Anti-pinch control method and device, electronic equipment and vehicle

Abstract

The invention provides an anti-pinch control method and device, electronic equipment and a vehicle. The method comprises the following steps: acquiring a motor driving duty ratio adjustment value and a motor speed change value of a target object in a preset detection window; duty ratio fuzzy grade data are determined according to the motor driving duty ratio adjustment value and a preset duty ratio fuzzy set library, and speed fuzzy grade data are determined according to the motor speed change value and a preset speed fuzzy set library; determining an anti-pinch fuzzy level based on the duty ratio fuzzy level data, the speed fuzzy level data and a preset fuzzy rule, and determining an anti-pinch control probability; and the anti-pinch control state is determined based on all the anti-pinch control probabilities so as to perform anti-pinch control on the target object, so that anti-pinch can be effectively identified when the motor output torque does not pinch a person, and the risk of pinching the person is reduced.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to an anti-pinch control method, device, electronic device, and vehicle. Background Technology

[0002] The anti-pinch function of a car's power tailgate is a crucial component of automotive functional safety. During the opening and closing of the tailgate, the center of gravity and the angle of the electric strut change in real time, as does the load. Furthermore, different power tailgate systems vary significantly, making it difficult to mathematically abstract the load variation patterns. Related technologies employ setting the operating speed of the power tailgate and monitoring the motor's speed in real time, supplemented by PID (proportional, integral, derivative) adjustment, to maintain a relatively uniform speed during the tailgate's movement. Specifically, the anti-pinch function is implemented by detecting the Hall effect feedback signal from the strut motor, calculating the motor's operating speed, and determining that the anti-pinch function is activated when the motor speed decreases to a certain level.

[0003] However, if the duty cycle of the tailgate strut motor is adjusted by PID, and a person is caught in the door, the load gradually increases due to the flexibility of the human body, and the actual running speed of the motor gradually decreases. During this process, the PID adjustment will increase the motor torque output to reach the set speed, which may result in a large motor output torque that could injure a person.

[0004] It is evident that the relevant technologies cannot detect pinch prevention when the motor output torque is not high enough to injure a person, thus posing a risk of pinching a person. Summary of the Invention

[0005] This application provides an anti-pinch control method, device, electronic device, and vehicle to solve the technical problem in the related art that when the motor output torque is not enough to pinch a person, the anti-pinch function cannot be detected, and there is a risk of pinching a person.

[0006] This application provides an anti-pinch control method, the method comprising: acquiring the motor drive duty cycle adjustment value and motor speed change value of a target object in a preset detection window; determining duty cycle fuzzy level data based on the motor drive duty cycle adjustment value and a preset duty cycle fuzzy set library, and determining speed fuzzy level data based on the motor speed change value and a preset speed fuzzy set library; determining an anti-pinch fuzzy level based on the duty cycle fuzzy level data, speed fuzzy level data, and preset fuzzy rules, and determining an anti-pinch control probability; and determining an anti-pinch control state based on all the anti-pinch control probabilities, so as to perform anti-pinch control on the target object.

[0007] In one embodiment of this application, determining the anti-pinch control state based on all the anti-pinch control probabilities includes: determining the average value of all the anti-pinch control probabilities; if the average value is greater than a preset second control quantity threshold, determining the anti-pinch control state as triggered anti-pinch; or, counting the number of anti-pinch control probabilities that are greater than a preset first control quantity threshold; if the number is greater than a preset number threshold, determining the anti-pinch control state as triggered anti-pinch.

[0008] In one embodiment of this application, determining the anti-pinch control state based on all the anti-pinch control probabilities includes: storing the anti-pinch control probabilities in order through a pre-created first-in-first-out queue; processing the anti-pinch control probabilities in the first-in-first-out queue using a moving average filter to obtain an average value; and determining the anti-pinch control state as triggered anti-pinch if the average value is greater than a preset second control quantity threshold.

[0009] In one embodiment of this application, after determining the anti-pinch control state based on all the anti-pinch control probabilities, the method further includes: if the anti-pinch control state is determined to be triggered anti-pinch, updating the anti-pinch control flag to triggered, and triggering anti-pinch control on the target object according to the anti-pinch trigger control parameters; after completing the anti-pinch control, the method further includes at least one of the following: clearing the first-in-first-out queue, resetting the anti-pinch control flag, and performing anti-pinch control on the target object according to the initial anti-pinch control parameters.

[0010] In one embodiment of this application, anti-pinch control of the target object includes at least one of the following: Control the drive motor of the target object to stop working, or control the drive motor of the target object to reverse; trigger the generation and display of alarm information.

[0011] In one embodiment of this application, the determination of the anti-pinch control probability includes any one of the following: obtaining a preset control probability corresponding to the anti-pinch fuzziness level, determining the preset control probability as the anti-pinch control probability, and pre-configuring a corresponding preset control probability for each anti-pinch fuzziness level; determining the anti-pinch control probability based on the duty cycle fuzziness level, the duty cycle fuzziness level membership degree, the speed fuzziness level, the speed fuzziness level membership degree, and a preset membership function corresponding to the anti-pinch fuzziness level, and pre-configuring a corresponding preset membership function for each anti-pinch fuzziness level, wherein the duty cycle fuzziness level data includes the duty cycle fuzziness level and the duty cycle fuzziness level membership degree, and the speed fuzziness level data includes the speed fuzziness level and the speed fuzziness level data membership degree.

