A DPF differential pressure sensor welding point detection system

Abstract

The present invention belongs to the field of DPF differential pressure sensors and relates to a welding spot detection technology, which is used to solve the problem that a traditional detection system cannot prioritize detection processes according to execution data of different defects, resulting in the inability to improve the overall detection efficiency. The present invention is a DPF differential pressure sensor welding spot detection system, comprising a welding spot detection platform, wherein the welding spot detection platform is communicatively connected with an accuracy detection module, a welding spot detection module and a database; the accuracy detection module is used to detect and analyze the measurement accuracy of the DPF differential pressure sensor: the DPF differential pressure sensor to be detected is marked as an object to be detected, the measurement accuracy of the object to be detected is verified and the object to be detected is marked as an object with normal accuracy or an object with abnormal accuracy; the present invention can perform welding spot detection and analysis on an object with abnormal accuracy, and detect and analyze multiple welding spot quality parameters of the object with abnormal accuracy in combination with a variety of detection and analysis processes, thereby improving the accuracy and reliability of the welding spot detection results.

Description

Technical Field

[0001] The invention belongs to the field of DPF differential pressure sensors, relates to welding point detection technology, and specifically is a DPF differential pressure sensor welding point detection system. Background Art

[0002] The ‌DPF differential pressure sensor‌ is mainly used to detect the pressure difference between the inlet and outlet of the DPF (diesel particulate filter) to monitor the DPF blockage and provide data for DPF regeneration and OBD diagnosis. The working principle of this sensor is based on the piezoresistive effect of semiconductors and works by measuring the pressure difference between the two ends of the DPF.

[0003] There are many methods for detecting the welding points of DPF differential pressure sensors. The principles and detection parameters of different detection methods are different. Therefore, the traditional DPF differential pressure sensor welding point detection system generally needs to combine multiple detection methods to perform a comprehensive analysis and evaluation of the overall quality of the welding points of the DPF differential pressure sensor.

[0004] The invention patent with announcement number CN116012285A discloses a BGA solder joint defect detection system and detection method based on laser ultrasound. The detection system uses a machine learning method to perform model training on defective and non-defective samples in advance, and automatically identifies whether the sample contains defects during the detection process; however, when the detection system is executed, it is unable to analyze the detection execution efficiency and recognition success rate of different defects. Therefore, the traditional detection system cannot prioritize the detection process according to the execution data of different defects, resulting in the inability to reduce the detection processing task volume and improve the overall detection efficiency.

[0005] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0006] The purpose of the present invention is to provide a DPF differential pressure sensor welding point detection system, which is used to solve the problem that the traditional detection system cannot prioritize the detection process according to the execution data of different defects, resulting in the inability to improve the overall detection efficiency;

[0007] The technical problem to be solved by the present invention is: how to provide a DPF differential pressure sensor welding point detection system which can prioritize the detection process according to the execution data of different defects to improve the overall detection efficiency.

[0008] The purpose of the present invention can be achieved through the following technical solutions:

[0009] A DPF differential pressure sensor welding point detection system, comprising a welding point detection platform, wherein the welding point detection platform is communicatively connected with an accuracy detection module, a welding point detection module, a detection evaluation module and a database;

[0010] The accuracy detection module is used to detect and analyze the measurement accuracy of the DPF differential pressure sensor: mark the DPF differential pressure sensor to be detected as an object to be detected, perform measurement accuracy verification on the object to be detected and mark the object to be detected as an object with normal accuracy or an object with abnormal accuracy;

[0011] The solder joint detection module is used to perform solder joint detection and analysis on objects with abnormal precision: the solder joint detection and analysis process includes a solder joint height detection process, a solder joint surface detection process, and a solder joint internal detection process;

[0012] The detection evaluation module is used to evaluate and analyze the detection effectiveness of the solder joint detection module: generate an evaluation cycle, obtain the total number of manually verified objects output by the solder joint detection module within the evaluation cycle and mark it as a reserved value, obtain the total number of precision abnormal objects output by the precision detection module within the evaluation cycle and mark it as a processed value, mark the ratio of the reserved value to the processed value as the effective coefficient of the evaluation cycle, and determine whether the detection effectiveness of the solder joint detection module within the evaluation cycle meets the requirements through the effective coefficient; if the requirements are met, generate a process optimization signal and send the process optimization signal to the process optimization module through the solder joint detection platform;

[0013] The process optimization module is used to optimize and analyze the detection process of the solder joint detection module.

