Method, device, vehicle, medium and product for learning pressure-speed mapping relationship

Abstract

The application discloses a pressure-speed mapping relationship learning method and device, a vehicle, a medium and a product, and relates to the technical field of vehicles. The method comprises the following steps: querying a preset initial pressure-speed mapping relationship to obtain a plurality of learning pressure vectors, and detecting a learning speed vector corresponding to each of the plurality of learning pressure vectors; determining a plurality of learned true value vectors based on the learning pressure vectors and the learning speed vectors; in the case where the monotonicity trend corresponding to each of the learning pressure vectors and the learning speed vectors is an upward trend, determining a plurality of to-be-learned true value vectors; screening a target true value vector that matches the to-be-learned true value vector from the plurality of learned true value vectors, and determining a target pressure vector and a target speed vector corresponding to the to-be-learned true value vector based on the target true value vector; and updating the target pressure-speed relationship based on the target pressure vectors and the target speed vectors. The application can significantly improve the learning efficiency of the pressure-speed mapping relationship.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and more particularly to a method for learning pressure-speed mapping relationships, electronic devices, vehicles, storage media, and computer program products. Background Technology

[0002] With the continuous development of the automotive industry, in order to ensure that the vehicle's clutch can work stably under different operating conditions, a special learning process is needed to establish a precise pressure-speed mapping relationship, thereby providing a reliable decision-making basis for the transmission system and ensuring the continuity of the vehicle's power output.

[0003] In related technologies, technicians typically use sensors to collect at least 20 consecutive clutch pressure vectors and oil pump speed vectors during the learning process of pressure-speed mapping, thereby determining the mapping relationship between clutch pressure and oil pump speed based on each clutch pressure vector and oil pump speed vector.

[0004] However, the learning process requires a large number of data points, which leads to a long learning time and low learning efficiency. Summary of the Invention

[0005] The main objective of this application is to provide a method for learning pressure-speed mapping relationships, as well as electronic devices, vehicles, storage media, and computer program products, aiming to solve the technical problem of low learning efficiency of pressure-speed mapping relationships in related technologies.

[0006] To achieve the above objectives, this application proposes a method for learning the pressure-speed mapping relationship, wherein the method for learning the speed mapping relationship includes: Multiple learning pressure vectors are obtained by querying the preset initial pressure-speed mapping relationship, and the learning speed vectors corresponding to each of the multiple learning pressure vectors are detected. Based on each of the learning pressure vectors and each of the learning speed vectors, determine the multiple learned true value vectors contained in the initial pressure-speed mapping relationship, and determine the monotonicity trend corresponding to each of the learning pressure vectors and each of the learning speed vectors; If the monotonicity trend of each learning pressure vector and each learning speed vector is detected to be an upward trend, then the multiple true value vectors to be learned contained in the initial pressure-speed mapping relationship are determined. Filter out the target truth vector that matches the truth vector to be learned from the multiple learned truth vectors, and determine the target pressure vector and target speed vector corresponding to the truth vector to be learned based on the target truth vector; Based on the target pressure vector and the target rotation speed vector, the target pressure-rotation speed relationship is obtained by updating the true value vector to be learned.

[0007] In one embodiment, the step of filtering the target truth vector that matches the truth vector to be learned from the plurality of learned truth vectors includes: Determine the positional relationship between multiple learned truth vectors and the truth vector to be learned, wherein the positional relationship is such that the learned truth vector is in front of the truth vector to be learned or the learned truth vector is behind the truth vector to be learned; Based on the positional relationship, a target truth vector that matches the truth vector to be learned is selected from multiple learned truth vectors.

[0008] In one embodiment, the step of filtering target truth vectors that match the truth vector to be learned from a plurality of learned truth vectors based on the positional relationship includes: When it is detected that the positional relationship is that the learned truth vector is behind the truth vector to be learned, a first truth vector and a second truth vector are selected from multiple learned truth vectors, wherein the second truth vector is behind the first truth vector; or, When it is detected that the positional relationship is that the learned truth vector is in front of the truth vector to be learned, a third truth vector and a fourth truth vector are selected from multiple learned truth vectors, wherein the third truth vector is adjacent to the truth vector to be learned, and the fourth truth vector is adjacent to the third truth vector.

[0009] In one embodiment, the step of determining the target pressure vector and target rotational speed vector corresponding to the target true value vector based on the target true value vector includes: If the target truth vector is detected to be the first truth vector and the second truth vector, the first learning pressure vector and the first learning speed vector corresponding to the first truth vector are determined, and the second learning pressure vector and the second learning speed vector corresponding to the second truth vector are determined. The first calculation weight is determined based on the first truth vector and the second truth vector; The first calculated weight, the first learning pressure vector, and the second learning pressure vector are combined and linear interpolation is performed to obtain the target pressure vector corresponding to the ground value vector to be learned. The first calculated weight, the first learned speed vector, and the second learned speed vector are combined and linear interpolation is performed to obtain the target speed vector corresponding to the true value vector to be learned.

[0010] In one embodiment, the step of determining the target pressure vector and target rotational speed vector corresponding to the target true value vector based on the target true value vector includes: When the target truth vector is detected to be the third truth vector and the fourth truth vector, the third learning pressure vector and the third learning speed vector corresponding to the third truth vector are determined, and the fourth learning pressure vector and the fourth learning speed vector corresponding to the fourth truth vector are determined. Obtain the preset second calculation weight; Linear interpolation is performed by combining the second calculated weight, the third learning pressure vector, and the fourth learning pressure vector to obtain the target pressure vector corresponding to the true value vector to be learned; The second calculated weight, the third learned speed vector, and the fourth learned speed vector are combined to perform linear interpolation to obtain the target speed vector corresponding to the true value vector to be learned.

[0011] In one embodiment, after the step of determining the positional relationship between the plurality of learned truth vectors and the truth vector to be learned, the method further includes: When it is detected that the positional relationship is such that the learned truth vector is on both sides of the truth vector to be learned, the fifth truth vector and the sixth truth vector are selected from the multiple learned truth vectors, wherein the fifth truth vector is in front of the truth vector to be learned and the sixth truth vector is behind the truth vector to be learned. Determine the fifth learning pressure vector and the fifth learning speed vector corresponding to the fifth truth vector, and determine the sixth learning pressure vector and the sixth learning speed vector corresponding to the sixth truth vector; The third calculation weight is determined based on the fifth truth vector and the sixth truth vector; Linear interpolation is performed by combining the third calculated weight, the fifth learning pressure vector, and the sixth learning pressure vector to obtain the target pressure vector corresponding to the true value vector to be learned; The target speed vector corresponding to the true value vector to be learned is obtained by combining the third calculated weight, the fifth learned speed vector, and the sixth learned speed vector through linear interpolation.

[0012] In addition, to achieve the above objectives, this application also proposes an electronic device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the learning method for the pressure-speed mapping relationship as described above.

[0013] In addition, to achieve the above objectives, this application also proposes a vehicle that includes the electronic equipment described above.

[0014] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the learning method for the pressure-speed mapping relationship as described above.

[0015] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the learning method for the pressure-speed mapping relationship as described above.

[0016] The pressure-speed mapping relationship learning method provided in this application embodiment obtains multiple learning pressure vectors by querying a preset initial pressure-speed mapping relationship, and detects the learning speed vectors corresponding to each of the multiple learning pressure vectors; based on each learning pressure vector and each learning speed vector, it determines multiple learned truth vectors contained in the initial pressure-speed mapping relationship, and determines the monotonicity trend corresponding to each learning pressure vector and each learning speed vector; when the monotonicity trend corresponding to each learning pressure vector and each learning speed vector is detected to be an upward trend, it determines multiple unlearned truth vectors contained in the initial pressure-speed mapping relationship; it filters target truth vectors that match the unlearned truth vectors from the multiple learned truth vectors, and determines the target pressure vector and target speed vector corresponding to the unlearned truth vector based on the target truth vector; based on each target pressure vector and each target speed vector, it updates each unlearned truth vector to obtain the target pressure-speed relationship.

