A vehicle energy distribution method and system

Abstract

The present invention provides a vehicle energy distribution method and system. The method includes: when it is detected in real time that the vehicle is in a driving state, obtaining in real time vehicle information corresponding to the vehicle and environmental information around the vehicle; performing fusion processing on the vehicle information and the environmental information based on a preset rule to generate a corresponding target data packet in real time, and in real time matching in a preset database the historical vehicle energy consumption corresponding to the vehicle according to the target data packet; predicting in real time, according to the target data packet through a preset prediction model, the predicted vehicle energy consumption adapted to the vehicle, and in real time calculating the actual vehicle energy consumption corresponding to the vehicle according to the historical vehicle energy consumption and the predicted vehicle energy consumption; calculating in real time the actual output power corresponding to the drive motor inside the vehicle according to the actual vehicle energy consumption, and in real time calculating the power that the battery pack inside the vehicle needs to output according to the actual output power. The present invention can improve the user experience.

Description

Technical Field

[0001] The present invention relates to the technical field of automobiles, and particularly relates to a vehicle energy distribution method and system. Background Art

[0002] With the progress of technology and the rapid development of productivity, automobiles have been popularized in people's daily lives and have become one of the essential means of transportation for people's daily travel, greatly facilitating people's lives.

[0003] Among them, the production technology of new energy vehicles has become increasingly mature and has been popularized in people's daily lives. Specifically, in order to meet different usage requirements, people have developed electric light trucks for urban logistics distribution or short-distance transportation, and due to their advantages of zero emissions and low noise, they have been widely used.

[0004] Furthermore, in the actual use of existing electric light trucks, the quality of energy management directly affects the vehicle's performance. Based on this, the prior art will set corresponding energy management systems inside the vehicle. However, most of the prior art will set fixed parameters and management rules in the existing energy management system. However, this management method is only applicable to stable driving conditions. When the vehicle encounters complex driving conditions, it cannot accurately output the power actually required by the vehicle, correspondingly increasing the vehicle's energy consumption and reducing the user's driving experience. Summary of the Invention

[0005] Based on this, the purpose of the present invention is to provide a vehicle energy distribution method and system to solve the problem that when the vehicle encounters complex driving conditions in the prior art, it cannot accurately output the power actually required by the vehicle, resulting in an increase in the vehicle's energy consumption.

[0006] The first aspect of the embodiment of the present invention proposes:

[0007] A vehicle energy distribution method, wherein the method includes:

[0008] When it is detected in real time that the vehicle is in a driving state, obtain in real time the vehicle information corresponding to the vehicle and the environmental information around the vehicle;

[0009] Based on a preset rule, perform fusion processing on the vehicle information and the environmental information to generate a corresponding target data packet in real time, and in real time match the historical vehicle energy consumption corresponding to the vehicle in a preset database according to the target data packet;

[0010] Based on a preset prediction model, the predicted vehicle energy consumption adapted to the vehicle is predicted in real time according to the target data packet, and the actual vehicle energy consumption corresponding to the vehicle is calculated in real time according to the historical vehicle energy consumption and the predicted vehicle energy consumption.

[0011] The actual output power corresponding to the drive motor inside the vehicle is calculated in real time according to the actual vehicle energy consumption, and the power that the battery pack inside the vehicle needs to output is calculated in real time according to the actual output power.

[0012] The beneficial effect of the present invention is that by detecting the state of the vehicle in real time during the actual driving of the vehicle, the vehicle information and environmental information corresponding to the current vehicle can be obtained accordingly. Based on this, in order to comprehensively analyze the current vehicle, the two types of information will be further fused, and the historical vehicle energy consumption corresponding to the current vehicle and the predicted vehicle energy consumption that the current vehicle may need will be further matched. Based on this, the actual vehicle energy consumption corresponding to the current vehicle can be finally calculated, and the actual output power and actual power that the current vehicle actually needs can be finally calculated, so as to effectively complete the distribution of the power output by the battery pack, thereby effectively improving the cruising range and correspondingly improving the user experience.

