Parameter error quantification method of control algorithm and related equipment

Abstract

The invention provides a parameter error quantification method of a control algorithm and related equipment, and relates to the technical field of control algorithms, the method comprises the following steps: determining a target control algorithm to select an interest quantity in the target control algorithm, and under the condition that the target control algorithm comprises a distributed electric vehicle stability control algorithm, determining the interest quantity in the target control algorithm; the interest quantity comprises motor torque, vehicle speed and tire cornering rigidity; a precision tracking contribution ratio, an integral error contribution ratio, and a confidence of the integral error contribution ratio for error propagation of the amount of interest are determined. The influence of different parameter errors in the control model on the upper-layer control effect is researched, and the precision tracking contribution ratio, the integral error contribution ratio and the confidence coefficient thereof are solved based on the influence and are used for guiding the development work of a control algorithm. Therefore, the influence of error transmission of different parameters on final control is quantified, a developer is helped to intuitively understand the influence of each parameter on the control effect and judge whether main influence factors exist or not, and thus key influence parameters for improving the control effect are accurately positioned.

Description

Technical Field

[0001] The present invention relates to the technical field of control algorithms, and more specifically, to a method for quantizing parameter errors of a control algorithm, a device for quantizing parameter errors of a control algorithm, an electronic device, and a storage medium. Background Art

[0002] A control algorithm can be represented as a system that accepts control variables and parameters as input. It achieves its control objectives by transferring models, defining optimization objectives, and inversely determining the control variables. For example, in the field of vehicle control, the inputs to a distributed electric vehicle stability control algorithm might be the torque of the four-wheel motors. Parameters include vehicle speed, vehicle mass, vehicle moment of inertia, and tire cornering stiffness. The optimization objectives might be tracking the yaw rate and limiting the sideslip angle at the center of mass.

[0003] The control effect of the algorithm is measured by the error from the target. The inevitable errors can generally be divided into the following three categories: 1. Modeling error: the error between the mathematical model and the actual problem; 2. Observation error: error caused by inaccurate measuring tools and methods; 3. Truncation error and rounding error: numerical error caused by simplified calculations.

[0004] In vehicle control, control algorithms often operate in a hierarchical relationship. For example, parameters such as vehicle speed required by a vehicle stability control algorithm are provided by other algorithms. This transfer of control between these algorithms inevitably results in errors. For example, inaccurate estimates of parameters such as vehicle speed, tire cornering stiffness, and mass can lead to poor stability control algorithm performance.

[0005] Therefore, a parameter error quantification method is urgently needed to solve the above technical problems. Summary of the Invention

[0006] The Summary of the Invention introduces a series of simplified concepts that will be further described in the Detailed Description of the Invention. The Summary of the Invention is not intended to limit the key features and essential features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0007] In a first aspect, the present invention proposes a method for quantifying parameter errors of a control algorithm, comprising: determining a target control algorithm to select a quantity of interest in the target control algorithm, where the target control algorithm includes a distributed electric vehicle stability control algorithm, the quantity of interest includes motor torque, vehicle speed, and tire cornering stiffness; The accuracy tracking contribution ratio, the integral error contribution ratio, and the confidence level of the integral error contribution ratio of the error propagation of the quantity of interest are determined.

[0008] In some embodiments, the method further comprises: Select specific working conditions; Under the same specific working condition, multiple groups of test signals of the quantity of interest are collected.

[0009] In some embodiments, Determine the accuracy tracking contribution ratio, the integral error contribution ratio, and the confidence level of the integral error contribution ratio of the error propagation of the quantity of interest, including: Discretely sample multiple groups of test signals to obtain a sample data set corresponding to each quantity of interest; Determine the first Gaussian distribution feature of each quantity of interest based on the sampled data set; Determine the second Gaussian distribution characteristics of unit error; determining a third Gaussian distribution feature of the transmission error based on the first Gaussian distribution feature and the second Gaussian distribution feature; Based on the third Gaussian distribution characteristics, the accuracy tracking contribution ratio, the integral error contribution ratio and the confidence level of the integral error contribution ratio are determined.