[0017] In this embodiment, when learning the pressure-speed mapping relationship, the electronic device can first query a preset initial pressure-speed mapping relationship to determine multiple learning pressure vectors that need to be learned, and detect the learning speed vectors generated under the multiple learning pressure vectors. Then, the electronic device associates each learning pressure vector with each learning speed vector to determine the learned true value vector within the initial pressure-speed mapping relationship that correctly represents the mapping relationship between the learning speed vector and the learning pressure vector. Simultaneously, the electronic device determines the monotonicity trend generated between each learning pressure vector and each learning speed vector. Then, when the electronic device detects that the monotonicity trend is an upward trend... In this case, the electronic device determines the learning truth vector of the unlearned speed-pressure mapping relationship within the initial pressure-speed mapping relationship. Then, the electronic device filters multiple learned truth vectors to obtain the target truth vector for deriving the correct pressure-speed mapping relationship corresponding to the learning truth vector. Based on the learned pressure vector and learned speed vector corresponding to the target truth vector, the electronic device calculates the target pressure vector and target speed vector corresponding to the learning truth vector. Finally, the electronic device updates the initial pressure-speed mapping relationship based on the target pressure vector and target speed vector to obtain the target pressure-speed mapping relationship, thereby completing the learning operation of the pressure-speed mapping relationship.

[0018] Thus, this application solves the technical problem of low learning efficiency of pressure-speed mapping relationship in related technologies. Specifically, this application, when detecting that the monotonicity trend of the pressure vector and speed vector corresponding to the learned truth vector is upward, calculates the pressure vector and speed vector corresponding to the unknown truth vector to be learned based on the known pressure vector and speed vector corresponding to the learned truth vector. This allows the electronic device to calculate the truth vector of the remaining unlearned data points based only on the truth vector of a small number of learned data points without having to learn all the data points in the pressure-speed mapping relationship learning process. This significantly reduces the number of data points required in the pressure-speed mapping relationship learning process, significantly reduces the time spent in the learning process, and improves the learning efficiency of the pressure-speed mapping relationship. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1It is a schematic flow chart provided for the first embodiment of the learning method of the pressure - speed mapping relationship of the present application.

[0022] Figure 2 It is a schematic structural diagram of a transmission control system involved in an embodiment of the learning method of the pressure - speed mapping relationship of the present application.

[0023] Figure 3 It is a detailed schematic flow chart of the learning method of the pressure - speed mapping relationship of the present application.

[0024] Figure 4 It is a schematic module structure diagram of the learning system of the pressure - speed mapping relationship in an embodiment of the present application.

[0025] Figure 5 It is a schematic device structure diagram of the hardware operating environment involved in the learning method of the pressure - speed mapping relationship in an embodiment of the present application.

[0026] The realization of the purpose, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. Specific embodiments

[0027] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0028] In order to better understand the technical solutions of the present application, the following will be described in detail with reference to the accompanying drawings of the specification and specific embodiments.

[0029] In this embodiment, for the sake of convenience of description, the following takes mobile terminals, data storage control terminals, PCs, etc. configured in in - vehicle electronic devices or connected to an electronic control unit supporting the electronic device as the execution subject for elaboration. It can be understood that the electronic device can be connected to the transmission control system in the vehicle to collect the pressure vector and speed vector generated during the learning process of the pressure - speed mapping relationship. Among them, please refer to Figure 2 , Figure 2 It is a schematic structural diagram of a transmission control system involved in an embodiment of the learning method of the pressure - speed mapping relationship of the present application. As Figure 2 shown, the transmission control system can include an oil pump motor, an oil pump, a pressure sensor and a clutch. Among them, the oil pump motor is used to rotate the oil pump, and when the oil pump rotates, the clutch pressure increases, and the pressure sensor is used to detect the clutch pressure.

[0030] Based on the above - mentioned electronic device, the overall concept of the learning method of the pressure - speed mapping relationship of the present application is proposed here.

[0031] With the continuous development of the automotive industry, to ensure the stable operation of vehicle clutches under different working conditions, a specialized learning process is needed to establish a precise pressure-speed mapping relationship. This provides a reliable decision-making basis for the transmission system and ensures the continuity of the vehicle's power output. In related technologies, technicians typically use sensors to collect at least 20 consecutive clutch pressure vectors and oil pump speed vectors during the pressure-speed mapping relationship learning process. Based on these vectors, the mapping relationship between clutch pressure and oil pump speed is determined. However, this learning process requires a large number of data points, resulting in a significant time consumption and low learning efficiency.

[0032] To address the above issues, this application provides a method for learning a pressure-speed mapping relationship. The method includes: querying a preset initial pressure-speed mapping relationship to obtain multiple learning pressure vectors; detecting the learning speed vectors corresponding to each of the multiple learning pressure vectors; determining multiple learned ground truth vectors included in the initial pressure-speed mapping relationship based on each of the learning pressure vectors and each of the learning speed vectors, and determining the monotonicity trend corresponding to each of the learning pressure vectors and each of the learning speed vectors; when the monotonicity trend corresponding to each of the learning pressure vectors and each of the learning speed vectors is detected to be an upward trend, determining multiple ground truth vectors to be learned included in the initial pressure-speed mapping relationship; filtering target ground truth vectors that match the ground truth vectors to be learned from the multiple learned ground truth vectors, and determining the target pressure vector and target speed vector corresponding to the ground truth vectors to be learned based on the target ground truth vectors; and updating each ground truth vector to be learned based on each target pressure vector and each target speed vector to obtain a target pressure-speed relationship.

[0033] Thus, this application solves the technical problem of low learning efficiency of pressure-speed mapping relationship in related technologies. Specifically, this application, when detecting that the monotonicity trend of the pressure vector and speed vector corresponding to the learned truth vector is upward, calculates the pressure vector and speed vector corresponding to the unknown truth vector to be learned based on the known pressure vector and speed vector corresponding to the learned truth vector. This allows the electronic device to calculate the truth vector of the remaining unlearned data points based only on the truth vector of a small number of learned data points without having to learn all the data points in the pressure-speed mapping relationship learning process. This significantly reduces the number of data points required in the pressure-speed mapping relationship learning process, significantly reduces the time spent in the learning process, and improves the learning efficiency of the pressure-speed mapping relationship.

