Internet of Things-based Home Processing Technology Management Method and System

Abstract

The present invention discloses a home processing technology management method and system based on the Internet of Things, specifically related to the field of processing process flow management, including obtaining first process parameters through a plurality of pre-deployed wireless sensor nodes, inputting them into a dynamic resource allocation model according to a process parameter database to obtain second process parameters; analyzing each home processing link implementing the second process parameters to obtain second key process features; and analyzing the home processing production process in real time to construct a home processing technology control strategy. The home processing technology management method and system based on the Internet of Things enhance the flexibility of equipment monitoring and layout adaptability by using flexible electronic technology to construct a dynamically reconfigurable Internet of Things perception layer; adopt a dynamic optimization model with a three-level optimization architecture to achieve precise adaptive adjustment of process parameters; and construct a dynamic optimization and closed-loop control system to significantly improve the product quality stability and production efficiency of home processing.

Description

Technical Field

[0001] The present invention relates to the technical field of processing process management, and more specifically, to a home furnishing processing technology management method and system based on the Internet of Things. Background Art

[0002] With the digital transformation of the home furnishing industry and the advancement of intelligent manufacturing, the intelligent management of home furnishing processing technology has become a key requirement for improving production efficiency and ensuring product quality. In the traditional home furnishing processing field, the technical solutions mostly rely on the combination of traditional automation equipment and manual labor. However, there are generally problems such as insufficient real-time performance and weak cross-device collaboration ability, which are difficult to meet the requirements of process dynamic optimization and efficient resource allocation in complex home furnishing processing scenarios.

[0003] Currently, the current mainstream home furnishing processing technology management is usually a process management solution that combines manual and semi-automation. During the production process, it mainly relies on the experience of workers and regular equipment inspections to control the processing technology. In the field of automation, a hierarchical architecture is adopted. The device layer collects the status data of processing equipment through sensors, the network layer transmits the data to the local server through transmission protocols, and the application layer analyzes the data based on preset rules and generates control instructions.

[0004] However, when it is actually used, there are still some drawbacks. For example, the adjustment of process parameters dominated by manual experience lacks standardization and replicability, and it is easy to introduce quality fluctuations due to subjective judgment differences of operators, and it is impossible to achieve real-time closed-loop control of the entire process. The preset rule library depends on fixed thresholds and cannot dynamically optimize resource allocation based on real-time working conditions, resulting in resource contention for key processes. The full volume of original data is uploaded to the server for processing without local preprocessing, which exacerbates the network bandwidth pressure and reduces the timeliness of anomaly detection. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a home furnishing processing technology management method and system based on the Internet of Things, through the following solutions to solve the problems raised in the above background art.

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

[0007] A home furnishing processing technology management method based on the Internet of Things, comprising:

[0008] S1: Obtain first process parameters, and the first process parameters are obtained through a plurality of pre-deployed wireless sensor nodes;

[0009] S2: Perform a pre-screening operation on the first process parameters to obtain first key process features corresponding to the first process parameters;

[0010] S3: Store the process parameters corresponding to the processing equipment at each stage and the product quality feedback data of various combinations of process parameters to construct a process parameter database;

[0011] S4: Obtain a dynamic resource allocation model, and input the first key process feature into the dynamic resource allocation model to obtain the second process parameter;

[0012] S5: Trace and analyze each home furnishing processing link implementing the second process parameter to obtain the second key process feature corresponding to the second process parameter;

[0013] S6: Based on the second key process feature, analyze the home furnishing processing production process in real time to dynamically generate the third process parameter;

[0014] S7: Construct a home furnishing processing process control strategy based on the third process parameter and dynamically update the process parameter database.

[0015] Preferably, in S2, the first key process feature includes the real-time operating power of the equipment, the operating stability index of the processing equipment, the equipment failure frequency, the label of the processing process, the processing process parameters under the normal operating state of the processing equipment, and the abnormal data mark.

[0016] Preferably, in S3, constructing the process parameter database specifically includes:

[0017] Obtain multiple process parameter databases, and the multiple process parameter databases include a target furniture process database, and the target furniture process database is the process parameter database corresponding to the target furniture among the multiple process parameter databases.

[0018] Preferably, in S4, before obtaining the second process parameter, obtaining the dynamic resource allocation model specifically includes:

[0019] Perform data analysis operations on the first key process feature to obtain the process feature attribute set corresponding to the first key process feature, and the process feature attribute set is the equipment operation attribute set corresponding to the first key process feature, and the equipment operation attribute set includes the real-time operating power of the equipment, the operating stability index of the equipment, and the equipment failure frequency;

[0020] Obtain the influencing factor set corresponding to the equipment operation attribute set;

[0021] According to the influencing factor set, obtain the resource demand weight corresponding to the equipment operation attribute set;

[0022] Construct a dynamic resource allocation model according to the resource demand weight.

