A precision detection method, device and medium of a linear guide rail

Abstract

This invention discloses a method, device, and medium for precision testing of linear guideways, relating to the field of guideway precision testing technology. The method includes: constructing a separable multi-condition detection input; acquiring five-component observation errors along the travel path under each condition; defining different travel positions of the linear guideway; constructing an event type library, including zero-crossing events and straightness extreme value events; defining corresponding event functions and event conditions for each event; and performing deduplication processing on selected event state sequences. The method of this invention maps the raw output of multi-channel sensors to straightness and attitude error expressions with clear engineering semantics based on a unified measurement model, enabling comparability and composability of observations with different dimensions and installation positions within the same error space.

Description

Technical Field

[0001] This invention relates to the field of guide rail accuracy testing technology, and in particular to a method, apparatus, equipment and medium for accuracy testing of linear guide rails. Background Technology

[0002] As a key basic component of CNC machine tools, precision measuring equipment, semiconductors, and automated assembly platforms, linear guides directly determine the consistency of the positional error of the actuator and the machining / inspection results through their motion accuracy. Related inspection technologies have evolved from linearity inspection using mechanical gauges and comparators to laser interferometry, electronic levels, grating rulers, and multi-sensor fusion measurement. In this process, the inspection target has also gradually expanded from straightness in a single direction to a comprehensive error expression that simultaneously characterizes straightness in two directions and multi-degree-of-freedom posture. However, existing linear guide rail accuracy testing still faces several key shortcomings. First, there is significant overlap between guide rail body error, measurement chain error, installation geometric error, and force response. Error curves obtained under a single working condition often lack separability, causing different error sources to absorb each other during fitting, making it difficult to form an absolutely meaningful body accuracy output. Second, at the guide rail end, in local smooth sections, or under the influence of controlled deviations, the event conditions are prone to root non-uniqueness or root drift, which will cause instability in the number of multiple events within the same working condition and the number of events across working conditions, resulting in non-closed constraints and non-convergence of the solution, making it difficult to simultaneously constrain body error and measurement chain error within the same parameter system. Summary of the Invention

[0003] In view of the aforementioned existing problems, the present invention is proposed.

[0004] Therefore, this invention provides a method, device, equipment, and medium for detecting the accuracy of linear guideways. This addresses the significant overlap between guideway body errors, measurement chain errors, installation geometric errors, and force responses. Error curves obtained under single working conditions often lack separability, leading to mutual absorption of different error sources during fitting, making it difficult to form an absolutely meaningful body accuracy output. Furthermore, at guideway ends, in locally smooth sections, or under controlled deviations, event conditions are prone to non-unique roots or root drift, causing instability in the number of multiple events within the same working condition and across working conditions. This results in non-closed constraints and non-convergence of the solution, making it difficult to simultaneously constrain body errors and measurement chain errors within the same parameter system.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for detecting the accuracy of a linear guide rail, comprising: Construct a separable multi-condition detection input and collect five-component observation error along the stroke under each condition; Define different travel positions of the linear guide, construct an event type library including zero-crossing events and straightness extreme value events, define corresponding event functions and event conditions for the events, and perform deduplication processing on the selected event state sequence; The degradation segment identification and screening event state sequences are introduced, and the different equivalent event states under the same event are analyzed for insertion and filling. The structured observation model is constructed based on the observation error, controlled deviation and measurement chain error term of the guide rail body, and the joint solution of the accuracy error of the guide rail body is performed. The error output is converted to the slider coordinate system and logged.

[0006] As a preferred embodiment of the accuracy detection method for linear guide rails described in this invention, the acquisition of five-component observation errors includes: Based on the guide rail motion error in linear motion as the detection target, and considering the aliasing of guide rail error, measurement chain error, installation and force response, the straightness error is statistically analyzed. Combined with the three components of angle error, a five-component vector is constructed, and a five-component observation error vector is constructed based on the observation data.

