RV reducer information management system based on edge calculation and big data algorithm

Abstract

The invention discloses an RV reducer information management system based on edge calculation and a big data algorithm. A digital workshop system comprises a part management module, an authority management module, a product assembly module, a product inventory management module and a product detection module. Two-dimensional code marks capable of being read are printed on key parts, namely a cycloidal gear, a pin wheel housing and a planet carrier, of the speed reducer respectively. According to the invention, a client module system is set for each production link of a manufacturing workshop by using an edge calculation method to carry out information management and tracing on part assembly, part processing and product in-out and warehousing links of the speed reducer; and the data processing efficiency is improved by adopting bidirectional mapping of an intelligent big data algorithm in data processing. According to the invention, the information sharing of the RV speed reducer from the business process to each link of production and manufacturing and the accurate direction of the information flow are ensured, and the product information management efficiency and the tracing accuracy of an RV speed reducer manufacturing enterprise are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of information management of business processes and product production processes in RV reducer manufacturing enterprises. Specifically, the present invention relates to an RV reducer information management system based on edge computing and big data algorithms. Background Art

[0002] In recent years, with the continuous development of RV reducer related technologies, related enterprises have also increased. The continuous improvement of business process management and product manufacturing efficiency requirements for RV reducer manufacturers has led to increasing market competition in the RV reducer industry. Therefore, RV reducer manufacturers urgently need to use information management technology to improve enterprise information management efficiency and product traceability management;

[0003] With the continuous improvement of RV reducer production requirements, traditional management systems are no longer suitable for information traceability management of RV reducer manufacturers. Traditional information management systems have problems such as data silos, complex management, and low information traceability. They cannot meet the requirements for product information traceability management of RV reducers, and it is difficult to coordinate the information flow between the business management level and the production line, which is not conducive to the efficient management and decision-making of the manufacturing workshop by the business senior management.

[0004] In order to solve the above technical defects and realize the informatization of RV reducer related manufacturing enterprises, a technical solution is proposed. Summary of the invention

[0005] The purpose of this invention is to provide an RV reducer information management system based on edge computing and big data algorithms to ensure that RV reducer manufacturers can store and manage information in each business process and each production link in a timely manner. It also realizes information traceability and management of the RV reducer product production process, solves the problems of information sharing between business processes and manufacturing workshops and data islands, and improves the efficiency of enterprise information management and system execution.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An RV reducer information traceability management system for the manufacturing process includes the following steps:

[0008] (1) A client is configured in each production link in the manufacturing workshop, and each client corresponds to the management module of each production link; each client is configured with a QR code recognition device; the RV reducer production process is traced from product parts processing to product storage and warehousing using QR codes, and this layer interacts with the client of each production link in the manufacturing workshop;

[0009] Data collection module: Each production link of the manufacturing workshop production line is equipped with a data collection module, which uses the identification equipment to identify the part QR code information and pre-process it to obtain the original information corresponding to each part. The data collection module integrates the data uploaded to the database by the QR code, and uniformly manages the scattered data sources of each production link of the RV reducer;

[0010] Parts management module: The parts management module includes a parts entry process submodule and a production task display submodule. The parts entry process submodule facilitates the entry of processing information, processing personnel and processing time of processed parts; the production task display submodule allows processing personnel to complete the corresponding number of processed parts; the display window of the entry process submodule can publish, modify and delete part information; the parts management module is distributed in the processing links of the cycloid wheel, planetary carrier and pinion housing for later part processing traceability confirmation.

[0011] Product assembly management module: The product assembly management module is distributed on the assembly line. During the assembly process, the assemblers use a barcode scanner to scan the QR code information of each assembly part to match the reducer parts. The assembly management module matches the product information based on the unique identification QR code information of the parts.

[0012] Product Inventory Management Module: The inventory management module is responsible for the in-and-out operation and management of RV reducer products. The information management system issues outbound tasks to the outbound management module based on production plans, production orders, and inventory factors. Users view detailed product information based on the outbound order number and perform outbound tasks by scanning the QR code on the reducer, thereby recording and tracking each outbound order, including order status, outbound time, outbound quantity, and inbound time.

