Vehicle energy distribution method and system

Abstract

The invention provides a vehicle energy distribution method and system, and the method comprises the steps: obtaining vehicle information corresponding to a vehicle and environment information around the vehicle in real time when the vehicle is detected to be in a driving state in real time; 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, and matching historical vehicle energy consumption corresponding to the vehicle in a preset database in real time according to the target data packet; predicted vehicle energy consumption matched with the vehicle is correspondingly predicted in real time according to the target data packet through a preset prediction model, and actual vehicle energy consumption corresponding to the vehicle is calculated in real time according to historical vehicle energy consumption and the predicted vehicle energy consumption; actual output power corresponding to a driving motor in the vehicle is calculated in real time according to the actual vehicle energy consumption, and the electric quantity needing to be output by a battery pack in the vehicle is correspondingly calculated in real time according to the actual output power. The user experience can be improved.

Description

Technical Field

[0001] The present invention relates to the field of automobile technology, and in particular to a vehicle energy distribution method and system. Background Art

[0002] With the advancement of science and technology and the rapid development of productivity, cars have become popular in people's daily lives and have become one of the indispensable means of transportation for people's daily travel, greatly facilitating people's lives.

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

[0004] Furthermore, in the actual use of existing electric light trucks, the quality of energy management directly affects the performance of the vehicle. Based on this, the existing technology will set a corresponding energy management system inside the vehicle. However, most of the existing technologies 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, which increases the energy consumption of the vehicle and reduces 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, the prior art cannot accurately output the power actually required by the vehicle, resulting in a corresponding increase in vehicle energy consumption.

[0006] The first aspect of the embodiment of the present invention proposes: A vehicle energy distribution method, wherein the method comprises: When it is detected in real time that the vehicle is in a driving state, vehicle information corresponding to the vehicle and environmental information around the vehicle are acquired in real time; The vehicle information and the environmental information are fused and processed based on preset rules to generate a corresponding target data packet in real time, and the historical vehicle energy consumption corresponding to the vehicle is matched in real time in a preset database according to the target data packet; Predicting the predicted vehicle energy consumption adapted to the vehicle in real time according to the target data packet through a preset prediction model, and calculating the actual vehicle energy consumption corresponding to the vehicle in real time according to the historical vehicle energy consumption and the predicted vehicle energy consumption; The actual output power corresponding to the driving motor inside the vehicle is calculated in real time based on the actual vehicle energy consumption, and the amount of electricity required to be output by the battery pack inside the vehicle is calculated in real time based on the actual output power.

[0007] 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. Based on this, in order to be able to conduct a comprehensive analysis of the current vehicle, the two current information will be further integrated, and the historical vehicle energy consumption corresponding to the current vehicle and the predicted vehicle energy consumption that the current vehicle may need can 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 actually required by the current vehicle can be finally calculated, so that the distribution of the battery pack output power can be effectively completed, and then the cruising range can be effectively improved, which correspondingly improves the user experience.

[0008] Furthermore, the step of fusing the vehicle information and the environment information based on a preset rule to generate a corresponding target data packet in real time includes: When the vehicle information is acquired in real time, the vehicle information is analyzed and processed in real time to detect a plurality of first data sequences contained in the vehicle information; When the environmental information is acquired in real time, the environmental information is analyzed and processed in real time to detect a plurality of second data sequences contained in the environmental information; A plurality of the first data sequences and a plurality of the second data sequences are fused in real time to generate the target data packet accordingly, and the target data packet is unique.

[0009] Furthermore, the step of performing real-time fusion processing on a plurality of the first data sequences and a plurality 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 acquired, a vehicle model corresponding to the vehicle is detected in real time; According to the vehicle model, a target fusion template adapted to a plurality of the first data sequences and a plurality of the second data sequences is matched in real time in a preset template database; A plurality of the first data sequences and a plurality of the second data sequences are correspondingly embedded into the target fusion template to correspondingly generate the target data packet, and the target fusion template is unique.

