Home processing technology management method and system based on Internet of Things

Abstract

The invention discloses a home processing technology management method and system based on the Internet of Things, and particularly relates to the field of processing technological process management, and the method comprises the steps: inputting a first technological parameter obtained by a plurality of pre-deployed wireless sensor nodes into a dynamic resource distribution model according to a technological parameter database, and obtaining a second technological parameter; analyzing each home processing link for implementing the second process parameter to obtain a second key process feature; the home processing production process is analyzed in real time, and a home processing technology control strategy is constructed. According to the home processing technology management method and system based on the Internet of Things, an Internet of Things sensing layer capable of being dynamically reconfigured is constructed by using a flexible electronic technology, so that the equipment monitoring flexibility and the layout adaptability are enhanced; a dynamic optimization model of a three-level optimization architecture is adopted, and precise self-adaptive adjustment of technological parameters is achieved; and a dynamic optimization and closed-loop control system is constructed, so that the product quality stability and the production efficiency of home processing are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of processing technology flow 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 smart manufacturing, intelligent management of home furnishing processing technology has become a key requirement for improving production efficiency and ensuring product quality. The traditional home furnishing processing field mostly relies on technical solutions that combine traditional automated equipment with manual labor. However, there are generally problems such as insufficient real-time performance and weak cross-device collaboration capabilities, which make it difficult to meet the needs of dynamic process optimization and efficient resource allocation in complex home furnishing processing scenarios.

[0003] At present, the current mainstream home furnishing processing technology management technology is usually a process management solution that combines manual and semi-automatic processes. During the production process, the processing technology is mainly controlled by the workers' experience and regular equipment inspections; in the field of automation, a layered architecture is adopted. The equipment layer collects the status data of the processing equipment through sensors, the network layer transmits the data to the local server through the transmission protocol, and the application layer performs data analysis based on preset rules and generates control instructions.

[0004] However, it still has some shortcomings in actual use. For example, the process parameter adjustment dominated by manual experience lacks standardization and replicability, which is easy to introduce quality fluctuations due to differences in subjective judgment of operators, and cannot achieve real-time closed-loop control of the entire process; the preset rule base relies on fixed thresholds and cannot dynamically optimize resource allocation based on real-time working conditions, resulting in contention for key process resources; the original data is fully uploaded to the server for processing without local preprocessing, which increases 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, and solves the problems raised in the above-mentioned background technology through the following scheme.

[0006] To achieve the above object, the present invention provides the following technical solutions: The home furnishing processing technology management method based on the Internet of Things includes: S1: Acquire a first process parameter, where the first process parameter is acquired through a plurality of pre-deployed wireless sensor nodes; S2: performing a pre-screening operation on the first process parameter to obtain a first key process feature corresponding to the first process parameter; S3: Store the process parameters corresponding to the processing equipment at each stage and the product quality feedback data of multiple process parameter combinations to build a process parameter database; S4: obtaining a dynamic resource allocation model, and inputting the first key process feature into the dynamic resource allocation model to obtain a second process parameter; S5: tracing back and analyzing each furniture processing link implementing the second process parameter to obtain a second key process feature corresponding to the second process parameter; S6: Based on the second key process feature, the furniture processing production process is analyzed in real time to dynamically generate the third process parameter; S7: constructing a furniture processing technology control strategy based on the third process parameter, and dynamically updating the process parameter database.

[0007] Preferably, the S2, 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 procedure, the processing parameters of the processing equipment under normal operating conditions, and the abnormal data mark.

[0008] Preferably, the step S3, constructing a process parameter database, specifically includes: A plurality of process parameter databases are obtained, wherein the plurality of process parameter databases include a target furniture process database, and the target furniture process database is a process parameter database corresponding to the target furniture in the plurality of process parameter databases.

[0009] Preferably, before obtaining the second process parameter, the step S4, obtaining the dynamic resource allocation model, specifically includes: Performing a data analysis operation on the first key process feature to obtain a process feature attribute set corresponding to the first key process feature, the process feature attribute set being an equipment operation attribute set corresponding to the first key process feature, the equipment operation attribute set including equipment real-time operation power, equipment operation stability index, and equipment failure frequency; Obtaining a set of influencing factors corresponding to the device operation attribute set; According to the influencing factor set, obtaining the resource demand weight corresponding to the device operation attribute set; A dynamic resource allocation model is constructed according to the resource demand weights.

