Method, system and equipment for determining state of automobile battery and medium

Abstract

The embodiment of the invention discloses an automobile battery state determination method, system, device and medium, the method is applied to a vehicle-mounted controller of the automobile battery state determination system, the automobile battery state determination system comprises a plurality of vehicle-mounted controllers and a central server, and the method comprises the following steps: obtaining original battery data of an automobile battery, generating quantum battery data according to the original battery data; performing feature extraction on the quantum battery data to obtain quantization features; based on the quantization characteristics, the quantum state of the automobile battery is determined through a pre-trained edge state prediction model, model parameters of the edge state prediction model are determined through a center state prediction model of a center server, and the center state prediction model is obtained through training according to historical battery data of the automobile battery managed by each vehicle-mounted controller; and determining the battery state of the automobile battery according to the quantum state. According to the technical scheme provided by the invention, the determination speed and accuracy of the battery state can be improved.

Description

Technical Field

[0001] This invention relates to the field of quantum computing, and more particularly to a method, system, device, and medium for determining the state of an automotive battery. Background Technology

[0002] Battery state monitoring is a core function of a battery management system. The background is that power batteries, as key components of electric vehicles, are characterized by high cost, complex electrochemical properties, and sensitivity to operating conditions. The fundamental significance of this work lies in ensuring safety by preventing dangers caused by battery overheating or overcharging through real-time monitoring. It is also crucial for maintaining battery life by accurately estimating remaining charge and health status, enabling intelligent charging and discharging and equalization management, and slowing down battery degradation.

[0003] Current real-time data monitoring methods have low accuracy, and high-precision monitoring and detection algorithms are computationally expensive and require a lot of resources. Summary of the Invention

[0004] This invention provides a method, system, device, and medium for determining the state of an automotive battery. By introducing quantum computing into the monitoring and prediction of battery state, the accuracy and efficiency of battery state determination can be improved, while saving computing resources.

[0005] In a first aspect, embodiments of the present invention provide a method for determining the state of an automotive battery, applied to an on-board controller of an automotive battery state determination system. The automotive battery state determination system includes multiple on-board controllers and a central server, comprising:

[0006] Obtain raw battery data of a car battery, and generate quantum battery data based on the raw battery data, wherein the quantum battery data characterizes the quantum encoding result of the raw battery data;

[0007] Feature extraction is performed on the quantum battery data to obtain quantized features;

[0008] Based on the quantum characteristics, the quantum state of the vehicle battery is determined by a pre-trained edge state prediction model. The model parameters of the edge state prediction model are determined by the central state prediction model of the central server. The central state prediction model is trained based on the historical battery data of the vehicle batteries managed by each vehicle controller.

[0009] The battery state of the car battery is determined based on the quantum state.

[0010] Secondly, embodiments of the present invention provide a system for determining the state of an automotive battery. The system includes multiple on-board controllers and a central server. The on-board controllers include:

[0011] An acquisition module is used to acquire raw battery data of a car battery and generate quantum battery data based on the raw battery data, wherein the quantum battery data characterizes the quantum encoding result of the raw battery data;

[0012] The feature extraction module is used to extract features from the quantum battery data to obtain quantized features;

[0013] The prediction module is used to determine the quantum state of the vehicle battery based on the quantum characteristics and through a pre-trained edge state prediction model. The model parameters of the edge state prediction model are determined by the central state prediction model of the central server. The central state prediction model is trained based on the historical battery data of the vehicle batteries managed by each vehicle controller.

[0014] A determination module is used to determine the battery state of the automotive battery based on the quantum state.

[0015] Thirdly, embodiments of the present invention provide an electronic device, the electronic device comprising:

[0016] At least one processor; and,

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method for determining the state of a car battery as described in any one of the embodiments of the present invention.

[0019] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions, which are used to cause a processor to execute the method for determining the state of an automobile battery as described in any one of the embodiments of the present invention.