[0023] Preferably, in S4, obtaining the second process parameter specifically includes:

[0024] Based on the first key process feature and the preset constraint conditions, and according to the matching relationship between the processing equipment and the processing task, to obtain a processing process parameter adjustment scheme, the processing process parameter adjustment scheme includes the mapping relationship between the first key process feature and the second process parameter, the second process parameter, the optimal matching relationship between the processing equipment and the processing task, and the real-time updated constraint conditions;

[0025] The preset constraint conditions include equipment load constraint, task dependency constraint, and quality deviation constraint.

[0026] Preferably, in step S6, dynamically generating a third process parameter specifically includes:

[0027] Based on the real-time load monitoring data and the second key process feature, constructing a dynamic optimization model, the dynamic optimization model is a three-level optimization architecture, and the three-level optimization architecture includes a compensatory adjustment layer, an adaptive strategy layer, and a constraint condition update layer;

[0028] The compensatory adjustment layer generates compensation parameters for immediate anomalies through a preset control algorithm;

[0029] The adaptive strategy layer is a machine learning model trained based on historical data stored in the system operation database, and dynamically adjusts the detection frequency and the processing process parameter adjustment scheme;

[0030] The constraint condition update layer corrects the processing process parameter thresholds and priority rules in real time according to the changes in the production environment.

[0031] Preferably, in step S6, generating the compensation parameters in the compensatory adjustment layer specifically includes:

[0032] When the vibration energy monitored in the second key process feature shows an offset compared to the standard vibration energy calculate the power compensation amount , specifically expressed as:

[0033] ;

[0034] wherein, represents a preset vibration energy anomaly correction coefficient;

[0035] When the monitored temperature gradient in the second key process feature exceeds the standard, calculate the rotational speed fine-tuning value , specifically expressed as:

[0036] ;

[0037] wherein, represents a preset temperature gradient correction coefficient, Denoted as the temperature rise rate, i.e., the rate of change of temperature with time;

[0038] When the load pressure in the monitored equipment of the second key process feature fluctuates, calculate the tool offset compensation , specifically expressed as:

[0039] ;

[0040] Among them, Denoted as the preset load fluctuation correction coefficient, Denoted as the standard deviation of the tool pressure in the cutting equipment, Denoted as the average value of the tool pressure in the cutting equipment.

[0041] Preferably, the S7 constructs a home processing technology control strategy, specifically including:

[0042] The equipment adjustment instruction consists of a processing equipment control instruction and a process parameter database iteration instruction.

[0043] To achieve the above object, the present invention provides the following technical solutions: A home processing technology management system based on the Internet of Things, including a system operation database, a system central processing module, and a user information terminal. Implementing the above-mentioned home processing technology management method based on the Internet of Things further includes:

[0044] Reconfigurable deployment module: Used to obtain the first process parameters through multiple wireless sensor nodes pre-deployed on home processing equipment;

[0045] Pre-screening module: Used to perform pre-screening operations on the first process parameters to obtain the first key process features corresponding to the first process parameters;

[0046] Process parameter database construction module: Used to store the process parameters corresponding to the processing equipment at each stage in the home processing production process and the product quality feedback data corresponding to various process parameter combinations, and construct a process parameter database;

[0047] Dynamic resource scheduling module: Used to obtain a dynamic resource allocation model, input the first key process features into the dynamic resource allocation model, and obtain the second process parameters;

[0048] Production process data traceability module: Used to trace and analyze each home processing link implementing the second process parameters to obtain the second key process features corresponding to the second process parameters;

[0049] Process parameter dynamic optimization module: Based on the second key process features, analyze the home processing production process in real time to dynamically generate the third process parameters;

[0050] Real-time home processing technology control strategy generation module: used to construct a home processing technology control strategy based on the third process parameter and dynamically update the process parameter database;

[0051] The system operation database includes all data texts of the home processing technology management system and collects information texts output by each module in real time. The system central processing module is used to control the information text instructions output by each module in the control method, and the user information terminal is an information output device for receiving the home processing technology management system.

[0052] Preferably, in the production process data traceability module, the second key process features include processing equipment operation features, quality correlation features, and timing abnormal signals.

[0053] Technical effects and advantages of the present invention:

[0054] 1. In the present invention, by using flexible electronic technology to construct a dynamically reconfigurable Internet of Things perception layer in S1, the flexibility of device monitoring and the adaptability of layout are enhanced;

[0055] 2. In the present invention, by adopting a dynamic optimization model with a three-level optimization architecture in S6, precise adaptive adjustment of process parameters is achieved;

[0056] 3. In the present invention, by S6 and S7, a dynamic optimization and closed-loop control system is constructed, significantly improving the product quality stability and production efficiency of home processing. Description of the drawings

[0057] Figure 1 It is a flowchart of the implementation steps of the home processing technology management method based on the Internet of Things provided by the embodiment of the present application;

[0058] Figure 2 It is a schematic structural diagram of the home processing technology management system based on the Internet of Things provided by the embodiment of the present application;

[0059] Figure 3 It is a schematic structural diagram of an electronic device provided by the embodiment of the present application. The electronic device includes a system central processor, a communication bus, a system operation database, and a user information terminal.