[0010] In some embodiments, determining a third Gaussian distribution characteristic of the transmission error based on the first Gaussian distribution characteristic and the second Gaussian distribution characteristic includes: Restoring the first Gaussian distribution feature to a first sinusoidal signal; Restoring the second Gaussian distribution feature into a second sinusoidal signal; Substituting the second sinusoidal signal into the first sinusoidal signal to obtain a transfer error; The transmission error is discretely sampled and Gaussian fitted to obtain the third Gaussian distribution characteristics.

[0011] In some embodiments, substituting the second sinusoidal signal into the first sinusoidal signal to obtain a transfer error comprises: Substituting the second sinusoidal signal into the first sinusoidal signals corresponding to the plurality of quantities of interest in sequence to determine an error-added output result for each quantity of interest; Based on each quantity of interest, obtain an error-free output result; The difference between the output result with and without error addition is calculated to obtain the transfer error.

[0012] In some embodiments, determining the accuracy tracking contribution ratio, the integral error contribution ratio, and the confidence level of the integral error contribution ratio based on the third Gaussian distribution characteristic includes calculating the accuracy tracking contribution ratio, the integral error contribution ratio, and the confidence level of the integral error contribution ratio using the following formula: , as well as ,in, represents the precision tracking contribution ratio, represents the integral error contribution ratio, Indicates confidence, Indicates the The expectation of the transmission error corresponding to the quantity of interest, Indicates the The standard deviation of the transmission error corresponding to the quantity of interest, Indicates the number of selected interest quantities.

[0013] In some embodiments, the test durations of the multiple groups of test signals are equal.

[0014] Secondly, a parameter error quantization device for a control algorithm is also proposed, including: a first determining module, configured to determine a target control algorithm to select an interest quantity in the target control algorithm, wherein when the target control algorithm includes a distributed electric vehicle stability control algorithm, the interest quantity includes motor torque, vehicle speed, and tire cornering stiffness; The second determination module is used to determine the accuracy tracking contribution ratio, the integral error contribution ratio and the confidence level of the integral error contribution ratio of the error transfer of the interest quantity.

[0015] In a third aspect, an electronic device is proposed, comprising a processor and a memory, wherein the memory stores computer program instructions, and the computer program instructions are used by the processor to execute the parameter error quantization method of the control algorithm described above when the processor is running.

[0016] In a fourth aspect, a storage medium is also proposed, on which program instructions are stored. The program instructions are used to execute the parameter error quantization method of the control algorithm described above during operation.

[0017] According to the above technical solution, a target control algorithm is determined to select the quantity of interest within the target control algorithm and determine the precision tracking contribution ratio, integral error contribution ratio, and confidence level of the integral error contribution ratio of the error transfer of the quantity of interest. The impact of different parameter errors in the control model on the upper-level control effect is studied. Based on the magnitude of the impact, the precision tracking contribution ratio and integral error contribution ratio, as well as their confidence levels, are calculated to guide control algorithm development. Therefore, quantifying the impact of error transfer of different parameters on the final control helps developers intuitively understand the impact of each parameter on the control effect, determine whether there are major influencing factors, and accurately locate the key influencing parameters for improving control effectiveness.

[0018] The parameter error quantization method of the control algorithm of the present invention, and other advantages, objectives and features of the present invention will be reflected in part through the following description, and in part will be understood by those skilled in the art through research and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0020] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0021] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the exemplary embodiments below. The accompanying drawings are for illustration purposes only and are not to be considered limiting of the present application. Throughout the accompanying drawings, the same reference symbols denote the same components. In the accompanying drawings: Figure 1 A schematic flow chart of a method for quantifying parameter errors of a control algorithm provided in an embodiment of the present application; Figure 2 A schematic block diagram of a parameter error quantization device for a control algorithm provided in an embodiment of the present application; Figure 3 A schematic block diagram of an electronic device according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0022] In order to more clearly understand the above-mentioned objectives, features and advantages of the present application, the scheme of the present application will be further described below. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0023] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the embodiments described are only some embodiments of the present application, not all embodiments.