[0013] Further, the step of fusing the vehicle information and the environmental information based on a preset rule to generate a corresponding target data packet in real time includes:

[0014] When the vehicle information is obtained in real time, the vehicle information is parsed and processed in real time to detect a number of first data sequences included in the vehicle information.

[0015] When the environmental information is obtained in real time, the environmental information is parsed and processed in real time to detect a number of second data sequences included in the environmental information.

[0016] The number of first data sequences and the number of second data sequences are fused and processed in real time to generate the target data packet, and the target data packet is unique.

[0017] Further, the step of fusing the number of first data sequences and the number of second data sequences in real time to generate the target data packet includes:

[0018] When the first data sequence and the second data sequence are respectively obtained, the vehicle model corresponding to the vehicle is detected in real time.

[0019] In the preset template database, in real time, match a target fusion template adapted to a number of the first data sequences and a number of the second data sequences according to the vehicle model;

[0020] Embed a number of the first data sequences and a number of the second data sequences into the target fusion template correspondingly to generate the target data packet correspondingly. The target fusion template is unique.

[0021] Further, the step of embedding a number of the first data sequences and a number of the second data sequences into the target fusion template correspondingly includes:

[0022] When the target fusion template is obtained in real time, perform a full scan on the target fusion template to detect in real time a number of first data channels and a number of second data channels correspondingly included inside the target fusion template. Among them, the number of the first data channels and the number of the second data channels are distributed in a crisscross manner with each other;

[0023] Add corresponding first identifiers to a number of the first data sequences respectively, and add corresponding second identifiers to a number of the second data sequences respectively;

[0024] Fill each of the first data sequences into the inside of each of the first data channels correspondingly according to the first identifier, and fill each of the second data sequences into the inside of each of the second data channels correspondingly according to the second identifier to generate the target data packet correspondingly. Each of the first data sequences and each of the second data sequences is unique.

[0025] Further, the step of predicting in real time a predicted vehicle energy consumption adapted to the vehicle according to the target data packet through the preset prediction model includes:

[0026] When the target data packet is obtained in real time, perform a full scan on the target data packet to detect in real time a target data sequence correspondingly included inside the target data packet;

[0027] Detect in real time a starting point and an ending point corresponding to the target data sequence, and convert the target data sequence into a corresponding target curve graph in real time according to the starting point and the ending point;

[0028] Predict a predicted vehicle energy consumption adapted to the vehicle according to the target curve graph and the preset prediction model.

[0029] Further, the step of predicting a predicted vehicle energy consumption adapted to the vehicle according to the target curve graph and the preset prediction model includes:

[0030] When the target curve graph is obtained in real time, several maximum points and several minimum points included in the target curve graph are detected in real time;

[0031] Several of the maximum points and several of the minimum points are correspondingly set as target feature values adapted to the target data packet, and the predicted vehicle energy consumption adapted to the vehicle is predicted in real time according to the preset prediction model and each target feature value.

[0032] Further, the step of predicting the predicted vehicle energy consumption adapted to the vehicle in real time according to the preset prediction model and each target feature value includes:

[0033] When each target feature value is obtained in real time, the target neural network included in the preset prediction model is detected in real time;

[0034] Several network nodes included inside the target neural network are detected in real time, and the initial network parameters respectively included in each network node are detected in real time;

[0035] Each initial network parameter is correspondingly replaced with each target feature value, so that the preset prediction model outputs the predicted vehicle energy consumption.

[0036] The second aspect of the embodiments of the present invention proposes:

[0037] A vehicle energy distribution system, wherein the system includes:

[0038] An acquisition module, configured to, when it is detected in real time that the vehicle is in a driving state, acquire the vehicle information corresponding to the vehicle and the environmental information around the vehicle in real time;

[0039] A fusion module, configured to perform a fusion process on the vehicle information and the environmental information based on a preset rule to generate a corresponding target data packet in real time, and match in real time in a preset database the historical vehicle energy consumption corresponding to the vehicle according to the target data packet;

[0040] A prediction module, configured to predict in real time the predicted vehicle energy consumption adapted to the vehicle according to the target data packet through a preset prediction model, and calculate in real time the actual vehicle energy consumption corresponding to the vehicle according to the historical vehicle energy consumption and the predicted vehicle energy consumption;

[0041] A calculation module, configured to calculate in real time the actual output power corresponding to the drive motor inside the vehicle according to the actual vehicle energy consumption, and calculate in real time the electric quantity that the battery pack inside the vehicle needs to output according to the actual output power.