[0060] Description of the reference numerals: 300, electronic device; 301, system central processor; 302, communication bus; 303, system operation database, and the system operation database includes a home processing technology management system, a network communication module, and a user interface module; 304, user information terminal. Detailed implementation manners

[0061] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0062] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. As used in the specification of the present application, the singular forms "a", "an", "the", "above-mentioned", "said", and "this" are also intended to include the plural forms, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more of the listed items.

[0063] Hereinafter, the terms "first", "second", and "third" are only used for descriptive purposes, and cannot be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first", "second", and "third" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0064] As shown in the attached Figure 1 The home processing technology management method based on the Internet of Things includes inputting the first process parameters obtained by multiple pre-deployed wireless sensor nodes into the dynamic resource allocation model according to the process parameter database to obtain the second process parameters; analyzing each home processing link implementing the second process parameters to obtain the second key process features; and analyzing the home processing production process in real time to construct a home processing technology control strategy.

[0065] S1: Obtain the first process parameters, and the first process parameters are obtained by multiple pre-deployed wireless sensor nodes.

[0066] Specifically, wireless sensor nodes are installed on the key process equipment in the home processing process through flexible electronic technology to construct a dynamically reconfigurable Internet of Things perception layer, realizing real-time acquisition of multi-dimensional process parameters corresponding to multiple home processing devices, obtaining the first process parameters, and the first process parameters include temperature and humidity data of the operating environment of the processing device, pressure data of each component of the processing device, vibration frequency and amplitude data of the processing device in each processing link, rotational speed of the processing device in each processing link, temperature of the processing device in each processing link, start and end times of the processing device in each processing link, and position information of each working component of the processing device in each processing link.

[0067] Example 1: Taking the production of metal furniture as an example, the first process parameters obtained in the home furnishing processing technology management method based on the Internet of Things in this embodiment are as follows: The process of producing metal furniture includes processes such as stamping, welding, and painting. On the stamping equipment, a pressure sensor and a vibration sensor are deployed. The pressure sensor is installed in a snap-on manner and positioned at the contact part of the stamping die to measure the pressure change during the stamping process in real time. The vibration sensor is installed on the body of the stamping equipment to monitor the vibration generated during the operation of the equipment. On the welding equipment, a temperature sensor, a position sensor, and an operation duration timing unit are installed respectively. The temperature sensor is installed on the welding electrode and the heat dissipation part of the welding equipment to monitor the key temperature points of the equipment in real time. The position sensor is installed at the arm joint of the welding robot to control the welding position. The operation duration timing unit is integrated into the processing equipment control system to automatically record the operation time of the equipment. On the painting equipment, key process data such as painting speed and paint flow rate are continuously collected at a frequency of once per minute.

[0068] Example 2: Taking the production of wooden furniture as an example, the first process parameters obtained in the home furnishing processing technology management method based on the Internet of Things in this embodiment are as follows: The process of producing wooden furniture includes processes such as wood cutting, carving, and polishing. On the wood cutting equipment, temperature and humidity sensors, pressure sensors, and vibration sensors are integrated. The temperature and humidity sensors are installed in a magnetic adsorption manner around the equipment operation area to monitor the ambient temperature and humidity changes in real time. The pressure sensor is installed on the wood fixing part to measure the pressure exerted on the wood during the cutting process. The vibration sensor is placed at the handle of the cutting tool to monitor the vibration frequency and amplitude of the tool. On the carving equipment, a rotational speed sensor, a temperature sensor, and a position sensor are deployed. The rotational speed sensor is installed on the rotating shaft of the carving motor to monitor the motor speed in real time. The temperature sensor is installed on the motor housing and the cooling system of the carving tool respectively to monitor the operating temperature of the equipment. The position sensor is installed on the moving track of the carving tool to record the tool position information. The polishing equipment continuously collects key processing technology data such as polishing speed and polishing force at a frequency of once per second.

[0069] S2: Perform a pre-screening operation on the first process parameters to obtain the first key process features corresponding to the first process parameters.

[0070] Specifically, after the home furnishing processing technology management system obtains the first process parameters transmitted by the dynamically reconfigurable Internet of Things perception layer, through preprocessing operations, multiple key process feature sets corresponding to the first process parameters can be obtained, that is, the first key process features. The first key process features include the real-time operating power of the equipment, the processing equipment operation stability index, the equipment failure frequency, the labels of the processing processes, the processing technology parameters under the normal operating state of the processing equipment, and the abnormal data marks. The steps of the preprocessing are as follows:

[0071] Noise filtering: Mean filtering, median filtering and other methods are used to remove abnormal fluctuation data caused by factors such as sensor noise and electromagnetic interference; Feature extraction: From the data after noise filtering, through a preset feature extraction algorithm, information related to the key features of the equipment operation status and the process is identified and extracted; Feature indicators reflecting the operation stability of the processing equipment are extracted from the vibration data and temperature data of the processing equipment, and the real-time operation power feature is extracted from the equipment operation power data; Based on the mean value of the real-time power of the processing equipment and standard deviation , the mean value of vibration and standard deviation , the stability index of the processing equipment is obtained, specifically expressed as: ; Divide the time window, set the statistical period and rolling window, and calculate the failure frequency; Anomaly detection: Use machine learning algorithms to identify outliers in the data, and mark and remove them; Through a preset normal range threshold, data points exceeding the threshold are determined as outliers and removed to ensure the reliability of the data.