[0012] In one embodiment of this application, if the method for determining the anti-pinch control probability includes determining the anti-pinch control probability based on the duty cycle fuzziness level, the duty cycle fuzziness level membership degree, the velocity fuzziness level, the velocity fuzziness level membership degree, and a preset membership function corresponding to the anti-pinch fuzziness level, the method for determining the anti-pinch control probability includes: determining a corresponding anti-pinch fuzziness level based on the anti-pinch fuzziness level, the velocity fuzziness level, and a preset fuzziness rule to obtain multiple inference rules; determining the smaller value between the duty cycle fuzziness level membership degree and the velocity fuzziness level membership degree corresponding to a inference rule as the initial strength of the inference rule; determining the rule strength based on the initial strength and a preset weight corresponding to the anti-pinch fuzziness level of the inference rule; if the rule strength is greater than a preset strength value... The initial intensity is recorded as the activation intensity of the inference rule, and the inference rule is determined as the activation rule. A preset membership function corresponding to the anti-pinch fuzziness level of the activation rule is obtained, and the preset membership function is truncated based on the activation intensity to obtain a truncation result. The truncation results of all activation rules are aggregated to obtain the final truncation result corresponding to the preset output value. The preset output value's sampling point weighting value is determined based on the preset output value and the final truncation result corresponding to the preset output value. The sampling point weighting value is determined based on all sampling point weighting values, and the final truncation result sum is determined based on the final truncation result sum corresponding to all preset output values. The anti-pinch control probability is determined based on the sampling point weighting value and the final truncation result sum.

[0013] This application embodiment also provides an anti-pinch control device, the device comprising: an acquisition module, configured to acquire the motor drive duty cycle adjustment value and motor speed change value of a target object within a preset detection window; a first determination module, configured to determine duty cycle fuzzy level data based on the motor drive duty cycle adjustment value and a preset duty cycle fuzzy set library, and to determine speed fuzzy level data based on the motor speed change value and a preset speed fuzzy set library; a second determination module, configured to determine an anti-pinch fuzzy level based on the duty cycle fuzzy level data, the speed fuzzy level data, and a preset fuzzy rule, and to determine an anti-pinch control probability; and an anti-pinch control module, configured to determine an anti-pinch control state based on all the anti-pinch control probabilities, so as to perform anti-pinch control on the target object.

[0014] This application also provides a computer-readable storage medium storing a computer program thereon, characterized in that the computer program, when executed by a processor, implements the steps of the method described in any of the above embodiments.

[0015] This application also provides a computer-readable storage medium storing a computer program thereon, characterized in that the computer program, when executed by a processor, implements the steps of the method described in any of the above embodiments.

[0016] This application also provides a vehicle that includes the electronic equipment described in the above embodiments, or performs the steps of the method described in any of the above embodiments.

[0017] Beneficial Effects: This application proposes an anti-pinch control method, device, electronic device, and vehicle. The method acquires the motor drive duty cycle adjustment value and motor speed change value of the target object within a preset detection window; determines the duty cycle fuzzy level data based on the motor drive duty cycle adjustment value and a preset duty cycle fuzzy set library, and determines the speed fuzzy level data based on the motor speed change value and a preset speed fuzzy set library; determines the anti-pinch fuzzy level based on the duty cycle fuzzy level data, speed fuzzy level data, and preset fuzzy rules, and determines the anti-pinch control probability; and determines the anti-pinch control state based on all anti-pinch control probabilities to perform anti-pinch control on the target object. This effectively identifies anti-pinch when the motor output torque is not high enough to injure a person, reducing the risk of injury. Attached Figure Description

[0018] 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. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0019] In the attached diagram: Figure 1 This is a schematic diagram illustrating an application scenario of an anti-pinch control method provided in an embodiment of this application. Figure 2 A schematic flowchart of an anti-pinch control method provided in an embodiment of this application; Figure 3 A schematic flowchart of a specific anti-pinch control method provided in an embodiment of this application; Figure 4 A schematic diagram of the anti-pinch control device provided in one embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0020] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0021] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. The drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0022] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the present application. However, it will be apparent to those skilled in the art that embodiments of the present application may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the present application.

[0023] The anti-pinch function of a power tailgate is an important component of automotive functional safety. During the opening and closing of the tailgate, the center of gravity and the angle of the electric strut change in real time, as does the load. Furthermore, different power tailgate systems vary significantly, making it difficult to mathematically abstract the load variation patterns of a single system. The industry generally uses a set operating speed for the power tailgate, real-time monitoring of the motor's speed, and PID control to maintain a relatively uniform speed during operation.

[0024] The tailgate anti-pinch function works by detecting the Hall feedback signal of the strut motor and calculating the motor speed. When the motor speed decreases to a certain level, the anti-pinch function is activated.

[0025] However, when adjusting the duty cycle of the tailgate strut motor via PID control, if a person is caught in the door, the load gradually increases due to the flexibility of the human body, and the actual operating speed of the motor gradually decreases. During this process, the PID control will increase the motor torque output to reach the set speed, which may result in a large motor output torque that could injure a person.

[0026] The inventors discovered that the problem stems from the independent operation of the PID control system and the anti-pinch system. This results in the inability to detect the anti-pinch function when the motor output torque is not high enough to pinch a person, thus posing a risk of injury.

[0027] In view of this, an anti-pinch control method is proposed, which is a tailgate anti-pinch detection scheme based on fuzzy control algorithm. Combining PID control and tailgate running speed detection, by reasonably setting fuzzy control parameters, it can effectively identify the anti-pinch function when the motor output torque is not high enough to pinch a person, thus reducing the risk of injury. In terms of implementation, as an example, the anti-pinch control can be based on the motor drive duty cycle adjustment value (characterizing PID control) and the motor speed change value (characterizing tailgate running speed). By acquiring the motor drive duty cycle adjustment value and motor speed change value of the target object in a preset detection window, and then determining the duty cycle fuzzy level data and speed fuzzy level data based on preset duty cycle fuzzy set libraries and preset speed fuzzy set libraries respectively, the anti-pinch fuzzy level is determined based on these data and preset fuzzy rules. The anti-pinch control probability is then determined based on this anti-pinch fuzzy level, and the anti-pinch control state is determined based on all the obtained anti-pinch control probabilities to perform anti-pinch control on the target object. This allows for a comprehensive judgment on whether to trigger anti-pinch control by combining the operation of the PID control system and the anti-pinch system. It can identify anti-pinch when the motor output torque is not high enough to injure a person, further reducing the risk of injury, improving vehicle safety, and enhancing user experience. Furthermore, this method has a relatively small computational load, saving system computing resources and improving the execution efficiency of anti-pinch control.