[0013] System permission module: The system permission module is divided into three sub-modules: permission code, role matching permission and user role addition; permission code is responsible for the permission management of each system module and system use, and different codes have different system permission operations; role matching permission means that different roles have different permission codes; user role addition is responsible for the number of roles each user has.

[0014] Performance detection module: The detection module is used to analyze the basic data of the reducer test and detection process. The performance detection module is connected to the database of the performance test bench of the detection center to collect the test data of the RV reducer transmission error and torsional stiffness and calculate the transmission performance and stiffness characteristics of the RV reducer.

[0015] (2) Construct a data processing algorithm at the data source to index the collected raw data. The raw data includes: part serial number, part type (cycloid gear, planetary carrier, pinion housing), part tension value, part torque value, processing time, processing personnel, and basic part model. The efficiency calculation formula for processing parts per hour is:

[0016]

[0017] In the formula, s l Indicates the number of parts processed per hour; l takes values ​​of 1, 2, and 3; s1 indicates the number of cycloid wheels processed; s2 indicates the number of planetary carriers processed; s3 indicates the number of pinion housings processed; the original data and part processing efficiency are stored in the target database.

[0018] Refined processing of assembly information: information management of the product assembly process. The original data of the assembly process includes: finished product serial number, matching parts (cycloid gear, planetary carrier, pinion housing), assembly personnel, and assembly time. The assembly personnel use the QR code device to scan the assembly parts one by one and match the parts information. At the same time, the hourly assembly efficiency is calculated; the parts matching information and assembly efficiency are stored in the target database. The hourly assembly efficiency calculation formula is:

[0019]

[0020] In the formula, f o Indicates the number of products assembled per hour; o indicates the model of the reducer;

[0021] Refined processing of outbound information: outbound information determines outbound tasks based on outbound order numbers. Outbound information corresponding to outbound order numbers includes: product model, product name, product outbound quantity, and outbound time. Outbound work efficiency is calculated based on outbound quantity and outbound time; outbound records and outbound work efficiency data are stored in the target database. The outbound efficiency calculation formula per hour is:

[0022]

[0023] In the formula, p o Indicates the number of product assemblies per hour;

[0024] Refined processing method of performance test information: The performance test items of RV reducer include transmission error and stiffness characteristics. The transmission error includes the forward transmission error, reverse transmission error and return error of the reducer. The stiffness characteristics include the stiffness, idle stroke value and tooth gap of the reducer. The performance test module obtains refined performance data by processing the test data collected by the acquisition module. The specific data processing method of the performance test module is as follows:

[0025]

[0026] Where, I is the reduction ratio of the RV reducer; TE + Indicates positive transmission error (when the output end of the reducer rotates counterclockwise); TE - Represents reverse transmission error (when the output end of the reducer rotates clockwise); θ + out Indicates the counterclockwise rotation angle of the reducer output end; θ - out Indicates the clockwise rotation angle of the reducer output end; θ + in Indicates the rotation angle of the input end when the output end of the reducer rotates counterclockwise; θ - in It indicates the rotation angle of the input end when the output end of the reducer rotates clockwise; HTE indicates the return transmission error; K indicates the torsional stiffness of the reducer; T indicates the rated torque; θ 1 (T) represents the torsion angle at the output end of the reducer under torque T; BL represents the backlash; ED represents the lost motion value; when a torque varying from 0 to T is applied in a counterclockwise direction at the output end of the reducer, θ 1 (0.5T) is the torsion angle generated when a 0.5T torque is applied; θ 2 (0) and θ 2 (0.03T) They represent the torsion angles generated at 0 and 0.03T torques when the output end of the reducer is reduced from T to 0 torque; θ 1 (0) and θ 1 (0.03T) They represent the torsion angles generated by the torques of 0 and 0.03T when the torque from 0 to T is applied counterclockwise at the output end of the reducer; θ 3 (0.03T) It indicates the torsion angle generated by 0.03T torque when the torque varying from 0 to T is applied clockwise at the output end of the reducer; θ 4 (0.03T) It indicates the torsion angle produced by 0.03T torque when the rated torque at the output end is reduced from T to 0.

[0027] The above performance test data including: original test data, forward error, reverse error, return error, torsional stiffness, backlash, and lost motion value are stored in the target database.

