An automated detection and early warning method and system for SMT production lines

Abstract

The present invention discloses an automated detection and early warning method and system for SMT production lines, belonging to the field of production line detection technology. In the method part, unified coding is implemented based on the product component list, and the position information of the sample BOM patch position diagram and the patch position diagram of the product to be detected are compared; components with inconsistent positions are collected to generate a sample set to be calibrated, and converted into a connection tree relationship diagram to generate a sample set to be evaluated; the abnormal impact range value of the production line is evaluated, and an automated detection curve function is depicted; the abnormal fluctuation degree is analyzed, the early warning object is marked, and a list of early warning objects is generated. The system includes a database module, a data processing module, an automated detection and calibration center module, and an early warning module, which cooperates with the implementation of the method part to respectively realize data storage, information generation and processing, abnormal impact evaluation, and early warning functions. The present invention improves the production efficiency and product quality of the SMT production line and the timeliness of the production line early warning through automated detection and early warning.

Description

Technical Field

[0001] The present invention relates to the field of production line detection technology, and in particular to an automatic detection and early warning method and system for an SMT production line. Background Art

[0002] In the SMT (surface mount technology) production line, precise placement of product components is crucial to ensuring product quality and performance; however, with the miniaturization and complexity of electronic products, the types and quantities of product components continue to increase, and the requirements for placement accuracy are becoming higher and higher, which brings huge challenges to production line detection and early warning.

[0003] Traditional manual inspection methods are not only inefficient but also prone to errors, and cannot meet the high efficiency and high precision requirements of modern SMT production lines. Therefore, the industry has begun to explore automated inspection and early warning technologies to achieve fast and accurate inspection of the placement of components on production line products, and to provide timely warnings of potential quality issues.

[0004] However, most of the existing automated detection and early warning technologies are based on image recognition algorithms. They detect the consistency of component placement by comparing the sample BOM (bill of materials) placement map with the placement map of the product to be tested. These technologies often only focus on the placement of a single component and ignore the connection relationship between components. In particular, once the SMT production line is started, it will enter an automated batch production state. The continuous output mode of the product makes it difficult to grasp the impact range of the production line abnormality in a timely manner, resulting in the accuracy and reliability of the early warning results need to be improved. Summary of the invention

[0005] The object of the present invention is to provide an automated detection and early warning method and system for an SMT production line to solve the problems raised in the above-mentioned background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] An automated detection and early warning system for SMT production lines, the system comprising: a database module, a data processing module, an automated detection and calibration center module and an early warning module;

[0008] The database module is used to store the coding information of each product component, the patch location diagram of the product to be tested and the sample BOM patch location diagram, and is used to compare the location information of each product component in the sample BOM patch location diagram and the patch location diagram of the product to be tested;

[0009] The data processing module is used to generate a sample set to be calibrated and a sample set to be evaluated;

[0010] The automated detection calibration center module is used to evaluate the production line abnormality impact range value when the product components are used as calibration objects, and based on the production line abnormality impact range value, generate an automated detection curve function, and analyze the abnormal fluctuation of the patch position diagram of the product to be detected;

[0011] The warning module is used to mark the warning objects and generate a warning object list.

[0012] Further, the database module includes an import interface unit and an image interface unit;

[0013] The import interface unit is used to import the product component list and uniformly encode each product component in the product component list;

[0014] The image interface unit is used to construct a unified two-dimensional coordinate system in the image software, import the sample BOM patch position diagram and the patch position diagram of the product to be tested into the image software respectively, and compare the position information of each product component.

[0015] Further, the data processing module includes an information collection unit and an information screening unit;

[0016] The information collection unit collects components of each product with inconsistent positions based on the comparison result to generate a sample set to be calibrated;

[0017] The information screening unit converts the connection relationship of each product component in the drawn product circuit board into a connection tree relationship diagram based on the sample BOM patch position diagram; based on the sample set to be calibrated and the connection tree relationship diagram, the product components in the sample set to be calibrated are used as calibration objects, and the product components that are inconsistent with the previous position and the product components that are inconsistent with the next position in the connection relationship with the product components are captured in the connection tree relationship diagram, and a sample set to be evaluated is generated.