[0014] Furthermore, the specific process of measuring the accuracy of the object to be inspected includes: using a calibrated standard DPF differential pressure sensor to connect to the DPF system for detection and measure the differential pressure value, marking the measured differential pressure value as the differential pressure standard value, and then connecting all the objects to be inspected to the DPF system for detection in turn and measuring the differential pressure values, marking the measured differential pressure value as the differential pressure detection value of the object to be inspected, marking the absolute value of the difference between the differential pressure detection value and the differential pressure standard value as the accuracy deviation value, marking the ratio of the accuracy deviation value to the differential pressure standard value as the accuracy deviation coefficient of the object to be inspected, retrieving the accuracy deviation threshold value through the database, comparing the accuracy deviation coefficient of the object to be inspected with the accuracy deviation threshold value, and marking the object to be inspected as an object with normal accuracy or an object with abnormal accuracy through the comparison result.

[0015] Furthermore, the specific process of comparing the accuracy deviation coefficient of the object to be inspected with the accuracy deviation threshold includes: if the accuracy deviation coefficient is less than the accuracy deviation threshold, it is determined that the measurement accuracy of the object to be inspected meets the requirements, and the corresponding object to be inspected is marked as an object with normal accuracy; if the accuracy deviation coefficient is greater than or equal to the accuracy deviation threshold, it is determined that the measurement accuracy of the object to be inspected does not meet the requirements, and the corresponding object to be inspected is marked as an object with abnormal accuracy.

[0016] Furthermore, the weld height detection process includes: marking the welds of the precision abnormal objects as height detection objects, drawing a circle with the highest point of the height detection object as the center and r1 as the radius to obtain a reference area, r1 is a numerical constant, and the specific value of r1 is set by the management personnel, randomly selecting a number of detection points on the edge line of the reference area, measuring the height value of the detection point relative to the welding plane by laser and marking it as the high inspection value of the detection point, summing and averaging the high inspection values ​​of all the detection points to obtain the height performance value of the height detection object, obtaining the height standard value through the database, marking the absolute value of the difference between the height performance value and the height standard value as the high inspection coefficient, marking the precision abnormal objects whose weld height detection results do not meet the requirements as height rejection objects through the height coefficient, and rejecting the height rejection objects from the precision abnormal objects.

[0017] Furthermore, the solder joint surface detection process includes: capturing an image of an object with abnormal precision, marking the captured image as a detection image, amplifying the detection image into a pixel grid image and performing grayscale transformation, extracting the solder joint area of ​​the detection image by a binary method, obtaining the grayscale range of the solder joint through a database, marking the pixel grids in the solder joint area whose grayscale values ​​are outside the grayscale range as welding defect grids, marking the ratio of the number of welding defect grids in the solder joint area to the number of all pixel grids in the solder joint area as a welding defect coefficient of the solder joint area, marking the precision abnormal objects whose solder joint surface detection results do not meet the requirements as surface rejection objects by the welding defect coefficient, and rejecting the surface rejection objects from the precision abnormal objects.

[0018] Furthermore, the internal inspection process of the solder joint includes: utilizing the property that X-rays can penetrate materials and attenuate in materials to detect defects inside the solder joints; if no internal defects are detected in all solder joints, it is determined that the internal inspection results of the solder joints of the precision abnormal objects meet the requirements; otherwise, it is determined that the internal inspection results of the solder joints of the precision abnormal objects do not meet the requirements, and the corresponding precision abnormal objects are marked as internal rejection objects, and the internal rejection objects are removed from the precision abnormal objects.