[0010] Furthermore, the step of correspondingly embedding a plurality of the first data sequences and a plurality of the second data sequences into the target fusion template includes: When the target fusion template is acquired in real time, the target fusion template is fully scanned to detect in real time a plurality of first data channels and a plurality of second data channels correspondingly contained in the target fusion template, wherein the plurality of first data channels and the plurality of second data channels are crisscrossed with each other; adding corresponding first identifiers to a number of the first data sequences respectively, and adding corresponding second identifiers to a number of the second data sequences respectively; According to the first identifier, each of the first data sequences is filled into the interior of each of the first data channels, and according to the second identifier, each of the second data sequences is filled into the interior of each of the second data channels to generate the target data packets accordingly. Each of the first data sequences and each of the second data sequences is unique.

[0011] Furthermore, the step of predicting the predicted vehicle energy consumption adapted to the vehicle in real time according to the target data packet by using a preset prediction model includes: When the target data packet is acquired in real time, the target data packet is fully scanned to detect the target data sequence contained in the target data packet in real time; Detecting the starting point and the ending point corresponding to the target data sequence in real time, and converting the target data sequence into a corresponding target curve graph in real time according to the starting point and the ending point; The predicted vehicle energy consumption suitable for the vehicle is predicted according to the target curve graph and the preset prediction model.

[0012] Furthermore, the step of predicting the predicted vehicle energy consumption adapted to the vehicle according to the target curve graph and the preset prediction model includes: When the target curve graph is acquired in real time, a number of maximum value points and a number of minimum value points corresponding to the target curve graph are detected in real time; A plurality of the maximum value points and a plurality of the minimum value points are correspondingly set as target characteristic values ​​adapted to the target data packet, and a predicted vehicle energy consumption adapted to the vehicle is predicted in real time according to the preset prediction model and each of the target characteristic values.

[0013] Furthermore, the step of predicting the predicted vehicle energy consumption adapted to the vehicle in real time according to the preset prediction model and each of the target characteristic values ​​includes: When each of the target feature values ​​is acquired in real time, the target neural network corresponding to the preset prediction model is detected in real time; Detecting in real time a number of corresponding network nodes contained in the target neural network, and detecting in real time the initial network parameters contained in each of the network nodes; Each of the initial network parameters is replaced with each of the target characteristic values ​​so that the preset prediction model outputs the predicted vehicle energy consumption accordingly.

[0014] The second aspect of the embodiment of the present invention proposes: A vehicle energy distribution system, wherein the system comprises: An acquisition module, used for acquiring vehicle information corresponding to the vehicle and environmental information around the vehicle in real time when it is detected in real time that the vehicle is in a driving state; A fusion module, used for fusing the vehicle information and the environmental information based on preset rules to generate a corresponding target data packet in real time, and matching the historical vehicle energy consumption corresponding to the vehicle in a preset database in real time according to the target data packet; A prediction module, used to predict the predicted vehicle energy consumption adapted to the vehicle in real time according to the target data packet through a preset prediction model, and calculate the actual vehicle energy consumption corresponding to the vehicle in real time according to the historical vehicle energy consumption and the predicted vehicle energy consumption; The calculation module is used to calculate the actual output power corresponding to the driving motor inside the vehicle in real time according to the actual vehicle energy consumption, and calculate the power required to be output by the battery pack inside the vehicle in real time according to the actual output power.

[0015] Furthermore, the fusion module is specifically used for: When the vehicle information is acquired in real time, the vehicle information is analyzed and processed in real time to detect a plurality of first data sequences contained in the vehicle information; When the environmental information is acquired in real time, the environmental information is analyzed and processed in real time to detect a plurality of second data sequences contained in the environmental information; A plurality of the first data sequences and a plurality of the second data sequences are fused in real time to generate the target data packet accordingly, and the target data packet is unique.

[0016] Furthermore, the fusion module is specifically used for: When the first data sequence and the second data sequence are respectively acquired, a vehicle model corresponding to the vehicle is detected in real time; According to the vehicle model, a target fusion template adapted to a plurality of the first data sequences and a plurality of the second data sequences is matched in real time in a preset template database; A plurality of the first data sequences and a plurality of the second data sequences are correspondingly embedded into the target fusion template to correspondingly generate the target data packet, and the target fusion template is unique.