[0020] This invention provides a method, system, device, and medium for determining the state of an automotive battery. The method is applied to the on-board controller of an automotive battery state determination system, which includes multiple on-board controllers and a central server. The method includes: acquiring raw battery data of the automotive battery; generating quantum battery data based on the raw battery data, wherein the quantum battery data characterizes the quantum encoding result of the raw battery data; extracting features from the quantum battery data to obtain quantized features; determining the quantum state of the automotive battery based on the quantized features using a pre-trained edge state prediction model, wherein the model parameters of the edge state prediction model are determined by a central state prediction model on the central server, and the central state prediction model is trained based on historical battery data of automotive batteries managed by each on-board controller; and determining the battery state of the automotive battery based on the quantum state. Specifically, by introducing quantum computing into the monitoring and prediction of battery state, the accuracy and efficiency of battery state determination can be improved. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart illustrating a method for determining the state of an automotive battery according to Embodiment 1 of the present invention;

[0023] Figure 2 A flowchart illustrating a method for determining the state of an automotive battery according to Embodiment 2 of the present invention;

[0024] Figure 3 A schematic diagram of a system for determining the state of an automotive battery provided in an embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram of a system for determining the state of an automotive battery according to Embodiment 3 of the present invention;

[0026] Figure 5 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] It should be noted that the collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0030] Example 1

[0031] Figure 1 This is a flowchart of a method for determining the state of a car battery according to Embodiment 1 of the present invention. The method is specifically applicable to the detection of the state of a car battery. The method is applied to the on-board controller of the car battery state determination system. The car battery state determination system includes multiple on-board controllers and a central server. The system is composed of software and / or hardware.

[0032] Step 110: Obtain the raw battery data of the car battery, and generate quantum battery data based on the raw battery data, wherein the quantum battery data characterizes the quantum encoding result of the raw battery data.

[0033] Raw battery data consists of unprocessed data collected from the vehicle's battery sensors, representing the battery's real-time state information, typically in binary or hexadecimal format. Raw battery data may include voltage, current, temperature, etc. Quantum battery data, on the other hand, is the raw battery data processed through quantum encoding. It represents the quantized state of the battery data, and the quantum encoding result can be a sequence of qubits or a quantum state representation. Quantum encoding can employ methods such as variable quantum encoding or amplitude encoding.

[0034] Specifically, existing technologies typically transmit raw battery data, which is prone to corruption and consumes a lot of resources. Quantum data transmission utilizes quantum coding to enhance data security and anti-interference capabilities, improve processing efficiency, and support parallel computing, thereby enabling faster and more accurate analysis of battery status.

[0035] Step 120: Extract features from the quantum battery data to obtain quantized features.

[0036] Among them, quantized features are quantum properties extracted from quantum battery data through feature extraction, characterizing the quantum properties of the battery data. Quantized features can include quantum amplitude and phase, etc.

[0037] Step 130: Based on the quantum characteristics, determine the quantum state of the vehicle battery through a pre-trained edge state prediction model, wherein the model parameters of the edge state prediction model are determined by the central state prediction model of the central server, and the central state prediction model is trained based on the historical battery data of the vehicle batteries managed by each vehicle controller.

[0038] Both the edge state prediction model and the central state prediction model are quantum neural network models with identical internal structures. Furthermore, the central state prediction model is trained using historical battery data from the vehicles managed by each onboard controller. Therefore, the training set for the central state prediction model comprises historical battery data from all vehicles managed by the onboard controllers, resulting in a large and highly universal dataset, leading to a more accurate central state prediction model. Once the central state prediction model is successfully trained, its parameters can be directly distributed to the edge state prediction model, significantly improving its accuracy. It's important to note that the inputs and outputs of both the edge and central state prediction models are not ordinary binary or decimal data, but rather quantum data. Therefore, the model's computation and prediction processes are extremely fast, consuming far fewer resources than ordinary neural network models. The onboard controller is the electronic control unit within the vehicle, representing the management node of the vehicle system, used to monitor and control the vehicle battery. The onboard controller includes the battery management system.

[0039] Step 140: Determine the battery state of the car battery based on the quantum state.

[0040] Specifically, the quantum state is measured multiple times to determine the probability distribution of the car battery in each state; based on the probability distribution, the battery state of the car battery is determined.