[0042] Further, the fusion module is specifically configured to:

[0043] When the vehicle information is obtained in real time, perform real-time parsing and processing on the vehicle information to correspondingly detect a number of first data sequences included in the vehicle information;

[0044] When the environmental information is obtained in real time, perform real-time parsing and processing on the environmental information to correspondingly detect a number of second data sequences included in the environmental information;

[0045] Perform real-time fusion processing on a number of the first data sequences and a number of the second data sequences to correspondingly generate the target data packet, and the target data packet is unique.

[0046] Further, the fusion module is specifically configured to:

[0047] When the first data sequence and the second data sequence are respectively obtained, detect in real time the vehicle model corresponding to the vehicle;

[0048] In the preset template database, match in real time a target fusion template adapted to a number of the first data sequences and a number of the second data sequences according to the vehicle model;

[0049] Embed a number of the first data sequences and a number of the second data sequences into the target fusion template correspondingly to generate the target data packet, and the target fusion template is unique.

[0050] Further, the fusion module is specifically configured to:

[0051] When the target fusion template is obtained in real time, perform a full scan on the target fusion template to detect in real time a number of first data channels and a number of second data channels included inside the target fusion template, wherein the number of the first data channels and the number of the second data channels are distributed in a crisscross manner;

[0052] Add corresponding first identifiers to a number of the first data sequences respectively, and add corresponding second identifiers to a number of the second data sequences respectively;

[0053] Fill each of the first data sequences into the inside of each of the first data channels according to the first identifier, and fill each of the second data sequences into the inside of each of the second data channels according to the second identifier to generate the target data packet correspondingly, and each of the first data sequences and each of the second data sequences is unique.

[0054] Further, the prediction module is specifically configured to:

[0055] When the target data packet is obtained in real time, a full scan is performed on the target data packet to detect in real time the target data sequence contained therein;

[0056] The starting point and the ending point corresponding to the target data sequence are detected in real time, and the target data sequence is converted into a corresponding target curve graph in real time according to the starting point and the ending point;

[0057] According to the target curve graph and the preset prediction model, the predicted vehicle energy consumption adapted to the vehicle is predicted.

[0058] Furthermore, the prediction module is specifically configured to:

[0059] When the target curve graph is obtained in real time, several maximum value points and several minimum value points included in the target curve graph are detected in real time;

[0060] The several maximum value points and the several minimum value points are set as target feature values adapted to the target data packet, and the predicted vehicle energy consumption adapted to the vehicle is predicted in real time according to the preset prediction model and each target feature value.

[0061] Furthermore, the prediction module is specifically configured to:

[0062] When each target feature value is obtained in real time, the target neural network included in the preset prediction model is detected in real time;

[0063] Several network nodes included in the target neural network are detected in real time, and the initial network parameters respectively included in each network node are detected in real time;

[0064] Each initial network parameter is replaced with each target feature value so that the preset prediction model outputs the predicted vehicle energy consumption.

[0065] The third aspect of the embodiments of the present invention proposes:

[0066] A computer, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the computer program, the vehicle energy distribution method as described above is implemented.

[0067] The fourth aspect of the embodiments of the present invention proposes:

[0068] A readable storage medium, on which a computer program is stored, wherein when the program is executed by a processor, the vehicle energy distribution method as described above is implemented.

[0069] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A flowchart of a vehicle energy distribution method provided for the first embodiment of the present invention;

[0071] Figure 2 A structural block diagram of a vehicle energy distribution system provided for the third embodiment of the present invention.

[0072] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. SPECIFIC EMBODIMENTS

[0073] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Several embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided so that the disclosure of the present invention will be thorough and comprehensive.