[0018] Further, the automated detection and calibration center module includes an abnormal impact range assessment unit, a curve characterization unit, and an abnormal fluctuation assessment unit;

[0019] The abnormal impact range evaluation unit evaluates the production line abnormal impact range value when the product components are used as calibration objects based on the sample set to be evaluated;

[0020] The curve drawing unit is used to draw an automated detection curve of the patch position diagram of the product to be detected with the number of the product component as the horizontal coordinate value and the value of the production line abnormality impact range when the product component is used as the calibration object as the vertical coordinate value, and convert it into an automated detection curve function;

[0021] The abnormal fluctuation evaluation unit analyzes the abnormal fluctuation degree of the patch position diagram of the product to be detected based on the automated detection curve function.

[0022] Further, the warning module includes a judgment marking unit and a warning list generating unit;

[0023] The judgment and marking unit is used to preset an abnormal fluctuation threshold, and if the abnormal fluctuation degree is greater than or equal to the abnormal fluctuation threshold, the patch position diagram of the product to be detected is marked as a warning object;

[0024] The warning list generating unit is used to generate a warning object list and send it to the staff port.

[0025] An automated detection and early warning method for an SMT production line, the method comprising the following steps:

[0026] Step S1: Based on the product component list, uniformly encode the components of each product; compare the location information of the components of each product with the sample BOM patch location diagram and the patch location diagram of the product to be tested, and output the comparison result;

[0027] Step S2: Based on the comparison results, collect the product components with inconsistent positions to generate a sample set to be calibrated; convert the connection relationship of each product component in the drawn product circuit board into a connection tree relationship diagram, and generate a sample set to be evaluated based on the sample set to be calibrated and the connection tree relationship diagram;

[0028] Step S3: Based on the sample set to be evaluated, evaluate the production line abnormality impact range value when the product components are used as the calibration object, and construct a two-dimensional coordinate system to depict the automated detection curve function of the patch position map of the product to be detected;

[0029] Step S4: Based on the automated detection curve function, analyze the abnormal fluctuation of the patch position diagram of the product to be detected, and mark the warning objects to generate a warning object list.

[0030] Furthermore, the specific implementation process of step S1 includes:

[0031] Import the product component list into the automated testing database, which will uniformly encode each product component;

[0032] In the image software, a unified two-dimensional coordinate system is constructed, and the sample BOM patch position diagram and the patch position diagram of the product to be tested are respectively imported into the image software. The image software captures the position information of each product component in the sample BOM patch position diagram and the patch position diagram of the product to be tested in turn according to the coding order of the product components, and compares the position information of each product component, and sends the comparison results to the automated detection database, the comparison results including consistent positions and inconsistent positions.

[0033] Furthermore, the specific implementation process of step S2 includes:

[0034] Based on the comparison results, the components of each product with inconsistent positions are collected to generate a sample set to be calibrated, which is recorded as CS = {C i |i∈[1,I]}, and imported into the automated detection and calibration center, where C i Represents the i-th product component, where i is the code number of the product component;

[0035] Based on the sample BOM patch location diagram, the connection relationship of each product component in the drawn product circuit board is converted into a connection tree relationship diagram; based on the sample set to be calibrated CS and the connection tree relationship diagram, the product components C in the sample set to be calibrated are i To calibrate the object, capture the product component C in the connection tree diagram i There are product components with inconsistent previous positions and product components with inconsistent subsequent positions in the connection relationship, and a sample set to be evaluated is generated, which is recorded as ES(C i ),and

[0036] Furthermore, the specific implementation process of step S3 includes:

[0037] Based on the sample set to be evaluated, evaluate product components C i The impact range of production line abnormality when used as the calibration object:

[0038]

[0039] In the formula, RP(C i ) indicates product component C i As the calibration object, the production line abnormality impact range value, C j represents the jth product component, C j ∈CS and C j ≠C i , ES(C j ) indicates product component C j As the sample set to be evaluated when it is used as the calibration object, NUM[ES(C i )∩ES(C j )] represents the sample set to be evaluated ES(C i ) and the sample set to be evaluated ES(C j ) The number of product components contained in the intersection set, NUM[ES(C i )∪ES(C j )] represents the sample set to be evaluated ES(C i ) and the sample set to be evaluated ES(C j ) The number of product components contained in the union set;

[0040] In the above method, the data source for evaluating the production line abnormality impact range value is the product to be tested with inconsistent position information. Based on the line connection relationship, the abnormal impact range of any product component is weighed by the components with inconsistent position information. The larger the production line abnormality impact range value when the product component is used as the calibration object, the greater the impact of the product component in the line relationship. This impact is determined based on the position information, and can quickly perceive the unstable operation of the production line equipment.