[0024] In the following description, many specific details are set forth to facilitate a full understanding of the present application, but the present application can also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present application, not all of the embodiments.

[0025] In order to solve the above technical problems, according to the first aspect of the present application, a method for quantifying parameter errors of a control algorithm is proposed. Figure 1 This is a schematic flow chart of a method for quantifying parameter errors of a control algorithm provided in an embodiment of the present application. For example, Figure 1 As shown, the method may include the following steps.

[0026] Step S110 , determining a target control algorithm to select an interest quantity in the target control algorithm.

[0027] For example, assume that a distributed electric vehicle stability control algorithm has been completed by the developer. Based on the onboard prototype of the distributed electric vehicle stability control algorithm, the torque of the four-wheel hub motor is selected as the control quantity. 、 、 、 , vehicle speed and tire cornering stiffness As the quantity of interest. It should be noted that the quantity of interest can be an important control variable or parameter that the developer wants to study, and can be reasonably set according to different control algorithms.

[0028] Step S120 , determining the accuracy tracking contribution ratio, the integral error contribution ratio, and the confidence level of the integral error contribution ratio of the error transfer of the variable of interest.

[0029] For example, when the quantity of interest is the above-mentioned multiple parameters, it can be determined that the goal of the task is to achieve the center of mass side slip angle Suppression of, for example, the target center of mass sideslip angle From this, the torque of the four-wheel hub motor can be determined 、 、 、 , vehicle speed and tire cornering stiffness The error transfer contribution ratio to the center of mass sideslip angle precision tracking, the integral error contribution ratio and the confidence level. Among them, for the precision tracking contribution ratio, it can be used to measure the influence of the error of the quantity of interest on the precision tracking effect of the center of mass sideslip angle suppression target. The larger the contribution ratio, the greater the precision tracking effect of the unit error on the quantity of interest. For the integral error contribution ratio, it is used to measure the influence of the error of the quantity of interest on the integral error effect of the center of mass sideslip angle suppression target. The larger the contribution ratio, the greater the integral of the transfer error generated by the unit error on the quantity of interest over a period of time. The confidence level is used to measure the confidence level of the two effects of precision tracking and integral error, and indicates the credibility of the corresponding effect of the error on the target on the quantity of interest.

[0030] According to the above technical solution, a target control algorithm is determined to select the quantity of interest within the target control algorithm and determine the precision tracking contribution ratio, integral error contribution ratio, and confidence level of the integral error contribution ratio of the error transfer of the quantity of interest. The impact of different parameter errors in the control model on the upper-level control effect is studied. Based on the magnitude of the impact, the precision tracking contribution ratio and integral error contribution ratio, as well as their confidence levels, are calculated to guide control algorithm development. Therefore, quantifying the impact of error transfer of different parameters on the final control helps developers intuitively understand the impact of each parameter on the control effect, determine whether there are major influencing factors, and accurately locate the key influencing parameters for improving control effectiveness.

[0031] In some embodiments, the method may further include: selecting a specific operating condition; and collecting multiple groups of test signals of the quantity of interest under the same specific operating condition.

[0032] For example, the simulation evaluation method can be selected as follows: For example, a distributed electric vehicle model U001, the speed limit range is 40km / h-70km / h, and the road adhesion coefficient is 0.3. In this case, the following can be completed: Group( The test is conducted under dual lane shift control conditions (where is an adjustable parameter). During the test, six variables of interest are recorded: the torque of the four-wheel hub motors 、 、 、 , vehicle speed and tire cornering stiffness of It should be noted that the amount of interest can be selected according to actual needs or experience. The above 6 are only exemplary and do not mean that the number of amounts of interest is limited. In some embodiments, the duration of each group of experiments is the same. Therefore, the accuracy of each set of test signals collected for subsequent calculations can be guaranteed by controlling variables, providing reliable guarantees for the accuracy of subsequent calculation results.