[0074] It should be noted that when an element is referred to as being "fixedly provided on" another element, it can be directly on the other element or there can also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0075] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0076] Please refer to Figure 1 , which shows a vehicle energy distribution method provided for the first embodiment of the present invention. The vehicle energy distribution method provided in this embodiment can accurately calculate the real-time energy consumption required by the vehicle, so as to be able to correspondingly control the output power of the battery pack, and then be able to reasonably complete the energy distribution, correspondingly greatly improving the user experience.

[0077] Specifically, this embodiment provides:

[0078] A vehicle energy distribution method, specifically including the following steps:

[0079] Step S10: When it is detected in real time that the vehicle is in a driving state, obtain in real time the vehicle information corresponding to the vehicle and the environmental information around the vehicle;

[0080] Step S20: Based on preset rules, perform fusion processing on the vehicle information and the environmental information to generate a corresponding target data packet in real time, and in real time match the historical vehicle energy consumption corresponding to the vehicle in a preset database according to the target data packet;

[0081] Step S30: Use a preset prediction model to predict in real time the predicted vehicle energy consumption adapted to the vehicle according to the target data packet, and in real time calculate the actual vehicle energy consumption corresponding to the vehicle according to the historical vehicle energy consumption and the predicted vehicle energy consumption;

[0082] Step S40: Calculate in real time the actual output power corresponding to the drive motor inside the vehicle according to the actual vehicle energy consumption, and in real time calculate the power that the battery pack inside the vehicle needs to output according to the actual output power.

[0083] Specifically, in this embodiment, it should be noted first that in order to quickly and effectively complete the energy distribution inside the vehicle, it is necessary to accurately obtain the vehicle information generated during the real-time driving of the vehicle, and further complete the real-time energy distribution according to the obtained information, so as to correspondingly improve the cruising range of the vehicle and at the same time improve the user experience. Among them, it should be pointed out that the vehicle energy distribution method provided by the present invention is specifically applied to various new energy electric vehicles and is used to control the output power of the battery pack in real time to correspondingly improve the performance of new energy vehicles. Based on this, in the actual application process, the present invention will collect the vehicle information of the vehicle and the environmental information around the current vehicle in real time through an in-vehicle controller preset inside the vehicle. Among them, it should be noted that the vehicle information disclosed by the present invention may specifically include specific data such as vehicle speed, output torque, and battery pack temperature. Correspondingly, the above environmental information may include specific data such as road type and road gradient. Based on this, in order to comprehensively complete the energy consumption analysis of the current vehicle, the present invention will further perform corresponding fusion processing on the current vehicle information and environmental information according to the preset rules, and can further fuse the required target data packet for subsequent processing.

[0084] Further, after the target data packet needed is obtained in real time through the above steps, corresponding data analysis and processing need to be carried out immediately. Preferably, since in the prior art, vehicle data generated during vehicle driving is collected and stored in real time, a database adapted to the current vehicle can be formed accordingly. Specifically, the database internally contains data such as the historical energy consumption and historical driving mileage of the vehicle. Based on this, the present invention can further match the historical vehicle energy consumption corresponding to the current vehicle in the above database in real time according to the current target data packet. Based on this, the present invention will further call out a pre-trained prediction model. At the same time, the prediction vehicle energy consumption adapted to the current vehicle can be predicted in real time according to the current target data packet through the prediction model. Based on this, the present invention will further calculate the average value between the current historical vehicle energy consumption and the current predicted vehicle energy consumption, and set this average value as the actual vehicle energy consumption required by the current vehicle. On this basis, the present invention can further calculate the actual output power required by the drive motor inside the current vehicle in real time according to the existing electrical principles based on the current actual vehicle energy consumption. Similarly, the power output required by the battery pack inside the current vehicle can be calculated in real time according to the current real-time output power, so as to reasonably complete the distribution of the power output by the battery pack and correspondingly improve the user experience.

[0085] Second Embodiment

[0086] Further, the step of performing fusion processing on the vehicle information and the environment information based on a preset rule to generate a corresponding target data packet in real time includes:

[0087] When the vehicle information is obtained in real time, perform real-time parsing and processing on the vehicle information to correspondingly detect a number of first data sequences included in the vehicle information;

[0088] When the environment information is obtained in real time, perform real-time parsing and processing on the environment information to correspondingly detect a number of second data sequences included in the environment information;

[0089] Perform real-time fusion processing on the number of first data sequences and the number of second data sequences to correspondingly generate the target data packet, and the target data packet is unique.