[0017] Furthermore, the fusion module is specifically used for: When the target fusion template is acquired in real time, the target fusion template is fully scanned to detect in real time a plurality of first data channels and a plurality of second data channels correspondingly contained in the target fusion template, wherein the plurality of first data channels and the plurality of second data channels are crisscrossed with each other; adding corresponding first identifiers to a number of the first data sequences respectively, and adding corresponding second identifiers to a number of the second data sequences respectively; According to the first identifier, each of the first data sequences is filled into the interior of each of the first data channels, and according to the second identifier, each of the second data sequences is filled into the interior of each of the second data channels to generate the target data packets accordingly. Each of the first data sequences and each of the second data sequences is unique.

[0018] Furthermore, the prediction module is specifically used for: When the target data packet is acquired in real time, the target data packet is fully scanned to detect the target data sequence contained in the target data packet in real time; Detecting the starting point and the ending point corresponding to the target data sequence in real time, and converting the target data sequence into a corresponding target curve graph in real time according to the starting point and the ending point; The predicted vehicle energy consumption suitable for the vehicle is predicted according to the target curve graph and the preset prediction model.

[0019] Furthermore, the prediction module is specifically used for: When the target curve graph is acquired in real time, a number of maximum value points and a number of minimum value points corresponding to the target curve graph are detected in real time; A plurality of the maximum value points and a plurality of the minimum value points are correspondingly set as target characteristic values ​​adapted to the target data packet, and a predicted vehicle energy consumption adapted to the vehicle is predicted in real time according to the preset prediction model and each of the target characteristic values.

[0020] Furthermore, the prediction module is specifically used for: When each of the target feature values ​​is acquired in real time, the target neural network corresponding to the preset prediction model is detected in real time; Detecting in real time a number of corresponding network nodes contained in the target neural network, and detecting in real time the initial network parameters contained in each of the network nodes; Each of the initial network parameters is replaced with each of the target characteristic values ​​so that the preset prediction model outputs the predicted vehicle energy consumption accordingly.

[0021] The third aspect of the embodiment of the present invention proposes: A computer comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the vehicle energy distribution method as described above is implemented when the processor executes the computer program.

[0022] The fourth aspect of the embodiments of the present invention proposes: A readable storage medium stores a computer program thereon, wherein the program, when executed by a processor, implements the vehicle energy distribution method as described above.

[0023] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A flow chart of a vehicle energy distribution method provided by a first embodiment of the present invention; Figure 2 This is a structural block diagram of a vehicle energy distribution system provided in the third embodiment of the present invention.

[0025] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0026] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given 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, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0027] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art 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.

[0029] See also Figure 1 , shown is the vehicle energy distribution method provided by the first embodiment of the present invention. The vehicle energy distribution method provided by this embodiment can accurately calculate the real-time energy consumption required by the vehicle, so as to control the output power of the battery pack accordingly, and then reasonably complete the energy distribution, which greatly improves the user experience.

[0030] Specifically, this embodiment provides: A vehicle energy distribution method comprises the following steps: Step S10, when it is detected in real time that the vehicle is in a driving state, obtaining vehicle information corresponding to the vehicle and environmental information around the vehicle in real time; Step S20, fusing the vehicle information and the environmental information based on preset rules to generate a corresponding target data packet in real time, and matching the historical vehicle energy consumption corresponding to the vehicle in a preset database in real time according to the target data packet; Step S30, predicting the predicted vehicle energy consumption adapted to the vehicle in real time according to the target data packet through a preset prediction model, and calculating the actual vehicle energy consumption corresponding to the vehicle in real time according to the historical vehicle energy consumption and the predicted vehicle energy consumption; Step S40, calculating in real time the actual output power corresponding to the driving motor inside the vehicle according to the actual vehicle energy consumption, and calculating in real time the amount of electricity that the battery pack inside the vehicle needs to output according to the actual output power.