[0072] S3: Store the process parameters corresponding to the processing equipment at each stage and the product quality feedback data of various process parameter combinations to construct a process parameter database.

[0073] Specifically, by collecting and sorting out the average processing process parameters of various home furnishing products from raw material processing to finished product assembly, the average processing process parameters are the average values of the specific process parameters implemented for multiple furniture products, obtain the value range of each average processing process parameter in each furniture product processing process, and obtain the home furnishing processing production quality feedback data corresponding to the value range of each average processing process parameter, and establish a traceable parameter-quality association model in the system operation database, that is, a process parameter database.

[0074] In a possible implementation manner, constructing a process parameter database includes: obtaining multiple process parameter databases, the multiple process parameter databases include a target furniture process database, the target furniture process database is the process parameter database corresponding to the target furniture in the multiple process parameter databases, and the process parameter database includes but is not limited to quality indicators of each processing process, influence coefficients of the process on product quality, raw material characteristic data of home furnishing products, various processing process parameter data, home furnishing processing production quality feedback data, material characteristics and tool life of processing equipment, and processing constraints, etc.

[0075] Example 3: Taking the production of metal furniture as an example, the process parameter database for constructing the welding process in the home furnishing processing process management method based on the Internet of Things described in this embodiment is as follows:

[0076] Associate the processing equipment parameters with the quality inspection results through the labels of the processing procedures, and establish the dynamic adjustment rule of the welding current as , where represents the current value after dynamic adjustment, represents the base current value, represents the ambient temperature; when the current fluctuation exceeds ±5%, trigger a real-time alarm and record it in the metal furniture welding process parameter database. At the same time, set the voltage fluctuation threshold to ±3%, and automatically correct the wire feeding speed when it exceeds the limit; Collect about 100,000 new welding data per week, and use the Apriori algorithm to re-mining association rules to ensure that the support degree ≥ 0.1 and the confidence degree ≥ 0.7.

[0077] S4: Obtain the dynamic resource allocation model, and input the first key process feature into the dynamic resource allocation model to obtain the second process parameter.

[0078] Specifically, the dynamic resource allocation model is a pre-constructed data analysis model. By inputting the first key process feature into the dynamic resource allocation model, the parameter configuration of the processing processes of multiple processing equipment is adjusted and optimized. The second process parameter is the parameter configuration of the processing processes corresponding to each processing equipment after passing through the dynamic resource allocation model, and the second process parameter is transmitted to the corresponding processing equipment.

[0079] In a possible implementation manner, obtaining the second process parameter includes: performing a data analysis operation on the first key process feature to obtain a set of process feature attributes corresponding to the first key process feature; the set of process feature attributes is a set of equipment operation attributes corresponding to the first key process feature, and the set of equipment operation attributes includes equipment operation power, equipment operation stability index, and equipment failure frequency; obtaining a set of influencing factors corresponding to the set of equipment operation attributes, and the set of influencing factors includes the process production cycle, the degree of influence of the process on product quality, and the urgency of the production task; according to the set of influencing factors, obtaining the resource demand weight corresponding to the set of equipment operation attributes; according to the resource demand weight, constructing a dynamic resource allocation model.

[0080] It should be noted that the process production cycle is the time required to complete a processing procedure, which is determined by the start and end times of the procedure; the degree of influence of the process on product quality is determined by the evaluation of historical product quality data in the process parameter database; the urgency of the production task is determined according to the order delivery time and the order product quantity. Orders with a near delivery date and a large product quantity have a higher urgency of the production task.

[0081] Example 4: Taking the production of wooden furniture as an example, the pre-constructed dynamic resource allocation model in the home furnishing processing technology management method based on the Internet of Things described in this embodiment is as follows:

[0082] By obtaining the real-time operating power of a numerical control cutting machine, a woodworking milling machine, and a painting device in real time; the vibration amplitude of the numerical control cutting machine remains within a small range during normal processing, the woodworking milling machine has a certain degree of vibration instability due to tool wear, and the painting device operates relatively stably. Then, the stability indicators of the numerical control cutting machine, the woodworking milling machine, and the painting device are set to 0.2, 0.4, and 0.15 respectively; it is obtained from the equipment maintenance record system that the numerical control cutting machine has had 2 minor faults in the past month, the woodworking milling machine has had 3 faults during the same period, and the painting device is relatively reliable and has only had 1 fault; the production cycle of the cutting tabletop process is recorded as 30 minutes; the production cycle of the milling machine processing table legs is 20 minutes; the painting process production cycle is 15 minutes; by analyzing the historical product quality data in the process parameter database, it can be obtained that the cutting process will directly affect the splicing and overall flatness of the tabletop, and the influence degree coefficient on the final quality of the dining table is 0.3; the milling machine processing table leg process affects the dimensional accuracy and surface smoothness of the table legs, and the influence degree coefficient is 0.25; the painting process is crucial for the appearance quality of the furniture, and the influence degree coefficient is 0.4.

