CN-121980185-A - Intelligent analysis method and system for silencer manufacturing process data

CN121980185ACN 121980185 ACN121980185 ACN 121980185ACN-121980185-A

Abstract

The invention is applicable to the technical field of muffler manufacturing, and particularly relates to an intelligent analysis method and system for muffler manufacturing process data, wherein the method comprises the steps of obtaining installation information of an exhaust system, wherein the installation information at least comprises user information, model and installation time, acquiring historical muffling data by using sensing equipment which is arranged in the exhaust system in advance, and establishing a corresponding relation between the model and the historical muffling data, wherein the historical muffling data at least comprises noise and vibration; position data of the exhaust system are acquired and clustered into a plurality of scene categories. According to the invention, by deploying the test equipment, the exhaust system of the equipment to be delivered can be personalized adjusted according to the historical noise elimination data of the exhaust system, the matching degree of the muffler and the exhaust system is further enhanced, the use experience of a user is improved, the personalized requirements of the user can be met, different selling points can be provided for the muffler, and the brand value is improved.

Inventors

CHU JIE
GAO XIANG
WANG XIAOJUAN
CHU QINGLE
WANG JIE
JIAO MINGXING
CHEN QINGGUANG

Assignees

山东洁静环保设备有限公司

Dates

Publication Date: 20260505
Application Date: 20260126

Claims (10)

1. An intelligent analysis method for silencer manufacturing process data, which is characterized by comprising the following steps: acquiring installation information of an exhaust system, wherein the installation information at least comprises user information, model and installation time, acquiring historical noise elimination data by using sensing equipment which is arranged in the exhaust system in advance, and establishing a corresponding relation between the model and the historical noise elimination data, wherein the historical noise elimination data at least comprises noise and vibration; Acquiring position data of an exhaust system, clustering the position data into a plurality of scene categories, selecting a test period, defining a high-frequency category, inquiring a preset comparison table to obtain an adjustment rule, and issuing the adjustment rule to a preset terminal; Hashing the historical muffling data to obtain a verification value, establishing a mapping between the verification value and the historical muffling data, and constructing a preliminary verification mechanism based on similarity; Establishing a corresponding relation between position data and historical noise elimination data through a preset timestamp, constructing a test data set corresponding to each scene category one by one, sending the test data set to preselected test equipment, searching out a to-be-delivered silencer based on the model, defining the to-be-delivered silencer as a processing object, collecting attribute data of the processing object, acquiring an evaluation result of the processing object in the test equipment, comparing the historical noise elimination data with the test result, obtaining a distinguishing item, and adjusting the attribute data.
2. The intelligent analysis method of muffler manufacturing process data according to claim 1, wherein the step of collecting historical muffling data using sensing apparatus pre-deployed in an exhaust system comprises: And collecting using data of the exhaust system by using the sensing equipment, configuring a behavior characteristic mode, and shifting the distinguishing item.
3. The intelligent analysis method of muffler manufacturing process data according to claim 1, wherein the steps of collecting historical muffler data and establishing a correspondence between model numbers and the historical muffler data comprise: dividing the historical noise elimination data to obtain a common segment and an abnormal segment; and determining risk factors of the abnormal section through the position data, integrating the position data, the abnormal section and the risk factors, generating a risk report, and sending the risk report to a preset terminal.
4. The intelligent analysis method for silencer manufacturing process data according to claim 3, wherein the steps of obtaining the position data of the exhaust system, clustering the position data into a plurality of scene categories, selecting a test period, defining a high-frequency category, inquiring a preset comparison table to obtain an adjustment rule, and issuing the adjustment rule to a preset terminal comprise: generating a moving path diagram by utilizing the position data, determining the terrain change, and dividing the moving path diagram into a plurality of segments; Traversing the scene category corresponding to each segment, counting the number of the segments corresponding to each scene category, and defining a high-frequency category.
5. The intelligent analysis method for muffler manufacturing process data according to claim 3, wherein the step of hashing the historical muffling data to obtain a verification value, establishing a mapping of the verification value and the historical muffling data, and constructing a preliminary verification mechanism based on similarity comprises: Selecting a hash function, carrying out hash on the historical noise elimination data, defining a hash result as a verification value, integrating all verification values, and generating an index library; And acquiring real-time noise elimination data, carrying out hash to obtain target data, inquiring an index library, determining a verification value with highest similarity with the target data, and transmitting attribute data to a source terminal of the real-time noise elimination data through the distinguishing item.
6. The intelligent analysis method of muffler manufacturing process data according to claim 1, wherein the steps of comparing the historical muffler data with the test results, obtaining a distinction term, and adjusting the attribute data include: recording an adjustment process of attribute data, and collecting usage data after delivery is completed; and integrating the use data and the historical noise elimination data, generating a reference feature set, and writing the adjustment rules and the adjustment process into the reference feature set.
7. An intelligent analysis system for muffler manufacturing process data, the system comprising: The system comprises an establishing module, a control module and a control module, wherein the establishing module is used for acquiring installation information of an exhaust system, the installation information at least comprises user information, model and installation time, acquiring historical silencing data by using sensing equipment which is arranged in the exhaust system in advance, and establishing a corresponding relation between the model and the historical silencing data, wherein the historical silencing data at least comprises noise and vibration; The issuing module is used for acquiring the position data of the exhaust system, clustering the position data into a plurality of scene categories, selecting a test period, defining a high-frequency category, inquiring a preset comparison table, obtaining an adjustment rule and issuing the adjustment rule to a preset terminal; The construction module is used for carrying out hash on the historical noise elimination data to obtain a verification value, establishing a mapping between the verification value and the historical noise elimination data and constructing a preliminary verification mechanism based on similarity; The adjusting module is used for establishing a corresponding relation between the position data and the historical noise elimination data through a preset timestamp, constructing a test data set corresponding to each scene category one by one, sending the test data set to the preselected test equipment, searching out the to-be-delivered muffler based on the model, defining the to-be-delivered muffler as a processing object, collecting attribute data of the processing object, acquiring an evaluation result of the processing object in the test equipment, comparing the historical noise elimination data with the test result, obtaining a distinguishing item, and adjusting the attribute data.
8. The muffler manufacturing process data intelligent analysis system of claim 7, wherein the setup module comprises: The deviation unit is used for collecting the use data of the exhaust system by using the sensing equipment, configuring a behavior characteristic mode and deviating the distinguishing item; the obtaining unit is used for dividing the historical noise elimination data to obtain a common segment and an abnormal segment; And the sending unit is used for determining risk factors of the abnormal section through the position data, integrating the position data, the abnormal section and the risk factors, generating a risk report and sending the risk report to a preset terminal.
9. The muffler manufacturing process data intelligent analysis system of claim 8, wherein the issuing module comprises: The segmentation unit is used for generating a moving path diagram by utilizing the position data, determining the terrain change and segmenting the moving path diagram into a plurality of segments; The definition unit is used for traversing the scene category corresponding to each segment, counting the number of the segments corresponding to each scene category and defining the high-frequency category.
10. The muffler manufacturing process data intelligent analysis system of claim 8, wherein the build module comprises: The generation unit is used for selecting a hash function, carrying out hash on the historical noise elimination data, defining a hash result as a verification value, integrating all verification values and generating an index library; The hash unit is used for acquiring the real-time noise elimination data, carrying out hash to obtain target data, inquiring an index library, determining a verification value with highest similarity with the target data, and sending the attribute data to a source terminal of the real-time noise elimination data through the distinguishing item.