[0034] Based on the overall concept of the pressure-speed rotational speed mapping relationship learning method of this application, the embodiments of this application provide a method for learning the pressure-speed rotational speed mapping relationship, referring to... Figure 1, Figure 1 This is a flowchart illustrating the first embodiment of the pressure-speed rotational speed mapping relationship learning method of this application. In this embodiment, the pressure-speed rotational speed mapping relationship learning method includes steps S10~S50: Step S10: Query the preset initial pressure-speed mapping relationship to obtain multiple learning pressure vectors, and detect the learning speed vector corresponding to each of the multiple learning pressure vectors; Step S20: Based on each learned pressure vector and each learned speed vector, determine the multiple learned true value vectors contained in the initial pressure-speed mapping relationship, and determine the monotonicity trend corresponding to each learned pressure vector and each learned speed vector; Step S30: If the monotonicity trend of each learning pressure vector and each learning speed vector is detected to be an upward trend, determine the multiple true value vectors to be learned contained in the initial pressure-speed mapping relationship. Step S40: Select the target truth vector that matches the truth vector to be learned from multiple learned truth vectors, and determine the target pressure vector and target speed vector corresponding to the truth vector to be learned based on the target truth vector; It should be noted that the initial pressure-speed mapping relationship is a pre-defined mapping table containing at least 20 fixed pressure scales and corresponding learned pressure vectors. Specifically, this initial pressure-speed mapping relationship can contain three 20-dimensional vectors: the initial pressure vector AxisPIncRaw (i.e., the pressure vector under the fixed pressure scale, such as [0, 1, 2, 3, ..., 19] bar), the learned speed vector AxisSpdIncRaw, and the truth vector BAxisPSel_T. Furthermore, the learned truth vector is the vector element with a value of "1" in multiple dimensions of the initial pressure-speed mapping relationship. This means that the learned truth vector accurately represents the learned speed vector and learned pressure vector for the corresponding data point. Similarly, the truth vector to be learned is the vector element with a value of "0" in multiple dimensions of the initial pressure-speed mapping relationship. This means that the truth vector to be learned does not accurately represent the corresponding speed vector and pressure vector for the corresponding data point. Furthermore, the learned pressure vector is the clutch pressure value corresponding to the learned true value vector in the initial pressure-speed mapping relationship; similarly, the learned speed vector is the oil pump speed value corresponding to the learned true value vector in the initial pressure-speed mapping relationship. Additionally, the monotonicity trend represents the changing pattern of the learned pressure vector and learned speed vector after multiple learned true value vectors are sorted by index order. An upward trend indicates that the learned pressure vector at the end of the sorted sequence is greater than the learned pressure vector at the beginning of the sorted sequence; similarly, a downward trend indicates that the learned pressure vector at the beginning of the sorted sequence is greater than the learned pressure vector at the end of the sorted sequence.

[0035] In this embodiment, when the electronic device performs the pressure-speed mapping relationship learning operation, it first reads its own configured storage module to obtain a preset initial pressure-speed mapping relationship, determines the initial pressure-speed mapping relationship, and determines multiple learning pressure vectors in the initial pressure-speed mapping relationship. The electronic device controls the oil pump motor to adjust the clutch pressure to the scale corresponding to each learning pressure vector and stabilize it, thereby detecting the learning speed vector matched by each learning pressure vector. Then, based on the acquired learning pressure vectors and learning speed vectors, the electronic device marks the truth vector corresponding to the detected learning pressure vector in the initial pressure-speed mapping relationship as a learned truth vector with a value of 1. At the same time, the electronic device sorts the learned truth vectors, thereby comparing the learning pressure vectors corresponding to each of two adjacent learned truth vectors and comparing the learning speed vectors corresponding to each of two adjacent learned truth vectors, and then detecting... If the learning pressure vector of a later point is greater than that of a previous point, and the learning speed vector of a later point is also greater than that of a previous point, then the monotonicity trend of each learning pressure vector and each learning speed vector is determined to be an upward trend. Next, the electronic device determines multiple truth vectors with values ​​of 0 corresponding to multiple data points within the pressure-speed mapping relationship. The electronic device then selects the foremost truth vector from among the selected truth vectors with values ​​of 0, and designates this foremost truth vector as the learning truth vector for the corresponding pressure and speed values. Then, the electronic device filters each learned truth vector for the learning truth vector to be calculated, selecting the target truth vector for learning the learning truth vector. Finally, based on a preset linear interpolation algorithm, the learning pressure vector and learning speed vector corresponding to each target truth vector are processed to obtain the target pressure vector and target speed vector corresponding to the learning truth vector.

[0036] For example, please refer to Figure 3 , Figure 3 A detailed flowchart illustrating the learning method for the pressure-speed mapping relationship in this application is shown below. Figure 3As shown, when the electronic device performs the pressure-speed mapping relationship learning operation, it first reads the aforementioned storage module to obtain the preset initial pressure-speed mapping relationship, determines the initial pressure-speed mapping relationship, and determines that there are 20 truth vectors BAxisPSel_T that need to be learned within the initial pressure-speed mapping relationship. At this time, the electronic device reads the learning pressure vectors corresponding to the 7th, 8th, and 20th initial pressure vectors AxisPIncRaw7, AxisPIncRaw8, and AxisPIncRaw20, and controls the oil pump motor to adjust the clutch pressure to the scale corresponding to the learning pressure vectors AxisPIncRaw7, AxisPIncRaw8, and AxisPIncRaw20. And stabilize, simultaneously, the electronic device acquires the learned speed vectors AxisSpdIncRaw7, AxisSpdIncRaw8, and AxisSpdIncRaw20 generated under the learned pressure vectors AxisPIncRaw7, AxisPIncRaw8, and AxisPIncRaw20 via the speed sensor. Then, the electronic device further uses the learned pressure vectors AxisPIncRaw7, AxisPIncRaw8, and AxisPIncRaw20, and the learned speed vectors AxisSpdIncRaw7, AxisSpdIncRaw8, and AxisSpdIncRaw20 to map the true value vector B within the initial pressure-speed mapping relationship. The values ​​of AxisPSel_T7, truth vector BAxisPSel_T8, and truth vector BAxisPSel_T20 are marked as 1, while other truth vectors within the initial pressure-speed vector are marked as 0. At this point, the electronic device determines the truth vector BAxisPSel_T within the initial pressure-speed vector as BAxisPSel_T = 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1. The electronic device then selects the learned truth vectors BAxisPSel_T7 and BAxisPSel_T8, and sets the learned truth vectors BAxisPSel_T7 and BAxisPSel_T20 to 1. The speed vectors AxisSpdIncRaw7 and AxisSpdIncRaw8 corresponding to l_T8 are compared. Simultaneously, the pressure vectors AxisPIncRaw7 and AxisPIncRaw8 corresponding to the learned true value vectors BAxisPSel_T7 and BAxisPSel_T8 are compared. Therefore, if it is detected that both speed vector AxisSpdIncRaw7 and pressure vector AxisSpdIncRaw8 are less than each other, the monotonicity trend is determined to be an upward trend. At this point, the electronic device will consider the true value vectors with values ​​of 0.The first data point at the forefront is used as the ground truth vector to be learned. Then, the electronic device filters the ground truth vectors BAxisPSel_T for each dimension based on the ground truth vector BAxisPSel_T1 to be calculated. The learned ground truth vectors BAxisPSel_T7 and BAxisPSel_T8, which are closest to the ground truth vector BAxisPSel_T1, are used as the target ground truth vectors for calculation. Then, the electronic device calculates the target rotational speed vector AxisSpdIncRaw1 corresponding to the ground truth vector BAxisPSel_T1 based on the rotational speed vectors AxisSpdIncRaw7 and AxisSpdIncRaw8, and the target pressure vector AxisPIncRaw1 corresponding to the ground truth vector BAxisPSel_T1 based on the pressure vectors AxisPIncRaw7 and AxisPIncRaw8.

[0037] In this way, the electronic device can accurately determine the learned truth vector of known accurate data and the truth vector to be learned of the data to be solved. Based on the rotational speed vector and pressure vector connected by the truth values, it detects the monotonicity trend. Thus, when the monotonicity trend is determined to be an upward trend, that is, the changes in pressure and rotational speed conform to physical laws, the learned truth vector is matched with the truth vector to be learned. Then, a linear interpolation algorithm is used to process a small number of pressure and rotational speed vectors, thereby deriving the pressure and rotational speed vectors corresponding to the truth vectors with a value of 0. In this way, without completing the learning of all data points, the truth vectors of the remaining unlearned data points can be derived only from the truth vectors of a small number of learned data points. This greatly reduces the number of data points required in the learning process of the pressure-rotational speed mapping relationship, significantly reduces the time spent in the learning process, and improves the learning efficiency of the pressure-rotational speed mapping relationship.

