Remote supervision system for industrial intelligent manufacturing

Abstract

The invention discloses an industrial intelligent manufacturing remote supervision system, and relates to the field of intelligent manufacturing, and the system comprises an industrial production platform which is provided with a manufacturing management module, a manufacturing sensing module, a manufacturing analysis module, a manufacturing decision module, a manufacturing transmission module and a manufacturing response module. The manufacturing management module is used for acquiring industrial intelligent manufacturing information and constructing a manufacturing event type decision tree; the manufacturing sensing module is used for collecting industrial production data; the manufacturing analysis module is used for acquiring a manufacturing data packet in an industrial production process; the manufacturing decision-making module is used for generating auxiliary decision-making information corresponding to the corresponding production process according to the manufacturing data packet; the manufacturing transmission module is used for carrying out planning transmission on the generated auxiliary decision-making information; the manufacturing response module is used for acquiring corresponding auxiliary decision-making information, generating remote response information and regulating and controlling the production process in real time; according to the invention, the supervision efficiency of remote supervision is improved to a certain extent.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing technology, and in particular to an industrial intelligent manufacturing remote monitoring system. Background Art

[0002] With the continuous development of modern science and technology, the manufacturing industry has gradually shifted from traditional production methods to intelligent production methods. Under this new production method, industrial intelligent manufacturing systems have become an important means. This system can realize intelligent management of the production process through technologies such as automation and data collection, thereby improving production efficiency and quality. However, this intelligent production method also brings new problems, that is, how to effectively supervise and manage the production process.

[0003] After searching, the invention patent with Chinese patent number CN116859857A discloses an intelligent management system for a fuel pump assembly production line based on remote control, including a management platform, a server, a conveying risk unit, an assembly supervision unit, a production line analysis unit, an assessment and analysis unit, an early warning management unit and a workstation self-inspection unit are arranged inside the management platform; the present invention collects the conveying data of the assembly production line and conducts conveying risk supervision and analysis to ensure the conveying safety and assembly efficiency of the assembly production line, and conducts self-inspection on abnormal assembly efficiency, that is, conducts supervision, early warning, feedback and evaluation analysis on the status data of the equipment at each workstation of the production line to improve the assembly efficiency of the production line and the operation safety of each workstation, and integrates and analyzes the production line data by data integration, so as to reasonably supervise the production line, so as to remind the operation management personnel to reasonably and targetedly supervise the production line, so as to reasonably arrange the operation management personnel.

[0004] Compared with the existing technology, the invention patent with Chinese patent number CN116859857A can reasonably supervise each step in the industrial production process so that the operation and management personnel can make reasonable arrangements, thereby completing the intelligent management of the corresponding production line.

[0005] However, in actual use, the above system can only perform self-inspection on abnormal situations in assembly efficiency and determine whether the corresponding assembly efficiency is abnormal, but cannot obtain the specific abnormal situation. As a result, remote management personnel need professional technicians to judge abnormalities in the production process. In this process, the efficiency of remote supervision in the industrial production process is reduced to a certain extent, and resources are wasted to a certain extent. Summary of the invention

[0006] The purpose of the present invention is to solve the shortcoming of low remote supervision efficiency in the prior art and to propose an industrial intelligent manufacturing remote supervision system.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] An industrial intelligent manufacturing remote supervision system includes an industrial production platform, wherein the industrial production platform is provided with a manufacturing management module, a manufacturing perception module, a manufacturing analysis module, a manufacturing decision module, a manufacturing transmission module and a manufacturing response module;

[0009] The manufacturing management module is used to input corresponding industrial intelligent manufacturing information, set corresponding production management space according to the industrial intelligent manufacturing information, and construct corresponding manufacturing event type decision tree;

[0010] The manufacturing perception module is used to deploy corresponding sensor devices according to industrial intelligent manufacturing information and collect industrial production data through the corresponding sensor devices;

[0011] The manufacturing analysis module is used to analyze and process the collected industrial production data according to the manufacturing event type decision tree to obtain the manufacturing data packets in the industrial production process;

[0012] The manufacturing decision module is used to obtain a manufacturing data packet, analyze and process the manufacturing data packet, obtain the severity coefficient corresponding to the corresponding production process in the production process, and generate auxiliary decision information according to the severity coefficient of the production process and the corresponding manufacturing data packet;

[0013] The manufacturing transmission module is used to perform transmission planning for the generated auxiliary decision information, adjust the transmission resources according to the planning results, and transmit the data corresponding to the production process according to the adjustment results;

[0014] The manufacturing response module is used to remotely control the production process corresponding to each production process according to the transmission results, generate remote response information from the control results, and perform real-time control on the production process corresponding to the production process according to the remote response information.