[0007] As a preferred embodiment of the accuracy detection method for linear guideways described in this invention, the method involves defining different travel positions of the linear guideway, constructing an event type library including zero-crossing events and straightness extreme value events, and defining corresponding event functions and event conditions for each event, including... Based on different working conditions, discretization sampling is performed using a five-component observation error vector. For zero-crossing events, the observation angle error component is used as the event function. For straightness extreme events, a continuous piecewise function is constructed using a piecewise cubic spline algorithm. Based on the event type library, the sampling node of the straightness extreme event is evaluated, and event conditions are defined for candidate interval detection. The event location of the candidate interval is determined by finding the root using the bisection method.

[0008] In a preferred embodiment of the accuracy detection method for linear guide rails described in this invention, the deduplication process includes: Set a minimum event location interval and perform deduplication on the event location sequence.

[0009] In a preferred embodiment of the accuracy detection method for linear guides described in this invention, the step of analyzing different equivalent event states under the same event and inserting / filling includes: In the processed event state sequence, degradation segment identification is introduced, degradation criteria are defined to determine the degradation domain, and it is decomposed into several non-overlapping continuous intervals. Sampling nodes are screened, and equivalent event positions and equivalent event states are defined for insertion and filling.

[0010] As a preferred embodiment of the accuracy detection method for linear guideways described in this invention, the step of constructing a structured observation model based on the observation error, controlled deviation, and measurement chain error term of the guideway body, and jointly solving for the accuracy error of the guideway body, includes: Differential observation of the event state sequence is performed by differential calculation to clarify the controlled deviation vector of the linear guide rail. A structured observation model is constructed by combining the observation error, measurement chain error term and response term caused by the controlled input. The optimal estimate of the model parameters is obtained by solving the weighted least squares method, and the accuracy error vector of the guide rail body is output.

[0011] In a preferred embodiment of the accuracy detection method for the linear guide rail described in this invention, the error output converted to the slider coordinate system includes: The pose transformation is performed based on the accuracy error vector of the guide rail body, and the slider coordinate system of the linear guide rail is mapped accordingly.

[0012] In a preferred embodiment of the accuracy detection method for linear guides described in this invention, the step of logging includes: Based on the mapping of the guide rail body's accuracy error vector in the slider coordinate system, and the mapping of the controlled deviation vector directly acquired by the sensor in the slider coordinate system, a precision detection data pair for the linear guide rail is formed, and the detection log is output and uploaded to the cloud.

[0013] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the accuracy detection method for linear guide rails as described in the first aspect of the present invention.

[0014] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the accuracy detection method for linear guides as described in the first aspect of the present invention.

[0015] The beneficial effects of this invention are as follows: By mapping the raw output of multi-channel sensors to linearity and attitude error expressions with clear engineering semantics according to a unified measurement model, observations with different dimensions and installation positions are comparable and combinable in the same error space. By introducing zero-crossing geometric states and linearity local extreme geometric states as complementary anchor points, the five-component observation errors under different working conditions no longer depend on sampling point alignment but on geometric state alignment. Thus, even when changes in working conditions cause curve amplitude, phase, or local shape drift, the reproducible consistency of event sequence and position is maintained. By identifying degenerate intervals such as plateau segments and gradually changing segments that cause event roots to be non-unique or prone to drift, and filtering multiple unstable events within the degenerate intervals, and inserting equivalent event states, the stability of the number and order of nodes across working conditions is ensured. This makes the event system naturally satisfy the requirements of one-to-one correspondence and reversibility, structurally eliminating the constraint non-closure problem caused by the drift of the number of events in a segment within the same working condition and across working conditions. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the accuracy testing method for linear guides in Example 1.

[0018] Figure 2 This is a flowchart illustrating the degradation segment identification process of the linear guide rail accuracy detection method in Example 1.

[0019] Figure 3 This is a schematic diagram of the event filtering process for the accuracy detection method of the linear guide rail in Example 1.