[0028] (3) Constructing edge network nodes: In the edge network of the discrete manufacturing workshop production process, the edge network deployment is S = (W, R), the set of connection paths of S in the edge network deployment is represented by R, the set of network nodes of S in the edge network deployment is represented by W, and D represents the set of edge nodes; the number of network nodes is n (corresponding to the n production links in claim 3 of claim 1, each network node represents a specific production link of the manufacturing workshop), then w i {i=1,2,…,n} represents, so the network node set W=(w1,w2,…,w n ), (i, j) represents the communication transmission between any two points in the edge network (i.e., the data interaction between the manufacturing workshop management modules), where x ij represents the data transmitted between the i-th edge network node and the j-th edge network node, and the value of (i, j) ranges from 1 to n; each network node has computing power (i.e., data processing of each management module); there are m tasks in the edge network of the manufacturing workshop production process, where m≤n, d i {i=1,2,…,m} represents the amount of data collected by each task (corresponding to the data collected by the data collection module for the production link);

[0029] The business processes performed by edge computing include:

[0030] ① The business management of the enterprise edits, signs and submits the production task information;

[0031] ② According to the content of each production task information, the production task information is read in the target network node and the production task information is read in the edge network node, and each module displays the production task information;

[0032] ③ After the manufacturing workshop staff uses the modules corresponding to each production link and successfully performs data operations on the target network node according to the production task information, the target edge computing node saves the processed data in a temporary data table;

[0033] ④ The edge computing node deletes, adds, stores, and calculates data in the temporary data table (i.e., the refined calculation processing method of the data source), and responds to the data management table of each module, and finally stores the processed data in the database;

[0034] The edge computing principle is integrated into the RV reducer information management system, which is characterized in that the data processing of various management modules includes the storage, management, query, data operation and acquisition of production data. The connection path is the interface accessed by the database server, and the data interaction between different modules is realized through the interface through the sqlClient function; the data flow is the data for data interaction between different data storage tables in the database.

[0035] (4) Construct the data interaction between the business layer of the big data algorithm and the manufacturing workshop: establish the forward mapping and reverse mapping between the source database and the target database; the source data model and the target data model are as follows:

[0036] A={a1(x1,y1,z1),a2(x2,y2,z2),...,a n (x n ,y n ,z n )} (6)

[0037] B={b1(f1,g1,e1),b2(f2,g2,e2),...,b n (f n ,g n ,e n )} (7)

[0038] In the formula, A represents the source data model; B represents the target data model; a n Indicates the demand information of the business layer for the nth production link (the n in A and B corresponds to the number of production links in claim 1, requirement 3); b n Indicates the feedback information from the nth production link to the business layer under the information demand of the business layer; x n ,y n , z n , f n , g n , e n Indicates the attributes of the corresponding data (i.e., detailed information queried under the business layer demand information (x n ,y n , z n ), which respectively represent the production and processing efficiency, the serial number SN of the reducer finished product and parts, and the production information of the reducer; the corresponding (f n , g n , e n ) respectively represent the production efficiency, production information and product performance information of the current demand).

[0039] The forward mapping between the business layer and the manufacturing workshop represents the mapping from the source data model to the target data model, which is expressed as:

[0040] α i =(A,B,δ) (8)

[0041] In the formula, α i It represents the forward mapping of the information demand of the business layer to the i-th production link, and δ represents the forward mapping rule (i.e., the permission of users at the business layer to access the i-th production link; the permission is formulated and managed by the permission management module).

[0042]

[0043] In the formula, α i -1 It represents the backward mapping of information feedback from the i-th production link to the business layer according to the information demand, and ε represents the backward mapping rule (i.e., the login permission and data operation permission of the management module corresponding to the i-th production link).

[0044] The data decision model based on information demand as described in claim 6 of claim 1 is characterized in that:

[0045] The information relationship between production links is calculated using the n-order matrix X=(x ij ) n×n It means the direct influence relationship between the elements of different production links in the manufacturing workshop (in the matrix X, x ij represents the data transmitted between the i-th production link and the j-th production link, and vice versa. ji Represents data transmitted between the j-th production link and the ith production; corresponding to the transmission data between edge nodes described in the feature of claim 2 of claim 1); wherein the information demand of the business layer represents an information query for each production link of the manufacturing workshop (the demand information includes: parts production status, product performance status, product warehousing status).