[0083] In a possible implementation manner, obtaining the second process parameter further includes: based on the first key process feature and a preset constraint condition, and according to the matching relationship between the processing equipment and the processing task, to obtain a processing process parameter adjustment plan, the processing process parameter adjustment plan includes the mapping relationship between the first key process feature and the second process parameter, the second process parameter, the optimal matching relationship between the processing equipment and the processing task, and the real-time updated constraint condition;

[0084] It should be noted that the matching relationship between the processing equipment and the processing task ensures the maximization of the overall production efficiency and resource utilization rate under the premise of satisfying the load, timing, and quality constraints through a process of combining equipment-task matching and constraint optimization.

[0085] Furthermore, the preset constraint conditions include equipment load constraint, task dependency constraint, and quality deviation constraint. Among them, the equipment load constraint is expressed as ; represents the total number of processing tasks to be allocated, represents the index of the processing task to be allocated, represents the index of the processing equipment that needs to execute the processing task, represents the th processing equipment and the th processing task matching relationship, The value of is 0 or 1. 0 means not matching, and 1 means matching. represents the th processing task at the th processing equipment execution time, Denoted as the maximum allowable load of the th processing device; the task dependency constraint is that task must be completed before task , expressed by the formula: , Denoted as the start time of the th processing task, Denoted as the th processing task's execution time on the th processing device, Denoted as the start time of the th processing task; the quality deviation constraint is expressed as , Denoted as the quality index of the th processing operation, Denoted as the minimum allowable quality index of the th processing operation, Denoted as the quality deviation coefficient, with a value of 0.8 in this embodiment, Denoted as the quality influence coefficient of the th processing operation.

[0086] Embodiment 5: As shown in the production of wooden furniture, the second process parameters obtained through the dynamic resource allocation model in the home furnishing processing technology management method based on the Internet of Things described in this embodiment are as follows:

[0087] Input the above first key process feature data into the dynamic resource allocation model. According to the current equipment operation status, order urgency, and process importance, adjust the parameter configuration of the processing technology of each processing device as follows: increase the operating power of the CNC cutting machine to 13 kW, and at the same time slightly reduce the cutting accuracy requirement, and raise the priority of the cutting process to the highest to ensure that the cutting task is completed first to meet the order delivery time requirement; since the failure frequency of the woodworking milling machine equipment is relatively high and the current stability is poor, reduce the operating power to 7 kW, extend the production cycle of the process of processing table legs to 22 minutes, and arrange it to start after a part of the CNC cutting process is completed to avoid competing for resources with other key processes; keep the operating power of the painting equipment unchanged at 5 kW, but increase the inspection frequency of the painting process from originally sampling 1 piece out of every 10 pieces to sampling 1 piece out of every 5 pieces, and at the same time, according to the order production progress, reasonably arrange the painting time to ensure that painting can be carried out in a timely manner after other processes are completed, and avoid excessive waiting time of the product affecting the overall production efficiency.

[0088] S5: Trace and analyze each home furnishing processing link implementing the second process parameters to obtain the second key process features corresponding to the second process parameters.

[0089] Specifically, after receiving the second process parameters, they are input into the corresponding processing equipment in each home furnishing processing link. During the implementation process, through the sensors of the reconfigurable device Internet of Things deployed in S1 and the work logs of multiple processing equipment, the pressure data of each component, vibration frequency and amplitude data, rotational speed, temperature, and position information of each working component of the processing equipment implementing the second process parameters are collected in real time. And the start and end times of the processing equipment in each processing link, as well as equipment failure records, are obtained from the work logs of multiple processing equipment to obtain real-time load monitoring data; using big data analysis technology, the real-time load monitoring data is analyzed and processed to extract the second key process features.