[0015] The above technical solution further includes: the process of setting up a production management space and constructing a corresponding manufacturing event type decision tree includes:

[0016] Enter the corresponding industrial intelligent manufacturing information, which includes the corresponding enterprise qualification information, manufacturing equipment information, manufacturing process information, manufacturing evaluation information and historical production information;

[0017] Set up corresponding production management space according to enterprise qualification information, and analyze and manage the industrial production process within the enterprise through the production management space;

[0018] Acquire manufacturing equipment information and manufacturing process information, set a process equipment mapping table corresponding to the manufacturing process information and the manufacturing equipment information, set corresponding process nodes according to the process equipment mapping table, and set corresponding manufacturing process links according to each process node;

[0019] According to the matching analysis between the corresponding process nodes in the manufacturing process link and the corresponding manufacturing evaluation information and historical production information, the obtained historical production information is analyzed for manufacturing event types according to the manufacturing evaluation information, the historical production information obtained by the process nodes is set to multiple historical production information sets, and the obtained multiple historical production information sets are analyzed and processed from multi-dimensional perspectives respectively;

[0020] The trunk branches are set according to the manufacturing process links, the trunk nodes are set according to the process nodes, the analysis results of the multi-dimensional angles in the trunk nodes are obtained, and the branch nodes are set according to the corresponding multi-dimensional angles. The branch nodes are provided with corresponding manufacturing event types and corresponding event feature data. The obtained data are integrated to construct an event type decision tree.

[0021] Furthermore, the process of collecting industrial production data includes:

[0022] Construct an industrial production data set of the corresponding manufacturing equipment according to the historical production information obtained, obtain the production process data of the corresponding manufacturing equipment according to the industrial production data set, obtain the change interval of the manufacturing equipment to which each production process data belongs, perform intersection processing on each change interval, obtain the corresponding deployment position, and install the corresponding sensor equipment according to the deployment position;

[0023] Industrial production data during the production process of corresponding manufacturing equipment is collected through sensor equipment.

[0024] Furthermore, the process of obtaining manufacturing data packets in the industrial production process includes:

[0025] Obtain the corresponding manufacturing process link in the process equipment mapping table, obtain the corresponding industrial production data according to the corresponding process node in the manufacturing process link, input the obtained industrial production data into the event type decision tree, perform matching analysis according to the event feature data stored in each branch node in the corresponding trunk node, and obtain the manufacturing event type category data according to the matching results;

[0026] Obtaining the collection time of industrial production data, setting a directed number axis according to the collection time, performing visual analysis on the manufacturing event type corresponding to the corresponding process node according to the directed number axis, and obtaining the manufacturing event type continuous data corresponding to the corresponding manufacturing event type according to the visual analysis result;

[0027] The obtained manufacturing event type category data and manufacturing event type continuous data are used to generate a manufacturing data packet, and sent to the manufacturing decision module.

[0028] Furthermore, the process of obtaining the severity coefficient corresponding to the corresponding production process includes:

[0029] Obtain the historical manufacturing data package corresponding to the corresponding process node, obtain the correlation data between the corresponding manufacturing event type and the corresponding production process according to the historical manufacturing data package, obtain the basic severity coefficient of the corresponding manufacturing event type in the corresponding process node according to the correlation data, and set the basic increment of the corresponding manufacturing event type;

[0030] According to the continuous data of manufacturing event types in the manufacturing data package, the corresponding event severity data is obtained according to the corresponding basic severity coefficient and basic increment. The event severity data corresponding to each manufacturing event type data are integrated to obtain the corresponding severity coefficient in the corresponding process node.

[0031] Furthermore, the process of generating auxiliary decision information includes:

[0032] Set a severe threshold according to the historical manufacturing data package corresponding to the production process, compare and analyze the severe coefficient corresponding to the production process with the severe threshold, and determine whether to generate process abnormality information based on the comparative analysis results;

[0033] Obtain the location of the manufacturing process link to which the process exception information corresponds to the production process, reverse the starting process node of the corresponding manufacturing event type based on the production process, and mark the starting process node for auxiliary decision-making;

[0034] The process exception information and auxiliary decision corresponding to the corresponding production process in the manufacturing process link are marked as the auxiliary decision information corresponding to the corresponding manufacturing process link.

[0035] Furthermore, the process of transmitting the data corresponding to the production process includes:

[0036] In the manufacturing transmission module, corresponding fixed transmission resources and flexible transmission resources are set according to each manufacturing process link in operation during the industrial production process. The fixed transmission resources are used to transmit data to the target transmission address corresponding to the fixed manufacturing process link; the flexible transmission resources are used to transmit data to the target transmission address corresponding to each manufacturing process link as needed;

[0037] Obtain auxiliary decision information, set corresponding transmission priority according to corresponding process exception information and auxiliary decision mark in the auxiliary decision information, set a queue to be transmitted according to the transmission priority, and obtain the transmission data volume corresponding to the queue to be transmitted;

[0038] Compare and analyze the obtained transmission data volume with the corresponding fixed transmission resources, and determine whether to generate resource scheduling information according to the comparison and analysis results, wherein the resource scheduling information is to call the corresponding elastic transmission resources to transmit the corresponding transmission data volume;

[0039] The corresponding data is transmitted by transmitting the analysis results.