[0090] Further, the step of performing real-time fusion processing on the number of first data sequences and the number of second data sequences to correspondingly generate the target data packet includes:

[0091] When the first data sequence and the second data sequence are respectively obtained, the vehicle model corresponding to the vehicle is detected in real time;

[0092] Match in real time in a preset template database a target fusion template adapted to a number of the first data sequences and a number of the second data sequences according to the vehicle model;

[0093] Embed a number of the first data sequences and a number of the second data sequences into the target fusion template correspondingly to generate the target data packet correspondingly, and the target fusion template is unique.

[0094] Further, the step of embedding a number of the first data sequences and a number of the second data sequences into the target fusion template correspondingly includes:

[0095] When the target fusion template is obtained in real time, perform a full scan on the target fusion template to detect in real time a number of first data channels and a number of second data channels correspondingly included inside the target fusion template, wherein the number of the first data channels and the number of the second data channels are distributed in a crisscross manner with each other;

[0096] Add corresponding first identifiers to a number of the first data sequences respectively, and add corresponding second identifiers to a number of the second data sequences respectively;

[0097] Fill each of the first data sequences into the inside of each of the first data channels correspondingly according to the first identifier, and fill each of the second data sequences into the inside of each of the second data channels correspondingly according to the second identifier to generate the target data packet correspondingly, and each of the first data sequences and each of the second data sequences are unique.

[0098] Further, the step of predicting in real time a predicted vehicle energy consumption adapted to the vehicle according to the target data packet through a preset prediction model includes:

[0099] When the target data packet is obtained in real time, perform a full scan on the target data packet to detect in real time a target data sequence correspondingly included inside the target data packet;

[0100] Detect in real time a starting point and an ending point corresponding to the target data sequence, and convert the target data sequence into a corresponding target curve graph in real time according to the starting point and the ending point;

[0101] Predict a predicted vehicle energy consumption adapted to the vehicle according to the target curve graph and the preset prediction model.

[0102] Further, the step of predicting a predicted vehicle energy consumption adapted to the vehicle according to the target curve graph and the preset prediction model includes:

[0103] When the target curve graph is obtained in real time, several maximum points and several minimum points included in the target curve graph are detected in real time;

[0104] Several of the maximum points and several of the minimum points are correspondingly set as target feature values adapted to the target data packet, and the predicted vehicle energy consumption adapted to the vehicle is predicted in real time according to the preset prediction model and each target feature value.

[0105] Further, the step of predicting the predicted vehicle energy consumption adapted to the vehicle in real time according to the preset prediction model and each target feature value includes:

[0106] When each target feature value is obtained in real time, the target neural network included in the preset prediction model is detected in real time;

[0107] Several network nodes included inside the target neural network are detected in real time, and the initial network parameters respectively included in each network node are detected in real time.

[0108] Each of the initial network parameters is correspondingly replaced with each target feature value, so that the preset prediction model outputs the predicted vehicle energy consumption.

[0109] In addition, in this embodiment, it should also be noted that after obtaining the required vehicle information and environmental information through the above steps respectively, in order to quickly and effectively complete the fusion process between the current vehicle information and the current environmental information, preferably, the present invention will further perform corresponding extraction processes on the current vehicle information and the current environmental information respectively, so as to be able to extract the corresponding first data sequence and second data sequence respectively. Further, since the current first data sequence and the current second data sequence are both independent data, based on this, in order to be able to correspondingly establish the connection between the two, the present invention will further detect the specific model of the current vehicle, and can further match in real time in a pre-set template database the target fusion template adapted to the current two data sequences according to the vehicle model of the current vehicle. Based on this, it is also possible to further parse the current template and detect the internal corresponding several first data channels and several second data channels included in the current template. Among them, it should be pointed out that the current several first data channels and the current several second data channels are distributed in a criss-cross manner with each other, so as to form a corresponding network structure. Correspondingly, the present invention will further add a first identifier to each of the above first data sequences and add a second identifier to each of the above second data sequences. Based on this, the current first data sequence can be correspondingly filled into the interior of the current first data channel according to the first identifier, and correspondingly, the current second data sequence can be correspondingly filled into the interior of the current second data channel according to the current second identifier, so as to be able to correspondingly complete the data filling, and further be able to correspondingly generate the required target data packet for subsequent processing.