[0010] Preferably, the step S4, obtaining the second process parameter, specifically includes: Based on the first key process feature and the preset constraint condition, and according to the matching relationship between the processing equipment and the processing task, a processing process parameter adjustment plan is obtained, and the processing process parameter adjustment plan includes a mapping relationship between the first key process feature and the second process parameter, the second process parameter, the matching relationship between the optimal processing equipment and the processing task, and the constraint condition updated in real time; The preset constraints include equipment load constraints, task dependency constraints, and quality deviation constraints.

[0011] Preferably, the step S6, dynamically generating the third process parameter, specifically includes: Based on real-time load monitoring data and the second key process characteristics, a dynamic optimization model is constructed. The dynamic optimization model is a three-level optimization architecture, which includes a compensatory adjustment layer, an adaptive strategy layer, and a constraint condition update layer. The compensatory adjustment layer generates compensation 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 to dynamically adjust the inspection frequency and processing parameter adjustment plan; The constraint update layer is to modify the processing parameter thresholds and priority rules in real time according to changes in the production environment.

[0012] Preferably, the step S6, generating the compensation parameter in the compensatory adjustment layer, specifically includes: When the vibration energy monitored in the second critical process characteristic Compared to standard vibration energy When an offset occurs, calculate the power compensation amount , specifically expressed as: ; in, It is expressed as a preset vibration energy anomaly correction coefficient; When the temperature gradient monitored in the second key process feature exceeds the limit, calculate the speed fine-tuning value , specifically expressed as: ; in, Expressed as the preset temperature gradient correction coefficient, It is expressed as the rate of temperature rise, which is the rate of change of temperature over time; Calculate tool deflection compensation when load pressure in the equipment monitored in the second critical process feature fluctuates , specifically expressed as: ; in, Shown as the preset load fluctuation correction factor, Expressed as the standard deviation of the pressure of the tool in the cutting device, Expressed as the mean pressure of the tool in the cutting equipment.

[0013] Preferably, the step S7, constructing a furniture processing technology control strategy, specifically includes: Equipment adjustment instructions consist of processing equipment control instructions and process parameter database iteration instructions.

[0014] 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, and implementing the above-mentioned home processing technology management method based on the Internet of Things, further comprising: A reconfigurable deployment module: used to obtain a first process parameter through a plurality of wireless sensor nodes pre-deployed on the home processing equipment; Pre-screening module: used for performing a pre-screening operation on the first process parameter to obtain a first key process feature corresponding to the first process parameter; Process parameter database construction module: used to store the process parameters corresponding to the processing equipment at each stage of the home furnishing processing and production process and the product quality feedback data corresponding to the combination of multiple process parameters, and to build a process parameter database; 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; Production process data tracing module: used to trace and analyze each furniture processing link implementing the second process parameter to obtain the second key process feature corresponding to the second process parameter; Process parameter dynamic optimization module: Based on the second key process characteristics, the furniture processing production process is analyzed in real time to dynamically generate the third process parameters; Real-time furniture processing technology control strategy generation module: used to build furniture processing technology control strategy based on the third process parameter 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 text output by each module in real time. The system central processing module is used for the information text instructions output by each module in the central control method, and the user information terminal is an information output device for receiving the home processing technology management system.

[0015] Preferably, in the production process data traceability module, the second key process characteristics include processing equipment operation characteristics, quality-related characteristics and timing abnormal signals.

[0016] Technical effects and advantages of the present invention: 1. The present invention uses flexible electronic technology in S1 to construct a dynamically reconfigurable IoT perception layer, thereby enhancing the flexibility of equipment monitoring and layout adaptability; 2. The present invention realizes accurate adaptive adjustment of process parameters by adopting a dynamic optimization model with a three-level optimization architecture in S6; 3. The present invention constructs a dynamic optimization and closed-loop control system through S6 and S7, which significantly improves the product quality stability and production efficiency of home furnishing processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A diagram of implementation steps of a home furnishing processing technology management method based on the Internet of Things provided according to an embodiment of the present application; Figure 2 It is a structural diagram of a home processing technology management system based on the Internet of Things provided according to an embodiment of the present application; Figure 3 This is a structural diagram of an electronic device provided according to an 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.