[0028] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating an application scenario of an anti-pinch control method provided in an embodiment of this application. For example... Figure 1 As shown, taking the application of this method to a vehicle as an example, the vehicle at least includes a tailgate 110 and / or a front hatch 120 controlled by a motor. The anti-pinch control method provided in this embodiment is illustrated with the tailgate 110 and / or the front hatch 120 as the target object. See also... Figure 1 During the closing of the tailgate 110 and / or the hood 120, relevant parameters are collected and the motor drive duty cycle adjustment value and motor speed change value are calculated. This data is collected according to preset collection rules. The acquired motor drive duty cycle adjustment value is matched with a preset duty cycle fuzzy set library to obtain the duty cycle fuzzy level, and the motor speed change value is matched with a preset speed fuzzy set library to obtain the speed fuzzy level. Then, the anti-pinch fuzzy level is determined using the duty cycle fuzzy level, the speed fuzzy level, and preset fuzzy rules. This anti-pinch fuzzy level is converted into an anti-pinch control probability, thereby determining the anti-pinch control state and achieving anti-pinch control of the tailgate 110 and / or the hood 120. By comprehensively considering the operation of the PID control system and the anti-pinch system, anti-pinch control can be implemented, enabling the identification of anti-pinch when the motor output torque is not high enough to injure a person, further reducing the risk of injury and improving vehicle safety.

[0029] It should be noted that the above scenario is only an example of an application scenario provided by the embodiments of this application. The embodiments of this application do not limit the actual form of various devices, components, etc. included in the scenario. In the specific application of the solution, it can be set according to actual needs. This solution can be applied to the tailgate of a vehicle, or to other devices with similar tailgate control principles, or to other motor-driven components of the vehicle. The above is just one example and does not limit the method to vehicles.

[0030] Please see Figure 2 , Figure 2 A flowchart illustrating an anti-pinch control method provided in an embodiment of this application is shown below. Figure 2 As shown, the method includes the following steps: Step S210: Obtain the motor drive duty cycle adjustment value and motor speed change value of the target object in the preset detection window.

[0031] As an example, the preset detection window includes one or more motor control cycles. When the preset detection window includes multiple motor control cycles, the anti-pinch control state needs to be determined by considering the probability of each anti-pinch control corresponding to the preset detection window.

[0032] In one embodiment, the preset detection window can be a time-based process that acquires data for a preset time length. The preset detection window can also be determined by limiting the number of acquired motor drive duty cycle adjustment values ​​and motor speed change values, such as acquiring 5 sets of data. The specific settings of the preset detection window can be configured by those skilled in the art as needed; the above are merely examples.

[0033] As an example, a pair of data sets, which are pre-associated with the motor drive duty cycle adjustment value and the motor speed change value, are obtained. Then, the duty cycle fuzziness level and speed fuzziness level are determined for this data set, as well as the subsequent anti-pinch fuzziness level and anti-pinch control probability.

[0034] As another example, a set of motor drive duty cycle adjustment values ​​and motor speed change values ​​can be correlated by matching the timestamps of the original data used to calculate the motor drive duty cycle adjustment values ​​and motor speed change values. The timestamps of the original data corresponding to the two sets of data can be the same, or they can have differences within a preset range.

[0035] As another example, the motor drive duty cycle adjustment value can be calculated by obtaining the duty cycle of the motor in two adjacent cycles, and the motor speed in the two cycles can be obtained to calculate the motor speed change value. Then, the motor drive duty cycle adjustment value and the motor speed change value are used as the basis for calculating the anti-pinch control probability data of this set of data.

[0036] In one embodiment, the motor drive duty cycle adjustment value is determined as follows: Δduty=duty1-duty2 formula (1), Where Δduty is the motor drive duty cycle adjustment value, duty1 is the duty cycle output in the current cycle, and duty2 is the duty cycle output in the previous cycle.

[0037] In one embodiment, the change in motor speed is determined as follows: Formula (2) for Δv=v1-v2 Where Δv is the change in motor speed, v1 is the motor speed detected in this cycle, and v2 is the motor speed in the previous cycle.

[0038] It should be noted that the target object can be a vehicle's tailgate, hood, or other components controlled by a motor, or other components using the same control principle as a tailgate. The target object is also not limited to vehicles; it can also be other openable / closing components that require anti-pinch protection and are driven by a motor, employing a PID control principle.

[0039] When multiple sets of motor drive duty cycle adjustment values ​​and motor speed change values ​​are obtained, it is necessary to correlate these data in advance to understand the correlation between the motor drive duty cycle adjustment values ​​and motor speed change values. The subsequent determination of the anti-pinch control probability is based on the motor drive duty cycle adjustment values ​​and motor speed change values ​​in one set of data.

[0040] As an example, this scheme can be executed in real time. That is, when applied to the tailgate of a vehicle, the motor drive duty cycle adjustment value and motor speed change value are acquired in real time during the tailgate closing process, and then the anti-pinch control probability is determined. In other words, matching is performed directly according to the order of data acquisition. For example, two buffer queues can be maintained to store the motor drive duty cycle adjustment value and the motor speed change value, respectively. The two types of data are collected and calculated at the same acquisition rhythm, and the order of the queues can be used directly as the matching rule. Another example is to maintain the timestamps of the original data corresponding to the calculation of the motor drive duty cycle adjustment value and the motor speed change value, and then match them using the timestamps. Of course, other methods known to those skilled in the art can also be used; this is just an example.