[0031] Specifically, in this embodiment, it should be noted that, in order to quickly and effectively complete the distribution of energy inside the vehicle, it is necessary to accurately obtain the vehicle information corresponding to the vehicle in the process of real-time driving, and further complete the real-time distribution of energy according to the obtained information, so as to correspondingly improve the vehicle's cruising range and improve the user's experience. Among them, it should be pointed out that the vehicle energy distribution method provided by the present invention is specifically applied to various types of 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 process of actual application, the present invention will collect the vehicle information of the vehicle and the environmental information around the current vehicle in real time through the on-board controller pre-set inside the vehicle. Among them, it should be noted that the vehicle information disclosed in the present invention can specifically include specific data such as vehicle speed, output torque and battery pack temperature, and correspondingly, the above-mentioned environmental information can include specific data such as the type of road and the slope of the road. Based on this, in order to be able to comprehensively complete the energy consumption analysis of the current vehicle, the present invention will further immediately perform corresponding fusion processing on the current vehicle information and environmental information according to the pre-set rules, and can further fuse the required target data packets for subsequent processing.

[0032] Furthermore, after the required target data packets are obtained in real time through the above steps, corresponding data analysis and processing must be performed immediately. Preferably, since the prior art collects and stores corresponding vehicle data in real time during vehicle driving, a database adapted to the current vehicle can be formed. Specifically, the database contains data such as the vehicle's historical energy consumption and historical mileage. Based on this, the present invention can further match the historical vehicle energy consumption corresponding to the current vehicle in the above-mentioned 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, it can synchronously predict the predicted vehicle energy consumption adapted to the current vehicle 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 the average value to the actual vehicle energy consumption required by the current vehicle. On this basis, the present invention can further calculate the actual output power required to be output by the drive motor inside the current vehicle in real time according to the current actual vehicle energy consumption according to the existing electrical principles. Similarly, it can also calculate the amount of electricity required to be output by the battery pack inside the current vehicle in real time according to the current real-time output power, so that the distribution of the electricity output by the battery pack can be reasonably completed, which correspondingly improves the user experience.

[0033] Second embodiment Furthermore, the step of fusing the vehicle information and the environment information based on a preset rule to generate a corresponding target data packet in real time includes: When the vehicle information is acquired in real time, the vehicle information is analyzed and processed in real time to detect a plurality of first data sequences contained in the vehicle information; When the environmental information is acquired in real time, the environmental information is analyzed and processed in real time to detect a plurality of second data sequences contained in the environmental information; A plurality of the first data sequences and a plurality of the second data sequences are fused in real time to generate the target data packet accordingly, and the target data packet is unique.

[0034] Furthermore, the step of performing real-time fusion processing on a plurality of the first data sequences and a plurality 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 acquired, a vehicle model corresponding to the vehicle is detected in real time; According to the vehicle model, a target fusion template adapted to a plurality of the first data sequences and a plurality of the second data sequences is matched in real time in a preset template database; A plurality of the first data sequences and a plurality of the second data sequences are correspondingly embedded into the target fusion template to correspondingly generate the target data packet, and the target fusion template is unique.

[0035] Furthermore, the step of correspondingly embedding a plurality of the first data sequences and a plurality of the second data sequences into the target fusion template includes: When the target fusion template is acquired in real time, the target fusion template is fully scanned to detect in real time a plurality of first data channels and a plurality of second data channels correspondingly contained in the target fusion template, wherein the plurality of first data channels and the plurality of second data channels are crisscrossed with each other; adding corresponding first identifiers to a number of the first data sequences respectively, and adding corresponding second identifiers to a number of the second data sequences respectively; According to the first identifier, each of the first data sequences is filled into the interior of each of the first data channels, and according to the second identifier, each of the second data sequences is filled into the interior of each of the second data channels to generate the target data packets accordingly. Each of the first data sequences and each of the second data sequences is unique.

[0036] Furthermore, the step of predicting the predicted vehicle energy consumption adapted to the vehicle in real time according to the target data packet by using a preset prediction model includes: When the target data packet is acquired in real time, the target data packet is fully scanned to detect the target data sequence contained in the target data packet in real time; Detecting the starting point and the ending point corresponding to the target data sequence in real time, and converting the target data sequence into a corresponding target curve graph in real time according to the starting point and the ending point; The predicted vehicle energy consumption suitable for the vehicle is predicted according to the target curve graph and the preset prediction model.

[0037] Furthermore, the step of predicting the predicted vehicle energy consumption adapted to the vehicle according to the target curve graph and the preset prediction model includes: When the target curve graph is acquired in real time, a number of maximum value points and a number of minimum value points corresponding to the target curve graph are detected in real time; A plurality of the maximum value points and a plurality of the minimum value points are correspondingly set as target characteristic values ​​adapted to the target data packet, and a predicted vehicle energy consumption adapted to the vehicle is predicted in real time according to the preset prediction model and each of the target characteristic values.