[0033] In some embodiments, step S120 of determining the accuracy tracking contribution ratio, the integral error contribution ratio, and the confidence level of the integral error contribution ratio of the error propagation of the quantity of interest may include: Step S121 : Discretely sample multiple groups of test signals to obtain a sample data set corresponding to each quantity of interest.

[0034] For example, the quantity of interest under a specific working condition can be regarded as a continuous random variable that conforms to the Gaussian distribution. In order to obtain the Gaussian distribution characteristics of each quantity of interest, the statistical information of these quantities of interest can be obtained by the discretized sampling method. For example, The group test collected The experimental signal is set and the discrete sampling is performed. , each set of test signals can be obtained n Sampling data. Finally, each quantity of interest can get a sampling data set including N data .

[0035] Step S122: determining a first Gaussian distribution feature of each quantity of interest based on the sampled data set.

[0036] For example, for each sample data set of interest, the expected value can be obtained using the following formula: and standard deviation : , , , ,in, The first Then we can get the first Gaussian distribution characteristics of the six quantities of interest: 、 、 、 、 as well as , where N indicates that the corresponding interest quantity obeys Gaussian distribution.

[0037] Step S123: determine the second Gaussian distribution characteristics of the unit error.

[0038] For example, the error propagation experiment is a set of comparative experiments, in which the unit error e is added to the first Gaussian distribution feature as the input of the system to obtain the output, and the output is compared with the test results of the interest quantity input without adding the unit error e to directly analyze the effect of error propagation. Among them, the unit error e can be a Gaussian random variable with mean and variance of 1, which can be expressed as .

[0039] Step S124, determining a third Gaussian distribution feature of the transmission error based on the first Gaussian distribution feature and the second Gaussian distribution feature, may include the following steps.

[0040] Step S124a: restore the first Gaussian distribution feature to a first sinusoidal signal.

[0041] For example, in order to study the influence of error transmission of a single variable of interest on the control effect, it is necessary to keep the input of other variables of interest unchanged. To this end, the variable of interest that follows the Gaussian distribution can be restored to a sinusoidal continuous signal as the input of the system, that is, the first Gaussian distribution feature can be restored to the first sinusoidal signal. For example, restore to ,in, , , . represents the amplitude of the sine function, represents the angular frequency of the sine function, represents the offset of the sine function, It is an adjustable parameter, which represents the duration of the sinusoidal signal under working conditions. When different quantities of interest are restored to continuous sinusoidal signals, the corresponding expectation and standard deviation values are taken, so that the six first sinusoidal signals corresponding to the six quantities of interest can be obtained.

[0042] Step S124b: restore the second Gaussian distribution feature to a second sinusoidal signal.

[0043] Exemplarily, similar to the above restoration method, the second Gaussian distribution feature can be restored to a second sinusoidal signal. , , , .in, The value of The value remains consistent.

[0044] Step S124c: Substitute the second sinusoidal signal into the first sinusoidal signal to obtain a transfer error.

[0045] For example, in order to study the effect of error transfer of a single variable of interest on the control effect, a control variable method is used. The second sinusoidal signal is sequentially introduced into the first sinusoidal signals corresponding to multiple variables of interest to determine the error-added output result of each variable of interest; based on each variable of interest, the output result without error addition is obtained; the difference between the error-added output result and the error-free output result is calculated to obtain the transfer error. For example, a unit error is added to the continuous sinusoidal signal of only one variable of interest at a time. , ensuring that the other five quantities of interest remain unchanged, complete a set of comparative tests under the above specific working conditions. Repeat the test 6 times to obtain the comparative results of the output signals of 6 sets of comparative tests. Each set of comparative tests contains two output results with and without unit error. The difference between the two output results can be used to obtain the unit error after the transfer of the quantity of interest, that is, the transfer error .

[0046] Step S124d: performing discrete sampling and Gaussian fitting on the transmission error to obtain a third Gaussian distribution feature.