[0041] In one embodiment, before step S210, the closed state of the target object can be obtained. When the target object begins to execute the closing command, step S210 is triggered. That is, this method is not triggered during the opening process of the tailgate, etc., but is triggered when the tailgate is closed, or in other scenarios where anti-pinch is required.

[0042] Step S220: Determine the duty cycle fuzzy level data based on the motor drive duty cycle adjustment value and the preset duty cycle fuzzy set library, and determine the speed fuzzy level data based on the motor speed change value and the preset speed fuzzy set library.

[0043] It's understandable that a pre-maintained preset duty cycle fuzzy set library and a preset speed fuzzy set library are used. During method execution, these two libraries are directly called to calculate the corresponding duty cycle fuzziness level data and speed fuzziness level data. The preset duty cycle fuzzy set library includes the universe of discourse corresponding to different duty cycle fuzziness levels, and the preset speed fuzzy set library includes the universe of discourse corresponding to different speed fuzziness levels. It should be noted that the number of duty cycle fuzziness levels and speed fuzziness levels can be the same or different. For example, there could be three duty cycle fuzziness levels and four speed fuzziness levels. Alternatively, both duty cycle fuzziness levels and speed fuzziness levels could be five.

[0044] As an example, the calibration parameters (configured fuzzy levels, i.e., their fuzzy sets) for the motor drive duty cycle adjustment value Δduty are NB (negative large), NS (negative small), Z (zero), PS (positive small), and PB (positive large). The calibration parameters (configured fuzzy levels, i.e., their fuzzy sets) for the motor speed change value Δv are also NB (negative large), NS (negative small), Z (zero), PS (positive small), and PB (positive large). The corresponding typical membership function center points / peak values ​​(or universe of discourse ranges) are [-8%, -4%, 0, 3%, 6%] and [-3cm / s, -1.8cm / s, 0cm / s, 1cm / s, 3cm / s], respectively. Please refer to Tables 1 and 2. Table 1 is an example of a preset duty cycle fuzzy set library, and Table 2 is an example of a preset speed fuzzy set library.

[0045] Table 1

[0046] Table 2

[0047] As an example, based on Tables 1 and 2 above, the membership degree corresponding to the currently acquired motor drive duty cycle adjustment value and motor speed change value at different levels can be calculated.

[0048] Fuzzy sets allow elements to partially belong to a set, rather than using traditional binary logic (either belong or not). Each element's membership degree is between 0 and 1, indicating the degree to which the element belongs to the set.

[0049] Membership functions can be selected from those known to those skilled in the art. The following provides a method for calculating membership degree using triangular membership functions as an example.

[0050] Formula (3), in, Let x be the membership degree of fuzzy set A, with a value range of [0, 1]. Let x be the currently acquired motor drive duty cycle adjustment value or motor speed change value. Fuzzy set A corresponds to the duty cycle fuzzy level or speed fuzzy level based on the value of x. Let b and c be the membership function centers of the fuzzy set A and its two adjacent levels, respectively. For example, taking x as the currently acquired motor drive duty cycle adjustment value, and A as the duty cycle fuzzy level NS, then... b, c are -8%, -4%, and 0, respectively.

[0051] Taking Tables 1 and 2 as examples, if the currently acquired motor drive duty cycle adjustment value is less than -8%, then its membership degree to NB is 1, and the membership degree to NS, Z, PS, and PB is 0. Similarly, if the currently acquired motor drive duty cycle adjustment value is greater than 6%, then its membership degree to PB is 1, and the membership degree to NS, Z, PS, and NB is 0. The motor speed change value is similar and will not be elaborated further. That is, when Δduty ≤ -8%, the membership degree to NB is 1; it linearly decays to 0 in the range of -8% to -4%; when Δv ≤ -3cm / s, the membership degree to NB is 1; it linearly decays to 0 in the range of -3cm / s to -1.8cm / s.

[0052] Formula (3) can be used to calculate the membership degree of the obtained motor drive duty cycle adjustment value to different duty cycle fuzzy levels, and can also be used to calculate the membership degree of the obtained motor speed change value to different speed fuzzy levels.

[0053] As an example, duty cycle fuzziness level data includes duty cycle fuzziness level and its membership degree, while velocity fuzziness level data includes velocity fuzziness level and its membership degree.

[0054] In another embodiment, the duty cycle fuzzy level membership degree can be determined through a pre-configured duty cycle membership degree mapping relationship, and the speed fuzzy level membership degree can be determined through a pre-configured speed membership degree mapping relationship. For example, the duty cycle membership degree mapping relationship includes membership degree evaluation values ​​for different duty cycle fuzzy levels corresponding to different duty cycle adjustment value ranges. Then, the ratio of the membership degree evaluation values ​​of the currently acquired motor drive duty cycle adjustment value corresponding to different duty cycle fuzzy levels to the sum of all its corresponding membership degree evaluation values ​​is used to determine the duty cycle fuzzy level and duty cycle fuzzy level membership degree corresponding to the acquired motor drive duty cycle adjustment value. Similarly, the speed membership degree mapping relationship includes membership degree evaluation values ​​for different speed fuzzy levels corresponding to different speed change value ranges. Then, the ratio of the membership degree evaluation values ​​of the currently acquired motor drive speed change value corresponding to different speed fuzzy levels to the sum of all its corresponding membership degree evaluation values ​​is used to determine the speed fuzzy level and speed fuzzy level membership degree corresponding to the acquired motor drive speed change value.

[0055] Continuing with Table 1 as an example, when Δduty is less than -8%, the membership evaluation value for NB is 1, and the membership evaluation value for NS, Z, PS, and PB is 0. Similarly, when Δduty is less than -8% but greater than -4%, the membership evaluation value for NB is 0.9, for NS it is 0.7, and for Z, PS, and PB it is 0, and so on. Therefore, if Δduty is -5%, the duty cycle fuzzy levels are NB and NS, and the corresponding duty cycle fuzzy level membership values ​​(rounded to two decimal places) are: 0.9 / (0.9+0.7+0+0+0) = 0.56 and 0.7 / (0.9+0.7+0+0+0) = 0.43.