[0019] Furthermore, the specific process of determining whether the detection effectiveness of the solder joint detection module within the evaluation period meets the requirements includes: obtaining the effective threshold through the database, and comparing the effective coefficient with the effective threshold: if the effective coefficient is less than the effective threshold, then it is determined that the detection effectiveness of the solder joint detection module within the evaluation period does not meet the requirements, and a solder joint detection optimization signal is generated and sent to the mobile phone terminal of the administrator through the solder joint detection platform; if the effective coefficient is greater than or equal to the effective threshold, then it is determined that the detection effectiveness of the solder joint detection module within the evaluation period meets the requirements.

[0020] Furthermore, the specific process of the process optimization module optimizing and analyzing the detection process of the solder joint detection module includes: marking the solder joint height detection process, the solder joint surface detection process and the solder joint internal detection process as solder joint detection processes, obtaining the efficiency data XL and the screening data SC of the solder joint detection process in the evaluation cycle, and performing numerical calculation to obtain the priority coefficient YX of the solder joint detection process; arranging the solder joint height detection process, the solder joint surface detection process and the solder joint internal detection process in descending order according to the priority coefficient YX values ​​of the corresponding solder joint detection processes to obtain a priority management sequence, and when the solder joint quality inspection is performed through the solder joint detection module in the next evaluation cycle, the corresponding solder joint detection process is executed according to the priority management sequence.

[0021] The present invention has the following beneficial effects:

[0022] 1. The accuracy detection module can be used to detect and analyze the measurement accuracy of the DPF differential pressure sensor. The DPF differential pressure sensor is connected to the DPF system for detection, and the measured values ​​are recorded. Then, they are compared with the standard values ​​one by one. The objects to be inspected are marked differently according to the degree of deviation between the measured values ​​and the standard values, so as to screen the workpieces that need to be inspected for welding point detection;

[0023] 2. The solder joint detection module can be used to detect and analyze the solder joints of objects with abnormal precision. By combining multiple detection and analysis processes, multiple solder joint quality parameters of objects with abnormal precision can be detected and analyzed to improve the accuracy and reliability of solder joint detection results.

[0024] 3. The detection evaluation module can be used to evaluate and analyze the detection effectiveness of the solder joint detection module, and the number of workpieces with abnormal solder joints screened out by the precision abnormal objects in all solder joint detection processes can be counted to obtain the effective coefficient. According to the effective coefficient, the necessity of optimizing the execution of the solder joint detection process can be fed back, and the detection execution process of the solder joint detection module can be optimized when necessary;

[0025] 4. The process optimization module can be used to optimize and analyze the inspection process of the solder joint inspection module, analyze the inspection efficiency and defect screening rate corresponding to different inspection processes to obtain the priority coefficient, and sort the execution order of the inspection processes according to the priority coefficient, so that the inspection processes with higher execution efficiency and higher defect screening rate can be executed earlier, thereby reducing the overall screening task volume and improving the overall inspection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0027] Figure 1 is a system block diagram of Embodiment 1 of the present invention;

[0028] Figure 2 This is a system block diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0029] The technical scheme of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than 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.

[0030] Embodiment 1: Figure 1 As shown, a DPF differential pressure sensor welding point detection system includes a welding point detection platform, and the welding point detection platform is communicatively connected with an accuracy detection module, a welding point detection module and a database.