[0040] Furthermore, the process of real-time control of the production process includes:

[0041] Obtain corresponding auxiliary decision information from the corresponding target transmission address, obtain the manufacturing event type corresponding to the corresponding production process in the production process according to the auxiliary decision information and generate production warning information, remotely control the production process corresponding to the manufacturing equipment of the corresponding production process according to the manufacturing event type, and generate remote response information with the control result;

[0042] The generated remote response information is fed back to the process node corresponding to the corresponding production process in the corresponding manufacturing process link, and the process node performs real-time regulation of the corresponding manufacturing equipment based on the remote response information.

[0043] The present invention has the following beneficial effects:

[0044] 1. In the present invention, by setting a process equipment mapping table, the production steps of each product in the corresponding enterprise and the manufacturing equipment involved are visualized according to the process equipment mapping table, so as to clearly set the corresponding manufacturing process link, and set the corresponding event type decision tree for the corresponding production process according to the set manufacturing process link. According to the event type decision tree, a preliminary analysis is performed on the corresponding manufacturing event type in the production process, and the manufacturing data packet corresponding to the corresponding production process is obtained according to the preliminary analysis results, so that the remote monitoring management process can manage the corresponding manufacturing process link more efficiently.

[0045] 2. In the present invention, by analyzing and processing the production situation of the corresponding production process through the duration of the type corresponding to the corresponding manufacturing event type in the corresponding production process, the error in the data analysis process is avoided to a certain extent, thereby improving the accuracy of the analysis and processing of each production process.

[0046] 3. In the present invention, by setting the priority of the auxiliary decision-making information corresponding to the corresponding production process in the transmission process for the severity coefficient corresponding to different manufacturing event types in the corresponding production process, and setting the queue to be transmitted according to the priority corresponding to the auxiliary decision-making information, remote supervision driven by the manufacturing event type is indirectly realized to a certain extent, and the transmission process is dynamically adjusted through flexible transmission resources, which improves the real-time performance of the remote supervision process to a certain extent and reduces the waste of resources in the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a structural schematic diagram of an industrial intelligent manufacturing remote monitoring system proposed by the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] Embodiment 1

[0050] like Figure 1 As shown, an industrial intelligent manufacturing remote supervision system proposed in the present invention includes an industrial production platform, in which a manufacturing management module, a manufacturing perception module, a manufacturing analysis module, a manufacturing decision module, a manufacturing transmission module and a manufacturing response module are arranged.

[0051] In this embodiment, the industrial production platform sets the equipment analysis process in the corresponding data according to each module to complete the remote supervision in the industrial intelligent manufacturing process. The specific implementation process includes:

[0052] The manufacturing management module is used to input corresponding industrial intelligent manufacturing information and construct a corresponding manufacturing event type decision tree according to the industrial intelligent manufacturing information. The specific implementation process includes:

[0053] Set up a manufacturing management unit and a production analysis unit;

[0054] The manufacturing management unit is provided with a manufacturing input subunit and a manufacturing analysis subunit, wherein:

[0055] Enter corresponding industrial intelligent manufacturing information through the manufacturing entry subunit, wherein the industrial intelligent manufacturing information includes corresponding enterprise qualification information, manufacturing equipment information, manufacturing process information, manufacturing evaluation information and historical production information;

[0056] The manufacturing analysis subunit obtains corresponding industrial intelligent manufacturing information, analyzes and processes the obtained industrial intelligent manufacturing information, and determines whether the corresponding industrial intelligent manufacturing information is valid;

[0057] Obtain the corresponding enterprise qualification information, which includes the basic enterprise qualification, industry production qualification, quality management system qualification, etc. The obtained enterprise qualification information is queried and verified through the corresponding official channels. If the verification is passed, the enterprise is granted access to the industrial production platform, and a production management space is set up for the corresponding enterprise. The industrial intelligent manufacturing information is input into the corresponding production management space, and the production management space stores the corresponding industrial intelligent manufacturing information;

[0058] The production analysis unit is used to analyze and process the industrial intelligent manufacturing information in the production management space corresponding to each enterprise, obtain the manufacturing event types in different production processes, and construct a manufacturing event type decision tree. The specific implementation process includes:

[0059] Obtain the corresponding production management space and the corresponding industrial intelligent manufacturing information in the production management space, isolate the production management space from other production management spaces, and avoid data leakage in different enterprises in the industrial production platform. The specific implementation process includes:

[0060] Obtain the enterprise qualification information of the enterprise corresponding to the production management space, set up the corresponding virtual LAN and the corresponding firewall policy according to the enterprise qualification information, and isolate the corresponding production management space in the industrial production platform according to the corresponding virtual LAN and the corresponding firewall policy;

[0061] The production management space analyzes and processes the corresponding industrial intelligent manufacturing information and sets the corresponding manufacturing event information. The specific implementation process includes:

[0062] Obtain each manufacturing process information and manufacturing equipment information in the corresponding industrial intelligent manufacturing information, obtain the manufacturing equipment involved in the corresponding process steps in the manufacturing process information, and set a process equipment mapping table corresponding to the manufacturing process information and manufacturing equipment information according to the results involved;

[0063] The corresponding horizontal and vertical coordinates in the process equipment mapping table correspond to the corresponding manufacturing equipment and manufacturing process respectively, and the corresponding process step is marked in the corresponding cell according to the manufacturing equipment corresponding to the process step;

[0064] According to the marking results of each manufacturing equipment in the vertical direction corresponding to the corresponding manufacturing process information in the process equipment mapping table, corresponding process nodes are set for each manufacturing process information;

[0065] The obtained process nodes are used to generate manufacturing process links according to the marking results corresponding to the process steps. It should be further explained that in the specific implementation process, the process nodes are also used to analyze and process the production process in the corresponding production process;

[0066] Obtain each manufacturing process link, analyze and process the process nodes corresponding to each manufacturing process link, and build an event type decision tree;

[0067] Obtain the corresponding manufacturing process link in the process equipment mapping table, perform matching analysis according to the corresponding process node in the manufacturing process link and the corresponding manufacturing evaluation information and historical production information, perform manufacturing event type analysis on the obtained historical production information according to the manufacturing evaluation information, set multiple historical production information sets for the historical production information obtained by the process node, and analyze and process the multiple historical production information sets obtained from multiple dimensions, including production efficiency, production quality and production cost;

[0068] According to the corresponding alignment angles, the corresponding historical production information sets are analyzed and processed in sequence based on the deep learning algorithm to obtain the manufacturing event types and corresponding event feature types corresponding to the multi-dimensional angles;

[0069] Obtain a historical production information set corresponding to a multi-dimensional perspective, perform single variable division processing on the historical production information in the historical production information set, obtain a corresponding historical production information subset based on the single variable division processing, perform analysis and processing on the historical production information subset, obtain the manufacturing event types corresponding to different historical production information subsets, perform feature extraction on the historical production information subset corresponding to the manufacturing event type, and obtain event feature data corresponding to the corresponding manufacturing event type;

[0070] The obtained event characteristic data is associated and stored with the corresponding manufacturing event type;

[0071] Obtain the corresponding manufacturing process link, set the trunk branch according to the corresponding manufacturing process link, and set the corresponding trunk nodes on the trunk branch in sequence according to the corresponding process nodes in the manufacturing process link;

[0072] Obtaining the analysis results of the multi-dimensional angles in the trunk node, and setting the branch nodes according to the corresponding multi-dimensional angles; the manufacturing event types corresponding to the corresponding multi-dimensional angles and the corresponding event feature data are set in the branch nodes;

[0073] The corresponding manufacturing event types and the corresponding event feature data are mapped to the corresponding trunk nodes and branch nodes in turn, the corresponding event type decision tree is obtained, and the obtained event type decision tree is updated and stored.

[0074] The manufacturing perception module is used to deploy corresponding sensor devices according to industrial intelligent manufacturing information and collect industrial production data through the corresponding sensor devices. The specific implementation process includes:

[0075] Setting up a sensing deployment unit and a sensing collection unit;

[0076] The sensing deployment unit is used to obtain the corresponding manufacturing process link in the corresponding process equipment mapping table, and deploy the corresponding sensor according to the manufacturing equipment corresponding to the obtained manufacturing process link;

[0077] Obtain the number of manufacturing process links corresponding to the manufacturing equipment, and obtain the historical production information of the process nodes corresponding to each manufacturing process link corresponding to the corresponding manufacturing equipment;

[0078] An industrial production data set of the corresponding manufacturing equipment is constructed based on the historical production information obtained, and the corresponding historical production information in the industrial production data set is analyzed and processed in sequence, and the production process data corresponding to the corresponding process nodes in the corresponding manufacturing equipment are obtained respectively, and the corresponding production process data is analyzed and processed to complete the corresponding equipment analysis process;

[0079] Obtain the change interval of the manufacturing equipment to which each production process data belongs, perform intersection processing on the change intervals corresponding to each production process data in the industrial production data set, and obtain the corresponding deployment position;

[0080] According to the deployment positions obtained by each manufacturing device, corresponding sensor devices are set, corresponding sensors are installed, and they are associated with corresponding manufacturing devices;

[0081] The sensing and collecting unit is used to obtain the sensor devices set at the corresponding deployment positions of each manufacturing equipment, and obtain the data information monitored by the corresponding sensor devices. The specific implementation process includes:

[0082] Acquire corresponding industrial production data through corresponding sensor devices, wherein the industrial production data includes equipment operation data, production material data and production product data, and send the acquired industrial production data to the manufacturing analysis module;

[0083] It should be further explained that, in the specific implementation process, the sensor device includes multiple ones, which are respectively used to collect different types of industrial production data.