[0056] As another example, the duty cycle fuzziness level data includes the duty cycle fuzziness level, the speed fuzziness level data includes the speed fuzziness level, different duty cycle fuzziness levels are preset for different motor drive duty cycle adjustment value ranges, and different speed fuzziness levels are preset for different motor speed change values.

[0057] Step S230: Determine the anti-pinch fuzziness level based on the duty cycle fuzziness level data, the speed fuzziness level data, and the preset fuzziness rules, and determine the anti-pinch control probability.

[0058] The preset fuzzy rule can be a combination of the duty cycle fuzzy level, the speed fuzzy level and the corresponding anti-pinch fuzzy level, which can be pre-set by those skilled in the art.

[0059] Taking the anti-pinch blur level P_pinch set to VL (very low), L (low), M (medium), H (high), VH (highest) as an example, please refer to Table 3. Table 3 is an example of a preset blur rule. It should be noted that Table 3 is only an example. The specific settings of the anti-pinch blur level, as well as the anti-pinch blur levels corresponding to different duty cycle blur levels and speed blur levels, can be adjusted by those skilled in the art as needed, and are not limited here.

[0060] Table 3

[0061] As an example, pre-defined fuzzy rules can be formulated based on the following logic: When the duty cycle increases but the speed decreases (Δduty>0, Δv<0), the probability of someone being trapped is high. When the duty cycle decreases but the speed increases (Δduty<0, Δv>0) → the probability of someone being trapped is low; When duty cycle and velocity change in the same direction → medium probability.

[0062] In one embodiment, the method for determining the anti-pinch control probability includes any one of the following: The first method: Obtain the preset control probability corresponding to the anti-pinch fuzziness level, and determine the preset control probability as the anti-pinch control probability. Each anti-pinch fuzziness level is pre-configured with a corresponding preset control probability. The second method: Determine the anti-pinch control probability based on the duty cycle fuzziness level, duty cycle fuzziness level membership degree, speed fuzziness level, speed fuzziness level membership degree, and the preset membership function corresponding to the anti-pinch fuzziness level. Each anti-pinch fuzziness level is pre-configured with a corresponding preset membership function. The duty cycle fuzziness level data includes the duty cycle fuzziness level and the duty cycle fuzziness level membership degree, and the speed fuzziness level data includes the speed fuzziness level and the speed fuzziness level data membership degree.

[0063] It should be noted that if multiple anti-pinch control probabilities are determined using multiple sets of data, the same method for determining the anti-pinch control probability can be chosen, or different methods can be chosen. For example, some data can be determined using the first method mentioned above, while some data can be determined using the second method mentioned above. Alternatively, all data can be determined using either the first or second method mentioned above.

[0064] In one embodiment, for the first method described above, if there is only one determined anti-pinch ambiguity level, then the preset control probability corresponding to that anti-pinch ambiguity level is directly used as the anti-pinch control probability. If there are multiple determined anti-pinch ambiguity levels, the anti-pinch control probability can be obtained by weighting the different pre-set weights of the anti-pinch ambiguity levels and the preset control probabilities corresponding to each anti-pinch ambiguity level.

[0065] As an example, if the second method is included, the determination of the anti-pinch control probability includes: determining the corresponding anti-pinch fuzziness level based on the anti-pinch fuzziness level, the velocity fuzziness level, and the preset fuzziness rule, thus obtaining multiple inference rules; determining the smaller value between the duty cycle fuzziness level membership degree and the velocity fuzziness level membership degree corresponding to an inference rule as the initial strength of the inference rule; determining the rule strength based on the initial strength and the preset weight corresponding to the anti-pinch fuzziness level of the inference rule; if the rule strength is greater than the preset strength value, recording the initial strength as the activation strength of the inference rule, and determining the inference rule as the activation rule; obtaining the preset membership function corresponding to the anti-pinch fuzziness level of the activation rule, and truncating the preset membership function based on the activation strength to obtain the truncated result; performing maximum aggregation on all the truncated results of the activation rules to obtain the final truncated result; and defuzzifying the final truncated result to obtain the anti-pinch control probability.

[0066] In another embodiment, for the second method, fuzzy inference can be performed using methods such as Min-Max Composition. For example, a motor drive duty cycle adjustment value may correspond to one or more duty cycle fuzzy levels, and correspondingly, a motor speed change value may also correspond to one or more speed fuzzy levels. By combining the duty cycle fuzzy level and the speed fuzzy level in pairs with preset fuzzy rules, the corresponding anti-pinch fuzzy level can be obtained, resulting in multiple inference rules (if the duty cycle fuzzy level is A, the speed fuzzy level is B, and the anti-pinch fuzzy level is C, where the duty cycle fuzzy level is A and the speed fuzzy level is B are the condition parts, and the anti-pinch fuzzy level is C is the output result). Then, the smaller value between the membership degree of the duty cycle fuzzy level and the membership degree of the speed fuzzy level in each inference rule is taken as the initial strength of the inference rule. Based on the preset weight of the anti-pinch fuzzy level in the result part and the initial strength, the rule strength is determined. If the rule strength is greater than the preset strength value, the initial strength is recorded as the activation strength of the inference rule. For example, for an inference rule i, the product of the minimum membership value corresponding to the condition part and the weight corresponding to the output result part is determined as the rule strength. If the rule strength is greater than a preset strength value (this value can be set by those skilled in the art as needed, such as 0), the minimum membership value corresponding to the condition part is taken as the activation strength of inference rule i. Then, the anti-pinch fuzziness level corresponding to the inference rule i is obtained, and the preset membership function corresponding to the anti-pinch fuzziness level is obtained. The preset membership function is truncated based on the activation strength to obtain the truncated result. The truncated results corresponding to all inference rules are aggregated to obtain the final truncated result. Then, defuzzification is performed based on the final truncated result, for example, by calculating the centroid of the final truncated result as the final anti-pinch control probability.