[0018] Explanation of the reference numerals: 300, electronic device; 301, system central processing unit; 302, communication bus; 303, system operation database, which includes a home furnishing process management system, a network communication module and a user interface module; 304, user information terminal. DETAILED DESCRIPTION

[0019] 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.

[0020] 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 be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, 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 listed items.

[0021] In the following, the terms "first", "second", and "third" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", and "third" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0022] As attached Figure 1The home furnishing processing technology management method based on the Internet of Things shown includes obtaining a first process parameter through a plurality of pre-deployed wireless sensor nodes, inputting the first process parameter into a dynamic resource allocation model according to a process parameter database, and obtaining a second process parameter; analyzing each home furnishing processing link that implements the second process parameter to obtain a second key process feature; and analyzing the home furnishing processing production process in real time to construct a home furnishing processing technology control strategy.

[0023] S1: Acquire a first process parameter, where the first process parameter is acquired through a plurality of pre-deployed wireless sensor nodes.

[0024] Specifically, wireless sensor nodes are installed on key process equipment in the home processing flow through flexible electronic technology, and a dynamically reconfigurable Internet of Things perception layer is constructed to realize real-time collection of multi-dimensional process parameters corresponding to multiple home processing equipment, and obtain first process parameters. The first process parameters include temperature and humidity data of the operating environment of the processing equipment, pressure data of each component of the processing equipment, vibration frequency and amplitude data of the processing equipment in each processing link, rotation speed of the processing equipment in each processing link, temperature of the processing equipment in each processing link, start and end time of the processing equipment in each processing link, and position information of each working component of the processing equipment in each processing link.

[0025] Embodiment 1: Taking the production of metal furniture as an example, the first process parameter is obtained in the home processing technology management method based on the Internet of Things described in this embodiment as follows: the process of producing metal furniture includes stamping, welding, painting and other processes; on the stamping equipment, pressure sensors and vibration sensors are deployed, and the pressure sensors are installed in a snap-on manner and positioned at the contact part of the stamping die to measure the pressure changes during the stamping process in real time; the vibration sensor is installed on the body of the stamping equipment to monitor the vibration generated when the equipment is running; on the welding equipment, temperature sensors, position sensors and running time timing units are installed respectively, and the temperature sensors are installed on the welding electrodes and the heat dissipation parts of the welding equipment to monitor the key temperature points of the equipment in real time; the position sensor is installed at the arm joints of the welding robot to control the welding position; the running time timing unit is integrated in the processing equipment control system to automatically record the running time of the equipment; on the painting equipment, key process data such as painting speed and paint flow are continuously collected at a frequency of once per minute.

[0026] Embodiment 2: Taking the production of wooden furniture as an example, the first process parameter is obtained in the home processing technology management method based on the Internet of Things described in this embodiment as follows: the process of producing wooden furniture includes wood cutting, carving, polishing and other processes; temperature, humidity, pressure and vibration sensors are integrated on the wood cutting equipment, and the temperature and humidity sensors are magnetically installed around the equipment operation area to monitor the changes in environmental temperature and humidity in real time; the pressure sensor is installed on the wood fixing part to measure the pressure on the wood during the cutting process; the vibration sensor is placed on the handle of the cutting tool to monitor the vibration frequency and amplitude of the tool; on the carving equipment, speed, temperature and position sensors are deployed, and the speed sensor is installed on the rotating shaft of the carving motor to monitor the motor speed in real time; the temperature sensors are respectively installed on the motor housing and the cooling system of the carving tool 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 intensity at a frequency of once per second.

[0027] S2: performing a pre-screening operation on the first process parameter to obtain a first key process feature corresponding to the first process parameter.