[0046]

[0047] For the convenience of calculation, the matrix of formula (10) is normalized and defined as the direct influence matrix G = (g ij ) n×n :

[0048]

[0049] The comprehensive impact matrix is ​​recorded as Q. The comprehensive impact matrix is ​​obtained according to the direct impact matrix. The comprehensive impact matrix Q is:

[0050] Q=G+G 2 +…+G n =G(1-G) -1 (12)

[0051] Setting decision-making indicators: Impact D i 、Influence C i 、Center degree M i And the causal degree R i .

[0052]

[0053] In the formula, q ij represents the element in the i-th row and j-th column of the comprehensive influence matrix Q; q jirepresents the element in the jth row and ith column of the comprehensive influence matrix Q; D i The data representing the business layer's information demand for the i-th production link affects the production efficiency of the remaining production links; C i M represents the influence of the data of the business layer's information demand for the i-th production link on the production efficiency of the remaining production links; i The data representing the information demand of the business layer for the i-th production link is central to the production efficiency of the remaining production links; R i The data representing the business layer's information demand for the i-th production link affects the production efficiency of the remaining production links; (the values ​​of i and j are determined by the number of production links).

[0054] Technical effects and advantages of the present invention:

[0055] 1. The present invention provides an RV reducer information management system based on edge computing and big data algorithms. The manufacturing workshop of a manufacturing enterprise is divided into production links of parts management, parts assembly, product inventory, and product performance, and production data information of each link is collected. The edge computing model is used to process the data; a bidirectional mapping algorithm is used to exchange data and make data decisions between the business layer and the manufacturing workshop; according to data anomalies of product performance and product production process, the business layer issues production tasks and changes to each production link of the manufacturing workshop, thereby improving the production efficiency of the enterprise;

[0056] 2. The present invention helps enterprises to establish their own information processing advantages in the production of RV reducer products by improving the collection, organization, processing, analysis and visualization of data. It also improves the real-time nature of data in various departments of the enterprise through data sharing, optimizes the stability of system execution, and improves decision-making efficiency and information processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is the overall framework diagram of the system in the implementation of the present invention;

[0058] Figure 2 is a multi-client schematic diagram of a manufacturing workshop of the present invention;

[0059] Figure 3 This is a data interaction diagram based on a database of the present invention. DETAILED DESCRIPTION

[0060] The present invention is further described below in conjunction with the accompanying drawings and specific implementations;

[0061] This paper designs an RV reducer information management system based on edge computing and big data algorithms, with the aim of providing technical support for the digital transformation of the RV reducer manufacturing industry and improving enterprise management efficiency through the system. The main benefit of this system to enterprises is the transparency of information from business to product manufacturing processes of RV reducers, data management of the entire process and problem tracing;

[0062] According to the overall block diagram of the whole system Figure 1 As shown, the specific system implementation is as follows:

[0063] (1) In the data collection layer, a barcode scanner is used to extract the QR code information and establish a connection with the database with the client. Figure 2 As shown, the implementation of the client management module of each production link in the manufacturing workshop is as follows:

[0064] Data collection module: Each production link of the manufacturing workshop production line is equipped with a data collection module, which uses the QR code recognition equipment to identify the QR code information on the parts and pre-process it to obtain the original information corresponding to each part. The data collection module integrates the data uploaded to the database by the QR code, and uniformly manages the scattered data sources of each production link of the RV reducer;

[0065] Parts management module: The parts management module includes a parts entry process submodule and a production task display submodule. The parts entry process submodule facilitates the entry of processing information, processing personnel and processing time of processed parts; the production task display submodule allows processing personnel to complete the corresponding number of processed parts; the display window of the entry process submodule can publish, modify and delete part information; the parts management module is distributed in the processing links of the cycloid wheel, planetary carrier and pinion housing for later part processing traceability confirmation.

[0066] Product assembly management module: The product assembly management module is distributed on the assembly line. During the assembly process, the assemblers use a barcode scanner to scan the QR code information of each assembly part to match the reducer parts. The assembly management module matches the product information based on the unique identification QR code information of the parts.