[0041] Because quantum states possess unique properties, a single measurement can cause them to collapse into a specific eigenstate, thus losing the original superposition information. Therefore, to accurately obtain the probability distribution contained within a quantum state, multiple repeated measurements must be performed using quantum circuits. The true probability distribution is approximated by statistically analyzing the frequency of each measurement result. Based on this probability distribution, the state of the vehicle battery is determined.

[0042] For example, if a battery is in a healthy state, its quantum state probability distribution may be concentrated in a certain characteristic range; while the quantum state probability distribution of an aged or faulty battery may shift or spread. By comparing the difference between the measured probability distribution and the standard healthy probability distribution, the battery state can be detected and classified, such as corresponding to different types of battery states with different standard healthy probability distributions.

[0043] This invention provides a method for determining the state of a vehicle battery. The method is applied to the on-board controller of a vehicle battery state determination system, which includes multiple on-board controllers and a central server. The method includes: acquiring raw battery data of the vehicle battery; generating quantum battery data based on the raw battery data, wherein the quantum battery data characterizes the quantum encoding result of the raw battery data; extracting features from the quantum battery data to obtain quantized features; determining the quantum state of the vehicle battery based on the quantized features using a pre-trained edge state prediction model, wherein the model parameters of the edge state prediction model are determined by a central state prediction model on the central server, and the central state prediction model is trained based on historical battery data of the vehicle batteries managed by each on-board controller; and determining the battery state of the vehicle battery based on the quantum state. Specifically, by introducing quantum computing into the monitoring and prediction of battery state, the accuracy and efficiency of battery state determination can be improved.

[0044] Example 2

[0045] Figure 2 This is a flowchart of a method for determining the state of an automotive battery according to Embodiment 2 of the present invention. Based on the above embodiment, this method further defines the steps of the method for determining the state of an automotive battery, such as... Figure 2 As shown, it includes:

[0046] Step 210: Obtain the raw battery data of the car battery.

[0047] Step 220: Standardize the original battery data to obtain standardized battery data.

[0048] Specifically, the standardization process can include steps such as format standardization and data normalization. The sum of the squares of the data in each dimension of the standardized battery data is 1.

[0049] Step 230: Perform quantum encoding on the standardized battery data to obtain the quantum battery data corresponding to the standardized battery data.

[0050] Specifically, step 230 includes:

[0051] The number of qubits is determined based on the characteristic dimensions of the original battery data;

[0052] The standardized battery data is mapped to a quantum state consisting of the number of qubits by amplitude encoding to obtain the quantum battery data.

[0053] Among them, the quantum bit is the computing unit of quantum computing. Data storage can be completed by mapping features of different dimensions onto the quantum state of the quantum bit.

[0054] Amplitude encoding encodes the original battery data into the amplitude of each ground state of the quantum state.

[0055] For example, for 4-dimensional normalized battery data [a,b,c,d], it can be encoded into the four ground states of a quantum state consisting of two qubits: a|00〉+ b|01〉+ c|10〉+ d|11〉. 2 +b 2 +c 2 +d 2 =1.

[0056] Specifically, the relationship between the feature dimension N of the raw battery data and the number of qubits M is M = log2N.

[0057] Step 240: Based on the quantum characteristics, determine the quantum state of the vehicle battery through a pre-trained edge state prediction model, wherein the model parameters of the edge state prediction model are determined by the central state prediction model of the central server, and the central state prediction model is trained based on the historical battery data of the vehicle batteries managed by each vehicle controller.

[0058] The training method for the central state prediction model includes:

[0059] The system acquires historical battery data and corresponding historical battery states of a car battery from an edge server; it performs quantum encoding on the historical battery data to obtain historical quantum battery data; it acquires a basic quantum neural network model, and trains the basic quantum neural network model based on the historical quantum battery data and corresponding historical battery states to obtain a center state prediction model.

[0060] The basic quantum neural network model consists of a rotation gate, an amplitude gate, and a quantum simulation circuit; the specific structure is not limited here.