[0038] Furthermore, the step of predicting the predicted vehicle energy consumption adapted to the vehicle in real time according to the preset prediction model and each of the target characteristic values ​​includes: When each of the target feature values ​​is acquired in real time, the target neural network corresponding to the preset prediction model is detected in real time; Detecting in real time a number of corresponding network nodes contained in the target neural network, and detecting in real time the initial network parameters contained in each of the network nodes; Each of the initial network parameters is replaced with each of the target characteristic values ​​so that the preset prediction model outputs the predicted vehicle energy consumption accordingly.

[0039] In addition, in the present embodiment, it is also necessary to explain that after the required vehicle information and environmental information are respectively obtained through the above steps, in order to be able to quickly and effectively complete the fusion processing between the current vehicle information and the current environmental information, preferably, the present invention will further perform corresponding extraction processing on the current vehicle information and the current environmental information, so as to be able to extract the corresponding first data sequence and the second data sequence respectively. Furthermore, 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 construct the connection between the two, the present invention will further detect the specific model of the current vehicle, and can further match the target fusion template adapted to the current two data sequences in real time in a pre-set template database according to the vehicle model of the current vehicle. Based on this, it can also It is capable of further performing a corresponding analysis on the current template, and correspondingly detecting a plurality of first data channels and a plurality of second data channels contained in the interior of the current template, wherein it should be pointed out that the current plurality of first data channels and the current plurality of second data channels are crisscrossed with each other, so as to form a corresponding mesh structure, and correspondingly, the present invention will further add a first identifier to each of the above-mentioned first data sequences, and add a second identifier to each of the above-mentioned second data sequences, based on which, the current first data sequence can be filled into the interior of the current first data channel according to the first identifier, and correspondingly, the current second data sequence can be filled into the interior of the current second data channel according to the current second identifier, so as to complete the data filling, and then generate the required target data packet, so as to facilitate subsequent processing.

[0040] Furthermore, after the required target data packet is acquired 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 corresponding to the current target data packet, and at the same time, synchronously detect the starting point and the ending point of the current target data sequence, and further convert the current target data sequence into a corresponding target curve graph in real time within the range of the current starting point and the ending point. Based on this, it is also necessary to further detect a number of maximum points and a number of minimum points corresponding to the current target curve graph. It should be noted that the current maximum points and the current minimum points are the target characteristic values ​​corresponding to the above-mentioned target data packets. Based on this, the target neural network corresponding to the above-mentioned preset prediction model can be further called out, and the current target neural network corresponding to the internal several network nodes can be further detected. At the same time, the initial network parameters corresponding to the internal of each current network node are immediately detected. On this basis, each current initial network parameter is finally 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, so as to facilitate subsequent processing.

[0041] See also Figure 2 , the third embodiment of the present invention provides: A vehicle energy distribution system, wherein the system comprises: An acquisition module, used for acquiring vehicle information corresponding to the vehicle and environmental information around the vehicle in real time when it is detected in real time that the vehicle is in a driving state; A fusion module, used for fusing the vehicle information and the environmental information based on preset rules to generate a corresponding target data packet in real time, and matching the historical vehicle energy consumption corresponding to the vehicle in a preset database in real time according to the target data packet; A prediction module, used to predict the predicted vehicle energy consumption adapted to the vehicle in real time according to the target data packet through a preset prediction model, and calculate the actual vehicle energy consumption corresponding to the vehicle in real time according to the historical vehicle energy consumption and the predicted vehicle energy consumption; The calculation module is used to calculate the actual output power corresponding to the driving motor inside the vehicle in real time according to the actual vehicle energy consumption, and calculate the power required to be output by the battery pack inside the vehicle in real time according to the actual output power.

[0042] Furthermore, the fusion module is specifically used for: When the vehicle information is acquired in real time, the vehicle information is analyzed and processed in real time to detect a plurality of first data sequences contained in the vehicle information; When the environmental information is acquired in real time, the environmental information is analyzed and processed in real time to detect a plurality of second data sequences contained in the environmental information; A plurality of the first data sequences and a plurality of the second data sequences are fused in real time to generate the target data packet accordingly, and the target data packet is unique.