[0067] Product Inventory Management Module: The inventory management module is responsible for the in-and-out operation and management of RV reducer products. The information management system issues outbound tasks to the outbound management module based on production plans, production orders, and inventory factors. Users view detailed product information based on the outbound order number and perform outbound tasks by scanning the QR code on the reducer, thereby recording and tracking each outbound order, including order status, outbound time, outbound quantity, and inbound time.

[0068] System permission module: The system permission module is divided into three sub-modules: permission code, role matching permission and user role addition; permission code is responsible for the permission management of each system module and system use, and different codes have different system permission operations; role matching permission means that different roles have different permission codes; user role addition is responsible for the number of roles each user has.

[0069] Performance detection module: The detection module is used to analyze the basic data of the reducer test and detection process. The performance detection module is connected to the database of the performance test bench of the detection center to collect the test data of the RV reducer transmission error and torsional stiffness and calculate the transmission performance and stiffness characteristics of the RV reducer.

[0070] according to Figure 2 Multi-clients in the manufacturing workshop, building edge network nodes: In the edge network of the discrete manufacturing workshop production process, the edge network deployment is S = (W, R), the set of connection paths of S in the edge network deployment is represented by R, the set of network nodes of S in the edge network deployment is represented by W, and D represents the set of edge nodes; if the number of network nodes is n, then w i {i=1,2,…,n} represents, so the network node set W=(w1,w2,…,w n ), (i, j) represents the communication transmission between any two points in the edge network (i.e., the data interaction between the manufacturing workshop management modules), where x ij represents the data transmitted between the i-th edge network node and the j-th edge network node, and the value of (i, j) ranges from 1 to n; each network node has computing power (i.e., data processing of each management module); there are m tasks in the edge network of the manufacturing workshop production process, where m≤n, d i {i=1,2,…,m} represents the amount of data collected by each task (corresponding to the data collected by the data collection module for the production link);

[0071] The business processes performed by edge computing include:

[0072] ⑤ The business management of the enterprise edits, signs and submits the production task information;

[0073] ⑥ According to the content of each production task information, the production task information is read in the target network node and the production task information is read in the edge network node, and each module displays the production task information;

[0074] ⑦ After the manufacturing workshop staff uses the modules corresponding to each production link and successfully performs data operations on the target network node according to the production task information, the target edge computing node saves the processed data in a temporary data table;

[0075] ⑧The target edge computing node deletes, adds, stores, and calculates data in the temporary data table (i.e., the refined calculation processing method of the data source), responds to the data management table of each module, and finally stores the processed data in the database;

[0076] The edge computing principle is integrated into the RV reducer information management system, characterized in that the data processing of the various management modules includes the storage, management, query, data operation and acquisition of production data. The connection path is the interface for database server access. Figure 3 The data interaction relationship based on the database is realized by using the sqlClient function through the interface. The data flow is the data for data interaction between different data storage tables in the database.

[0077] Construct the data interaction between the business layer of the big data algorithm and the manufacturing workshop: establish the forward mapping and reverse mapping between the source database and the target database; the source data model and the target data model are as follows:

[0078] A={a1(x1,y1,z1),a2(x2,y2,z2),...,a n (x n ,y n ,z n )} (14)

[0079] B={b1(f1,g1,e1),b2(f2,g2,e2),...,b n (f n ,g n ,e n )} (15)

[0080] In the formula, A represents the source data model; B represents the target data model; a n Indicates the demand information of the business layer for the nth production link (n in A and B corresponds to the number of production links); b n Indicates the feedback information from the nth production link to the business layer under the information demand of the business layer; x n ,y n , z n , f n , g n , e n Indicates the attributes of the corresponding data (i.e., detailed information queried under the business layer demand information (x n ,y n , z n ), which respectively represent the production and processing efficiency, the serial number SN of the reducer finished product and parts, and the production information of the reducer; the corresponding (f n , g n , en ) respectively represent the production efficiency, production information and product performance information of the current demand).

[0081] The forward mapping between the business layer and the manufacturing workshop represents the mapping from the source data model to the target data model, which is expressed as:

[0082] α i =(A,B,δ) (16)

[0083] In the formula, α i It represents the forward mapping of the information demand of the business layer to the i-th production link, and δ represents the forward mapping rule (i.e., the permission of users at the business layer to access the i-th production link; the permission is formulated and managed by the permission management module).