[0110] Further, after the required target data packet is obtained in real time through the above steps, in order to accurately predict the predicted vehicle energy consumption required by the current vehicle, so as to further accurately calculate the actual vehicle energy consumption required by the current vehicle, based on this, the present invention will further detect the target data sequence contained in the current target data packet. At the same time, the starting point and the ending point of the current target data sequence are synchronously detected, and within the range of the current starting point and the ending point, the current target data sequence is further converted into a corresponding target curve graph in real time. Based on this, it is also necessary to further detect several maximum value points and several minimum value points contained in the current target curve graph. It should be noted that the current several maximum value points and the current several minimum value points are the target characteristic values corresponding to the above target data packet. Based on this, at this time, the target neural network contained in the above preset prediction model can be further called out, and several network nodes contained in the current target neural network can be further detected. At the same time, the initial network parameters respectively contained in each current network node are immediately detected. On this basis, finally, each current initial network parameter is respectively replaced with each current target characteristic value, and the current preset prediction model can complete the corresponding output, and further output the required predicted vehicle energy consumption for subsequent processing.

[0111] Please refer to Figure 2 , the third embodiment of the present invention provides:

[0112] A vehicle energy distribution system, wherein the system includes:

[0113] An acquisition module, configured to, when it is detected in real time that the vehicle is in a driving state, acquire in real time vehicle information corresponding to the vehicle and environmental information around the vehicle;

[0114] A fusion module, configured to perform a fusion process on the vehicle information and the environmental information based on a preset rule to generate a corresponding target data packet in real time, and in real time match the historical vehicle energy consumption corresponding to the vehicle in a preset database according to the target data packet;

[0115] A prediction module, configured to, through a preset prediction model, predict in real time the predicted vehicle energy consumption adapted to the vehicle according to the target data packet, and calculate in real time the actual vehicle energy consumption corresponding to the vehicle according to the historical vehicle energy consumption and the predicted vehicle energy consumption;

[0116] A calculation module, configured to calculate in real time the actual output power corresponding to the drive motor inside the vehicle according to the actual vehicle energy consumption, and calculate in real time the electric quantity required to be output by the battery pack inside the vehicle according to the actual output power.

[0117] Furthermore, the fusion module is specifically configured to:

[0118] When the vehicle information is obtained in real time, perform real-time parsing and processing on the vehicle information to correspondingly detect a number of first data sequences included in the vehicle information;

[0119] When the environmental information is obtained in real time, perform real-time parsing and processing on the environmental information to correspondingly detect a number of second data sequences included in the environmental information;

[0120] Perform real-time fusion processing on a number of the first data sequences and a number of the second data sequences to correspondingly generate the target data packet, and the target data packet is unique.

[0121] Furthermore, the fusion module is specifically configured to:

[0122] When the first data sequence and the second data sequence are respectively obtained, detect in real time the vehicle model corresponding to the vehicle;

[0123] In the preset template database, in real time match a target fusion template adapted to a number of the first data sequences and a number of the second data sequences according to the vehicle model;

[0124] Embed a number of the first data sequences and a number of the second data sequences into the target fusion template correspondingly to generate the target data packet, and the target fusion template is unique.

[0125] Furthermore, the fusion module is specifically configured to:

[0126] When the target fusion template is obtained in real time, perform a full scan on the target fusion template to detect in real time a number of first data channels and a number of second data channels correspondingly included inside the target fusion template, wherein the number of the first data channels and the number of the second data channels are distributed in a crisscross manner with each other;

[0127] Add corresponding first identifiers to a number of the first data sequences respectively, and add corresponding second identifiers to a number of the second data sequences respectively;

[0128] Fill each of the first data sequences into the inside of each of the first data channels correspondingly according to the first identifier, and fill each of the second data sequences into the inside of each of the second data channels correspondingly according to the second identifier to generate the target data packet, and each of the first data sequences and each of the second data sequences is unique.