[0090] Further, the second key process features are extracted as follows: Vibration energy anomaly index: Perform spectral analysis on the vibration frequency and amplitude data using the fast Fourier transform to convert the vibration signal in the time domain into a frequency domain signal; calculate the proportion of the energy in the 1 - 2 kHz frequency band to the total energy; if the proportion of the energy in the 1 - 2 kHz frequency band to the total energy is greater than 15%, then record that the vibration energy anomaly index exceeds the standard, indicating that the processing equipment has abnormal vibration; Temperature gradient overstandard rate: Calculate the temperature difference between adjacent time points to obtain the temperature change rate, and count the proportion of the number of times the temperature change rate is greater than 5°C / min to the total number of records, which is the temperature gradient overstandard rate; Load fluctuation coefficient: Calculate the mean and standard deviation of the pressure data of each component of the processing equipment, and calculate the ratio of the pressure standard deviation to the mean. If the ratio of the pressure standard deviation to the mean is greater than 20%, then record that the load fluctuation coefficient exceeds the standard, indicating that the equipment load is unstable and affects the processing quality; Process parameter - quality correlation rule: Perform correlation analysis on the real-time load monitoring data and product quality through the Apriori algorithm to obtain the correlation rule between the processing process parameters and product quality; Process parameter adjustment signal: Statistically calculate the qualified rate of the product in real time and compare it with the preset target qualified rate; if the actual qualified rate is lower than 2% of the preset target qualified rate, then obtain the process parameter adjustment signal; Parameter deviation signal: Calculate the deviation between the actual processing process parameters and the set second process parameters, and count the situations where the deviation continuously exceeds 3 times the standard deviation to obtain the parameter deviation signal of each processing process.

[0091] It should be noted that the real-time load monitoring data includes the pressure data of each component of the processing equipment, the vibration frequency and amplitude data of the processing equipment in each processing link, the rotational speed of the processing equipment in each processing link, the temperature of the processing equipment in each processing link, the position information of each working component of the processing equipment in each processing link, the start time and end time of the processing equipment in each processing link, and the equipment failure records; the second key process feature includes the processing equipment operation feature, the quality correlation feature, and the time-series abnormal signal, where the processing equipment operation feature includes, but is not limited to, vibration energy, temperature gradient, load pressure, etc.; the quality correlation feature includes, but is not limited to, the "process parameter-quality" correlation rule, the process parameter adjustment signal, etc., and the time-series abnormal signal includes the deviation signal of each processing process parameter.

[0092] S6: Based on the second key process feature, analyze the home furnishing processing production process in real time to dynamically generate the third process parameter.

[0093] Specifically, based on the real-time load monitoring data transmitted in S5 and the second key process feature, a dynamic optimization model is constructed. The dynamic optimization model is a three-level optimization architecture, and the three-level optimization architecture includes a compensatory adjustment layer, an adaptive strategy layer, and a constraint condition update layer. Among them, the compensatory adjustment layer generates compensatory parameters for immediate anomalies through a preset control algorithm; the adaptive strategy layer is a machine learning model trained based on historical data stored in the system operation database, and dynamically adjusts the detection frequency and the processing process parameter adjustment plan; the constraint condition update layer corrects the processing process parameter threshold and priority rule in real time according to the production environment change to achieve the optimization of the process parameter, and dynamically generates the optimized process parameter, that is, the third process parameter. The third process parameter includes compensatory parameters, adaptive strategy parameters, and constraint condition update signals; among them, the compensatory parameters include, but are not limited to, power compensation amount, rotational speed fine-tuning value, tool offset compensation, etc.; the adaptive strategy parameters include, but are not limited to, dynamic detection frequency, load balancing rule, etc.; the constraint condition update signal includes, but is not limited to, the maximum allowable vibration energy threshold, the emergency task queue-jumping rule.

[0094] In a possible implementation manner, dynamically generating the third process parameter includes: when the vibration energy monitored in the second key process feature Compared with the standard vibration energy When there is an offset, calculate the power compensation amount , specifically expressed as:

[0095] ;

[0096] Among them, is represented as a preset abnormal correction coefficient of vibration energy; it should be noted that the value of the preset abnormal vibration energy index is set to 0.03 in this embodiment, and the standard vibration energy is the maximum vibration energy that does not cause abnormal conditions stored in the process parameter database; when the monitored temperature gradient in the second key process feature exceeds the standard, calculate the rotational speed fine-tuning value , specifically represented as:

[0097] ;

[0098] Among them, is represented as a preset temperature gradient correction coefficient, is represented as the temperature rise rate, that is, the change rate of temperature over time; it should be noted that the preset temperature gradient correction coefficient is set to in this embodiment; when the load pressure in the monitored equipment in the second key process feature fluctuates, calculate the tool offset compensation , specifically represented as:

[0099] ;

[0100] Among them, is represented as a preset load fluctuation correction coefficient, is represented as the pressure standard deviation of the tool in the cutting equipment, is represented as the pressure mean value of the tool in the cutting equipment; it should be noted that the preset load fluctuation correction coefficient is set to 0.1mm in this embodiment.

[0101] It should be noted that based on the real-time load monitoring data, the real-time correction of the machining process parameter threshold and the priority rule in the embodiment includes: when the product defect rate of three consecutive shifts exceeds 3%, reduce the maximum allowable vibration energy threshold from the original 15% to 12% to improve the control requirements for equipment vibration; when the remaining delivery time is less than 2 hours and the order priority reaches level 3 or above, trigger the emergency task queue-jumping rule, allow the emergency task to preferentially occupy the equipment resources for production, and output the corrected machining process parameter threshold and priority rule as a constraint condition update signal.

[0102] S7: Construct a home furnishing processing process control strategy based on the third process parameter and dynamically update the process parameter database.