[0038] In one feasible implementation, the step of "selecting target truth vectors that match the truth vector to be learned from the plurality of learned truth vectors" in step S40 above may specifically include steps S401 to S402: Step S401: Determine the positional relationship between the plurality of learned truth vectors and the truth vector to be learned, wherein the positional relationship is that the learned truth vector is in front of the truth vector to be learned or the learned truth vector is behind the truth vector to be learned; Step S402: Based on the positional relationship, select a target truth vector that matches the truth vector to be learned from among the multiple learned truth vectors.

[0039] It should be noted that this positional relationship refers to the relative index order of the learned truth vector and the truth vector to be learned among multiple truth vectors. That is, if the index value of the learned truth vector is less than the index value of the truth vector to be learned, the learned truth vector precedes the truth vector to be learned; similarly, if the index value of the learned truth vector is greater than the index value of the truth vector to be learned, the learned truth vector follows the truth vector to be learned. Furthermore, the target truth vector is a specific learned truth vector selected from among all the learned truth vectors, used to derive the truth vector to be learned.

[0040] In this embodiment, when the electronic device detects that the monotonicity trend among the learned truth vectors is upward, it further determines the indices corresponding to the known learned truth vectors and the truth vectors to be learned, and sorts the learned truth vectors and the truth vectors to be learned according to the indices to determine the sorting relationship between the learned truth vectors and the truth vectors to be learned. The electronic device then determines the position of each learned truth vector in front of or behind the truth vector to be learned based on the sorting relationship, so as to obtain the relative positional relationship between the learned truth vectors and the truth vectors to be learned. Finally, the electronic device filters each learned truth vector based on the relative positional relationship, thereby selecting the target truth vector for calculating the truth vector to be learned.

[0041] For example, if an electronic device determines that the read 20-dimensional truth vector BAxisPSel_T is BAxisPSel_T=0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1, and the monotonicity trend among the learned truth vectors is upward, then it determines that the indices corresponding to each learned truth vector are 7, 8, and 20, and determines that the index of the foremost truth vector to be learned is 1. The electronic device then compares the indices of each learned truth vector with the truth vector to be learned, thereby determining that the index vectors of the learned truth vectors BAxisPSel_T7, BAxisPSel_T8, and BAxisPSel_T20 are all higher than the truth vector to be learned BAxisPSel_T. l_T1 is used to determine that the learned truth vectors BAxisPSel_T7, BAxisPSel_T8, and BAxisPSel_T20 are all located after the truth vector to be learned, BAxisPSel_T1. The positional relationship between each learned truth vector and the truth vector to be learned is determined to be that the learned truth vector is located after the truth vector to be learned. Finally, the electronic device filters the learned truth vectors BAxisPSel_T7, BAxisPSel_T8, and BAxisPSel_T20 based on this positional relationship to determine that the learned truth vectors BAxisPSel_T7 and BAxisPSel_T8, which are adjacent to the truth vector to be learned, are the target truth vectors for calculation. Similarly, if the 20-dimensional truth vector BAxisPSel_T = 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, and the monotonicity trend among the learned truth vectors is upward, then the electronic device determines the indices of each learned truth vector as 1 and 2, and the index of the foremost truth vector to be learned as 3. The electronic device then compares the indices of each learned truth vector with those of the truth vector to be learned, thereby determining the indices of the learned truth vectors BAxisPSel_T1 and BAxisPSel_T2. Since the learned truth vectors are all smaller than the truth vector to be learned, BAxisPSel_T3, it is determined that the learned truth vectors BAxisPSel_T1 and BAxisPSel_T2 are both in front of the truth vector to be learned, thus determining the positional relationship between each learned truth vector and the truth vector to be learned as the learned truth vector is in front of the truth vector to be learned. Based on this positional relationship, the learned truth vectors BAxisPSel_T1 and BAxisPSel_T2, which are adjacent to the truth vector to be learned, are determined as the target truth vectors for calculation.

[0042] In this way, the electronic device can accurately determine the learned truth vector of known accurate data and the learned truth vector of the data to be solved. Based on the rotational speed vector and pressure vector connected by the truth values, it can detect the monotonicity trend. Thus, when the monotonicity trend is determined to be an upward trend, that is, the changes in pressure and rotational speed conform to physical laws, the learned truth vector is matched with the learned truth vector. This provides an accurate input basis for the subsequent derivation of the correct pressure vector and rotational speed vector corresponding to the learned truth vector, thereby ensuring the accuracy of the pressure-rotational speed mapping relationship learning process and improving the efficiency of the pressure-rotational speed mapping relationship learning.

[0043] In one feasible implementation, step S402 above may specifically include steps S4021 to S4022: Step S4021: When it is detected that the positional relationship is that the learned truth vector is behind the truth vector to be learned, a first truth vector and a second truth vector are selected from multiple learned truth vectors, wherein the second truth vector is behind the first truth vector; or, Step S4021: When it is detected that the positional relationship is that the learned truth vector is in front of the truth vector to be learned, a third truth vector and a fourth truth vector are selected from multiple learned truth vectors, wherein the third truth vector is adjacent to the truth vector to be learned, and the fourth truth vector is adjacent to the third truth vector.

[0044] In this embodiment, after determining the positional relationship between each learned truth vector and the truth vector to be learned, if the electronic device detects that the positional relationship is that each learned truth vector is behind the truth vector to be learned, then it further arbitrarily selects a first truth vector behind the truth vector to be learned and a second truth vector behind the first truth vector as target truth vectors for learning the truth vector to be learned. Similarly, after determining the positional relationship between each learned truth vector and the truth vector to be learned, if the electronic device detects that the positional relationship is that each learned truth vector is in front of the truth vector to be learned, then it first selects the third truth vector that is in front of and adjacent to the truth vector to be learned, and determines the fourth truth vector that is adjacent to the third truth vector and in front of the truth vector to be learned as the target truth vector for learning the truth vector to be learned.

[0045] For example, if an electronic device determines that the read 20-dimensional truth vector BAxisPSel_T = 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, and determines that the positional relationship is that the learned truth vectors BAxisPSel_T7, BAxisPSel_T8, and BAxisPSel_T20 are all behind the truth vector BAxisPSel_T1 to be learned, then it can directly take the learned truth vector BAxisPSel_T7, which is behind the truth vector BAxisPSel_T1 to be learned, as the first truth vector, and take the learned truth vector BAxisPSel_T8, which is behind the truth vector BAxisPSel_T1 to be learned and the learned truth vector BAxisPSel_T7, as the second truth vector. or, If the electronic device determines that the read 20-dimensional truth vector BAxisPSel_T = 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, and the electronic device determines that the positional relationship is that the learned truth vectors BAxisPSel_T1 and BAxisPSel_T2 are in front of the truth vector BAxisPSel_T3 to be learned, then the electronic device can determine the learned truth vector BAxisPSel_T2, which is adjacent to the truth vector BAxisPSel_T3 to be learned, as the third truth vector, and determine the learned truth vector BAxisPSel_T1, which is in front of the truth vector BAxisPSel_T3 to be learned and adjacent to the learned truth vector BAxisPSel_T2, as the fourth truth vector.

[0046] In this way, the electronic device can accurately determine the learned truth vector of known accurate data and the learned truth vector of the data to be solved. Based on the rotational speed vector and pressure vector connected by the truth values, it can detect the monotonicity trend. Thus, when the monotonicity trend is determined to be an upward trend, that is, the changes in pressure and rotational speed conform to physical laws, the learned truth vector is matched with the learned truth vector. This provides an accurate input basis for the subsequent derivation of the correct pressure vector and rotational speed vector corresponding to the learned truth vector, thereby ensuring the accuracy of the pressure-rotational speed mapping relationship learning process and improving the efficiency of the pressure-rotational speed mapping relationship learning.