[0043] Furthermore, the fusion module is specifically used for: When the first data sequence and the second data sequence are respectively acquired, a vehicle model corresponding to the vehicle is detected in real time; According to the vehicle model, a target fusion template adapted to a plurality of the first data sequences and a plurality of the second data sequences is matched in real time in a preset template database; A plurality of the first data sequences and a plurality of the second data sequences are correspondingly embedded into the target fusion template to correspondingly generate the target data packet, and the target fusion template is unique.

[0044] Furthermore, the fusion module is specifically used for: When the target fusion template is acquired in real time, the target fusion template is fully scanned to detect in real time a plurality of first data channels and a plurality of second data channels correspondingly contained in the target fusion template, wherein the plurality of first data channels and the plurality of second data channels are crisscrossed with each other; adding corresponding first identifiers to a number of the first data sequences respectively, and adding corresponding second identifiers to a number of the second data sequences respectively; According to the first identifier, each of the first data sequences is filled into the interior of each of the first data channels, and according to the second identifier, each of the second data sequences is filled into the interior of each of the second data channels to generate the target data packets accordingly. Each of the first data sequences and each of the second data sequences is unique.

[0045] Furthermore, the prediction module is specifically used for: When the target data packet is acquired in real time, the target data packet is fully scanned to detect the target data sequence contained in the target data packet in real time; Detecting the starting point and the ending point corresponding to the target data sequence in real time, and converting the target data sequence into a corresponding target curve graph in real time according to the starting point and the ending point; The predicted vehicle energy consumption suitable for the vehicle is predicted according to the target curve graph and the preset prediction model.

[0046] Furthermore, the prediction module is specifically used for: When the target curve graph is acquired in real time, a number of maximum value points and a number of minimum value points corresponding to the target curve graph are detected in real time; A plurality of the maximum value points and a plurality of the minimum value points are correspondingly set as target characteristic values ​​adapted to the target data packet, and a predicted vehicle energy consumption adapted to the vehicle is predicted in real time according to the preset prediction model and each of the target characteristic values.

[0047] Furthermore, the prediction module is specifically used for: When each of the target feature values ​​is acquired in real time, the target neural network corresponding to the preset prediction model is detected in real time; Detecting in real time a number of corresponding network nodes contained in the target neural network, and detecting in real time the initial network parameters contained in each of the network nodes; Each of the initial network parameters is replaced with each of the target characteristic values ​​so that the preset prediction model outputs the predicted vehicle energy consumption accordingly.

[0048] A fourth embodiment of the present invention provides a computer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the vehicle energy distribution method as described above when executing the computer program.

[0049] A fifth embodiment of the present invention provides a readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the vehicle energy distribution method as described above.

[0050] To sum up, 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 control the output power of the battery pack accordingly, and then reasonably complete the energy distribution, which greatly improves the user experience.

[0051] It should be noted that the above modules can be functional modules or program modules, and can be implemented by software or hardware. For modules implemented by hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0052] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0053] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0054] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned 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, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0055] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does 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.