[0061] Specifically, the step of training the basic quantum neural network model based on the historical quantum battery data and the corresponding historical battery states to obtain a central state prediction model includes:

[0062] Historical quantum battery data is input into the basic quantum neural network model. Multiple quantum measurements are performed on the quantum states output by the basic quantum neural network model. Based on the results of the multiple quantum measurements of the quantum states, the probability distribution of the battery state is determined. The error is calculated based on the probability distribution of the battery state and the historical battery state. The model parameters of the basic quantum neural network model are updated based on the error. If the error meets the preset conditions, the basic quantum neural network model is determined as the center state prediction model.

[0063] Specifically, for a quantum state output during a single training process, it collapses into a specific state upon observation. Therefore, multiple quantum measurements are required on the quantum state. The probability distribution of the battery state is determined based on these multiple measurements, ultimately leading to the final predicted value for this training process. The error is then calculated based on the probability distribution of the battery state and the historical battery states. The model parameters of the basic quantum neural network model are updated based on the error, such as updating the amplitude and angle of each rotation gate and amplitude gate. Finally, if the error meets preset conditions, the basic quantum neural network model is determined as the center state prediction model.

[0064] For example, the basic quantum neural network model can be a quantum-optimized LSTM model, where the continuous weight parameters to be optimized are discretized and mapped to a set of binary variables (spins). By analyzing the shape of the loss function for battery state prediction near the current parameters, a corresponding physical model is constructed, whose energy is equivalent to the loss function value. In this physical model, the local magnetic field strength experienced by each spin is determined by the gradient of the loss function with respect to that parameter; the coupling strength between spins is determined by the interaction between parameters. Therefore, finding the spin configuration that minimizes the energy of the physical model is equivalent to finding new, better model parameters that result in a smaller loss function. The predefined model parameters (i.e., magnetic field and coupling strength) are input into a 16-qubit processor in a quantum computing cloud to execute a quantum approximation optimization algorithm. The qubits are initialized to a uniform superposition state and evolved within the quantum circuit. The intensity parameters under alternating action are adjusted using a classical optimizer so that the final quantum state collapses to the low-energy state of the model with the highest probability. By performing multiple measurements on the final quantum state and decoding the binary bit string with the highest probability, a set of optimized, discretized new model parameters can be obtained. These new model parameter sets are then used to update the classical LSTM network, thus obtaining the basic quantum neural network model.

[0065] Furthermore, the model parameters of the central state prediction model are sent to the vehicle controller, and the edge state prediction model uses the model parameters of the central state prediction model to make actual state predictions.

[0066] Step 250: Determine the battery state of the car battery based on the quantum state.

[0067] Optionally, control instructions for the vehicle battery are determined based on the battery identifier and battery status of the original battery data, wherein the control instructions are used to control the operating status of the vehicle battery.

[0068] The control command is sent to the vehicle battery.

[0069] Specifically, current electric vehicles generally include multiple batteries. The control commands for the vehicle batteries, such as continuing charging or stopping charging, can be determined based on the battery identifier and battery status in the original battery data.

[0070] For example, Figure 3 This diagram illustrates a system for determining the state of a vehicle battery according to an embodiment of the present invention. Specifically, the system first transmits sensor data from the vehicle battery to an edge node (vehicle controller) for filtering and feature extraction. The quantum-processed feature data is then protected by "HTTPS + quantum encryption" before being uploaded to a quantum computing cloud. The cloud uses a federated learning model to train the model based on a quantum neural network (central state prediction model) to obtain a central state prediction model. High-precision model parameters are then generated and distributed to the edge node's neural network model (edge ​​state prediction model). The edge node uses the edge state prediction model to perform real-time, high-precision estimation of the battery's state of charge locally and issues control commands (such as charging cut-off) to the vehicle battery accordingly, forming a precise management closed loop. Simultaneously, the cloud provides digital twin calibration parameters to the edge node daily, enabling the system to continuously learn and adaptively optimize.

[0071] This invention provides a method for determining the state of a car battery. By introducing quantum computing into a battery state prediction model based on federated learning for monitoring and predicting the battery state, the accuracy and efficiency of battery state determination can be improved.