[0028] Specifically, after obtaining the first process parameter transmitted by the dynamically reconfigurable IoT perception layer, the home processing technology management system can obtain a set of multiple key process features corresponding to the first process parameter through preprocessing operations, namely, the first key process feature, which 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 procedure, the processing parameters of the processing equipment under normal operating conditions, and the abnormal data mark. The preprocessing steps are as follows: Noise filtering: Use mean filtering, median filtering and other methods to remove abnormal fluctuations caused by sensor noise, electromagnetic interference and other factors; Feature extraction: From the noise-filtered data, use the preset feature extraction algorithm to identify and extract information related to the equipment operation status and key characteristics of the process; extract characteristic indicators reflecting the operation stability of the processing equipment from the vibration data and temperature data of the processing equipment, and extract real-time operation power characteristics from the equipment operation power data; based on the mean of the real-time power of the processing equipment and standard deviation , the mean value of vibration and standard deviation , obtain the stability index of the processing equipment, which is specifically expressed as: ; Divide the time window, set the statistical period and rolling window, and calculate the fault frequency; Anomaly detection: Use machine learning algorithms to identify outliers in the data, mark them, and remove them; Through the pre-set normal range threshold, data points that exceed the threshold are determined as outliers and removed to ensure the reliability of the data.

[0029] S3: Store the process parameters corresponding to the processing equipment at each stage and the product quality feedback data of multiple process parameter combinations to build a process parameter database.

[0030] Specifically, by collecting and sorting out the average processing parameters of various home furnishing products from raw material processing to finished product assembly, the average processing parameters are the average values ​​of specific process parameters implemented to complete multiple furniture products, and the value range of each average processing parameter in each furniture product processing procedure is obtained, and the home furnishing processing production quality feedback data corresponding to the value range of each average processing parameter is obtained, a traceable parameter-quality association model, namely, a process parameter database, is established in the system operation database.

[0031] In a possible implementation, constructing a process parameter database includes: acquiring multiple process parameter databases, the multiple process parameter databases include a target furniture process database, the target furniture process database is a process parameter database corresponding to the target furniture in the multiple process parameter databases, the process parameter database includes but is not limited to quality indicators of each processing procedure, the influence coefficient of the procedure on product quality, raw material characteristic data of home furnishing products, various types of processing process parameter data, home furnishing processing production quality feedback data, material characteristics and tool life of processing equipment, and processing constraints, etc.

[0032] Example 3: Taking the production of metal furniture as an example, the process parameter database of the welding process is constructed in the furniture processing technology management method based on the Internet of Things described in this embodiment as follows: By associating the processing equipment parameters with the quality inspection results through the processing step labels, the dynamic adjustment rules for welding current are established: ,in, Expressed as the dynamically adjusted current value, Expressed as the base current value, It is expressed as ambient temperature. When the current fluctuation exceeds ±5%, a real-time alarm is triggered and recorded in the metal furniture welding process parameter database. At the same time, the voltage fluctuation threshold is set to ±3%. When it exceeds the limit, the wire feeding speed is automatically corrected. About 100,000 new welding data are collected every week, and the Apriori algorithm is used to re-mine the association rules to ensure that the support degree is ≥0.1 and the confidence level is ≥0.7.

[0033] S4: Obtain a dynamic resource allocation model, and input the first key process feature into the dynamic resource allocation model to obtain a second process parameter.

[0034] Specifically, the dynamic resource allocation model is a pre-constructed data analysis model. The first key process feature is input into the dynamic resource allocation model to adjust and optimize the parameter configuration of the processing technology of multiple processing equipment. The second process parameter is the parameter configuration of the processing technology corresponding to each processing equipment after passing the dynamic resource allocation model, and the second process parameter is transmitted to the corresponding processing equipment.

[0035] In one possible implementation, 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 equipment operation attribute set includes equipment operation power, equipment operation stability index, and equipment failure frequency; obtaining a set of influencing factors corresponding to the equipment operation attribute set, and the influencing factor set includes a 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 a resource demand weight corresponding to the equipment operation attribute set; and constructing a dynamic resource allocation model according to the resource demand weight.

[0036] It should be noted that the process production cycle is the time required to complete a processing step, which is determined by the start and end time of the process; the impact 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 by the order delivery time and the number of ordered products. Orders with close delivery dates and large product quantities have corresponding production tasks with higher urgency.