[0020] Figure 4 This is a schematic diagram of the data flow and system interaction timing of the accuracy detection method for linear guide rails in Example 1. Detailed Implementation

[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0022] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0023] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0024] Example 1, referring to Figures 1 to 4 This is the first embodiment of the present invention, which provides a method for detecting the accuracy of a linear guide rail, including the following steps: S1, construct a separable multi-condition detection input, and collect five-component observation error along the stroke under each condition; S1.1, based on the guide rail motion error in linear motion as the detection target, and considering the aliasing of guide rail error, measurement chain error, installation and force response, the straightness error is statistically analyzed, and combined with the three-component angle error, a five-component vector is constructed, and a five-component observation error vector is constructed based on the observation data; Specifically, the accuracy monitoring based on linear guides usually uses the motion error of the guide rail in linear motion as the detection target. This usually includes guide rail error, measurement chain error, installation and force response, which cause the detected guide rail nodes to be unstable. Specifically, it consists of the deviation values ​​of the guide rail nodes in the explanatory direction and vertical direction detected by the sensor. In engineering applications, linear guide rail accuracy testing often involves a mixture of guide rail errors, measurement chain errors, and installation / stress responses. A five-component vector is constructed for the straightness and angular errors of the guide rail. The straightness error includes the Y-axis and Z-axis errors along the X-axis, while the angular error includes the rotational angle error around a specific axis (such as an axis in a coordinate system), the rotational angle error around another specific axis, and the angular error corresponding to the remaining rotational degrees of freedom. Specifically, regarding the five-component vector, based on the current sensor output, the observed five-component vector is initially constructed, with the installation height (or vertical spacing) of the two Y-axis probes as follows: and The lateral distance between the two probes in the Z-axis direction is respectively and Therefore, under the small-angle approximation, the displacement difference can be used to deduce the angular component, expressed as: in, This represents the rotational angle error about a specific axis A, x represents the travel position, and m represents the event condition index. and These represent the Z-axis displacements measured by the two probes at different lateral positions m. This indicates the effective distance between the two sides in the Z-axis direction. and These represent the Y-direction displacements measured by the two probes at different lateral positions m. This indicates the effective distance between the two sides in the Y-axis direction. This represents the rotational angle error about a specific axis B. The angular error representing the remaining rotational degrees of freedom, and These represent the directional displacements measured at different lateral positions m on both sides of the probe, representing the remaining rotational degrees of freedom. This indicates the left-right distance between the probes on both sides of the remaining rotational degree of freedom; Combining the linear guide rail's dimensional error, a five-component observation error vector is formed, expressed as: in, This represents the five-component observation error vector. and These represent the linear deviations of the guide rail along the X direction in the Y and Z directions, respectively; By mapping the raw output of multi-channel sensors to linearity and attitude error expressions with clear engineering semantics according to a unified measurement model, observations with different dimensions and installation positions are comparable and combinable in the same error space, avoiding the problem that traditional methods only obtain a single deviation curve and cannot support the subsequent stable event localization.