[0031] The accuracy detection module is used to detect and analyze the measurement accuracy of the DPF differential pressure sensor: mark the DPF differential pressure sensor to be detected as the object to be detected, use a calibrated standard DPF differential pressure sensor to connect to the detection DPF system and measure the differential pressure value, mark the measured differential pressure value as the differential pressure standard value, then connect all the objects to be detected to the detection DPF system in turn and measure the differential pressure value, mark the measured differential pressure value as the differential pressure detection value of the object to be detected, mark the absolute value of the difference between the differential pressure detection value and the differential pressure standard value as the accuracy deviation value, mark the ratio of the accuracy deviation value to the differential pressure standard value as the accuracy deviation coefficient of the object to be detected, retrieve the accuracy deviation threshold value through the database, and set the accuracy deviation coefficient of the object to be detected as the differential pressure value. The difference coefficient is compared with the accuracy deviation threshold: if the accuracy deviation coefficient is less than the accuracy deviation threshold, it is determined that the measurement accuracy of the object to be inspected meets the requirements, and the corresponding object to be inspected is marked as an object with normal accuracy; if the accuracy deviation coefficient is greater than or equal to the accuracy deviation threshold, it is determined that the measurement accuracy of the object to be inspected does not meet the requirements, and the corresponding object to be inspected is marked as an object with abnormal accuracy; all objects with abnormal accuracy are sent to the weld spot detection module through the weld spot detection platform; the DPF differential pressure sensor is connected to the DPF system for detection and the measured value is recorded, and then compared with the standard value one by one, and the objects to be inspected are differentiated and marked according to the degree of deviation between the measured value and the standard value, so as to screen the workpieces that need to be inspected for weld spot detection.

[0032] The solder joint detection module is used to perform solder joint detection and analysis on objects with abnormal accuracy: the solder joint detection and analysis process includes the solder joint height detection process, the solder joint surface detection process and the solder joint internal detection process.

[0033] The welding spot height detection process includes: marking the welding spots of the objects with abnormal accuracy as height detection objects, drawing a circle with the highest point of the height detection object as the center and r1 as the radius to obtain the reference area, r1 is a numerical constant, and the specific value of r1 is set by the management personnel, randomly selecting a number of detection points on the edge line of the reference area, measuring the height value of the detection point relative to the welding plane by laser and marking it as the high inspection value of the detection point, summing and averaging the high inspection values ​​of all the detection points to obtain the height performance value of the height detection object, obtaining the height standard value through the database, marking the absolute value of the difference between the height performance value and the height standard value as the high inspection coefficient, and The high detection threshold is obtained through the database, and the high detection coefficient of the height detection object is compared with the high detection threshold: if the high detection coefficient is less than the high detection threshold, the height detection object is marked as a highly normal object; if the high detection coefficient is greater than or equal to the high detection threshold, the height detection object is marked as a highly abnormal object; if all height detection objects of the precision abnormal object are marked as highly normal objects, it is determined that the solder point height detection result of the precision abnormal object meets the requirements; otherwise, it is determined that the solder point height detection result of the precision abnormal object does not meet the requirements, and the corresponding precision abnormal object is marked as a highly rejected object, and the highly rejected object is rejected from the precision abnormal object.

[0034] The solder joint surface detection process includes: taking an image of the precision abnormal object, marking the image obtained by taking the image as a detection image, enlarging the detection image into a pixel grid image and performing grayscale transformation, extracting the solder joint area of ​​the detection image by a binary method, obtaining the grayscale range of the solder joint through a database, marking the pixel grids whose grayscale values ​​in the solder joint area are outside the grayscale range as welding defect grids, marking the ratio of the number of welding defect grids in the solder joint area to the number of all pixel grids in the solder joint area as the welding defect coefficient of the solder joint area, obtaining the welding defect threshold through a database, and comparing the welding defect coefficient with the welding defect threshold: if the welding defect coefficient is less than the welding defect threshold, the corresponding solder joint area is marked as a normal welding area; if the welding defect coefficient is greater than or equal to the welding defect threshold, the corresponding solder joint area is marked as an abnormal welding area; if all solder joint areas of the precision abnormal object are marked as normal welding areas, it is determined that the solder joint surface detection result of the precision abnormal object meets the requirements; otherwise, it is determined that the solder joint surface detection result of the precision abnormal object does not meet the requirements, and the corresponding precision abnormal object is marked as a surface rejection object, and the surface rejection object is rejected from the precision abnormal object.

[0035] The internal inspection process of the solder joint includes: using the characteristics of X-rays that can penetrate materials and attenuate in materials to detect defects inside the solder joints. If no internal defects are detected in all solder joints, it is determined that the internal inspection results of the solder joints of the precision abnormal objects meet the requirements; otherwise, it is determined that the internal inspection results of the solder joints of the precision abnormal objects do not meet the requirements, and the corresponding precision abnormal objects are marked as internal rejection objects, and the internal rejection objects are removed from the precision abnormal objects.