[0084]

[0085] In the formula, α i -1 It represents the backward mapping of information feedback from the i-th production link to the business layer according to the information demand, and ε represents the backward mapping rule (i.e., the login permission and data operation permission of the management module corresponding to the i-th production link).

Claims

1. An RV reducer information management system based on edge computing and big data algorithm, characterized in that: include: S1. Multiple clients are set up in the RV reducer manufacturing workshop, and each client is equipped with a QR code device to collect production data of the product production process; S2. The edge computing edge node corresponding to each production link of the manufacturing workshop is used to obtain the production data of the data source; construct the source data processing method of edge computing in the product production process of the manufacturing workshop; S3. The manufacturing workshop is divided into multiple production links, wherein the multiple production links consist of n links, each link is configured with a client, that is, there are n client management modules; n is greater than or equal to 1; each client represents a production link; S4. Each production link stores the data processing results of the edge computing model in the target database while collecting the original production data; the data of the target database is fed back to the source database of the enterprise business layer, and the enterprise business layer issues production tasks and adjustments to the manufacturing workshop based on the production data; S5. The information interaction between the business layer and the manufacturing workshop, that is, the information sharing between the source database and the target database, is realized by constructing a bidirectional mapping algorithm; S6. By building a data decision model based on information needs, the business layer can intuitively understand the production status of the manufacturing workshop.