[0129] Furthermore, the prediction module is specifically configured to:

[0130] When the target data packet is obtained in real time, perform a full scan on the target data packet to detect in real time the target data sequence contained therein correspondingly.

[0131] Detect in real time the starting point and the ending point corresponding to the target data sequence, and convert the target data sequence into a corresponding target curve graph in real time according to the starting point and the ending point.

[0132] Predict the predicted vehicle energy consumption adapted to the vehicle according to the target curve graph and the preset prediction model.

[0133] Further, the prediction module is specifically configured to:

[0134] When the target curve graph is obtained in real time, detect in real time a number of maximum value points and a number of minimum value points contained therein correspondingly.

[0135] Set a number of the maximum value points and a number of the minimum value points as target feature values adapted to the target data packet, and predict the predicted vehicle energy consumption adapted to the vehicle in real time according to the preset prediction model and each target feature value.

[0136] Further, the prediction module is specifically configured to:

[0137] When each target feature value is obtained in real time, detect in real time the target neural network contained in the preset prediction model.

[0138] Detect in real time a number of network nodes contained therein correspondingly in the target neural network, and detect in real time the initial network parameters respectively contained in each network node.

[0139] Replace each initial network parameter with each target feature value so that the preset prediction model outputs the predicted vehicle energy consumption correspondingly.

[0140] The fourth embodiment of the present invention provides a computer, including a memory, a processor, and a computer program stored on the memory and executable on the processor. Wherein, when the processor executes the computer program, the vehicle energy distribution method as described above is implemented.

[0141] The fifth embodiment of the present invention provides a readable storage medium, on which a computer program is stored. Wherein, when the program is executed by a processor, the vehicle energy distribution method as described above is implemented.

[0142] In summary, the vehicle energy distribution method and system provided by the above embodiments of the present invention can accurately calculate the real-time energy consumption required by the vehicle, so as to correspondingly control the output power of the battery pack, and then reasonably complete the energy distribution, thereby greatly improving the user experience.

[0143] It should be noted that the above-mentioned each module can be a functional module or a program module, and can be implemented either by software or by hardware. For the modules implemented by hardware, the above-mentioned each module can be located in the same processor; or the above-mentioned each module can also be located in different processors in any combined form.

[0144] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

[0145] More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection part (electronic device) having one or more wirings, a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or otherwise processing it as appropriate, and then storing it in a computer memory.

[0146] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following techniques known in the art or a combination thereof can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0147] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0148] The above-described embodiments merely represent several implementation manners of the present invention. The description is relatively specific and detailed, but should not be construed as a limitation on the scope of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.

Claims

1. A vehicle energy distribution method, characterized in that, The method includes: When it is detected in real time that the vehicle is in a driving state, the vehicle information corresponding to the vehicle and the environmental information around the vehicle are obtained in real time; Based on preset rules, the vehicle information and the environmental information are fused to generate a corresponding target data packet in real time, and the historical vehicle energy consumption corresponding to the vehicle is matched in a preset database in real time according to the target data packet; According to the target data packet, a predicted vehicle energy consumption suitable for the vehicle is predicted in real time through a preset prediction model, and the actual vehicle energy consumption corresponding to the vehicle is calculated in real time according to the historical vehicle energy consumption and the predicted vehicle energy consumption; The actual output power corresponding to the drive motor inside the vehicle is calculated in real time according to the actual vehicle energy consumption, and the power that the battery pack inside the vehicle needs to output is calculated in real time according to the actual output power; The step of predicting the predicted vehicle energy consumption suitable for the vehicle in real time according to the target data packet through a preset prediction model includes: When the target data packet is obtained in real time, a full scan of the target data packet is performed to detect the target data sequence contained inside the target data packet in real time; The starting point and the ending point corresponding to the target data sequence are detected in real time, and the target data sequence is converted into a corresponding target curve graph according to the starting point and the ending point in real time; The predicted vehicle energy consumption suitable for the vehicle is predicted according to the target curve graph and the preset prediction model; The step of predicting the predicted vehicle energy consumption suitable for the vehicle according to the target curve graph and the preset prediction model includes: When the target curve graph is obtained in real time, a number of maximum points and a number of minimum points contained in the target curve graph are detected in real time; A number of the maximum points and a number of the minimum points are set as target characteristic values suitable for the target data packet, and the predicted vehicle energy consumption suitable for the vehicle is predicted in real time according to the preset prediction model and each target characteristic value; The step of predicting the predicted vehicle energy consumption suitable for the vehicle in real time according to the preset prediction model and each target characteristic value includes: When each target characteristic value is obtained in real time, the target neural network contained in the preset prediction model is detected in real time; A number of network nodes contained inside the target neural network are detected in real time, and the initial network parameters respectively contained inside each network node are detected in real time; Each initial network parameter is replaced with each target characteristic value so that the preset prediction model outputs the predicted vehicle energy consumption.