[0047] In one feasible implementation, the step S40 above, "determining the target pressure vector and target rotational speed vector corresponding to the target true value vector to be learned based on the target true value vector," may specifically include steps S403 to S406: Step S403: When the target truth vector is detected to be the first truth vector and the second truth vector, determine the first learning pressure vector and the first learning speed vector corresponding to the first truth vector, and determine the second learning pressure vector and the second learning speed vector corresponding to the second truth vector; Step S404: Determine the first calculation weight based on the first truth vector and the second truth vector; Step S405: Perform linear interpolation by combining the first calculated weight, the first learning pressure vector, and the second learning pressure vector to obtain the target pressure vector corresponding to the ground truth vector to be learned; Step S406: Perform linear interpolation by combining the first calculated weight, the first learned rotational speed vector, and the second learned rotational speed vector to obtain the target rotational speed vector corresponding to the true value vector to be learned.

[0048] In this embodiment, after determining the target truth vector for calculation, if the electronic device detects that the target truth vector to be used is the first truth vector and the second truth vector mentioned above, it first reads the first learning pressure vector and the first learning speed vector corresponding to the first truth vector, and then reads the second learning pressure vector and the second learning speed vector corresponding to the second truth vector. After that, the electronic device determines the index with the larger value in the first truth vector and the second truth vector, and determines the first calculation weight based on the index value. Finally, the electronic device uses a preset linear interpolation algorithm to calculate the first calculation weight, the first learning pressure vector, and the second learning pressure vector to obtain the target pressure vector corresponding to the truth vector to be learned. Similarly, the electronic device uses a preset linear interpolation algorithm to calculate the first calculation weight, the first learning speed vector, and the second learning speed vector to obtain the target speed vector corresponding to the truth vector to be learned.

[0049] For example, such as Figure 3As shown, after determining the target truth vector, if the electronic device detects the acquired 20-dimensional truth vector BAxisPSel_T = 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, and determines that the target truth vector to be used is the aforementioned first truth vector BAxisPSel_T7 and second truth vector BAxisPSel_T8, then it first determines the index value with the larger value, 8, from the first truth vector BAxisPSel_T7 and the second truth vector BAxisPSel_T8. Based on 8, it then determines the first calculation weight as 1 / (8+1). Simultaneously, the electronic device reads the first truth vector BAxisPSel_T7... The electronic device reads the first learning pressure vector AxisPIncRaw7 and the first learning rotational speed vector AxisSpdIncRaw7 corresponding to l_T7, and reads the second learning pressure vector AxisPIncRaw8 and the second learning rotational speed vector AxisSpdIncRaw8 corresponding to the second true value vector BAxisPSel_T8. Finally, the electronic device performs linear interpolation on the first learning pressure vector AxisPIncRaw7 and the second learning pressure vector AxisPIncRaw8 based on the first calculated weight to obtain the target pressure vector AxisPIncForPtoSpd1=1 / (8+1) corresponding to the true value vector BAxisPSel_T1 to be learned. (AxisPIncRaw8-AxisPIncRaw7)+AxisPIncRaw7; Similarly, the electronic device performs linear interpolation on the first learned rotational speed vector AxisSpdIncRaw7 and the second learned rotational speed vector AxisSpdIncRaw8 based on the first calculated weight, so as to obtain the target rotational speed vector AxisSpdIncForPtoSpd1=1 / (8+1) corresponding to the ground value vector BAxisPSel_T1 to be learned. (AxisSpdIncRaw8-AxisSpdIncRaw7)+AxisSpdIncRaw7.

[0050] Furthermore, in this embodiment and another embodiment, such as Figure 3 As shown, if the electronic device determines that the first truth vector obtained is the preset learned truth vector BAxisPSel_T[n] and the second truth vector obtained is the preset learned truth vector BAxisPSel_T[m], and the electronic device determines that the index value of the first truth vector and the second truth vector is larger than that of m, then the electronic device can determine the first calculation weight as 1 / (m+1) based on the index value.

[0051] In this way, electronic devices can use linear interpolation algorithms to process a small number of pressure vectors and rotational speed vectors, thereby deriving the pressure vector and rotational speed vector corresponding to the true value vector with a value of 0. This allows electronic devices to derive the true value vector of the remaining unlearned data points based only on the true value vector of a small number of learned data points without having to learn all the data points. This significantly reduces the number of data points required in the learning process of the pressure-rotational speed mapping relationship, significantly reduces the time spent in the learning process, and improves the learning efficiency of the pressure-rotational speed mapping relationship.

[0052] In one feasible implementation, the step S40 above, "determining the target pressure vector and target rotational speed vector corresponding to the target true value vector based on the target true value vector," may further include steps S407-S410: Step S407: When the target truth vector is detected to be the third truth vector and the fourth truth vector, determine the third learning pressure vector and the third learning speed vector corresponding to the third truth vector, and determine the fourth learning pressure vector and the fourth learning speed vector corresponding to the fourth truth vector; Step S408: Obtain the preset second calculation weight; Step S409: Perform linear interpolation by combining the second calculated weight, the third learning pressure vector, and the fourth learning pressure vector to obtain the target pressure vector corresponding to the ground truth vector to be learned; Step S410: Perform linear interpolation by combining the second calculated weight, the third learned rotational speed vector, and the fourth learned rotational speed vector to obtain the target rotational speed vector corresponding to the true value vector to be learned.

[0053] In this embodiment, after determining the target truth vector for calculation, if the electronic device detects that the target truth vector to be used is the aforementioned third truth vector and fourth truth vector, it first reads the third learning pressure vector and third learning speed vector corresponding to the third truth vector, and then reads the fourth learning pressure vector and fourth learning speed vector corresponding to the fourth truth vector. After that, the electronic device reads the storage module to obtain the preset second calculation weight. Finally, the electronic device uses a preset linear interpolation algorithm to calculate the second calculation weight, the third learning pressure vector, and the fourth learning pressure vector to obtain the target pressure vector corresponding to the truth vector to be learned. Similarly, the electronic device uses a preset linear interpolation algorithm to calculate the second calculation weight, the third learning speed vector, and the fourth learning speed vector to obtain the target speed vector corresponding to the truth vector to be learned.

[0054] For example, such as Figure 3As shown, after determining the target truth vector, if the electronic device detects the acquired 20-dimensional truth vector BAxisPSel_T = 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, and determines that the target truth vectors to be used are the aforementioned third truth vector BAxisPSel_T2 and fourth truth vector BAxisPSel_T1, then it first reads the aforementioned storage module to obtain the preset second weight coefficient of 2. Simultaneously, the electronic device reads the third learning pressure vector AxisPIn corresponding to the third truth vector BAxisPSel_T2. The electronic device reads the third learning speed vector AxisSpdIncRaw2 and the fourth learning pressure vector AxisPIncRaw1 and the fourth learning speed vector AxisSpdIncRaw1 corresponding to the fourth true value vector BAxisPSel_T1. Finally, the electronic device performs linear interpolation on the third learning pressure vector AxisPIncRaw2 and the fourth learning pressure vector AxisPIncRaw1 based on the second calculation weight to obtain the target pressure vector AxisPIncForPtoSpd1=2 corresponding to the true value vector BAxisPSel_T3 to be learned. (AxisPIncRaw2-AxisPIncRaw1)+AxisPIncRaw1; Similarly, the electronic device performs linear interpolation on the third learned rotational speed vector AxisSpdIncRaw3 and the fourth learned rotational speed vector AxisSpdIncRaw1 based on the second calculated weights to obtain the target rotational speed vector AxisSpdIncForPtoSpd1=2 corresponding to the ground truth vector BAxisPSel_T3 to be learned. (AxisSpdIncRaw2-AxisSpdIncRaw1)+AxisSpdIncRaw1.