2. According to claim 1, an RV reducer information management system based on edge computing and big data algorithm is characterized in that: The construction of n client management modules is matched with data acquisition equipment, and each management module is connected to a data table, namely the target database, for managing their own production data; the specific implementation process is as follows: Data collection module: Each production link of the production line in the manufacturing workshop is equipped with a data collection module, which uses recognition equipment to identify the QR code information on the parts and pre-process it to obtain the original information corresponding to each part; The data acquisition module integrates the data uploaded to the database by the QR code, and manages the scattered data sources of each production link of the RV reducer in a unified manner; Parts management module: The parts management module includes a parts entry process submodule and a production task display submodule. The parts entry process submodule facilitates the entry of processing information, processing personnel and processing time of processed parts. The display submodule allows the processing personnel to have a clear understanding of the number of completed parts processing; The display window of the input process submodule can publish, modify and delete part information; the parts management module is distributed in the production and processing links of the cycloid gear, planetary carrier and pinion housing for later part processing traceability confirmation; Product assembly management module: The product assembly management module is distributed on the assembly line. During the assembly process, the assemblers use a barcode scanner to scan the QR code information of each assembly part to match the reducer parts. The assembly management module matches the product information based on the unique identification QR code information of the parts. Product inventory management module: The inventory management module is responsible for the in-and-out operation and management of RV reducer products; the information management system issues outbound tasks to the outbound management module based on production plans, production orders and inventory factors; users view detailed product information based on the outbound order number and execute outbound tasks by scanning the QR code on the reducer, thereby recording and tracking each outbound order, including order status, outbound time, outbound quantity and inbound time; System permission module: The system permission module is divided into three sub-modules: permission code, role matching permission, and user role addition; The permission code is responsible for the permission management of each system module and system usage. Different codes have different module operation permissions. Role matching permissions means different roles have different permission codes; User role addition is responsible for the number of roles each user has; Performance detection module: The detection module is used to analyze the basic data of the reducer test and detection process; the performance detection module is connected to the database of the performance test bench of the detection center to collect the test data of the RV reducer transmission error and torsional stiffness and calculate the RV reducer transmission performance and stiffness characteristics; The specific data processing method of the data acquisition module configured by each client management module at the data source is: Refined processing method for parts information: indexing the collected raw data; The original data includes: part serial number, part type including cycloid gear, planet carrier and pinion housing, part tension value, part torque value, processing time, processing personnel, and basic part model; the efficiency calculation formula for processing parts per hour is: In the formula, s l Indicates the number of parts processed per hour; l takes the value of 1, 2, 3; s1 indicates the number of cycloid wheels processed; s2 indicates the number of planet carriers processed; s3 indicates the number of pinion housings processed; the original data and part processing efficiency are stored in the target database; Refined processing of assembly information: information management of the product assembly process; the original data of the assembly process includes: finished product serial number, part types including cycloid gear, planetary carrier and pinion housing, assembly personnel, and assembly time; use QR code equipment to scan assembly parts one by one, and match part information; calculate hourly assembly efficiency at the same time; store part matching information and assembly efficiency in the target database; the hourly assembly efficiency calculation formula is: In the formula, f o Indicates the number of products assembled per hour; o indicates the model of the reducer; Refined processing of outbound information: outbound information determines outbound tasks based on outbound order numbers; outbound information corresponding to outbound order numbers includes: product model, product name, product outbound quantity, and outbound time; outbound work efficiency is calculated based on outbound quantity and outbound time; outbound records and outbound work efficiency data are stored in the target database; the outbound efficiency calculation formula per hour is: In the formula, p o Indicates the number of product assemblies per hour; Refined processing method of performance test information: The performance test items of RV reducer include transmission error and stiffness characteristics; the transmission error includes the forward transmission error, reverse transmission error and return error of the reducer; the stiffness characteristics include the stiffness, idle stroke value and backlash of the reducer; the performance test module obtains refined performance data by processing the test data collected by the acquisition module; the specific data processing method of the performance test module is as follows: Where, I is the reduction ratio of the RV reducer; TE + Indicates positive transmission error, i.e. when the output end of the reducer rotates counterclockwise; TE - Indicates the reverse transmission error, that is, when the output end of the reducer rotates clockwise; θ + out Indicates the counterclockwise rotation angle of the reducer output end; θ - out Indicates the clockwise rotation angle of the reducer output end; θ + in Indicates the rotation angle of the input end when the output end of the reducer rotates counterclockwise; θ - in It indicates the rotation angle of the input end when the output end of the reducer rotates clockwise; HTE indicates the return transmission error; K indicates the torsional stiffness of the reducer; T indicates the rated torque; θ 1 (T) represents the torsion angle at the output end of the reducer under torque T; BL represents the backlash; ED represents the lost motion value; when a torque varying from 0 to T is applied in a counterclockwise direction at the output end of the reducer, θ 1 (0.5T) is the torsion angle generated when a 0.5T torque is applied; θ 2 (0) and θ 2 (0.03T) They represent the torsion angles generated at 0 and 0.03T torques when the output end of the reducer is reduced from T to 0 torque; θ 1 (0) and θ 1 (0.03T) They represent the torsion angles generated by the torques of 0 and 0.03T when the torque from 0 to T is applied counterclockwise at the output end of the reducer; θ 3 (0.03T) It indicates the torsion angle generated by 0.03T torque when the torque varying from 0 to T is applied clockwise at the output end of the reducer; θ 4 (0.03T) It indicates the torsion angle produced by 0.03T torque when the rated torque at the output end is reduced from T to 0; The above performance test data including: original test data, forward error, reverse error, return error, torsional stiffness, backlash, and lost motion value are stored in the target database.

3. The RV reducer information management system based on edge computing and big data algorithm according to claim 1 is characterized in that: In the edge network of the discrete manufacturing workshop production process, the edge network deployment is S = (W, R), the set of connection paths of S in the edge network deployment is represented by R, the set of network nodes of S in the edge network deployment is represented by W, and D represents the set of edge nodes; the number of network nodes is n corresponding to the n production links, and each network node represents a specific production link of the manufacturing workshop, then w i {i=1,2,…,n} represents, so the network node set W=(w1,w2,…,w n ), (i, j) represents the communication transmission between any two points in the edge network, that is, the data interaction between the manufacturing workshop management modules, where x ij represents the data transmitted between the i-th edge network node and the j-th edge network node, and the value of (i, j) ranges from 1 to n; each network node has computing power (i.e., data processing of each management module); there are m tasks in the edge network of the manufacturing workshop production process, where m≤n, d i {i=1,2,…,m} indicates that the amount of data collected by each task corresponds to the data collected by the data collection module for the production link; The business processes executed by the edge computing model include: ① The business management of the enterprise edits, signs and submits the production task information; ② According to the content of each production task information, the production task information is read in the target network node and the production task information is read in the edge network node, and each module displays the production task information; ③ After the manufacturing workshop staff uses the modules corresponding to each production link and successfully performs data operations on the target network node according to the production task information, the target edge computing node saves the processed data in a temporary data table; ④ The target edge computing node deletes, adds, stores, and calculates data in the temporary data table, responds to the data management table of each module, and finally stores the processed data in the database.