[0036] The precision abnormal objects that pass the solder joint height detection process, solder joint surface detection process and solder joint internal detection process are marked as manual verification objects, and a manual verification signal is generated and the manual verification signal and the manual verification object are sent to the manager's mobile terminal through the solder joint detection platform; it should be noted that the execution order of the solder joint height detection process, the solder joint surface detection process and the solder joint internal detection process is randomly generated; a variety of detection and analysis processes are combined to detect and analyze multiple solder joint quality parameters of precision abnormal objects, so as to improve the accuracy and reliability of solder joint detection results.

[0037] Embodiment 2: Figure 2 As shown, the solder joint detection platform is also communicatively connected to a detection and evaluation module and a process optimization module.

[0038] The detection evaluation module is used to evaluate and analyze the detection effectiveness of the solder joint detection module: generate an evaluation cycle, obtain the total number of manually verified objects output by the solder joint detection module within the evaluation cycle and mark it as a reserved value, obtain the total number of precision abnormal objects output by the precision detection module within the evaluation cycle and mark it as a processed value, mark the ratio of the reserved value to the processed value as the effective coefficient of the evaluation cycle, obtain the effective threshold through the database, and compare the effective coefficient with the effective threshold: if the effective coefficient is less than the effective threshold, it is determined that the detection effectiveness of the solder joint detection module within the evaluation cycle does not meet the requirements, generate a solder joint detection optimization signal and send the solder joint detection optimization signal to the mobile phone terminal of the manager through the solder joint detection platform; if the effective coefficient is greater than or equal to the effective threshold, it is determined that the detection effectiveness of the solder joint detection module within the evaluation cycle meets the requirements, generate a process optimization signal and send the process optimization signal to the process optimization module through the solder joint detection platform; the number of workpieces with abnormal solder joints screened out by precision abnormal objects in all solder joint detection processes is statistically analyzed to obtain the effective coefficient, and the necessity of optimizing the execution of the solder joint detection process is fed back based on the effective coefficient, and the detection execution process of the solder joint detection module is optimized when necessary.

[0039] The process optimization module is used to optimize and analyze the detection process of the solder joint detection module: mark the solder joint height detection process, the solder joint surface detection process and the solder joint internal detection process as solder joint detection processes, and obtain the efficiency data XL and screening data SC of the solder joint detection process within the evaluation period. The process of obtaining the efficiency data XL includes: summing up and averaging the execution time of all precision abnormal objects that use the solder joint detection process to perform solder joint quality detection within the evaluation period to obtain the efficiency data XL; the process of obtaining the screening data SC includes: marking the number of precision abnormal objects that use the solder joint detection process to perform solder joint quality detection within the evaluation period as the execution value, marking the number of precision abnormal objects that use the solder joint detection process to perform solder joint quality detection within the evaluation period and whose detection results do not meet the requirements as the screening value, and calculating the ratio of the screening value to the execution value. Marked as screening data SC; the priority coefficient YX of the solder joint inspection process is obtained through the formula YX=k1×SC-k2×XL, where k1 and k2 are both proportional coefficients, and k1>k2>1; the solder joint height inspection process, solder joint surface inspection process and solder joint internal inspection process are arranged in descending order according to the corresponding solder joint inspection process priority coefficient YX value to obtain a priority management sequence, and when the solder joint quality inspection is performed through the solder joint inspection module in the next evaluation cycle, the corresponding solder joint inspection process is executed according to the priority management sequence; the inspection efficiency and defect screening rate corresponding to different inspection processes are analyzed to obtain the priority coefficient, and the inspection process execution order is sorted according to the priority coefficient, so that the inspection process with higher execution efficiency and higher defect screening rate is executed earlier, reducing the overall screening task volume and improving the overall inspection efficiency. ‌