[0056] The above-described embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A vehicle energy distribution method, characterized in that: The method comprises: When it is detected in real time that the vehicle is in a driving state, vehicle information corresponding to the vehicle and environmental information around the vehicle are acquired in real time; The vehicle information and the environmental information are fused and processed based on preset rules to generate a corresponding target data packet in real time, and the historical vehicle energy consumption corresponding to the vehicle is matched in real time in a preset database according to the target data packet; Predicting the predicted vehicle energy consumption adapted to the vehicle in real time according to the target data packet through a preset prediction model, and calculating the actual vehicle energy consumption corresponding to the vehicle in real time according to the historical vehicle energy consumption and the predicted vehicle energy consumption; Calculate the actual output power corresponding to the drive motor inside the vehicle in real time according to the actual vehicle energy consumption, and calculate the power required to be output by the battery pack inside the vehicle in real time according to the actual output power; The step of predicting the predicted vehicle energy consumption adapted to the vehicle in real time according to the target data packet by using a preset prediction model comprises: When the target data packet is acquired in real time, the target data packet is fully scanned to detect the target data sequence contained in the target data packet in real time; Detecting the starting point and the ending point corresponding to the target data sequence in real time, and converting the target data sequence into a corresponding target curve graph in real time according to the starting point and the ending point; According to the target curve graph and the preset prediction model, a predicted vehicle energy consumption suitable for the vehicle is predicted; The step of predicting the predicted vehicle energy consumption adapted to the vehicle according to the target curve graph and the preset prediction model comprises: When the target curve graph is acquired in real time, a number of maximum value points and a number of minimum value points corresponding to the target curve graph are detected in real time; The plurality of maximum value points and the plurality of minimum value points are correspondingly set as target characteristic 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 of the target characteristic values; The step of predicting the predicted vehicle energy consumption adapted to the vehicle in real time according to the preset prediction model and each of the target characteristic values ​​includes: When each of the target feature values ​​is acquired in real time, the target neural network corresponding to the preset prediction model is detected in real time; Detecting in real time a number of corresponding network nodes contained in the target neural network, and detecting in real time the initial network parameters contained in each of the network nodes; Each of the initial network parameters is replaced with each of the target characteristic values ​​so that the preset prediction model outputs the predicted vehicle energy consumption accordingly.

2. The vehicle energy distribution method according to claim 1, characterized in that: The step of fusing the vehicle information and the environment information based on a preset rule to generate a corresponding target data packet in real time includes: When the vehicle information is acquired in real time, the vehicle information is analyzed and processed in real time to detect a plurality of first data sequences contained in the vehicle information; When the environmental information is acquired in real time, the environmental information is analyzed and processed in real time to detect a plurality of second data sequences contained in the environmental information; A plurality of the first data sequences and a plurality of the second data sequences are fused in real time to generate the target data packet accordingly, and the target data packet is unique.

3. The vehicle energy distribution method according to claim 2, characterized in that: The step of performing real-time fusion processing on a plurality of the first data sequences and a plurality of the second data sequences to correspondingly generate the target data packet comprises: When the first data sequence and the second data sequence are respectively acquired, a vehicle model corresponding to the vehicle is detected in real time; According to the vehicle model, a target fusion template adapted to a plurality of the first data sequences and a plurality of the second data sequences is matched in real time in a preset template database; A plurality of the first data sequences and a plurality of the second data sequences are correspondingly embedded into the target fusion template to correspondingly generate the target data packet, and the target fusion template is unique.

4. The vehicle energy distribution method according to claim 3, characterized in that: The step of correspondingly embedding a plurality of the first data sequences and a plurality of the second data sequences into the target fusion template comprises: When the target fusion template is acquired in real time, the target fusion template is fully scanned to detect in real time a plurality of first data channels and a plurality of second data channels correspondingly contained in the target fusion template, wherein the plurality of first data channels and the plurality of second data channels are crisscrossed with each other; adding corresponding first identifiers to a number of the first data sequences respectively, and adding corresponding second identifiers to a number of the second data sequences respectively; According to the first identifier, each of the first data sequences is filled into the interior of each of the first data channels, and according to the second identifier, each of the second data sequences is filled into the interior of each of the second data channels to generate the target data packets accordingly. Each of the first data sequences and each of the second data sequences is unique.

5. A vehicle energy distribution system, characterized in that: For implementing the vehicle energy distribution method according to any one of claims 1 to 4, the system comprises: An acquisition module, used for acquiring vehicle information corresponding to the vehicle and environmental information around the vehicle in real time when it is detected in real time that the vehicle is in a driving state; A fusion module, used for fusing the vehicle information and the environmental information based on preset rules to generate a corresponding target data packet in real time, and matching the historical vehicle energy consumption corresponding to the vehicle in a preset database in real time according to the target data packet; A prediction module, used to predict the predicted vehicle energy consumption adapted to the vehicle in real time according to the target data packet through a preset prediction model, and calculate the actual vehicle energy consumption corresponding to the vehicle in real time according to the historical vehicle energy consumption and the predicted vehicle energy consumption; The calculation module is used to calculate the actual output power corresponding to the driving motor inside the vehicle in real time according to the actual vehicle energy consumption, and calculate the power required to be output by the battery pack inside the vehicle in real time according to the actual output power.

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

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

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