[0067] As an example, one possible way to determine activation strength is as follows: the rule activation strength (α_i) is equal to the membership degree (μ) of the input variable Δduty on the fuzzy set A. A (Duty cycle fuzzy level membership degree) and the membership degree (μ) of the input variable Δv on the fuzzy set B B The smaller value in (velocity fuzzy level membership degree).

[0068] As an example, the activation strength of rule activation is determined as follows: Formula (4), in, Let i be the activation strength of inference rule i. Let the duty cycle fuzzy level membership degree of inference rule i be . Let be the membership degree of the fuzzy level of reasoning rule i.

[0069] One example of determining activation intensity is as follows: Based on duty cycle fuzziness level data, velocity fuzziness level data, and preset fuzzy rules, multiple inference rules can be obtained. Based on the duty cycle fuzziness level membership degree, velocity fuzziness level membership degree, and corresponding result weight of each inference rule, the activation state of the inference rule is determined. If the activation state is active (initial rule intensity is greater than the preset intensity value), then the inference rule is taken as the activated inference rule, and the smaller value between the duty cycle fuzziness level membership degree and the velocity fuzziness level membership degree is determined as the activation intensity of the inference rule.

[0070] The preset fuzzy rules can also be the anti-pinch fuzzy level and the corresponding weights that are pre-set by those skilled in the art, based on the duty cycle fuzzy level, the speed fuzzy level, and the corresponding weights.

[0071] As an example, the final truncation result is determined as follows: Formula (5), in, This represents the final truncation result for the output value y across all activated inference rules i. Let μ_C be the activation strength of inference rule i. i (y) represents the membership degree of the rule conclusion.

[0072] Among them, the membership degree of the rule conclusion can be determined by the membership degree of the output value y in the preset membership function corresponding to the anti-pinch fuzziness level of the inference rule i.

[0073] As an example, truncating the preset membership function based on activation intensity can be achieved by truncating the membership degree of a preset output value corresponding to the anti-pinch fuzziness level of an inference rule using activation intensity, thus obtaining the truncated result.

[0074] One example of determining the maximum membership degree (final truncation result) is as follows: obtain the membership degree of the preset output value corresponding to the preset membership degree function of the anti-pinch fuzziness level of the inference rule i, denoted as the rule conclusion membership degree; determine the intermediate membership degree (truncation result) based on the rule conclusion membership degree and the activation strength of the inference rule i; and determine the maximum membership degree (maximum aggregation) of the preset output value based on the intermediate membership degrees of all inference rules, which is the final truncation result of the preset output value.

[0075] As an example, the weights corresponding to the anti-pinch blur level are pre-set. Please refer to Table 4, which is an example.

[0076] Table 4

[0077] For example, taking the inference rule in the first row of Table 4 as an example, the membership degree to PS is 0.7 and the membership degree to NS is 0.5, so the initial strength is 0.5, and the rule strength is 0.5 * 1 = 0.5. If the preset strength value is 0, then the inference rule is an active rule, and the activation strength is 0.5.

[0078] In one embodiment, deblurring the final truncation result to obtain the anti-pinch control probability includes: determining the sampling point weighting value of the preset output value based on the preset output value and the final truncation result corresponding to the preset output value; determining the sampling point weighting sum based on the sampling point weighting values ​​of all preset output values; determining the final truncation result sum based on the final truncation result corresponding to all preset output values; and determining the anti-pinch control probability based on the sampling point weighting and the final truncation result sum.

[0079] As an example, the probability of anti-pinch control is calculated as follows: Formula (6), in, To control the probability of pinch prevention, y j The preset output value, This is the final truncation result at the preset output value (sampling point j).

[0080] In the above formula, the numerator is the weighted sum of all sampling points in the output universe; yj is the j-th discrete sampling point of the output variable (the probability of being trapped) in its universe (e.g., from 0 to 1); μ_agg(yj): the membership value of the total output fuzzy set obtained by rule aggregation at the j-th sampling point (a number between 0 and 1); the denominator is the sum of the membership values ​​of the aggregated output fuzzy set at all sampling points.

[0081] As an example, if the denominator in the above formula (6) is 0, the intermediate probability of 0.5 is returned.

[0082] As an example, the preset membership function (preset control probability) corresponding to the anti-pinch fuzziness level can be found in Table 5.

[0083] Table 5

[0084] Please refer to Table 6, which is a schematic diagram of the calculated anti-pinch control probability.

[0085] Table 6

[0086] Step S240: Determine the anti-pinch control state based on all anti-pinch control probabilities to perform anti-pinch control on the target object.

[0087] In one embodiment, determining the anti-pinch control state based on all anti-pinch control probabilities includes: Determine the average value of all anti-pinch control probabilities. If the average value is greater than the preset second control threshold, determine the anti-pinch control state as triggered anti-pinch. or, The number of anti-pinch control probabilities that are greater than the preset first control quantity threshold is counted. If the number is greater than the preset quantity threshold, the anti-pinch control state is determined to be triggered.

[0088] If the preset detection window determines multiple anti-pinch control probabilities, then the decision to perform anti-pinch operation can be based on the average of the multiple anti-pinch control probabilities, or based on the number of critical events (the number of which is greater than the preset number threshold).

[0089] In one embodiment, determining the anti-pinch control state based on all anti-pinch control probabilities includes: storing the anti-pinch control probabilities sequentially through a pre-created first-in-first-out queue; processing the anti-pinch control probabilities in the first-in-first-out queue using a moving average filter to obtain an average value; and determining the anti-pinch control state as triggered anti-pinch if the average value is greater than a preset second control quantity threshold.

[0090] Since the queue has a limited capacity, the oldest data will be discarded when the capacity is exceeded. A sliding time window is used to dynamically determine whether an anti-pinch operation is needed.