Description

Intelligent analysis method and system for silencer manufacturing process data Technical Field The invention relates to the technical field of muffler manufacturing, in particular to an intelligent analysis method and system for muffler manufacturing process data. Background The muffler is a key component installed in an exhaust system and has a main function of reducing noise generated in the operation process of equipment through the inner multi-layer cavity, the partition plate and the sound absorbing material. In the manufacturing process of the existing muffler, a standardized and integrated scheme is generally adopted, that is, exhaust systems of the same batch are generally provided with mufflers of the same specification and structure, the use scene of the exhaust systems is often single, and if the manufacturing process of the muffler can be adjusted according to the single scene, for example, the materials are selected, the structural design and the arrangement of the internal acoustic cavity are optimized in a targeted manner, so that the use experience of a user can be greatly improved. Therefore, "how to adjust the muffler manufacturing process according to the high frequency scenario of the exhaust system" is a technical problem to be solved by the present invention. Disclosure of Invention The invention aims to provide an intelligent analysis method and system for silencer manufacturing process data, which are used for solving the problem of how to adjust the silencer manufacturing process according to the high-frequency scene of an exhaust system in the background art. In order to achieve the above purpose, the present invention provides the following technical solutions: a method for intelligent analysis of muffler manufacturing process data, the method comprising: acquiring installation information of an exhaust system, wherein the installation information at least comprises user information, model and installation time, acquiring historical noise elimination data by using sensing equipment which is arranged in the exhaust system in advance, and establishing a corresponding relation between the model and the historical noise elimination data, wherein the historical noise elimination data at least comprises noise and vibration; Acquiring position data of an exhaust system, clustering the position data into a plurality of scene categories, selecting a test period, defining a high-frequency category, inquiring a preset comparison table to obtain an adjustment rule, and issuing the adjustment rule to a preset terminal; Hashing the historical muffling data to obtain a verification value, establishing a mapping between the verification value and the historical muffling data, and constructing a preliminary verification mechanism based on similarity; Establishing a corresponding relation between position data and historical noise elimination data through a preset timestamp, constructing a test data set corresponding to each scene category one by one, sending the test data set to preselected test equipment, searching out a to-be-delivered silencer based on the model, defining the to-be-delivered silencer as a processing object, collecting attribute data of the processing object, acquiring an evaluation result of the processing object in the test equipment, comparing the historical noise elimination data with the test result, obtaining a distinguishing item, and adjusting the attribute data. Further, the step of collecting historical muffling data by using a sensing device pre-deployed in the target device includes: And collecting using data of the exhaust system by using the sensing equipment, configuring a behavior characteristic mode, and shifting the distinguishing item. Further, the step of collecting the historical noise elimination data and establishing the corresponding relation between the model and the historical noise elimination data comprises the following steps: dividing the historical noise elimination data to obtain a common segment and an abnormal segment; and determining risk factors of the abnormal section through the position data, integrating the position data, the abnormal section and the risk factors, generating a risk report, and sending the risk report to a preset terminal. Further, the step of acquiring the position data of the exhaust system, clustering the position data into a plurality of scene categories, selecting a test period, defining a high-frequency category, inquiring a preset comparison table to obtain an adjustment rule, and issuing the adjustment rule to a preset terminal includes: generating a moving path diagram by utilizing the position data, determining the terrain change, and dividing the moving path diagram into a plurality of segments; Traversing the scene category corresponding to each segment, counting the number of the segments corresponding to each scene category, and defining a high-frequency category. Further, the step of hashing the historical mu