[0084] The manufacturing analysis module is used to analyze and process the collected industrial production data according to the manufacturing event type decision tree to obtain the manufacturing data packet in the industrial production process. The specific implementation process includes:

[0085] Obtain the corresponding manufacturing process link in the process equipment mapping table, and according to the corresponding process nodes in the manufacturing process link, perform statistical analysis on the industrial production data corresponding to the corresponding process nodes based on the corresponding event type decision tree to generate the corresponding manufacturing data packet;

[0086] Preset a directed number axis, set the obtained directed number axis to a corresponding unit interval distance according to the collection time of the industrial production data, and map the obtained unit interval distance to the corresponding directed number axis;

[0087] According to the collection time of industrial production data, the data is mapped to the corresponding position in the corresponding directed number axis, and the process node analyzes and processes the industrial production data at the corresponding position in the directed number axis, and maps the analysis result to the corresponding position in the directed number axis to obtain the statistical directed axis line, and generates the manufacturing data package with the obtained statistical directed axis line;

[0088] The process node obtains corresponding industrial production data, performs feature extraction on the obtained industrial production data, obtains corresponding feature variables, matches and analyzes the obtained feature variables with the event feature data corresponding to the corresponding manufacturing event type in the corresponding branch node in the event type decision tree, obtains the corresponding manufacturing event type according to the matching result, and maps it to the corresponding position in the directed number axis;

[0089] The process node obtains the manufacturing event type corresponding to the industrial production data at the corresponding position in the directed number axis, and obtains the visualization result of the corresponding manufacturing event type according to the directed number axis;

[0090] Perform statistical analysis based on the visualization results corresponding to the directed number axis to obtain the number of manufacturing event types and the corresponding types, and obtain the manufacturing event type data at the corresponding position;

[0091] Taking the corresponding position in the directed number axis as the center, obtaining the visualization results of the manufacturing event types at the positions adjacent to the starting point with the corresponding position as the starting point, if the corresponding manufacturing event types are consistent, then continue to obtain the visualization results of the manufacturing event types at the adjacent positions until the manufacturing event types are inconsistent, stop counting, and obtain continuous data of the corresponding manufacturing event types;

[0092] The obtained manufacturing event type category data and manufacturing event type continuous data are marked as statistical analysis results, and the obtained statistical analysis results are used to generate a manufacturing data packet and send it to the manufacturing decision module.

[0093] The manufacturing decision module is used to obtain a manufacturing data packet, analyze and process the manufacturing data packet, obtain the severity coefficient corresponding to the corresponding production process in the production process, and generate auxiliary decision information according to the severity coefficient of the production process and the corresponding manufacturing data packet. The specific implementation process includes:

[0094] Set up data analysis unit and decision support unit;

[0095] The data analysis unit is used to obtain the corresponding manufacturing data packet, and analyze and process the corresponding manufacturing data packet to obtain the severity coefficient corresponding to the manufacturing event type corresponding to the corresponding production process;

[0096] Obtain the historical manufacturing data packets corresponding to the corresponding production processes at each process node, obtain the correlation data between the corresponding manufacturing event type and the corresponding production process according to the historical manufacturing data packets, obtain the corresponding manufacturing event type in each historical manufacturing data packet, and mark the corresponding manufacturing event type as Y; mark the corresponding production process as X, obtain the number of samples in the historical manufacturing data packet, and mark the number of samples as k, and mark the correlation data between the production process and the corresponding manufacturing event type as R, where:

[0097]

[0098] According to the correlation data between the corresponding production process and the manufacturing event type, the corresponding basic severity coefficient is set and marked as S0, where S0 is numerically equal to R;

[0099] Obtain the basic increment corresponding to the corresponding manufacturing event type in the historical manufacturing data package, mark the basic increment as ΔS, and obtain the manufacturing event type data corresponding to the directed axis in the corresponding production process in the historical manufacturing data package;

[0100] Preset time period, obtain the data volume of the corresponding manufacturing event type in each time period, and mark the corresponding data volume as x t , x t is the number corresponding to the tth time period, and m is the corresponding number of time periods, where:

[0101]

[0102] Obtain manufacturing event type category data and manufacturing event type persistence data in the manufacturing data package corresponding to the corresponding production process;

[0103] According to the continuous data of manufacturing event type, the corresponding event severity data is obtained, and the event severity data is marked as S. According to the obtained basic severity coefficient and the corresponding basic increment, the event severity data of the corresponding manufacturing event type is obtained, and the continuous data of the manufacturing event type is marked as n, where:

[0104] S = S0 + ΔS × n;

[0105] The event severity data corresponding to the manufacturing event type are integrated and processed, and the event severity data are marked according to the corresponding manufacturing event type to obtain the corresponding S 类型According to the corresponding manufacturing event type data in the manufacturing data package, the event severity data corresponding to each manufacturing event type is integrated to obtain the corresponding severity coefficient, and the severity coefficient is marked as YZ, YZ = ∑S 类型 ;

[0106] Send the severity coefficient corresponding to each production process to the auxiliary decision-making unit;

[0107] The auxiliary decision unit is used to analyze and process the severity coefficients corresponding to each production process to obtain corresponding auxiliary decision information. The specific implementation process includes:

[0108] Obtain the corresponding manufacturing process link in the process equipment mapping table, and analyze and process the corresponding severity coefficient according to the edge processing node corresponding to the production process corresponding to the corresponding manufacturing process link;

[0109] Setting a critical threshold value according to the historical manufacturing data packet corresponding to the production process, wherein the critical threshold value is set for the historical data corresponding to the historical manufacturing data packet corresponding to the corresponding production process;

[0110] When the severity coefficient in the process node corresponding to the production process is greater than or equal to the severity threshold, process abnormality information is generated;

[0111] When the severity coefficient in the process node corresponding to the production process is less than the severity threshold, no process abnormality information is generated;

[0112] The auxiliary decision-making unit obtains the abnormal analysis of the severity coefficient of the process node corresponding to each production process according to the corresponding manufacturing process link, and obtains the manufacturing event type data of the process node corresponding to the process abnormal information corresponding to the manufacturing process link;

[0113] Analyze and process the obtained manufacturing event type data to obtain the process node corresponding to the starting production process corresponding to each type of manufacturing event type in the corresponding process node;

[0114] According to the manufacturing process link, the process exception information is used as the starting point, and the starting process node of the corresponding manufacturing event type is reversed according to the production process. That is, the process exception information is used as the starting point to obtain the manufacturing event type data corresponding to the previous process node. If the corresponding manufacturing event type also exists in the process node, the manufacturing event type data corresponding to the previous process node is continued to be obtained until the corresponding manufacturing event type does not exist in the corresponding process. Then, the process node that last obtains the corresponding manufacturing event type is marked as the starting process node;

[0115] Obtain the starting process node corresponding to the corresponding manufacturing event type in the process exception information, and mark it as an auxiliary decision-making node;

[0116] The obtained process abnormality information and the auxiliary decision mark corresponding to the corresponding production process are used to produce auxiliary decision information, and the obtained auxiliary decision information is sent to the manufacturing transmission module.

[0117] The manufacturing transmission module is used to perform transmission planning for the generated auxiliary decision information, adjust the transmission resources according to the planning results, and transmit the data corresponding to the production process according to the adjustment results. The specific implementation process includes:

[0118] Obtain auxiliary decision information and map the auxiliary decision information to each process node position corresponding to each manufacturing process link;

[0119] In the manufacturing transmission module, corresponding fixed transmission resources and elastic transmission resources are set according to the manufacturing process links running in the industrial production process. The elastic transmission resources can be used for data transmission in each manufacturing process link;

[0120] Obtain the corresponding auxiliary decision-making marks and process exception information in the corresponding manufacturing process link, and set the transmission priority for the process node corresponding to the process exception information;

[0121] Obtain the auxiliary decision mark, and set the corresponding transmission priority in sequence according to the production process of the process node corresponding to the auxiliary decision mark;

[0122] Generate a queue to be transmitted for the corresponding auxiliary decision information according to the corresponding transmission priority, obtain the transmission data volume of the queue to be transmitted, and compare and analyze the transmission data volume of the generated queue to be transmitted with the fixed transmission resources corresponding to the corresponding manufacturing process link:

[0123] If the amount of transmitted data is less than or equal to the fixed transmission resources, no resource scheduling information is generated, and the industrial production data and manufacturing data packets in the corresponding process nodes are transmitted in sequence according to the queue to be transmitted;

[0124] If the amount of transmitted data is greater than the fixed transmission resources, the difference information between the amount of transmitted data corresponding to the corresponding manufacturing process link and the fixed transmission resources is obtained, and resource scheduling information is generated according to the difference information;