[0025] S2, define different travel positions of the linear guide, construct an event type library, which includes at least zero-crossing events and straightness extreme value events, and define corresponding event functions and event conditions for the events, and perform deduplication processing on the selected event state sequence; S2.1, based on different working conditions, discretization sampling is performed using a five-component observation error vector. For zero-crossing events, the observation angle error component is used as the event function. For straightness extreme events, a continuous piecewise function is constructed using a piecewise cubic spline algorithm. Based on the event type library, the sampling node of the straightness extreme event is evaluated, and event conditions are defined for candidate interval detection. The event location of the candidate interval is determined by finding the root using the bisection method. Furthermore, based on the different travel positions of the linear guide under different working conditions, sampling nodes are used, and discretization sampling is performed based on the five-component observation error vector. Events are defined, including angle zero-crossing events and straightness extreme events. The zero-crossing event is the geometric state with a pitch angle of 0, and the straightness extreme event represents the geometric state with a local extreme of horizontal straightness. Zero-crossing events and straightness extremum events are used as event types, and different five-component observation error vectors are constructed as an event type library. Zero-crossing geometric state and straightness local extremum geometric state are introduced as complementary anchor points, and the event function, event condition and value caliber are uniformly constrained by the event type library, so that the five-component observation error under different working conditions no longer depends on the alignment of sampling points but on the alignment of geometric state, thus maintaining the reproducible consistency of event sequence number and position when the curve amplitude, phase or local shape drifts due to changes in working conditions. Specifically, a zero-crossing event can be represented as: in, The event scalar function represents event type 1, where V represents the sequence index of different biases in the five-component observation error vector, specifically in different zero-crossing events. or There is zero crossover; For straightness extrema events, it is necessary to discretize the five-component observation error vector and construct a continuous piecewise function using a piecewise cubic spline algorithm, expressed as: in, Represents a piecewise function. , , , These represent the endpoint function values, second-order coefficients, coefficients of variation, and derivative coefficients, respectively. Let the position of the i-th travel segment be represented by the nodal second derivative, which is used to solve for the piecewise function, as follows: in, express The second derivative value , This represents the probe spacing at the i-th travel position. This represents the deviation from the straight line. Based on the piecewise function value being directly defined as the node evaluation value, different sampling nodes are retrieved for candidate interval detection. All candidate intervals are sorted, and the event location of the candidate interval is determined by finding the root using the bisection method. The candidate interval detection performed can be represented as: in, Indicates the evaluation at the sampling node. and Let represent the positions of the i-th and i+1-th travel segments, respectively, forming a change interval. At the sampling node, candidate intervals are evaluated for satisfaction detection. When the satisfaction condition is less than 0, the event position of the candidate interval is determined by finding the root using a bisection method, denoted as . The five-component observation error vector of the event location is used as the selected event state sequence; By first constructing a second-order continuous piecewise function for the straightness component, then evaluating the change range of the positioning symbol at the sampling node, and using root-finding iteration within the range to obtain the event position, the extreme value positioning is transformed from discrete point jump to continuous domain stable positioning. This, together with the angle zero-crossing event, forms a cross-component constraint skeleton, making the event node insensitive to noise and less prone to generating pseudo-events in local plateau segments. S2.3, Set the minimum event position interval and perform deduplication processing on the event position sequence; Specifically, if the difference between adjacent event states satisfies the condition that it is less than the minimum interval, then deduplication is performed, where the minimum interval condition is determined based on historical experience. The five-component observation error vector is read from the retained valid event locations and a processed event state sequence is formed to avoid the inconsistency of the number of nodes in different working conditions, which would destroy the recognizability of subsequent alignment and differential constraints. Finally, the five-component observations of multiple working conditions are reconstructed from continuous curves into a sparse, stable, and alignable set of event states. This structurally improves the constraint consistency and solution uniqueness when decoupling the body error and the measurement chain error, resulting in a non-obvious robust absolute detection effect.

[0026] S3 introduces degradation segment identification and filters event state sequences, analyzes different equivalent event states under the same event for insertion and filling, constructs structured observation models based on the observation error, controlled deviation and measurement chain error terms of the guide rail body, and performs joint solution of the accuracy error of the guide rail body. S3.1 In the processed event state sequence, degenerate segment identification is introduced, a degenerate criterion is defined to determine the degenerate domain, and it is decomposed into several non-overlapping continuous intervals. Sampling nodes are screened, and equivalent event positions and equivalent event states are defined for insertion and filling. Specifically, in the filtered event state sequence, the sampling nodes at different event locations are evaluated. If the value is approximately 0, it indicates the presence of guide rail end, local smooth section, or controlled deviation condition. Therefore, a degradation segment identification is introduced for the event function under each event type. Based on the degradation criterion defined at the sampling node, the degradation domain is determined by the travel range of the sampling points, and then decomposed into several non-overlapping continuous intervals, represented as: in, Indicates a degradation domain. Indicates the travel range of the sampling node. For the initial range value, Indicates the maximum range value. This represents the evaluation at the sampling node for the j-th event type. This represents the minimum rate of change threshold, determined based on historical experience. This represents a list of degenerate segments. Denotes the set of closed intervals containing the degenerate point r. Indicates the starting position of the degenerate segment. Indicates the end position of the degenerate segment. Indicates the number of degenerate point intervals; Based on the filtered event state sequence, sampling nodes are filtered for each degenerate segment, as follows: in, This represents the set of event indices within the degenerate segment, where k represents the event index; Specifically, if If so, it means that degradation processing does not need to be triggered. If , it indicates the existence of one event node, and it is marked as a degenerate single node. This indicates that multiple event nodes occur within the same degenerate segment; For event state sequences that contain a single degenerate node or multiple event nodes within the same degenerate segment, degenerate segment compression is required. Preferably, the equivalent event position is defined as the midpoint of the degenerate segment. Simultaneously, an equivalent event state is constructed based on the endpoints of the corresponding five-component observation error vector, expressed as: in, Indicates the location of the equivalent event. Indicates the equivalent event state; For the event index set within the degenerate segment, the original corresponding event nodes are deleted, and compressed equivalent nodes are inserted, including the equivalent event location and equivalent event status. By identifying degenerate intervals such as platform segments and gradually changing segments that cause event roots to be non-unique or prone to drift, and by filtering multiple unstable events within the degenerate intervals and inserting equivalent event states, the number and order of nodes across operating conditions are ensured to be stable. This makes the event system naturally meet the requirements of one-to-one correspondence and reversibility, structurally eliminating the constraint non-closure problem caused by multiple events in the same operating condition and the drift of the number of events across operating conditions. In this way, the reversibility of the event chain is transformed from an empirical assumption into a computable guarantee.