[0055] Furthermore, in this embodiment and another embodiment, such as Figure 3 As shown, if the electronic device determines that the obtained third truth vector is the preset learned truth vector BAxisPSel_T[i-1] and the obtained fourth truth vector is the preset learned truth vector BAxisPSel_T[i-2], the electronic device can also directly determine that the second calculation weight is 2.

[0056] In this way, electronic devices can use linear interpolation algorithms to process a small number of pressure vectors and rotational speed vectors, thereby deriving the pressure vector and rotational speed vector corresponding to the true value vector with a value of 0. This allows electronic devices to derive the true value vector of the remaining unlearned data points based only on the true value vector of a small number of learned data points without having to learn all the data points. This significantly reduces the number of data points required in the learning process of the pressure-rotational speed mapping relationship, significantly reduces the time spent in the learning process, and improves the learning efficiency of the pressure-rotational speed mapping relationship.

[0057] Step S50: Based on each target pressure vector and each target rotation speed vector, update each ground value vector to be learned to obtain the target pressure-rotation speed relationship.

[0058] It should be noted that the final pressure-speed mapping relationship formed by the target pressure-speed relationship specifically includes an updated and integrated 20-dimensional truth vector, a 20-dimensional learned pressure vector, and a 20-dimensional learned speed vector. It can be understood that within the target pressure-speed relationship, the values ​​of the 20-dimensional truth vector BAxisPSel_T are all 1.

[0059] In this embodiment, after calculating the target pressure vector and the target speed vector, the electronic device writes each target pressure vector and target speed vector into the aforementioned initial pressure-speed mapping relationship. This fills in the corresponding target pressure vector and target speed vector in each learning truth vector within the initial pressure-speed mapping relationship. At the same time, the electronic device modifies the value of the learning truth vector filled with the target pressure vector and target speed vector from 0 to 1, thereby obtaining a target pressure-speed mapping relationship that contains all correct pressure-speed mapping relationships.

[0060] In this embodiment, when the electronic device performs the pressure-speed mapping relationship learning operation, it first reads its own configured storage module to obtain a preset initial pressure-speed mapping relationship and determines the initial pressure-speed mapping relationship. Multiple learning pressure vectors are then determined within this initial pressure-speed mapping relationship. The electronic device controls the oil pump motor to adjust the clutch pressure to the scale corresponding to each learning pressure vector and stabilize it, thereby detecting the learning speed vector matched by each learning pressure vector. Then, based on the acquired learning pressure vectors and learning speed vectors, the electronic device marks the truth vector corresponding to the detected learning pressure vector in the initial pressure-speed mapping relationship as a learned truth vector with a value of 1. Simultaneously, the electronic device sorts the learned truth vectors, comparing the learning pressure vectors corresponding to two adjacent learned truth vectors and comparing the learning speed vectors corresponding to two adjacent learned truth vectors. If it detects that the learning pressure vector at a later point is greater than the learning pressure vector at a previous point, and the learning speed vector at a later point is greater than the learning speed vector at a previous point, it determines that the monotonicity trend of each learning pressure vector and each learning speed vector is an upward trend. Afterwards, the electronic device... The sub-device identifies multiple truth vectors with values ​​of 0 corresponding to multiple data points within the pressure-speed mapping relationship. The electronic device then selects the leading truth vector from among these zero-valued vectors and designates it as the learning truth vector for the corresponding pressure and speed values. Next, the electronic device filters each learned truth vector to identify the target truth vectors for learning the learning truth vectors. Finally, it processes the learning pressure and speed vectors corresponding to each target truth vector using a preset linear interpolation algorithm. The electronic device calculates the target pressure vector and target speed vector corresponding to the true value vector to be learned. Finally, after calculating the target pressure vector and target speed vector, the electronic device writes each target pressure vector and target speed vector into the above-mentioned initial pressure-speed mapping relationship. This fills the corresponding target pressure vector and target speed vector into each true value vector to be learned in the initial pressure-speed mapping relationship. At the same time, the electronic device changes the value of the true value vector to be learned that has been filled with the target pressure vector and target speed vector from 0 to 1, thereby obtaining the target pressure-speed mapping relationship that contains all the correct pressure-speed mapping relationships.

[0061] Thus, this application solves the technical problem of low learning efficiency of pressure-speed mapping relationship in related technologies. Specifically, this application, when detecting that the monotonicity trend of the pressure vector and speed vector corresponding to the learned truth vector is upward, calculates the pressure vector and speed vector corresponding to the unknown truth vector to be learned based on the known pressure vector and speed vector corresponding to the learned truth vector. This allows the electronic device to calculate the truth vector of the remaining unlearned data points based only on the truth vector of a small number of learned data points without having to learn all the data points in the pressure-speed mapping relationship learning process. This significantly reduces the number of data points required in the pressure-speed mapping relationship learning process, significantly reduces the time spent in the learning process, and improves the learning efficiency of the pressure-speed mapping relationship.

[0062] Based on the first embodiment of this application, a second embodiment of this application is proposed herein. In this second embodiment, content that is the same as or similar to the embodiments described above can be referred to the above description and will not be repeated hereafter. Furthermore, after step S401, this application may further include steps A10 to A50: Step A10: When it is detected that the positional relationship is that the learned truth vector is on both sides of the truth vector to be learned, the fifth truth vector and the sixth truth vector are selected from the multiple learned truth vectors, wherein the fifth truth vector is in front of the truth vector to be learned and the sixth truth vector is behind the truth vector to be learned. Step A20: Determine the fifth learning pressure vector and the fifth learning speed vector corresponding to the fifth truth vector, and determine the sixth learning pressure vector and the sixth learning speed vector corresponding to the sixth truth vector; Step A30: Determine the third calculation weight based on the fifth truth vector and the sixth truth vector; Step A40: Perform linear interpolation by combining the third calculated weight, the fifth learning pressure vector, and the sixth learning pressure vector to obtain the target pressure vector corresponding to the ground truth vector to be learned; Step A50: Perform linear interpolation by combining the third calculated weight, the fifth learned rotational speed vector, and the sixth learned rotational speed vector to obtain the target rotational speed vector corresponding to the true value vector to be learned.

[0063] In this embodiment, after determining the positional relationship between the learned truth vector and the truth vector to be learned, if the electronic device detects that the learned truth vector is located on both sides of the truth vector to be learned, it further filters out the fifth valid truth vector in front of the truth vector to be learned and the sixth valid truth vector behind the truth vector to be learned from the learned truth vectors and determines them as the target truth vectors for learning the truth vector to be learned. Then, the electronic device reads the fifth learning pressure vector and the fifth learning speed vector corresponding to the fifth truth vector, and reads the sixth learning pressure vector and the sixth learning speed vector corresponding to the sixth truth vector. Then, the electronic device determines the index with the larger value in the fifth truth vector and the sixth truth vector, and determines the third calculation weight based on the index value. Finally, the electronic device uses a preset linear interpolation algorithm to process the third calculation weight, the fifth learning pressure vector, and the sixth learning pressure vector to obtain the target pressure vector corresponding to the truth vector to be learned. Similarly, the electronic device uses a preset linear interpolation algorithm to process the third calculation weight, the fifth learning speed vector, and the sixth learning speed vector to obtain the target speed vector corresponding to the truth vector to be learned.