[0125] Acquire resource scheduling information, acquire transmission resources within the elastic transmission resources according to the resource scheduling information, and transmit the acquired transmission resources with the corresponding elastic transmission resources;

[0126] If the remaining elastic transmission resources are still less than the corresponding amount of transmission data, the corresponding difference information in the adjacent process nodes is obtained, and the corresponding indirect transmission path is set according to the corresponding difference information. The indirect transmission path is to complete the transmission of the industrial production data and manufacturing data packets corresponding to the corresponding process nodes in the process node through the fixed transmission resources corresponding to other process nodes;

[0127] It should be further explained that, in the specific implementation process, each production process corresponds to a corresponding target transmission address, the corresponding data is transmitted according to the target transmission address, the indirect transmission path is changed to obtain the target transmission address corresponding to the corresponding production process, and after the transmission is completed, it is modified to the target transmission address corresponding to the corresponding process node.

[0128] The manufacturing response module is used to remotely control the production process corresponding to each production process according to the transmission result, generate remote response information based on the control result, and perform real-time control on the production process corresponding to the production process according to the remote response information. The specific implementation process includes:

[0129] Obtain corresponding auxiliary decision information from the corresponding target transmission address, obtain the manufacturing event type corresponding to the corresponding production process in the production process according to the auxiliary decision information and generate production warning information, remotely control the production process corresponding to the manufacturing equipment of the corresponding production process according to the manufacturing event type, and generate remote response information with the control result;

[0130] A control linear programming library for manufacturing event types corresponding to corresponding manufacturing equipment is preset, and the obtained manufacturing event types are combined with corresponding industrial production data and mapped into the control linear programming library to obtain the control results of the manufacturing equipment corresponding to the corresponding production process;

[0131] The staff corresponding to the target transmission address verifies and analyzes the obtained control results according to the production warning information, generates the final control node according to the staff's verification and analysis, and generates remote response information with the final control result;

[0132] Feedback the generated remote response information to the process node corresponding to the corresponding production process in the corresponding manufacturing process link, and the process node will adjust the corresponding manufacturing equipment in real time according to the remote response information;

[0133] A control feedback cycle is preset, and the manufacturing event type corresponding to each production process is obtained according to the control feedback cycle. When the production processes corresponding to each production process are normal, the remote control is stopped. Otherwise, each production process is monitored in real time.

[0134] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An industrial intelligent manufacturing remote monitoring system, including an industrial production platform, characterized in that: The industrial production platform is provided with a manufacturing management module, a manufacturing perception module, a manufacturing analysis module, a manufacturing decision module, a manufacturing transmission module and a manufacturing response module; The manufacturing management module is used to input corresponding industrial intelligent manufacturing information and construct a corresponding manufacturing event type decision tree according to the industrial intelligent manufacturing information; The manufacturing perception module is used to deploy corresponding sensor devices according to industrial intelligent manufacturing information and collect industrial production data through the corresponding sensor devices; The manufacturing analysis module is used to analyze and process the collected industrial production data according to the manufacturing event type decision tree to obtain the manufacturing data packets in the industrial production process; The manufacturing decision module is used to obtain a manufacturing data packet, analyze and process the manufacturing data packet, obtain the severity coefficient corresponding to the corresponding production process in the production process, and generate auxiliary decision information according to the severity coefficient of the production process and the corresponding manufacturing data packet; The manufacturing transmission module is used to perform transmission planning for the generated auxiliary decision information, adjust the transmission resources according to the planning results, and transmit the data corresponding to the production process according to the adjustment results; The manufacturing response module is used to remotely control the production process corresponding to each production process according to the transmission results, generate remote response information from the control results, and perform real-time control on the production process corresponding to the production process according to the remote response information.

2. The industrial intelligent manufacturing remote monitoring system according to claim 1 is characterized in that: The process of setting up the production management space and building the corresponding manufacturing event type decision tree includes: Enter the corresponding industrial intelligent manufacturing information, which includes corresponding manufacturing equipment information, manufacturing process information, manufacturing evaluation information and historical production information; Acquire manufacturing equipment information and manufacturing process information, set a process equipment mapping table corresponding to the manufacturing process information and the manufacturing equipment information, set corresponding process nodes according to the process equipment mapping table, and set corresponding manufacturing process links according to each process node; According to the matching analysis of each process node in the manufacturing process link with the corresponding manufacturing evaluation information and historical production information, the obtained historical production information is analyzed for manufacturing event types according to the manufacturing evaluation information, the historical production information obtained by the process node is set to multiple historical production information sets, and the obtained multiple historical production information sets are analyzed and processed from multi-dimensional perspectives respectively; The trunk branches are set according to the manufacturing process links, the trunk nodes are set according to the process nodes, the analysis results of the multi-dimensional angles in the trunk nodes are obtained, and the branch nodes are set according to the corresponding multi-dimensional angles. The branch nodes are provided with corresponding manufacturing event types and corresponding event feature data. The obtained data are integrated to construct an event type decision tree.