[0027] S3.2, differential observation of the event state sequence is performed through differential calculation to clarify the controlled deviation vector of the linear guide rail. A structured observation model is constructed by combining the observation error, measurement chain error term and response term caused by the controlled input. The optimal estimate of the model parameters is obtained by solving the weighted least squares method, and the guide rail body accuracy error vector is output. Specifically, based on the filtered event state sequence, differential observation is performed through differential calculation to obtain differential event states. At the same time, the controlled deviation vector (including horizontal and vertical deviation) directly collected by the linear guide rail through the sensor is used as the differential input through the difference of the controlled deviation vectors of different events. A structured observation model is constructed, which includes the observation error of the guide rail itself, measurement chain error terms (zero bias / scale / external parameters, etc.), and response terms caused by controlled inputs, expressed as: in This represents a filtered sequence of event states. This represents the observation error of the guide rail body, where The parameters represent the error function. Represents the measurement chain terms, where This represents the equivalent error introduced by zero bias, scale, extrinsic parameters, etc. This represents the response term caused by the controlled input, where Represents the controlled deviation vector; Based on the difference in the controlled deviation vector and the state of the difference event, the difference domain constraints are defined, and the joint objective function is constructed as follows: in, Describe the joint objective function. Ka represents the total number of operating conditions, and Ka represents the total number of events. This represents the importance coefficient of the difference constraint term. Represents the difference field constraint. Represents the differential event state of the response item caused by the controlled input; Based on minimizing the joint objective function, the ontology error parameters are obtained by solving the weighted least squares method. With measurement chain parameters The optimal estimate is obtained, and the guide rail body accuracy error vector is output, expressed as: in, This represents the accuracy error vector of the guide rail body. The parameters represent the error function of the optimal estimate; By performing differential observations on nodes with the same sequence number after degradation compression and binding the differential inputs to controlled bias vector differences, the common terms in the observations and the equivalent errors of the measurement chain are systematically suppressed in the differential domain, while the controlled input responses are strengthened into dominant information. Then, it is combined with the structured observation model in the event domain to enter the weighted least squares solution, so that the ontological error parameters and measurement chain parameters are constrained and separated in the same framework, which significantly reduces the risk of spurious solutions caused by mutual absorption of parameters.

[0028] S4, convert to the error output of the slider coordinate system and record it in the log; S4.1, Perform pose transformation based on the guide rail body accuracy error vector, and map the slider coordinate system of the linear guide rail respectively; S4.2, Based on the mapping of the guide rail body accuracy error vector in the slider coordinate system, and the mapping of the controlled deviation vector directly acquired by the sensor in the slider coordinate system, a precision detection data pair of the linear guide rail is formed, and the detection log is output and uploaded to the cloud.

[0029] This embodiment also provides a computer device applicable to the accuracy detection method of linear guide rails, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the accuracy detection method of linear guide rails as proposed in the above embodiment.