[0041] Evaluation Product Components C i The abnormal impact range value of the production line in each patch position diagram of the product to be detected is taken as the horizontal axis value, and the abnormal impact range value of the production line when the product component is used as the calibration object is taken as the vertical axis value. The patch position diagram D of any x-th product to be detected is depicted. x The automatic detection curve is converted into an automatic detection curve function, recorded as f(D x ).

[0042] Furthermore, the specific implementation process of step S4 includes:

[0043] Based on the automated detection curve function, analyze the abnormal fluctuation of the patch position diagram of the product to be detected:

[0044]

[0045] In the formula, AV(D x ) represents the patch position diagram of the product to be tested D x The abnormal fluctuation of y represents any y-th patch location map of the product to be detected, DS represents the set of tasks to be detected consisting of the patch location maps of the product to be detected, NUM(DS) represents the total number of patch location maps of the product to be detected contained in the set of tasks to be detected DS, f(D y ) represents the patch position diagram of the product to be tested D y The generated automated detection curve function;

[0046] In the above method, the production line abnormality impact range values ​​of several components constitute a curve function of the patch of the product to be tested. The overall continuous stability of the production line is characterized by analyzing the abnormal fluctuation of the patch position diagram of the product to be tested under the continuous output mode of the product. The abnormal fluctuation indicates that there are individual abnormal jumps in the overall continuous stability of the production line. The more warning objects are marked, the weaker the overall continuous stability of the production line.

[0047] A preset abnormal fluctuation threshold is set. If the abnormal fluctuation degree is greater than or equal to the abnormal fluctuation threshold, the patch position diagram of the product to be detected is marked as a warning object. A warning analysis is performed on each patch position diagram of the product to be detected in the detection task set DS, and a list of warning objects is output and sent to the staff port.

[0048] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: in the method and system for automatic detection and early warning of SMT production lines provided by the present invention, in the method part, unified coding is realized based on the product component list, and the position information of the patch position diagram of the sample BOM and the patch position diagram of the product to be detected is compared; components with inconsistent positions are collected to generate a sample set to be calibrated, and converted into a connection tree relationship diagram to generate a sample set to be evaluated; the abnormal impact range value of the production line is evaluated, and the automatic detection curve function is characterized; the abnormal fluctuation degree is analyzed, the early warning object is marked and a list of early warning objects is generated. The system includes a database module, a data processing module, an automatic detection and calibration center module and an early warning module, which cooperate with the implementation of the method part to realize data storage, information generation and processing, abnormal impact evaluation and early warning functions respectively. The present invention improves the production efficiency and product quality of the SMT production line and the timeliness of the production line early warning through automatic detection and early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0050] Figure 1 It is a schematic diagram of the steps of an automated detection and early warning method for an SMT production line of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] In the first embodiment of the present invention, an automatic detection and early warning system for an SMT production line is provided, the system comprising: a database module, a data processing module, an automatic detection and calibration center module and an early warning module;

[0053] A database module is used to store the coding information of each product component, the patch location diagram of the product to be tested and the patch location diagram of the sample BOM, and is used to compare the location information of each product component in the sample BOM patch location diagram and the patch location diagram of the product to be tested;

[0054] Wherein, the database module includes an import interface unit and an image interface unit;

[0055] The import interface unit is used to import the product component list and uniformly encode each product component in the product component list;

[0056] The image interface unit is used to construct a unified two-dimensional coordinate system in the image software, import the sample BOM patch position diagram and the patch position diagram of the product to be tested into the image software, and compare the position information of the components of each product;

[0057] A data processing module, used to generate a sample set to be calibrated and a sample set to be evaluated;

[0058] Among them, the data processing module includes an information collection unit and an information screening unit;

[0059] The information collection unit collects components of each product with inconsistent positions based on the comparison results to generate a sample set to be calibrated;

[0060] The information screening unit converts the connection relationship of each product component in the drawn product circuit board into a connection tree relationship diagram based on the sample BOM patch position diagram; based on the sample set to be calibrated and the connection tree relationship diagram, the product components in the sample set to be calibrated are used as calibration objects, and the product components that are inconsistent with the previous position and the product components that are inconsistent with the next position in the connection relationship with the product components are captured in the connection tree relationship diagram, and a sample set to be evaluated is generated;

[0061] The automated inspection and calibration center module is used to evaluate the production line abnormality impact range value when the product components are used as the calibration object, and based on the production line abnormality impact range value, generate the automated inspection curve function and analyze the abnormal fluctuation of the patch position map of the product to be inspected;