[0103] Specifically, based on the third process parameter, a home processing process control strategy is implemented for the processing equipment. The home processing process control strategy realizes the dynamic regulation of the processing equipment and the dynamic iteration of the process knowledge base. The equipment adjustment instruction consists of a processing equipment control instruction and a process parameter database iteration instruction. The processing equipment control instruction is to convert the third process parameter into control signals for execution equipment such as servo motors and laser calibrators. The iteration instructions of the process parameter database include, but are not limited to, the update of quality indicators for each processing procedure, the update of the influence coefficient of the procedure on product quality, the update of raw material characteristic data of home products, the update of various processing process parameter data, the update of home processing production quality feedback data, the update of the relationship between material characteristics and the tool life of processing equipment, and the update of processing constraint conditions, etc.

[0104] As shown in the Figure 2 Internet of Things-based home processing process management system shown, which includes a system operation database, a system central processing module, and a user information terminal, and also includes: a reconfigurable deployment module, a pre-screening module, a process parameter database construction module, a dynamic resource scheduling module, a production process data traceability module, a process parameter dynamic optimization module, and a real-time home processing process control strategy generation module.

[0105] Reconfigurable deployment module: used to obtain the first process parameter through multiple wireless sensor nodes pre-deployed on home processing equipment;

[0106] Pre-screening module: used to perform a pre-screening operation on the first process parameter to obtain the first key process feature corresponding to the first process parameter;

[0107] Process parameter database construction module: used to store the process parameters corresponding to the processing equipment at each stage in the home processing production process and the product quality feedback data corresponding to various process parameter combinations, and construct a process parameter database;

[0108] Dynamic resource scheduling module: used to obtain a dynamic resource allocation model, input the first key process feature into the dynamic resource allocation model, and obtain the second process parameter;

[0109] Production process data traceability module: used to trace and analyze each home processing link implementing the second process parameter to obtain the second key process feature corresponding to the second process parameter;

[0110] Process parameter dynamic optimization module: based on the second key process feature, analyze the home processing production process in real time to dynamically generate the third process parameter;

[0111] Real-time home processing process control strategy generation module: used to construct a home processing process control strategy based on the third process parameter and dynamically update the process parameter database;

[0112] The system operation database includes all data texts of the home processing technology management system and collects information texts output by each module in real time. The system central processing module is used to control the information text instructions output by each module in the control method, and the user information terminal is an information output device for receiving the home processing technology management system.

[0113] In this embodiment, the second key process features in the production process data traceability module include processing equipment operation features, quality correlation features, and timing abnormal signals.

[0114] In this embodiment, an electronic device is also disclosed. Refer to Figure 3 , this electronic device may include: at least one system central processor 301, at least one communication bus 302, a user information terminal 304, and at least one system operation database 303.

[0115] Among them, the system central processor 301 is the core operation and control unit of the entire home processing technology management system; it includes one or more processing cores, connects various parts within the entire system through various interfaces and lines; by running or executing instructions, programs, code sets or instruction sets stored in the system operation database, and being able to call the data stored therein, thereby executing various functions of the home processing technology management system, including receiving data from the real-time load monitoring module in real time, using this data to calculate with a preset algorithm model, and thus dynamically adjusting the production parameters of home processing. At the same time, the system central processor 301 is also responsible for efficient information interaction with the communication bus, user information terminal and system operation database to ensure the smooth operation of the entire home processing technology management system.

[0116] Among them, the communication bus 302 is used to realize connection communication between components.

[0117] Among them, the system operation database 303 is used to store a large amount of data related to home processing technology management, including raw material information, processing flow parameters, historical production records, equipment status monitoring data, as well as production goals and preference settings set by users, and store a large amount of historical operation data; when the system central processor executes various functions, it will frequently call these data from the system operation database for comparison analysis, formulating control strategies and other operations, so as to achieve precise control and efficient management of home processing technology management.

[0118] Among them, the user information terminal 304 provides an interface for the user to interact with the system by connecting external devices such as a display screen and a camera through a standard wired interface or wireless interface.

[0119] Secondly: In the accompanying drawings of the disclosed embodiments of the present invention, only the structures related to the disclosed embodiments are involved. For other structures, reference may be made to the general design. Without conflict, the same embodiment and different embodiments of the present invention may be combined with each other;