3. The industrial intelligent manufacturing remote monitoring system according to claim 2 is characterized in that: The process of collecting industrial production data includes: Construct an industrial production data set of the corresponding manufacturing equipment according to the historical production information obtained, obtain the production process data of the corresponding manufacturing equipment according to the industrial production data set, obtain the change interval of the manufacturing equipment to which each production process data belongs, perform intersection processing on each change interval, obtain the corresponding deployment position, and install the corresponding sensor equipment according to the deployment position; Industrial production data during the production process of corresponding manufacturing equipment is collected through sensor equipment.

4. The industrial intelligent manufacturing remote monitoring system according to claim 3 is characterized in that: The process of obtaining manufacturing data packages in industrial production processes includes: Obtain the corresponding manufacturing process link in the process equipment mapping table, obtain the corresponding industrial production data according to the corresponding process node in the manufacturing process link, input the obtained industrial production data into the event type decision tree, perform matching analysis according to the event feature data stored in each branch node in the corresponding trunk node, and obtain the manufacturing event type category data according to the matching results; Obtaining the collection time of industrial production data, setting a directed number axis according to the collection time, performing visual analysis on the manufacturing event type corresponding to the corresponding process node according to the directed number axis, and obtaining the manufacturing event type continuous data corresponding to the corresponding manufacturing event type according to the visual analysis result; The obtained manufacturing event type category data and manufacturing event type continuous data are used to generate a manufacturing data packet, and sent to the manufacturing decision module.

5. The industrial intelligent manufacturing remote monitoring system according to claim 4 is characterized in that: The process of obtaining the severity coefficient corresponding to the corresponding production process includes: Obtain the historical manufacturing data package corresponding to the corresponding process node, obtain the correlation data between the corresponding manufacturing event type and the corresponding production process according to the historical manufacturing data package, obtain the basic severity coefficient of the corresponding manufacturing event type in the corresponding process node according to the correlation data, and set the basic increment of the corresponding manufacturing event type; According to the continuous data of manufacturing event types in the manufacturing data package, the corresponding event severity data is obtained according to the corresponding basic severity coefficient and basic increment. The event severity data corresponding to each manufacturing event type data are integrated to obtain the corresponding severity coefficient in the corresponding process node.

6. The industrial intelligent manufacturing remote monitoring system according to claim 5 is characterized in that: The process of generating decision-support information includes: Set a severe threshold according to the historical manufacturing data package corresponding to the production process, compare and analyze the severe coefficient corresponding to the production process with the severe threshold, and determine whether to generate process abnormality information based on the comparative analysis results; Obtain the location of the manufacturing process link to which the process exception information corresponds to the production process, reverse the starting process node of the corresponding manufacturing event type based on the production process, and mark the starting process node for auxiliary decision-making; The process exception information and auxiliary decision corresponding to the corresponding production process in the manufacturing process link are marked as the auxiliary decision information corresponding to the corresponding manufacturing process link.

7. The industrial intelligent manufacturing remote monitoring system according to claim 6 is characterized in that: The process of transmitting data corresponding to the production process includes: In the manufacturing transmission module, corresponding fixed transmission resources and flexible transmission resources are set according to each manufacturing process link in operation during the industrial production process. The fixed transmission resources are used to transmit data to the target transmission address corresponding to the fixed manufacturing process link; the flexible transmission resources are used to transmit data to the target transmission address corresponding to each manufacturing process link as needed; Obtain auxiliary decision information, set corresponding transmission priority according to corresponding process exception information and auxiliary decision mark in the auxiliary decision information, set a queue to be transmitted according to the transmission priority, and obtain the transmission data volume corresponding to the queue to be transmitted; Comparing and analyzing the obtained transmission data volume with the corresponding fixed transmission resources, and judging whether to generate resource scheduling information according to the comparison and analysis result, wherein the resource scheduling information transmits the corresponding transmission data volume by calling the corresponding elastic transmission resources; The corresponding data is transmitted by transmitting the analysis results.

8. The industrial intelligent manufacturing remote monitoring system according to claim 7 is characterized in that: The process of real-time control of the production process includes: Obtain corresponding auxiliary decision information from the corresponding target transmission address, obtain the manufacturing event type corresponding to the corresponding production process in the production process according to the auxiliary decision information and generate production warning information, remotely control the production process corresponding to the manufacturing equipment of the corresponding production process according to the manufacturing event type, and generate remote response information with the control result; The generated remote response information is fed back to the process node corresponding to the corresponding production process in the corresponding manufacturing process link, and the process node performs real-time regulation of the corresponding manufacturing equipment based on the remote response information.

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