[0062] Among them, the automated detection and calibration center module includes an abnormal impact range assessment unit, a curve characterization unit, and an abnormal fluctuation assessment unit;

[0063] The abnormal impact range assessment unit assesses the abnormal impact range value of the production line when the product components are used as the calibration object based on the sample set to be assessed;

[0064] The curve drawing unit is used to draw the automatic detection curve of the patch position diagram of the product to be detected with the number of the product component as the horizontal coordinate value and the value of the abnormal impact range of the production line when the product component is used as the calibration object as the vertical coordinate value, and convert it into an automatic detection curve function;

[0065] The abnormal fluctuation evaluation unit analyzes the abnormal fluctuation of the patch position diagram of the product to be tested based on the automated detection curve function;

[0066] The early warning module is used to mark the early warning objects and generate a list of early warning objects;

[0067] Among them, the early warning module includes a judgment marking unit and an early warning list generation unit;

[0068] A judgment marking unit is used to preset an abnormal fluctuation threshold. If the abnormal fluctuation degree is greater than or equal to the abnormal fluctuation threshold, the patch position map of the product to be detected is marked as a warning object;

[0069] The warning list generation unit is used to generate a warning object list and send it to the staff port.

[0070] See also Figure 1 In the second embodiment, an automatic detection and early warning method for an SMT production line is provided to be applied to the first embodiment, and the method includes the following steps:

[0071] Step S1: Based on the product component list, uniformly encode the components of each product; compare the location information of the components of each product with the sample BOM patch location diagram and the patch location diagram of the product to be tested, and output the comparison result;

[0072] Exemplarily, a product component list is imported into an automated testing database, and the automated testing database uniformly codes each product component;

[0073] In the imaging software, a unified two-dimensional coordinate system is constructed, and the sample BOM patch position diagram and the patch position diagram of the product to be inspected are respectively imported into the imaging software. The imaging software captures the position information of each product component in the sample BOM patch position diagram and the patch position diagram of the product to be inspected in turn according to the coding order of the product components, and compares the position information of each product component. The comparison results are sent to the automated inspection database, and the comparison results include consistent and inconsistent positions.

[0074] Step S2: Based on the comparison results, collect the product components with inconsistent positions to generate a sample set to be calibrated; convert the connection relationship of each product component in the drawn product circuit board into a connection tree relationship diagram, and generate a sample set to be evaluated based on the sample set to be calibrated and the connection tree relationship diagram;

[0075] For example, based on the comparison results, the components of each product with inconsistent positions are collected to generate a sample set to be calibrated, which is recorded as CS = {C i |i∈[1,I]}, and imported into the automated detection and calibration center, where C i Represents the i-th product component, where i is the code number of the product component;

[0076] Based on the sample BOM patch location diagram, the connection relationship of each product component in the drawn product circuit board is converted into a connection tree relationship diagram; based on the sample set to be calibrated CS and the connection tree relationship diagram, the product components C in the sample set to be calibrated are i To calibrate the object, capture the product component C in the connection tree diagram i There are product components with inconsistent previous positions and product components with inconsistent subsequent positions in the connection relationship, and a sample set to be evaluated is generated, which is recorded as ES(C i ),and

[0077] Step S3: Based on the sample set to be evaluated, evaluate the production line abnormality impact range value when the product components are used as the calibration object, and construct a two-dimensional coordinate system to depict the automated detection curve function of the patch position map of the product to be detected;

[0078] For example, based on the sample set to be evaluated, evaluate the product component C i The impact range of production line abnormality when used as the calibration object:

[0079]

[0080] In the formula, RP(C i ) indicates product component C i As the calibration object, the production line abnormality impact range value, C j represents the jth product component, C j ∈CS and C j ≠C i , ES(C j ) indicates product component C j As the sample set to be evaluated when it is used as the calibration object, NUM[ES(C i )∩ES(C j )] represents the sample set to be evaluated ES(C i ) and the sample set to be evaluated ES(C j ) The number of product components contained in the intersection set, NUM[ES(C i )∪ES(C j )] represents the sample set to be evaluated ES(C i ) and the sample set to be evaluated ES(C j ) The number of product components contained in the union set;

[0081] Evaluation Product Components C i The abnormal impact range value of the production line in each patch position diagram of the product to be detected is taken as the horizontal axis value, and the abnormal impact range value of the production line when the product component is used as the calibration object is taken as the vertical axis value. The patch position diagram D of any x-th product to be detected is depicted.x The automatic detection curve is converted into an automatic detection curve function, recorded as f(D x ).