[0091] As an example, the average value is determined as follows: Formula (7), in, Let P_pinch(tk) be the average value, N be the number of anti-pinch control probabilities, and P_pinch(tk) be the anti-pinch control probability at time point tk. N can be determined by the window length of the moving average filter.

[0092] For example, the average of the 5 probability of being pinched (anti-pinch control probability) is compared with 70%. If the average is greater than 70%, the anti-pinch function is deemed effective; otherwise, the process continues and waits for the next determination.

[0093] Following the above embodiments, after determining the anti-pinch control state based on all anti-pinch control probabilities, the method further includes: if the anti-pinch control state is determined to be triggered anti-pinch, updating the anti-pinch control flag to triggered, and triggering anti-pinch control on the target object according to the anti-pinch trigger control parameters; after completing the anti-pinch control, the method further includes at least one of the following: clearing the first-in-first-out queue, resetting the anti-pinch control flag, and performing anti-pinch control on the target object according to the initial anti-pinch control parameters.

[0094] It is understandable that after the anti-pinch operation is completed, the relevant data bits will be reset and the next round of monitoring will begin.

[0095] In one embodiment, anti-pinch control of the target object includes at least one of the following: Control the drive motor of the target object to stop working, or control the drive motor of the target object to reverse; Trigger the generation and display of alarm information.

[0096] As an example, anti-pinch control could be an acoustic prompt (buzzer alert), a flashing LED light, or a notification from a host computer.

[0097] Please see Figure 3 , Figure 3 A schematic diagram illustrating a specific implementation process of the anti-pinch control method provided in this application embodiment is shown below. Figure 3 As shown, the process begins with input data (motor drive duty cycle adjustment value and motor speed change value). This data is then fuzzified to obtain fuzzy level data for the duty cycle (Δduty membership degree) and speed (Δv membership degree). Rule evaluation is then performed to obtain a series of inference rules. These rules are activated to obtain activation strengths, and then aggregated to obtain the final truncation result. The aggregated output is then defuzzified to obtain the anti-pinch control probability. A moving average is applied to the anti-pinch control probabilities obtained from multiple input data points to obtain the final probability output, which is then used to determine whether anti-pinch measures are needed. If the average of five consecutive moving averages is greater than 70%, anti-pinch measures are deemed effective, and anti-pinch actions are executed, such as motor stop or reverse rotation, alarm triggering, or system state reset. If the average of five consecutive moving averages is not greater than 70%, monitoring continues.

[0098] The anti-pinch control method provided in the above embodiments combines PID control of the electric tailgate motion with anti-pinch detection to avoid the problem of excessive anti-pinch force causing injury when the electric tailgate pinches a person. This is beneficial for the effective detection of pinching and improves the reliability of the anti-pinch algorithm. Fuzzy control is used to solve the problem of excessive anti-pinch force caused by PID adjustment of the electric tailgate motion.

[0099] In one embodiment, an anti-pinch control device is provided; please refer to [link / reference]. Figure 4 , Figure 4 A schematic diagram of the anti-pinch control device provided in one embodiment of this application is shown below. Figure 4As shown, the anti-pinch control device 400 includes: an acquisition module 410, used to acquire the motor drive duty cycle adjustment value and motor speed change value of the target object in a preset detection window; a first determination module 420, used to determine duty cycle fuzzy level data based on the motor drive duty cycle adjustment value and a preset duty cycle fuzzy set library, and to determine speed fuzzy level data based on the motor speed change value and a preset speed fuzzy set library; a second determination module 430, used to determine the anti-pinch fuzzy level based on the duty cycle fuzzy level data, speed fuzzy level data and preset fuzzy rules, and to determine the anti-pinch control probability; and an anti-pinch control module 440, used to determine the anti-pinch control state based on all anti-pinch control probabilities, so as to perform anti-pinch control on the target object.

[0100] Specific limitations regarding the anti-pinch control device can be found in the limitations of the anti-pinch control method described above, and will not be repeated here. Each module in the aforementioned anti-pinch control device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the electronic device in hardware form or independently of it, or stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of each module.

[0101] In this embodiment, the anti-pinch control device is essentially equipped with multiple modules to execute the anti-pinch control method in any of the above embodiments. The specific functions and technical effects can be referred to the above embodiments, and will not be repeated here.

[0102] See Figure 5 , Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown below. Figure 5 As shown, this embodiment of the invention also provides an electronic device 500, including a processor 501, a memory 502, and a communication bus 503; the communication bus 503 is used to connect the processor 501 and the memory 502; the processor 501 is used to execute a computer program stored in the memory 502 to implement the method provided in any of the above embodiments.

[0103] In one embodiment, a vehicle is provided, which includes the electronic equipment provided in any of the above embodiments, or performs the method provided in any of the above embodiments. The specific functions and technical effects of the vehicle can be referred to the above embodiments, and will not be repeated here.

[0104] This invention also provides a computer-readable storage medium having a computer program stored thereon, the computer program being used to cause a computer to perform the method described in any of the above embodiments.

[0105] This application also provides a computer-readable storage medium storing one or more modules (programs) that, when applied to a device, enable the device to execute the instructions included in the steps provided in this application.

[0106] This application also provides a computer program product, including a computer program or instructions, which, when executed by a processor, can implement the steps and corresponding content of the aforementioned method embodiments.

[0107] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, 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 device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0108] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0109] Computer program code for performing the operations of this disclosure 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).

[0110] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of this disclosure. 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, may 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.

[0111] It should be understood that the terms "first," "second," etc., used in this application are used to distinguish similar objects and do not necessarily indicate a specific order or sequence. The technical features to which these terms are used can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in a sequence other than that shown in the figures or text.

[0112] It should be understood that although the flowcharts provided in the embodiments of this application indicate the various steps with arrows, the order indicated by the arrows does not necessarily limit the implementation order of these steps. Those skilled in the art can perform these steps in other orders according to different implementation scenarios and requirements.