[0040] A DPF pressure difference sensor welding spot detection system, when working, detects and analyzes the measurement accuracy of the DPF pressure difference sensor and marks the object to be detected as an object with normal accuracy or an object with abnormal accuracy, performs welding spot detection and analysis on the object with abnormal accuracy, removes the object with abnormal accuracy whose welding spot detection results do not meet the requirements, generates an evaluation cycle, obtains the total number of manually verified objects output by the welding spot detection module within the evaluation cycle and marks it as a reserved value, obtains the total number of objects with abnormal accuracy output by the accuracy detection module within the evaluation cycle and marks it as a processed value, marks the ratio of the reserved value to the processed value as the effective coefficient of the evaluation cycle, judges whether the detection effectiveness of the welding spot detection module within the evaluation cycle meets the requirements through the effective coefficient, obtains the priority coefficient YX of each welding spot detection process when the requirements are met, and generates the priority management sequence of the next evaluation cycle according to the priority coefficient YX.

[0041] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0042] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula close to the real value. The coefficients in the formula are set by technicians in this field according to the actual situation; for example: formula YX=k1×SC-k2×XL; technicians in this field collect multiple groups of sample data and set corresponding priority coefficients for each group of sample data; substitute the set priority coefficients and the collected sample data into the formula, any two formulas constitute a set of two-variable linear equations, screen the calculated coefficients and take the average, and obtain the values ​​of k1 and k2 as 3.48 and 2.15 respectively;

[0043] The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the technical personnel in this field preliminarily set the corresponding priority coefficient for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantified value, such as the priority coefficient is proportional to the value of the screening data.

[0044] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0045] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A DPF differential pressure sensor welding point detection system, characterized in that: It includes a solder joint detection platform, which is communicatively connected with an accuracy detection module, a solder joint detection module, a detection evaluation module and a database; The accuracy detection module is used to detect and analyze the measurement accuracy of the DPF differential pressure sensor: mark the DPF differential pressure sensor to be detected as an object to be detected, perform measurement accuracy verification on the object to be detected and mark the object to be detected as an object with normal accuracy or an object with abnormal accuracy; The solder joint detection module is used to perform solder joint detection and analysis on objects with abnormal precision: the solder joint detection and analysis process includes a solder joint height detection process, a solder joint surface detection process, and a solder joint internal detection process; The detection evaluation module is used to evaluate and analyze the detection effectiveness of the solder joint detection module: generate an evaluation cycle, obtain the total number of manually verified objects output by the solder joint detection module within the evaluation cycle and mark it as a reserved value, obtain the total number of precision abnormal objects output by the precision detection module within the evaluation cycle and mark it as a processed value, mark the ratio of the reserved value to the processed value as the effective coefficient of the evaluation cycle, and determine whether the detection effectiveness of the solder joint detection module within the evaluation cycle meets the requirements through the effective coefficient; if the requirements are met, generate a process optimization signal and send the process optimization signal to the process optimization module through the solder joint detection platform; The process optimization module is used to optimize and analyze the detection process of the solder joint detection module.

2. A DPF differential pressure sensor welding point detection system according to claim 1, characterized in that: The specific process of measuring accuracy verification for the object to be inspected includes: using a calibrated standard DPF differential pressure sensor to connect to the DPF system for detection and measure the differential pressure value, marking the measured differential pressure value as the differential pressure standard value, and then connecting all the objects to be inspected to the DPF system for detection in turn and measuring the differential pressure values, marking the measured differential pressure value as the differential pressure detection value of the object to be inspected, marking the absolute value of the difference between the differential pressure detection value and the differential pressure standard value as the accuracy deviation value, marking the ratio of the accuracy deviation value to the differential pressure standard value as the accuracy deviation coefficient of the object to be inspected, retrieving the accuracy deviation threshold through the database, comparing the accuracy deviation coefficient of the object to be inspected with the accuracy deviation threshold, and marking the object to be inspected as an object with normal accuracy or an object with abnormal accuracy based on the comparison result.