[0082] Step S4: Based on the automated detection curve function, analyze the abnormal fluctuation of the patch position diagram of the product to be detected, and mark the warning objects to generate a warning object list;

[0083] Exemplarily, based on the automated detection curve function, the abnormal fluctuation of the patch position diagram of the product to be detected is analyzed:

[0084]

[0085] In the formula, AV(D x ) represents the patch position diagram of the product to be tested D x The abnormal fluctuation of y represents any y-th patch location map of the product to be detected, DS represents the set of tasks to be detected consisting of the patch location maps of the product to be detected, NUM(DS) represents the total number of patch location maps of the product to be detected contained in the set of tasks to be detected DS, f(D y ) represents the patch position diagram of the product to be tested D y The generated automated detection curve function;

[0086] A preset abnormal fluctuation threshold is set. If the abnormal fluctuation degree is greater than or equal to the abnormal fluctuation threshold, the patch position diagram of the product to be detected is marked as a warning object. A warning analysis is performed on each patch position diagram of the product to be detected in the detection task set DS, and a list of warning objects is output and sent to the staff port.

[0087] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0088] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An automated detection and early warning method for an SMT production line, characterized in that: The method comprises the following steps: Step S1: Based on the product component list, uniformly encode the components of each product; compare the location information of the components of each product with the sample BOM patch location diagram and the patch location diagram of the product to be tested, and output the comparison result; Step S2: Based on the comparison results, collect the product components with inconsistent positions to generate a sample set to be calibrated; convert the connection relationship of each product component in the drawn product circuit board into a connection tree relationship diagram, and generate a sample set to be evaluated based on the sample set to be calibrated and the connection tree relationship diagram; Step S3: Based on the sample set to be evaluated, evaluate the production line abnormality impact range value when the product components are used as the calibration object, and construct a two-dimensional coordinate system to depict the automated detection curve function of the patch position map of the product to be detected; Step S4: Based on the automated detection curve function, analyze the abnormal fluctuation of the patch position diagram of the product to be detected, and mark the warning objects to generate a warning object list.

2. The automatic detection and early warning method for SMT production line according to claim 1, characterized in that: The specific implementation process of step S1 includes: Import the product component list into the automated testing database, which will uniformly encode each product component; In the image software, a unified two-dimensional coordinate system is constructed, and the sample BOM patch position diagram and the patch position diagram of the product to be tested are respectively imported into the image software. The image software captures the position information of each product component in the sample BOM patch position diagram and the patch position diagram of the product to be tested in turn according to the coding order of the product components, and compares the position information of each product component, and sends the comparison results to the automated detection database, the comparison results including consistent positions and inconsistent positions.

3. The automatic detection and early warning method for SMT production line according to claim 2 is characterized in that: The specific implementation process of step S2 includes: Based on the comparison results, the components of each product with inconsistent positions are collected to generate a sample set to be calibrated, which is recorded as CS = {C i |i∈[1,I]}, and imported into the automated detection and calibration center, where C i Represents the i-th product component, where i is the code number of the product component; Based on the sample BOM patch location diagram, the connection relationship of each product component in the drawn product circuit board is converted into a connection tree relationship diagram; based on the sample set to be calibrated CS and the connection tree relationship diagram, the product components C in the sample set to be calibrated are i To calibrate the object, capture the product component C in the connection tree diagram i There are product components with inconsistent previous positions and product components with inconsistent subsequent positions in the connection relationship, and a sample set to be evaluated is generated, which is recorded as ES(C i ),and 4. The automatic detection and early warning method for SMT production line according to claim 3 is characterized in that: The specific implementation process of step S3 includes: Based on the sample set to be evaluated, evaluate product components C i The impact range of production line abnormality when used as the calibration object: In the formula, RP(C i ) indicates product component C i As the calibration object, the production line abnormality impact range value, C j represents the jth product component, C j ∈CS and C j ≠C i , ES(C j ) indicates product component C j As the sample set to be evaluated when it is used as the calibration object, NUM[ES(C i )∩ES(C j )] represents the sample set to be evaluated ES(C i ) and the sample set to be evaluated ES(C j ) The number of product components contained in the intersection set, NUM[ES(C i )∪ES(C j )] represents the sample set to be evaluated ES(C i ) and the sample set to be evaluated ES(C j ) The number of product components contained in the union set; Evaluation Product Components C i The abnormal impact range value of the production line in each patch position diagram of the product to be detected is taken as the horizontal axis value, and the abnormal impact range value of the production line when the product component is used as the calibration object is taken as the vertical axis value. The patch position diagram D of any x-th product to be detected is depicted. x The automatic detection curve is converted into an automatic detection curve function, recorded as f(D x ).

