperature to obtain the operating condition type weights.

5. The method as described in claim 1, characterized in that, After determining whether the parameter is less than the parameter deviation threshold within the working condition locking cycle, the method further includes: If not, the working condition switching mechanism is triggered to determine the target working condition based on the parameters within the working condition locking period. Within a preset cache transition period, the weight of the operating condition and the weight of the target operating condition are linearly adjusted. Based on the adjusted operating condition weights, the current operating condition of the vehicle is switched to the target operating condition.

6. The method as described in claim 1, characterized in that, The regional control power allocation based on the target operating condition using a preset diagnostic correction sequence includes: Based on the target operating condition, the power matrix corresponding to the current driving demand mode is invoked; Real-time collection of status parameters of each component, and determination of whether the status parameters of each part are overloaded; If so, then based on the power matrix, the upper limit threshold of the power of each component is lowered according to power priority; If the overload is detected but not resolved, regional control power distribution is performed using a preset diagnostic correction sequence.

7. The method as described in claim 1, characterized in that, After identifying the current operating condition of the vehicle through a preset operating condition judgment threshold table, the method further includes: When the operating condition is a single operating condition, the operating condition is periodically locked. Determine whether the parameter is less than the parameter deviation threshold within the working condition lock-in period; If so, the operating condition is taken as the target operating condition, and the corresponding power allocation strategy is matched according to the power allocation mapping table based on the target operating condition. Regional control power allocation is performed based on the power allocation strategy.

8. A regional control power distribution system, characterized in that, The system includes: The processing module is used to collect multi-dimensional operating parameters of the current vehicle through the vehicle bus and perform Kalman filtering preprocessing on the multi-dimensional operating parameters; The identification module is used to identify the current operating condition of the vehicle based on preprocessed multi-dimensional operating parameters; The identification module is further configured to determine the current operating condition through a dynamic weighted voting mechanism when the operating condition is an overlay operating condition, and to periodically lock the current operating condition. The judgment module is used to determine whether the parameter is less than the parameter deviation threshold within the working condition locking cycle; The allocation module is used to, if so, take the current operating condition as the target operating condition and perform regional control power allocation based on the target operating condition using a preset diagnostic correction sequence.

9. A regional control power distribution device, characterized in that, The device includes: a memory, a processor, and a regional control power allocation program stored in the memory and executable on the processor, the regional control power allocation program being configured to implement the steps of the regional control power allocation method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores a regional control power allocation program, which, when executed by a processor, implements the steps of the regional control power allocation method as described in any one of claims 1 to 7.

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