[0113] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for preventing pinching, characterized in that, The method includes: Obtain the motor drive duty cycle adjustment value and motor speed change value of the target object in the preset detection window; The duty cycle fuzziness level data is determined based on the motor drive duty cycle adjustment value and the preset duty cycle fuzzy set library, and the speed fuzziness level data is determined based on the motor speed change value and the preset speed fuzzy set library; The anti-pinch fuzziness level is determined based on the duty cycle fuzziness level data, the speed fuzziness level data, and the preset fuzziness rules, and the anti-pinch control probability is also determined. The anti-pinch control state is determined based on all the anti-pinch control probabilities in order to perform anti-pinch control on the target object.

2. The anti-pinch control method as described in claim 1, characterized in that, The anti-pinch control state is determined based on all the aforementioned anti-pinch control probabilities, including: Determine the average value of all the anti-pinch control probabilities. If the average value is greater than a preset second control threshold, determine the anti-pinch control state as triggering anti-pinch. or, The number of anti-pinch control probabilities that are greater than a preset first control quantity threshold is counted. If the number is greater than a preset quantity threshold, the anti-pinch control state is determined to be triggered.

3. The anti-pinch control method as described in claim 1, characterized in that, The anti-pinch control state is determined based on all the aforementioned anti-pinch control probabilities, including: The anti-pinch control probabilities are stored sequentially using a pre-created first-in-first-out queue; The anti-pinch control probability in the first-in-first-out queue is processed by a moving average filter to obtain an average value; If the average value is greater than the preset second control threshold, the anti-pinch control state is determined to be triggered.

4. The anti-pinch control method as described in claim 3, characterized in that, After determining the anti-pinch control state based on all the aforementioned anti-pinch control probabilities, the method further includes: If the anti-pinch control state is determined to be triggered anti-pinch, the anti-pinch control flag is updated to triggered, and the target object is subjected to anti-pinch control according to the anti-pinch trigger control parameters. After completing the anti-pinch control, the method further includes at least one of the following: clearing the first-in-first-out queue, resetting the anti-pinch control flag, and performing anti-pinch control on the target object according to the initial anti-pinch control parameters.

5. The anti-pinch control method according to any one of claims 1-4, characterized in that, Anti-pinch control of the target object includes at least one of the following: Control the drive motor of the target object to stop working, or control the drive motor of the target object to reverse; Trigger the generation and display of alarm information.

6. The anti-pinch control method according to any one of claims 1-4, characterized in that, The method for determining the anti-pinch control probability includes any one of the following: Obtain the preset control probability corresponding to the anti-pinch ambiguity level, and determine the preset control probability as the anti-pinch control probability. Each anti-pinch ambiguity level is pre-configured with a corresponding preset control probability. The anti-pinch control probability is determined based on the duty cycle fuzziness level, duty cycle fuzziness level membership degree, speed fuzziness level, speed fuzziness level membership degree, and the preset membership function corresponding to the anti-pinch fuzziness level. Each anti-pinch fuzziness level is pre-configured with a corresponding preset membership function. The duty cycle fuzziness level data includes the duty cycle fuzziness level and the duty cycle fuzziness level membership degree, and the speed fuzziness level data includes the speed fuzziness level and the speed fuzziness level data membership degree.

7. The anti-pinch control method as described in claim 6, characterized in that, If the method for determining the anti-pinch control probability includes determining the anti-pinch control probability based on the duty cycle fuzziness level, the duty cycle fuzziness level membership degree, the velocity fuzziness level, the velocity fuzziness level membership degree, and the preset membership function corresponding to the anti-pinch fuzziness level, then the method for determining the anti-pinch control probability includes: Based on the anti-pinch fuzziness level, the speed fuzziness level, and the preset fuzziness rule, the corresponding anti-pinch fuzziness level is determined, and multiple inference rules are obtained. The smaller value between the duty cycle fuzzy level membership degree and the velocity fuzzy level membership degree corresponding to a reasoning rule is determined as the initial strength of the reasoning rule; The rule strength is determined based on the initial strength and the preset weight corresponding to the anti-pinch fuzziness level of the inference rule; If the rule strength is greater than the preset strength value, the initial strength is recorded as the activation strength of the inference rule, and the inference rule is determined as the activation rule; Obtain the preset membership function corresponding to the anti-pinch fuzziness level of the activation rule, and truncate the preset membership function based on the activation intensity to obtain the truncation result; The truncation results of all activation rules are aggregated to obtain the final truncation result corresponding to the preset output value; The sampling point weighting value of the preset output value is determined based on the preset output value and the final truncation result corresponding to the preset output value. The sampling point weighting sum is determined based on all the sampling point weighting values. The final truncation result sum is determined based on all the preset output values. The anti-pinch control probability is determined based on the sampling point weighting and the final truncation result sum.

8. An anti-pinch control device, characterized in that, The device includes: The acquisition module is used to acquire the motor drive duty cycle adjustment value and motor speed change value of the target object in the preset detection window; The first determining module is used to determine duty cycle fuzzy level data based on the motor drive duty cycle adjustment value and a preset duty cycle fuzzy set library, and to determine speed fuzzy level data based on the motor speed change value and a preset speed fuzzy set library; The second determining module is used to determine the anti-pinch fuzziness level based on the duty cycle fuzziness level data, the speed fuzziness level data and the preset fuzziness rules, and to determine the anti-pinch control probability. An anti-pinch control module is used to determine the anti-pinch control state based on all the anti-pinch control probabilities, so as to perform anti-pinch control on the target object.

9. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the computer program in the memory to implement the steps of the method according to any one of claims 1 to 7.

10. A vehicle, characterized in that, The vehicle includes the electronic equipment as claimed in claim 9, or performs the steps of the method as claimed in any one of claims 1 to 7.

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