[0047] For example, The unit error after the transfer of the quantity of interest Discrete sampling and Gaussian fitting are performed. , we can get The sampling data set of data is also expected to find the expected value of the sampling data set. and standard deviation , get the unit error after transmission Obey the following Gaussian distribution: .

[0048] Step S125 , based on the third Gaussian distribution feature, determine the accuracy tracking contribution ratio, the integral error contribution ratio, and the confidence level of the integral error contribution ratio.

[0049] For example, in combination with the above, the goal of the distributed electric vehicle stability control algorithm is to suppress the vehicle's center of mass slip angle, and the effect of the suppression can be measured by two indicators. Among them, the precision tracking effect represents the average tracking deviation of the center of mass slip angle, and the integral error represents the cumulative tracking error of the center of mass slip angle over a period of time. Different quantities of interest have different effects on these two effect evaluation indicators, and the algorithm of this application realizes the quantification of the effects. The precision tracking contribution ratio of the quantity of interest and integral error contribution ratio and its confidence The calculation formula is as follows: , It represents the precision tracking contribution ratio, which is used to measure the The larger the contribution ratio, the greater the impact of the unit error on the tracking accuracy of the target with center of mass sideslip angle suppression.

[0050] , It represents the integral error contribution ratio, which is used to measure the The contribution ratio is the influence of the error of the interest quantity on the integral error effect of the center of mass sideslip angle suppression target. The larger the contribution ratio, the greater the integral of the transmission error caused by the unit error of the interest quantity over a period of time.

[0051] ,Since the input used in the above error transfer test is a sinusoidal continuous signal restored by a random variable, a confidence index is added to measure the above two effects, namely the confidence level of precision tracking and integral error, representing the first The credibility of the degree of influence of the error of the interest quantity on the target. Indicates the The expectation of the transmission error corresponding to the quantity of interest, Indicates the The standard deviation of the transmission error corresponding to the quantity of interest, Indicates the number of selected interest quantities.

[0052] This enables the quantification of the impact of error transmission of different parameters on the final control, helping developers to intuitively understand the impact of each parameter on the control effect, determine whether there are major influencing factors, and accurately locate the key influencing parameters for improving the control effect.

[0053] According to the second aspect of the present application, a device for quantifying parameter errors of a control algorithm is also proposed. Figure 2 This is a schematic block diagram of a parameter error quantization device for a control algorithm provided in an embodiment of the present application. For example, Figure 2 As shown, the apparatus 200 may include: a first determining module 210 for determining a target control algorithm to select a quantity of interest in the target control algorithm, wherein when the target control algorithm includes a distributed electric vehicle stability control algorithm, the quantity of interest includes motor torque, vehicle speed, and tire cornering stiffness; The second determination module 220 is configured to determine the accuracy tracking contribution ratio, the integral error contribution ratio, and the confidence level of the integral error contribution ratio of the error transfer of the variable of interest.

[0054] According to a third aspect of the present invention, an electronic device is further provided. Figure 3 FIG. 1 shows a schematic block diagram of an electronic device 300 according to an embodiment of the present invention. Figure 3As shown, the electronic device 300 may include a processor 310 and a memory 320. The memory 320 stores computer program instructions, which are used by the processor 310 to execute the parameter error quantization method of the control algorithm described above when the computer program instructions are executed.

[0055] According to a fourth aspect of the present invention, a storage medium is further provided, on which program instructions are stored, and the program instructions are used to execute the parameter error quantization method of the control algorithm described above when running. The storage medium may include, for example, a storage component of a tablet computer, a hard disk of a computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.

[0056] A person skilled in the art can understand the specific details and beneficial effects of the parameter error quantization device, electronic device and storage medium of the control algorithm by reading the above description of the parameter error quantization method of the control algorithm. For the sake of brevity, they will not be repeated here.