[0120] Finally: The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A home processing technology management method based on the Internet of Things, characterized in that, Including: S1: Obtain first process parameters, where the first process parameters are obtained by multiple pre-deployed wireless sensor nodes; S2: Perform a pre-screening operation on the first process parameters to obtain first key process features corresponding to the first process parameters; S3: Store the process parameters corresponding to the processing equipment at each stage and the product quality feedback data of various process parameter combinations to build a process parameter database; S4: Obtain a dynamic resource allocation model, and input the first key process features into the dynamic resource allocation model to obtain second process parameters; Obtaining the dynamic resource allocation model includes: performing a data analysis operation on the first key process features to obtain a set of equipment operation attributes corresponding to the first key process features, where the set of equipment operation attributes includes the real-time operating power of the equipment, the equipment operation stability index, and the equipment failure frequency; obtaining a set of influencing factors corresponding to the set of equipment operation attributes; according to the set of influencing factors, obtaining the resource demand weights corresponding to the set of equipment operation attributes; and building a dynamic resource allocation model according to the resource demand weights; Obtaining the second process parameters includes: based on the first key process features and preset constraint conditions, and according to the matching relationship between the processing equipment and the processing task, obtaining a processing process parameter adjustment plan, where the processing process parameter adjustment plan includes the mapping relationship between the first key process features and the second process parameters, the second process parameters, the optimal matching relationship between the processing equipment and the processing task, and the real-time updated constraint conditions; the preset constraint conditions include equipment load constraints, task dependency constraints, and quality deviation constraints; S7: Trace and analyze each home furnishing processing link implementing the second process parameters to obtain second key process features corresponding to the second process parameters; S8: Based on the second key process features, analyze the home furnishing processing production process in real time to dynamically generate third process parameters; The steps of dynamically generating the third process parameters are as follows: Based on the real-time load monitoring data and the second key process features, build a dynamic optimization model, where the dynamic optimization model is a three-level optimization architecture, including a compensatory adjustment layer that generates compensation parameters for instant anomalies through a preset control algorithm, an adaptive strategy layer that is a machine learning model trained based on historical data stored in the system operation database, and a constraint condition update layer that corrects the processing process parameter thresholds and priority rules in real time according to changes in the production environment; S7: Build a home furnishing processing process control strategy based on the third process parameters and dynamically update the process parameter database.

2. The method for managing a home processing technology based on the Internet of Things according to claim 1, characterized in that: In S2, the first key process features include the real-time operating power of the equipment, the processing equipment operation stability index, the equipment failure frequency, the label of the processing process, the processing process parameters under the normal operating state of the processing equipment, and the abnormal data mark.

3. The home processing technology management method based on the Internet of Things according to claim 1, characterized in that: In S3, building the process parameter database specifically includes: Obtain multiple process parameter databases, where the multiple process parameter databases include a target furniture process database, and the target furniture process database is the process parameter database corresponding to the target furniture in the multiple process parameter databases.

4. The method for managing a home processing technology based on the Internet of Things according to claim 1, characterized in that: In S6, generating the compensation parameters in the compensatory adjustment layer specifically includes: When the monitored vibration energy in the second key process feature shows an offset compared to the standard vibration energy a power compensation amount is calculated which is specifically expressed as: ， Among them, is expressed as a preset vibration energy anomaly correction coefficient; When the monitored temperature gradient in the second key process feature exceeds the standard, calculate the rotational speed fine-tuning value , which is specifically expressed as: Among them, is expressed as a preset temperature gradient correction coefficient, is expressed as the temperature rise rate, that is, the change rate of temperature with time; When the load pressure in the monitored equipment of the second key process feature fluctuates, calculate the tool offset compensation , which is specifically expressed as: ， Wherein, is expressed as a preset load fluctuation correction coefficient, is expressed as the standard deviation of the pressure of the cutting tool in the cutting device, is expressed as the average value of the pressure of the cutting tool in the cutting device.

5. The method for managing a home processing technology based on the Internet of Things according to claim 1, wherein: The S7 constructs a home processing technology control strategy, specifically including: The equipment adjustment instruction consists of a processing equipment control instruction and a process parameter database iteration instruction.

6. The home processing technology management system based on the Internet of Things includes a system operation database, a system central processing module, and a user information terminal, and adopts the home processing technology management method based on the Internet of Things according to any one of claims 1-5, characterized in that, It also includes: A reconfigurable deployment module: used to obtain the first process parameters through multiple wireless sensor nodes pre-deployed on home processing equipment; A pre-screening module: used to perform a pre-screening operation on the first process parameters to obtain the first key process features corresponding to the first process parameters; A process parameter database construction module: used to store the process parameters corresponding to the processing equipment at each stage in the home processing production process and the product quality feedback data corresponding to various process parameter combinations, and construct a process parameter database; A dynamic resource scheduling module: used to obtain a dynamic resource allocation model, input the first key process features into the dynamic resource allocation model, and obtain the second process parameters; A production process data traceability module: used to trace and analyze each home processing link implementing the second process parameters to obtain the second key process features corresponding to the second process parameters; A process parameter dynamic optimization module: based on the second key process features, analyze the home processing production process in real time to dynamically generate the third process parameters; A real-time home processing technology control strategy generation module: used to construct a home processing technology control strategy based on the third process parameters and dynamically update the process parameter database; The system operation database includes all data texts of the home processing technology management system and collects the information texts output by each module in real time. The system central processing module is used to control the information text instructions output by each module in the control method, and the user information terminal is an information output device for receiving the home processing technology management system.

7. The home processing technology management system based on the Internet of Things according to claim 6, characterized in that: In the production process data traceability module, the second key process features include processing equipment operation features, quality correlation features, and timing anomaly signals.

Images