4. The RV reducer information management system based on edge computing and big data algorithm according to claim 1 is characterized in that: The data processing of the various management modules includes the storage, management, query, data operation and acquisition of production data; the connection path is the interface accessed by the database server, through which the data interaction of different modules is realized through the sqlClient function; the data flow is the data for data interaction in different data storage tables of the database.

5. The RV reducer information management system based on edge computing and big data algorithm according to claim 1 is characterized in that: The bidirectional mapping algorithm of the big data algorithm realizes the data interaction between the business layer and the manufacturing workshop, which is characterized by: Establish forward mapping and reverse mapping between the source database and the target database; the source data model and the target data model are as follows: <h2 style=";text-align:left;direction:ltr">A = {a1(x1,y1,z1),a2(x2,y2,z2),...,a<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> (x<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> ,y<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> ,z<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> )} (6) B={b1(f1,g1,e1),b2(f2,g2,e2),...,b n (f n ,g n ,e n )} (7) In the formula, A represents the source data model; B represents the target data model; a n Indicates the demand information of the business layer for the nth production link; b n Indicates the feedback information from the nth production link to the business layer under the information demand of the business layer; (x n ,y n , z n ), which respectively represent the production and processing efficiency, the serial number SN of the reducer finished product and parts, and the production information of the reducer; the corresponding (f n , g n , e n ) represent the production efficiency, production information, and product performance information of the current demand respectively; The forward mapping between the business layer and the manufacturing workshop represents the mapping from the source data model to the target data model, which is expressed as: a i =(A,B,δ) (8) In the formula, α i represents the forward mapping of the information demand of the business layer to the i-th production link, δ represents the forward mapping rule (i.e., the permission of the user at the business layer to access the i-th production link; the permission is formulated and managed by the permission management module; In the formula, α i -1 It represents the backward mapping of information feedback from the i-th production link to the business layer according to the information demand, and ε represents the backward mapping rule, that is, the login permission and data operation permission of the management module corresponding to the i-th production link.

6. The RV reducer information management system based on edge computing and big data algorithm according to claim 1 is characterized in that: The information relationship between production links is calculated using the n-order matrix X=(x ij ) n×n express; Its meaning is the direct influence relationship between the elements of different production links in the manufacturing workshop; in the matrix X, x ij represents the data transmitted between the i-th production link and the j-th production link, and vice versa. ji Represents the data transmitted between the j-th production link and the i-th production; Corresponding to the transmission data between the edge nodes; The information demand of the business layer means issuing information query for each production link of the manufacturing workshop, including: parts production status, product performance status, product in and out of storage status; For the convenience of calculation, the matrix of formula (10) is normalized and defined as the direct influence matrix G = (g ij ) n×n : The comprehensive impact matrix is ​​recorded as Q; the comprehensive impact matrix is ​​obtained according to the direct impact matrix, and the comprehensive impact matrix Q is: Q=G+G 2 +…+G n =G(1-G) -1 (12) Setting decision-making indicators: Impact D i 、Influence C i 、Center degree M i And the causal degree R i ; In the formula, q ij represents the element in the i-th row and j-th column of the comprehensive influence matrix Q; q ji represents the element in the jth row and ith column of the comprehensive influence matrix Q; D i The data representing the business layer's information demand for the i-th production link affects the production efficiency of the remaining production links; C i M represents the influence of the data of the business layer's information demand for the i-th production link on the production efficiency of the remaining production links; i The data representing the information demand of the business layer for the i-th production link is central to the production efficiency of the remaining production links; R i The data representing the information demand of the business layer for the i-th production link is the causal degree of the production efficiency of the remaining production links; Centrality indicates the interaction between the factors in information demand, and the relationship between them is proportional to the centrality. Causality indicates the degree of influence of the factor on other factors. Causality greater than 0 indicates that the current factor has a greater influence on other factors. Causality less than 0 indicates that the factor is more susceptible to the influence of other factors.

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