2. The vehicle energy distribution method according to claim 1, wherein: The step of fusing the vehicle information and the environmental information based on preset rules to generate a corresponding target data packet in real time includes: When the vehicle information is obtained in real time, real-time parsing processing is performed on the vehicle information to detect a number of first data sequences contained in the vehicle information correspondingly; When the environmental information is obtained in real time, the environmental information is parsed and processed in real time to correspondingly detect a number of second data sequences included in the environmental information; Perform real-time fusion processing on a number of the first data sequences and a number of the second data sequences to correspondingly generate the target data packet, and the target data packet is unique.

3. The vehicle energy distribution method according to claim 2, wherein: The step of performing real-time fusion processing on a number of the first data sequences and a number of the second data sequences to correspondingly generate the target data packet includes: When the first data sequence and the second data sequence are respectively obtained, the vehicle model corresponding to the vehicle is detected in real time; In the preset template database, in real time, match a target fusion template adapted to a number of the first data sequences and a number of the second data sequences according to the vehicle model; Embed a number of the first data sequences and a number of the second data sequences into the target fusion template correspondingly to generate the target data packet, and the target fusion template is unique.

4. The vehicle energy distribution method according to claim 3, wherein: The step of embedding a number of the first data sequences and a number of the second data sequences into the target fusion template correspondingly includes: When the target fusion template is obtained in real time, perform a full scan on the target fusion template to detect in real time a number of first data channels and a number of second data channels correspondingly included inside the target fusion template, wherein the number of the first data channels and the number of the second data channels are distributed in a crisscross manner; Add corresponding first identifiers to a number of the first data sequences respectively, and add corresponding second identifiers to a number of the second data sequences respectively; Fill each of the first data sequences into the inside of each of the first data channels according to the first identifier, and fill each of the second data sequences into the inside of each of the second data channels according to the second identifier to generate the target data packet correspondingly, and each of the first data sequences and each of the second data sequences is unique.

5. A vehicle energy distribution system, characterized in that, A system for implementing the vehicle energy distribution method according to any one of claims 1 to 4, the system includes: An acquisition module, configured to, when it is detected in real time that the vehicle is in a driving state, acquire in real time the vehicle information corresponding to the vehicle and the environmental information around the vehicle; A fusion module, configured to perform fusion processing on the vehicle information and the environmental information based on a preset rule to generate a corresponding target data packet in real time, and in real time match in a preset database the historical vehicle energy consumption corresponding to the vehicle according to the target data packet; A prediction module, configured to predict in real time the predicted vehicle energy consumption adapted to the vehicle according to the target data packet through a preset prediction model, and calculate in real time the actual vehicle energy consumption corresponding to the vehicle according to the historical vehicle energy consumption and the predicted vehicle energy consumption; A calculation module, configured to calculate in real time the actual output power corresponding to the drive motor inside the vehicle according to the actual vehicle energy consumption, and calculate in real time the electric quantity that the battery pack inside the vehicle needs to output according to the actual output power.

6. A computer, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the vehicle energy distribution method according to any one of claims 1 to 4.

7. A readable storage medium, on which a computer program is stored, characterized in that, When the program is executed by the processor, it implements the vehicle energy distribution method according to any one of claims 1 to 4.

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