5. The automatic detection and early warning method for SMT production line according to claim 4, characterized in that: The specific implementation process of step S4 includes: Based on the automated detection curve function, analyze the abnormal fluctuation of the patch position diagram of the product to be detected: In the formula, AV(D x ) represents the patch position diagram of the product to be tested D x The abnormal fluctuation of y represents any y-th patch location map of the product to be detected, DS represents the set of tasks to be detected consisting of the patch location maps of the product to be detected, NUM(DS) represents the total number of patch location maps of the product to be detected contained in the set of tasks to be detected DS, f(D y ) represents the patch position diagram of the product to be tested D y The generated automated detection curve function; A preset abnormal fluctuation threshold is set. If the abnormal fluctuation degree is greater than or equal to the abnormal fluctuation threshold, the patch position diagram of the product to be detected is marked as a warning object. A warning analysis is performed on each patch position diagram of the product to be detected in the detection task set DS, and a list of warning objects is output and sent to the staff port.

6. An automated detection and early warning system for an SMT production line, executing an automated detection and early warning method for an SMT production line as claimed in any one of claims 1 to 5, characterized in that: The system includes: a database module, a data processing module, an automatic detection and calibration center module and an early warning module; The database module is used to store the coding information of each product component, the patch location diagram of the product to be tested and the sample BOM patch location diagram, and is used to compare the location information of each product component in the sample BOM patch location diagram and the patch location diagram of the product to be tested; The data processing module is used to generate a sample set to be calibrated and a sample set to be evaluated; The automated detection calibration center module is used to evaluate the production line abnormality impact range value when the product components are used as the calibration object, and based on the production line abnormality impact range value, generate an automated detection curve function, and analyze the abnormal fluctuation of the patch position diagram of the product to be detected; The warning module is used to mark the warning objects and generate a warning object list.

7. The automatic detection and early warning system for SMT production line according to claim 6, characterized in that: The database module includes an import interface unit and an image interface unit; The import interface unit is used to import the product component list and uniformly encode each product component in the product component list; The image interface unit is used to construct a unified two-dimensional coordinate system in the image software, import the sample BOM patch position diagram and the patch position diagram of the product to be tested into the image software respectively, and compare the position information of each product component.

8. The automatic detection and early warning system for SMT production line according to claim 6, characterized in that: The data processing module includes an information collection unit and an information screening unit; The information collection unit collects components of each product with inconsistent positions based on the comparison result to generate a sample set to be calibrated; The information screening unit converts the connection relationship of each product component in the drawn product circuit board into a connection tree relationship diagram based on the sample BOM patch location diagram; Based on the sample set to be calibrated and the connection tree relationship diagram, the product components in the sample set to be calibrated are taken as calibration objects, and the product components that are inconsistent with the previous position and the product components that are inconsistent with the next position in a connection relationship with the product components are captured in the connection tree relationship diagram, and a sample set to be evaluated is generated.

9. The automatic detection and early warning system for SMT production line according to claim 6, characterized in that: The automated detection and calibration center module includes an abnormal impact range assessment unit, a curve characterization unit, and an abnormal fluctuation assessment unit; The abnormal impact range evaluation unit evaluates the production line abnormal impact range value when the product components are used as calibration objects based on the sample set to be evaluated; The curve drawing unit is used to draw an automated detection curve of the patch position diagram of the product to be detected with the product component number as the horizontal coordinate value and the production line abnormality impact range value when the product component is used as the calibration object as the vertical coordinate value, and convert it into an automated detection curve function; The abnormal fluctuation evaluation unit analyzes the abnormal fluctuation degree of the patch position diagram of the product to be detected based on the automated detection curve function.

10. The automatic detection and early warning system for SMT production line according to claim 6, characterized in that: The early warning module includes a judgment marking unit and an early warning list generating unit; The judgment and marking unit is used to preset an abnormal fluctuation threshold, and if the abnormal fluctuation degree is greater than or equal to the abnormal fluctuation threshold, the patch position diagram of the product to be detected is marked as a warning object; The warning list generating unit is used to generate a warning object list and send it to the staff port.

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