[0064] For example, after determining the target truth vector, if the electronic device detects the acquired 20-dimensional truth vector BAxisPSel_T=1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, then it can further determine the positional relationship between the to-be-learned truth vector BAxisPSel_T3 and each of the already learned truth vectors BAxisPSel_T1, BAxisPSel_T2, and BAxisPSel_T4 as the learned truth vector BAxisPSel_T4. BAxisPSel_T1 and BAxisPSel_T2 are located before the truth vector BAxisPSel_T3 to be learned, and the already learned truth vector BAxisPSel_T4 is located after the truth vector BAxisPSel_T3 to be learned. At this point, the electronic device can also select the already learned truth vector BAxisPSel_T2, which is located before the truth vector BAxisPSel_T3 to be learned, as the fifth truth vector, and determine the truth vector BAxisPSel_T4 to be located after the truth vector BAxisPSel_T3 to be learned. The learned truth vector BAxisPSel_T4 is taken as the sixth truth vector. The fifth learned pressure vector AxisPIncRaw2 and the fifth learned rotational speed vector AxisSpdIncRaw2, corresponding to the fifth truth vector BAxisPSel_T2, are read. The sixth learned pressure vector AxisPIncRaw4 and the sixth learned rotational speed vector AxisSpdIncRaw4, corresponding to the sixth truth vector BAxisPSel_T4, are also read. Then, based on the fifth and sixth truth vectors BAxisPSel_T2 and BAxisPSel_T4, the electronic device determines the larger index value as 4. Based on 4, it determines the third calculation weight as 1 / 4. Finally, based on the third calculation weight, the electronic device performs linear interpolation on the fifth and sixth learned pressure vectors AxisPIncRaw2 and AxisPIncRaw4 to obtain the target pressure vector AxisPIncForPtoSpd1=1 / 4 corresponding to the truth vector BAxisPSel_T3 to be learned. (AxisPIncRaw4-AxisPIncRaw2)+AxisPIncRaw2; Similarly, the electronic device performs linear interpolation on the fifth learned rotational speed vector AxisSpdIncRaw2 and the sixth learned rotational speed vector AxisSpdIncRaw4 based on the third calculated weights to obtain the target rotational speed vector AxisSpdIncForPtoSpd1=1 / 4 corresponding to the ground truth vector BAxisPSel_T3 to be learned. (AxisSpdIncRaw4-AxisSpdIncRaw2)+AxisSpdIncRaw2.

[0065] In this way, electronic devices can use linear interpolation algorithms to process a small number of pressure vectors and rotational speed vectors, thereby deriving the pressure vector and rotational speed vector corresponding to the true value vector with a value of 0. This allows electronic devices to derive the true value vector of the remaining unlearned data points based only on the true value vector of a small number of learned data points without having to learn all the data points. This significantly reduces the number of data points required in the learning process of the pressure-rotational speed mapping relationship, significantly reduces the time spent in the learning process, and improves the learning efficiency of the pressure-rotational speed mapping relationship.

[0066] This application also provides a learning system for pressure-speed mapping relationships; please refer to [reference needed]. Figure 4 The learning system for the pressure-speed mapping relationship includes: The mapping and reading module 10 is used to query a preset initial pressure-speed mapping relationship to obtain multiple learning pressure vectors, and to detect the learning speed vectors corresponding to each of the multiple learning pressure vectors. The monotonicity identification module 20 is used to determine, based on each of the learning pressure vectors and each of the learning speed vectors, multiple learned true value vectors included in the initial pressure-speed mapping relationship, and to determine the monotonicity trend corresponding to each of the learning pressure vectors and each of the learning speed vectors; The target filtering module 30 is used to determine multiple true value vectors to be learned contained in the initial pressure-speed mapping relationship when the monotonicity trend corresponding to each of the learning pressure vectors and each of the learning speed vectors is detected to be an upward trend. The vector derivation module 40 is used to filter out the target truth vector that matches the truth vector to be learned from a plurality of learned truth vectors, and determine the target pressure vector and target rotation speed vector corresponding to the truth vector to be learned based on the target truth vector; The mapping update module 50 is used to update the true value vector to be learned based on the target pressure vector and the target rotation speed vector to obtain the target pressure-rotation speed relationship.

[0067] In one feasible implementation, the vector derivation module 40 described above is further configured to: Determine the positional relationship between multiple learned truth vectors and the truth vector to be learned, wherein the positional relationship is such that the learned truth vector is in front of the truth vector to be learned or the learned truth vector is behind the truth vector to be learned; Based on the positional relationship, a target truth vector that matches the truth vector to be learned is selected from multiple learned truth vectors.

[0068] In one feasible implementation, the vector derivation module 40 described above is further configured to: When it is detected that the positional relationship is that the learned truth vector is behind the truth vector to be learned, a first truth vector and a second truth vector are selected from multiple learned truth vectors, wherein the second truth vector is behind the first truth vector; or, When it is detected that the positional relationship is that the learned truth vector is in front of the truth vector to be learned, a third truth vector and a fourth truth vector are selected from multiple learned truth vectors, wherein the third truth vector is adjacent to the truth vector to be learned, and the fourth truth vector is adjacent to the third truth vector.

[0069] In one feasible implementation, the vector derivation module 40 described above is further configured to: If the target truth vector is detected to be the first truth vector and the second truth vector, the first learning pressure vector and the first learning speed vector corresponding to the first truth vector are determined, and the second learning pressure vector and the second learning speed vector corresponding to the second truth vector are determined. The first calculation weight is determined based on the first truth vector and the second truth vector; The first calculated weight, the first learning pressure vector, and the second learning pressure vector are combined and linear interpolation is performed to obtain the target pressure vector corresponding to the ground value vector to be learned. The first calculated weight, the first learned speed vector, and the second learned speed vector are combined and linear interpolation is performed to obtain the target speed vector corresponding to the true value vector to be learned.

[0070] In one feasible implementation, the vector derivation module 40 described above is further configured to: When the target truth vector is detected to be the third truth vector and the fourth truth vector, the third learning pressure vector and the third learning speed vector corresponding to the third truth vector are determined, and the fourth learning pressure vector and the fourth learning speed vector corresponding to the fourth truth vector are determined. Obtain the preset second calculation weight; Linear interpolation is performed by combining the second calculated weight, the third learning pressure vector, and the fourth learning pressure vector to obtain the target pressure vector corresponding to the true value vector to be learned; The second calculated weight, the third learned speed vector, and the fourth learned speed vector are combined to perform linear interpolation to obtain the target speed vector corresponding to the true value vector to be learned.

[0071] In one feasible implementation, the vector derivation module 40 described above is further configured to: When it is detected that the positional relationship is such that the learned truth vector is on both sides of the truth vector to be learned, the fifth truth vector and the sixth truth vector are selected from the multiple learned truth vectors, wherein the fifth truth vector is in front of the truth vector to be learned and the sixth truth vector is behind the truth vector to be learned. Determine the fifth learning pressure vector and the fifth learning speed vector corresponding to the fifth truth vector, and determine the sixth learning pressure vector and the sixth learning speed vector corresponding to the sixth truth vector; The third calculation weight is determined based on the fifth truth vector and the sixth truth vector; Linear interpolation is performed by combining the third calculated weight, the fifth learning pressure vector, and the sixth learning pressure vector to obtain the target pressure vector corresponding to the true value vector to be learned; The target speed vector corresponding to the true value vector to be learned is obtained by combining the third calculated weight, the fifth learned speed vector, and the sixth learned speed vector through linear interpolation.

[0072] The pressure-speed mapping relationship learning system provided in this application, employing the pressure-speed mapping relationship learning method described in the above embodiments, can solve the technical problem of low learning efficiency of pressure-speed mapping relationships in related technologies. Compared with the prior art, the beneficial effects of the pressure-speed mapping relationship learning system provided in this application are the same as those of the pressure-speed mapping relationship learning method provided in the above embodiments, and other technical features of the pressure-speed mapping relationship learning system are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0073] This application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the pressure-speed mapping relationship learning method in Embodiment 1 above.