[0030] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0031] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the accuracy detection method for linear guides as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0032] In summary, this invention maps the raw output of multi-channel sensors into straightness and attitude error expressions with clear engineering semantics based on a unified measurement model. This makes observations with different dimensions and installation positions comparable and combinable in the same error space. By introducing zero-crossing geometric states and straightness local extreme geometric states as complementary anchor points, the five-component observation errors under different working conditions no longer depend on sampling point alignment but on geometric state alignment. This maintains the reproducible consistency of event sequence and position even when changes in working conditions cause curve amplitude, phase, or local shape drift. By identifying degenerate intervals such as plateau segments and gradually changing segments that cause event roots to be non-unique or prone to drift, and filtering multiple unstable events within the degenerate intervals, as well as inserting equivalent event states, the number and order of nodes across working conditions are kept stable. This ensures that the event system naturally meets the requirements of one-to-one correspondence and reversibility, structurally eliminating the constraint non-closure problem caused by the drift in the number of events within a segment under the same working condition and across working conditions.

[0033] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A precision detection method of a linear guide rail, characterized by, include: Construct a separable multi-condition detection input and collect five-component observation error along the stroke under each condition; Define different travel positions of the linear guide, construct an event type library including zero-crossing events and straightness extreme value events, define corresponding event functions and event conditions for the events, and perform deduplication processing on the selected event state sequence; The degradation segment identification and screening event state sequences are introduced, and the different equivalent event states under the same event are analyzed for insertion and filling. The structured observation model is constructed based on the observation error, controlled deviation and measurement chain error term of the guide rail body, and the joint solution of the accuracy error of the guide rail body is performed. The error output is converted to the slider coordinate system and logged.

2. The precision detection method of a linear guide rail according to claim 1, wherein: The five-component observation error includes, Based on the guide rail motion error in linear motion as the detection target, and considering the aliasing of guide rail error, measurement chain error, installation and force response, the straightness error is statistically analyzed. Combined with the three components of angle error, a five-component vector is constructed, and a five-component observation error vector is constructed based on the observation data.

3. The precision detection method of a linear guide rail according to claim 2, characterized in that: The system defines different travel positions of the linear guide and constructs an event type library, which includes zero-crossing events and straightness extreme value events. Corresponding event functions and conditions are defined for each event. include, Based on different working conditions, discretization sampling is performed using a five-component observation error vector. For zero-crossing events, the observation angle error component is used as the event function. For straightness extreme events, a continuous piecewise function is constructed using a piecewise cubic spline algorithm. Based on the event type library, the sampling node of the straightness extreme event is evaluated, and event conditions are defined for candidate interval detection. The event location of the candidate interval is determined by finding the root using the bisection method.

4. The accuracy detection method for linear guides as described in claim 3, characterized in that: The deduplication process includes, Set a minimum event location interval and perform deduplication on the event location sequence.

5. The accuracy detection method for linear guides as described in claim 4, characterized in that: The analysis involves inserting and filling different equivalent event states under the same event. include, In the processed event state sequence, degradation segment identification is introduced, degradation criteria are defined to determine the degradation domain, and it is decomposed into several non-overlapping continuous intervals. Sampling nodes are screened, and equivalent event positions and equivalent event states are defined for insertion and filling.

6. The accuracy detection method for linear guides as described in claim 5, characterized in that: The structured observation model is constructed based on the observation error of the guide rail body, the controlled deviation, and the measurement chain error term, and a joint solution is performed for the accuracy error of the guide rail body. include, Differential observation of the event state sequence is performed by differential calculation to clarify the controlled deviation vector of the linear guide rail. A structured observation model is constructed by combining the observation error, measurement chain error term and response term caused by the controlled input. The optimal estimate of the model parameters is obtained by solving the weighted least squares method, and the accuracy error vector of the guide rail body is output.

7. The accuracy detection method for linear guides as described in claim 6, characterized in that: The error output converted to the slider coordinate system includes, The pose transformation is performed based on the accuracy error vector of the guide rail body, and the slider coordinate system of the linear guide rail is mapped accordingly.

8. The accuracy detection method for linear guides as described in claim 7, characterized in that: The logging process includes, Based on the mapping of the guide rail body's accuracy error vector in the slider coordinate system, and the mapping of the controlled deviation vector directly acquired by the sensor in the slider coordinate system, a precision detection data pair for the linear guide rail is formed, and the detection log is output and uploaded to the cloud.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the accuracy detection method for linear guide rails according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the accuracy detection method for linear guide rails according to any one of claims 1 to 8.

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