[0072] Example 3

[0073] Figure 4 This is a schematic diagram of a system for determining the state of an automotive battery according to Embodiment 3 of the present invention. Figure 4 As shown, the vehicle battery status determination system includes multiple on-board controllers and a central server. The on-board controllers include:

[0074] The acquisition module 310 is used to acquire raw battery data of a car battery and generate quantum battery data based on the raw battery data, wherein the quantum battery data represents the quantum encoding result of the raw battery data;

[0075] Feature extraction module 320 is used to extract features from the quantum battery data to obtain quantized features;

[0076] The prediction module 330 is used to determine the quantum state of the vehicle battery based on the quantum characteristics and through a pre-trained edge state prediction model. The model parameters of the edge state prediction model are determined by the central state prediction model of the central server. The central state prediction model is trained based on the historical battery data of the vehicle batteries managed by each vehicle controller.

[0077] The determination module 340 is used to determine the battery state of the car battery based on the quantum state.

[0078] This invention provides a system for determining the state of a vehicle battery. The system includes multiple vehicle controllers and a central server. The vehicle controllers perform the following steps: acquiring raw battery data of the vehicle battery; generating quantum battery data based on the raw battery data, wherein the quantum battery data represents the quantum encoding result of the raw battery data; extracting features from the quantum battery data to obtain quantized features; determining the quantum state of the vehicle battery based on the quantized features using a pre-trained edge state prediction model, wherein the model parameters of the edge state prediction model are determined by a central state prediction model on the central server, and the central state prediction model is trained based on historical battery data of the vehicle batteries managed by each vehicle controller; and determining the battery state of the vehicle battery based on the quantum state. Specifically, by introducing quantum computing into the monitoring and prediction of battery state, the accuracy and efficiency of battery state determination can be improved.

[0079] The acquisition module 310 includes:

[0080] A standardization unit is used to standardize the original battery data to obtain standardized battery data.

[0081] The encoding unit is used to perform quantum encoding on the standardized battery data to obtain the quantum battery data corresponding to the standardized battery data.

[0082] The coding unit specifically includes:

[0083] Determine the sub-unit, which is used to determine the number of qubits based on the characteristic dimensions of the original battery data;

[0084] The encoding subunit is used to map the standardized battery data to a quantum state consisting of the number of qubits through amplitude encoding, thereby obtaining the quantum battery data.

[0085] Optionally, the system includes: a training module, comprising:

[0086] Get single quote, used to retrieve historical battery data of the car battery on the edge server and the historical battery status corresponding to the historical battery data;

[0087] An encoding unit is used to perform quantum encoding on the historical battery data to obtain historical quantum battery data;

[0088] The training unit is used to acquire a basic quantum neural network model. Based on the historical quantum battery data and the historical battery states corresponding to the historical battery data, the basic quantum neural network model is trained to obtain a center state prediction model.

[0089] The training unit specifically includes:

[0090] The training subunit is used to input historical quantum battery data into the basic quantum neural network model, perform multiple quantum measurements on the quantum state output by the basic quantum neural network model, and determine the probability distribution of the battery state based on the multiple quantum measurement results of the quantum state.

[0091] An error calculation subunit is used to calculate the error based on the probability distribution of the battery state and the historical battery state, and to update the model parameters of the basic quantum neural network model based on the error.

[0092] The termination subunit is used to determine the basic quantum neural network model as the center state prediction model when the error meets the preset conditions.

[0093] Optionally, the determining module 340 includes:

[0094] A statistical unit is used to perform multiple measurements on the quantum state to determine the probability distribution of the car battery in each state;

[0095] A determining unit is used to determine the battery state of the vehicle battery based on the probability distribution.

[0096] The device further includes a control module, used to determine control commands for the vehicle battery based on the battery identifier and battery status of the original battery data, wherein the control commands are used to control the operating status of the vehicle battery; and to send the control commands to the vehicle battery.

[0097] The vehicle controller of the vehicle battery status determination system provided in the embodiments of the present invention can execute the vehicle battery status method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0098] Example 4

[0099] Figure 5 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0100] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0101] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0102] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for determining the state of a car battery.