[0074] The following is for reference. Figure 5 The diagram illustrates a structural schematic of an electronic device suitable for implementing the embodiments of this application. The electronic device in the embodiments of this application may include, but is not limited to, terminals such as electronic devices configured in a vehicle, or mobile terminals, data storage control terminals, PCs, etc., connected to an electronic control unit associated with the electronic device. Figure 5 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0075] like Figure 5As shown, the electronic device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the electronic device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. The communication device 1009 allows the electronic device to communicate wirelessly or wiredly with other devices to exchange data. Although the diagrams show electronic devices with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented alternatively.

[0076] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0077] The electronic device provided in this application employs the pressure-speed mapping relationship learning method described in the above embodiments, which can solve the technical problem of low learning efficiency of pressure-speed mapping relationships in related technologies. Compared with the prior art, the beneficial effects of the electronic device provided in this application are the same as those of the pressure-speed mapping relationship learning method provided in the above embodiments, and other technical features of this electronic device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0078] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0079] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0080] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the pressure-speed mapping relationship learning method in the above embodiments.

[0081] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0082] The aforementioned computer-readable storage medium may be included in an electronic device or may exist independently without being assembled into an electronic device.

[0083] The aforementioned computer-readable storage medium carries one or more programs. When the one or more programs are executed by an electronic device, the electronic device causes the following: it queries a preset initial pressure-speed mapping relationship to obtain multiple learning pressure vectors; it detects the learning speed vectors corresponding to each of the multiple learning pressure vectors; based on each learning pressure vector and each learning speed vector, it determines multiple learned truth vectors included in the initial pressure-speed mapping relationship, and determines the monotonicity trend corresponding to each learning pressure vector and each learning speed vector; when it detects that the monotonicity trend corresponding to each learning pressure vector and each learning speed vector is an upward trend, it determines multiple unlearned truth vectors included in the initial pressure-speed mapping relationship; it filters target truth vectors that match the unlearned truth vectors from the multiple learned truth vectors, and determines the target pressure vector and target speed vector corresponding to the unlearned truth vector based on the target truth vector; and it updates each unlearned truth vector based on each target pressure vector and each target speed vector to obtain a target pressure-speed relationship.

[0084] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0085] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0086] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0087] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described pressure-speed mapping relationship learning method, which can solve the technical problem of low learning efficiency of pressure-speed mapping relationships in related technologies. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the pressure-speed mapping relationship learning method provided in the above embodiments, and will not be repeated here.

[0088] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the learning method for the pressure-speed mapping relationship as described above.

[0089] The computer program product provided in this application can solve the technical problem of low learning efficiency of pressure-speed mapping relationship in related technologies. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the pressure-speed mapping relationship learning method provided in the above embodiments, and will not be repeated here.

[0090] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for learning the pressure-speed mapping relationship, characterized in that, The learning method for the rotational speed mapping relationship includes: Multiple learning pressure vectors are obtained by querying the preset initial pressure-speed mapping relationship, and the learning speed vectors corresponding to each of the multiple learning pressure vectors are detected. Based on each of the learning pressure vectors and each of the learning speed vectors, determine the multiple learned true value vectors contained in the initial pressure-speed mapping relationship, and determine the monotonicity trend corresponding to each of the learning pressure vectors and each of the learning speed vectors; If the monotonicity trend of each learning pressure vector and each learning speed vector is detected to be an upward trend, then the multiple true value vectors to be learned contained in the initial pressure-speed mapping relationship are determined. Filter out the target truth vector that matches the truth vector to be learned from the multiple learned truth vectors, and determine the target pressure vector and target rotation speed vector corresponding to the truth vector to be learned based on the target truth vector; Based on the target pressure vector and the target rotation speed vector, the target pressure-rotation speed relationship is obtained by updating the true value vector to be learned.

2. The method for learning the pressure-speed mapping relationship as described in claim 1, characterized in that, The step of filtering the target ground value vector that matches the ground value vector to be learned from the plurality of learned ground value vectors includes: Determine the positional relationship between multiple learned truth vectors and the truth vector to be learned, wherein the positional relationship is such that the learned truth vector is in front of the truth vector to be learned or the learned truth vector is behind the truth vector to be learned; Based on the positional relationship, a target truth vector that matches the truth vector to be learned is selected from multiple learned truth vectors.

3. The method for learning the pressure-speed mapping relationship as described in claim 2, characterized in that, The step of filtering target ground value vectors that match the ground value vector to be learned from multiple learned ground value vectors based on the positional relationship includes: When it is detected that the positional relationship is that the learned truth vector is behind the truth vector to be learned, a first truth vector and a second truth vector are selected from multiple learned truth vectors, wherein the second truth vector is behind the first truth vector; or, When it is detected that the positional relationship is that the learned truth vector is in front of the truth vector to be learned, a third truth vector and a fourth truth vector are selected from multiple learned truth vectors, wherein the third truth vector is adjacent to the truth vector to be learned, and the fourth truth vector is adjacent to the third truth vector.

4. The method for learning the pressure-speed mapping relationship as described in claim 3, characterized in that, The step of determining the target pressure vector and target rotational speed vector corresponding to the target true value vector based on the target true value vector includes: If the target truth vector is detected to be the first truth vector and the second truth vector, the first learning pressure vector and the first learning speed vector corresponding to the first truth vector are determined, and the second learning pressure vector and the second learning speed vector corresponding to the second truth vector are determined. The first calculation weight is determined based on the first truth vector and the second truth vector; The first calculated weight, the first learning pressure vector, and the second learning pressure vector are combined and linear interpolation is performed to obtain the target pressure vector corresponding to the ground value vector to be learned. The first calculated weight, the first learned speed vector, and the second learned speed vector are combined and linear interpolation is performed to obtain the target speed vector corresponding to the true value vector to be learned.

5. The method for learning the pressure-speed mapping relationship as described in claim 3, characterized in that, The step of determining the target pressure vector and target rotational speed vector corresponding to the target true value vector based on the target true value vector includes: When the target truth vector is detected to be the third truth vector and the fourth truth vector, the third learning pressure vector and the third learning speed vector corresponding to the third truth vector are determined, and the fourth learning pressure vector and the fourth learning speed vector corresponding to the fourth truth vector are determined. Obtain the preset second calculation weight; Linear interpolation is performed by combining the second calculated weight, the third learning pressure vector, and the fourth learning pressure vector to obtain the target pressure vector corresponding to the true value vector to be learned; The second calculated weight, the third learned speed vector, and the fourth learned speed vector are combined to perform linear interpolation to obtain the target speed vector corresponding to the true value vector to be learned.

6. The method for learning the pressure-speed mapping relationship as described in claim 2, characterized in that, After the step of determining the positional relationship between the plurality of learned truth vectors and the truth vector to be learned, the method further includes: When it is detected that the positional relationship is such that the learned truth vector is on both sides of the truth vector to be learned, the fifth truth vector and the sixth truth vector are selected from the multiple learned truth vectors, wherein the fifth truth vector is in front of the truth vector to be learned and the sixth truth vector is behind the truth vector to be learned. Determine the fifth learning pressure vector and the fifth learning speed vector corresponding to the fifth truth vector, and determine the sixth learning pressure vector and the sixth learning speed vector corresponding to the sixth truth vector; The third calculation weight is determined based on the fifth truth vector and the sixth truth vector; Linear interpolation is performed by combining the third calculated weight, the fifth learning pressure vector, and the sixth learning pressure vector to obtain the target pressure vector corresponding to the true value vector to be learned; The target speed vector corresponding to the true value vector to be learned is obtained by combining the third calculated weight, the fifth learned speed vector, and the sixth learned speed vector through linear interpolation.

7. An electronic device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the learning method for the pressure-speed mapping relationship as described in any one of claims 1 to 6.

8. A vehicle, characterized in that, The vehicle includes the electronic equipment as described in claim 7.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the learning method for the pressure-speed mapping relationship as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the learning method for the pressure-speed mapping relationship as described in any one of claims 1 to 6.

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