3. A DPF differential pressure sensor welding point detection system according to claim 2, characterized in that: The specific process of comparing the accuracy deviation coefficient of the object to be inspected with the accuracy deviation threshold includes: if the accuracy deviation coefficient is less than the accuracy deviation threshold, it is determined that the measurement accuracy of the object to be inspected meets the requirements, and the corresponding object to be inspected is marked as an object with normal accuracy; if the accuracy deviation coefficient is greater than or equal to the accuracy deviation threshold, it is determined that the measurement accuracy of the object to be inspected does not meet the requirements, and the corresponding object to be inspected is marked as an object with abnormal accuracy.

4. A DPF differential pressure sensor welding point detection system according to claim 3, characterized in that: The weld height detection process includes: marking the welds of the precision abnormal objects as height detection objects, drawing a circle with the highest point of the height detection object as the center and r1 as the radius to obtain a reference area, randomly selecting a number of detection points on the edge line of the reference area, measuring the height value of the detection point relative to the welding plane by laser and marking it as the high inspection value of the detection point, summing and averaging the high inspection values ​​of all the detection points to obtain the height performance value of the height detection object, obtaining the height standard value through the database, marking the absolute value of the difference between the height performance value and the height standard value as the high inspection coefficient, marking the precision abnormal objects whose weld height detection results do not meet the requirements as height rejection objects through the high inspection coefficient, and rejecting the height rejection objects from the precision abnormal objects.

5. A DPF differential pressure sensor welding point detection system according to claim 4, characterized in that: The solder joint surface detection process includes: taking an image of the precision abnormal object, marking the captured image as a detection image, enlarging the detection image into a pixel grid image and performing grayscale transformation, extracting the solder joint area of ​​the detection image by a binary method, obtaining the grayscale range of the solder joint through a database, marking the pixel grids in the solder joint area whose grayscale values ​​are outside the grayscale range as welding defect grids, marking the ratio of the number of welding defect grids in the solder joint area to the number of all pixel grids in the solder joint area as the welding defect coefficient of the solder joint area, marking the precision abnormal objects whose solder joint surface detection results do not meet the requirements as surface rejection objects through the welding defect coefficient, and rejecting the surface rejection objects from the precision abnormal objects.

6. A DPF differential pressure sensor welding point detection system according to claim 5, characterized in that: The internal inspection process of the solder joint includes: using the characteristics of X-rays that can penetrate materials and attenuate in materials to detect defects inside the solder joints. If no internal defects are detected in all solder joints, it is determined that the internal inspection results of the solder joints of the precision abnormal objects meet the requirements; otherwise, it is determined that the internal inspection results of the solder joints of the precision abnormal objects do not meet the requirements, and the corresponding precision abnormal objects are marked as internal rejection objects, and the internal rejection objects are removed from the precision abnormal objects.

7. A DPF differential pressure sensor welding point detection system according to claim 6, characterized in that: The specific process of determining whether the detection effectiveness of the solder joint detection module within the evaluation period meets the requirements includes: obtaining the effective threshold through the database, and comparing the effective coefficient with the effective threshold: if the effective coefficient is less than the effective threshold, then it is determined that the detection effectiveness of the solder joint detection module within the evaluation period does not meet the requirements, and a solder joint detection optimization signal is generated and sent to the mobile phone terminal of the administrator through the solder joint detection platform; if the effective coefficient is greater than or equal to the effective threshold, then it is determined that the detection effectiveness of the solder joint detection module within the evaluation period meets the requirements.

8. A DPF differential pressure sensor welding point detection system according to claim 7, characterized in that: The specific process of the process optimization module optimizing and analyzing the detection process of the solder joint detection module includes: marking the solder joint height detection process, the solder joint surface detection process and the solder joint internal detection process as solder joint detection processes, obtaining the efficiency data XL and the screening data SC of the solder joint detection process in the evaluation cycle, and performing numerical calculation to obtain the priority coefficient YX of the solder joint detection process; arranging the solder joint height detection process, the solder joint surface detection process and the solder joint internal detection process in descending order according to the priority coefficient YX values ​​of the corresponding solder joint detection processes to obtain a priority management sequence, and when the solder joint quality inspection is performed through the solder joint detection module in the next evaluation cycle, the corresponding solder joint detection process is executed according to the priority management sequence.

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