[0057] In the several embodiments provided in this application, it should be understood that the disclosed devices and / or equipment can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0058] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0059] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0060] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0061] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for quantifying parameter errors of a control algorithm, characterized in that: include: determining a target control algorithm to select a quantity of interest in the target control algorithm, wherein when the target control algorithm includes a distributed electric vehicle stability control algorithm, the quantity of interest includes motor torque, vehicle speed, and tire cornering stiffness; An accuracy tracking contribution ratio, an integral error contribution ratio, and a confidence level of the integral error contribution ratio of the error propagation of the quantity of interest are determined.

2. The parameter error quantification method of the control algorithm according to claim 1, characterized in that: The method further comprises: Select specific working conditions; Under the same specific working condition, multiple groups of test signals of the quantity of interest are collected.

3. The parameter error quantification method of the control algorithm according to claim 2, characterized in that: The determining of the accuracy tracking contribution ratio, the integral error contribution ratio, and the confidence level of the integral error contribution ratio of the error propagation of the quantity of interest includes: Discretely sampling the multiple groups of test signals to obtain a sample data set corresponding to each of the quantities of interest; Determining a first Gaussian distribution feature of each quantity of interest based on the sampled data set; Determine the second Gaussian distribution characteristics of unit error; determining a third Gaussian distribution feature of the transmission error based on the first Gaussian distribution feature and the second Gaussian distribution feature; Based on the third Gaussian distribution feature, the precision tracking contribution ratio, the integral error contribution ratio, and the confidence level of the integral error contribution ratio are determined.

4. The parameter error quantification method of the control algorithm according to claim 3, characterized in that: The determining, based on the first Gaussian distribution feature and the second Gaussian distribution feature, of a third Gaussian distribution feature of the transmission error includes: Restoring the first Gaussian distribution feature into a first sinusoidal signal; Restoring the second Gaussian distribution feature into a second sinusoidal signal; Substituting the second sinusoidal signal into the first sinusoidal signal to obtain the transfer error; Discrete sampling and Gaussian fitting are performed on the transmission error to obtain the third Gaussian distribution feature.

5. The parameter error quantification method of the control algorithm according to claim 4, characterized in that: Substituting the second sinusoidal signal into the first sinusoidal signal to obtain the transfer error comprises: Substituting the second sinusoidal signal into the first sinusoidal signals corresponding to the plurality of quantities of interest in sequence to determine an error-added output result for each quantity of interest; Based on each of the quantities of interest, obtaining an output result without adding an error; The difference between the output result with error added and the output result without error added is calculated to obtain the transmission error.

6. The method for quantifying parameter errors of a control algorithm according to claim 5, characterized in that: Determining the precision tracking contribution ratio, the integral error contribution ratio, and the confidence level of the integral error contribution ratio based on the third Gaussian distribution feature includes calculating the precision tracking contribution ratio, the integral error contribution ratio, and the confidence level of the integral error contribution ratio using the following formula: , as well as ,in, represents the precision tracking contribution ratio, represents the integral error contribution ratio, represents the confidence level, Indicates the The expectation of the transmission error corresponding to the quantity of interest, Indicates the The standard deviation of the transmission error corresponding to the quantity of interest, Indicates the number of selected interest quantities.

7. The method for quantifying parameter errors of a control algorithm according to claim 2, characterized in that: The test durations of the multiple groups of test signals are equal.

8. A device for quantifying parameter errors of a control algorithm, characterized in that: include: a first determining module, configured to determine a target control algorithm to select a quantity of interest in the target control algorithm, wherein when the target control algorithm includes a distributed electric vehicle stability control algorithm, the quantity of interest includes motor torque, vehicle speed, and tire cornering stiffness; The second determination module is used to determine the accuracy tracking contribution ratio, the integral error contribution ratio and the confidence of the integral error contribution ratio of the error transfer of the interest quantity.

9. An electronic device, characterized in that: The system comprises a processor and a memory, wherein the memory stores computer program instructions, and the computer program instructions are used by the processor to execute the parameter error quantization method of the control algorithm according to any one of claims 1 to 7 when the processor is running.

10. A storage medium having program instructions stored thereon, wherein the program instructions are used to execute the parameter error quantization method of the control algorithm according to any one of claims 1 to 7 when running.

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