[0103] In some embodiments, the method for determining the state of a vehicle battery may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded into and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for determining the state of a vehicle battery described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the method for determining the state of a vehicle battery by any other suitable means (e.g., by means of firmware).

[0104] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0105] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0106] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0107] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0108] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0109] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0110] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0111] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining the state of an automotive battery, characterized in that, An on-board controller for a system for determining the state of an automotive battery, the system comprising multiple on-board controllers and a central server, including: Obtain raw battery data of a car battery, and generate quantum battery data based on the raw battery data, wherein the quantum battery data characterizes the quantum encoding result of the raw battery data; Feature extraction is performed on the quantum battery data to obtain quantized features; Based on the quantum characteristics, the quantum state of the vehicle battery is determined by a pre-trained edge state prediction model. The model parameters of the edge state prediction model are determined by the central state prediction model of the central server. The central state prediction model is trained based on the historical battery data of the vehicle batteries managed by each vehicle controller. The battery state of the car battery is determined based on the quantum state.

2. The method according to claim 1, characterized in that, The process of generating quantum battery data based on the original battery data includes: The original battery data is standardized to obtain standardized battery data; The standardized battery data is quantum encoded to obtain the quantum battery data corresponding to the standardized battery data.

3. The method according to claim 2, characterized in that, The process of quantum encoding standardized battery data to obtain quantum battery data corresponding to the standardized battery data includes: The number of qubits is determined based on the characteristic dimensions of the original battery data; The standardized battery data is mapped to a quantum state consisting of the number of qubits by amplitude encoding to obtain the quantum battery data.

4. The method according to claim 1, characterized in that, The training method for the central state prediction model includes: Obtain historical battery data of the car battery from the edge server and the corresponding historical battery status. The historical battery data is quantum-encoded to obtain historical quantum battery data; A basic quantum neural network model is obtained, and the basic quantum neural network model is trained based on the historical quantum battery data and the historical battery states corresponding to the historical battery data to obtain a central state prediction model.

5. The method according to claim 4, characterized in that, The step of training the basic quantum neural network model based on the historical quantum battery data and the corresponding historical battery states to obtain a central state prediction model includes: Historical quantum battery data is input into the basic quantum neural network model, and multiple quantum measurements are performed on the quantum state output by the basic quantum neural network model. Based on the results of the multiple quantum measurements of the quantum state, the probability distribution of the battery state is determined. The error is calculated based on the probability distribution of the battery state and the historical battery state, and the model parameters of the basic quantum neural network model are updated based on the error. If the error meets the preset conditions, the basic quantum neural network model is determined as the central state prediction model.

6. The method according to claim 1, characterized in that, Determining the battery state of the vehicle battery based on the quantum state includes: The probability distribution of the car battery in each state is determined by performing multiple measurements on the quantum state. The battery state of the vehicle battery is determined based on the probability distribution.

7. The method according to claim 1, characterized in that, Also includes: Based on the battery identifier and battery status of the original battery data, the control command for the vehicle battery is determined, wherein the control command is used to control the operating status of the vehicle battery; The control command is sent to the vehicle battery.

8. A system for determining the state of an automotive battery, characterized in that, The system for determining the state of the vehicle battery includes multiple on-board controllers and a central server. The on-board controllers include: An acquisition module is used to acquire raw battery data of a car battery and generate quantum battery data based on the raw battery data, wherein the quantum battery data characterizes the quantum encoding result of the raw battery data; The feature extraction module is used to extract features from the quantum battery data to obtain quantized features; The prediction module is used to determine the quantum state of the vehicle battery based on the quantum characteristics and through a pre-trained edge state prediction model. The model parameters of the edge state prediction model are determined by the central state prediction model of the central server. The central state prediction model is trained based on the historical battery data of the vehicle batteries managed by each vehicle controller. A determination module is used to determine the battery state of the automotive battery based on the quantum state.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the method for determining the state of an automobile battery as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for determining the state of an automotive battery as described in any one of claims 1-7.

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