[0037] Example 4: Taking the production of wooden furniture as an example, the dynamic resource allocation model pre-constructed in the furniture processing technology management method based on the Internet of Things described in this example is as follows: By obtaining the real-time operating power of the CNC cutting machine, woodworking milling machine, and paint spraying equipment; the vibration amplitude of the CNC cutting machine is kept in a small range during normal processing, the woodworking milling machine has a certain degree of vibration instability due to tool wear, and the paint spraying equipment operates relatively stably, so the stability indexes of the CNC cutting machine, woodworking milling machine, and paint spraying equipment are set to 0.2, 0.4, and 0.15 respectively; from the equipment maintenance record system, it is obtained that the CNC cutting machine has had 2 minor faults in the past month, the woodworking milling machine has had 3 faults in the same period, and the paint spraying equipment has been relatively stable. It is reliable, with only one failure; by recording the production cycle of the table top cutting process is 30 minutes; the production cycle of the table leg processing process by milling machine is 20 minutes; the production cycle of the painting process is 15 minutes; analyzing the historical product quality data from the process parameter database, it can be obtained that the cutting process will directly affect the splicing and overall flatness of the table top, and the influence coefficient on the final quality of the dining table is 0.3; the process of milling machine processing table legs affects the dimensional accuracy and surface smoothness of the table legs, and the influence coefficient is 0.25; the painting process is crucial to the appearance quality of furniture, and the influence coefficient is 0.4.

[0038] In a possible implementation, obtaining the second process parameter further includes: obtaining a processing parameter adjustment scheme based on the first key process feature and the preset constraint condition and according to the matching relationship between the processing equipment and the processing task, wherein the processing parameter adjustment scheme includes a mapping relationship between the first key process feature and the second process parameter, the second process parameter, an optimal matching relationship between the processing equipment and the processing task, and a constraint condition updated in real time; It should be noted that the matching relationship between processing equipment and processing tasks is achieved through a process that combines equipment-task matching with constraint optimization to ensure that the overall production efficiency and resource utilization are maximized while meeting load, timing and quality constraints.

[0039] Furthermore, the preset constraints include equipment load constraints, task dependency constraints, and quality deviation constraints, where the equipment load constraint is expressed as ; Expressed as the total number of processing tasks that need to be assigned, Represents the index of the processing task that needs to be assigned. Represents the index of the processing equipment that needs to perform the processing task. Expressed as The processing equipment and The matching relationship of the processing tasks, The value is 0 or 1, 0 means no match, 1 means match, Expressed as The processing task is in The execution time of each processing device, Expressed as The maximum allowable load of a processing device; the task dependency constraint is the task Must be on task Completed before, the formula is expressed as: , Expressed as The start time of a processing task, Expressed as The processing task is in The execution time of each processing device, Expressed as The starting time of a processing task; the quality deviation constraint is expressed as , Expressed as The quality index of each processing step, Expressed as The minimum allowable quality index of each processing step, It is expressed as the mass deviation coefficient, which is 0.8 in this embodiment. Expressed as The quality influence coefficient of each processing step.

[0040] Example 5: Taking the production of wooden furniture as an example, the second process parameters obtained by the dynamic resource allocation model in the furniture processing technology management method based on the Internet of Things described in this embodiment are as follows: The first key process characteristic data mentioned above is input into the dynamic resource allocation model. According to the current equipment operation status, order urgency and process importance, the parameter configuration of the processing technology of each processing equipment is adjusted as follows: the operating power of the CNC cutting machine is increased to 13kW, while the cutting accuracy requirement is slightly reduced, and the priority of the cutting process is increased to the highest level to ensure that the cutting task is completed first to meet the order delivery time requirements; due to the relatively high failure frequency of the woodworking milling machine equipment and the current poor stability, the operating power is reduced to 7kW, the process production cycle of processing table legs is extended to 22 minutes, and it is arranged to start after part of the CNC cutting process is completed to avoid competing for resources with other key processes; the operating power of the painting equipment remains unchanged at 5kW, but the inspection frequency of the painting process is increased from the original 1 in 10 pieces to 1 in 5 pieces. At the same time, according to the order production progress, the painting time is reasonably arranged to ensure that painting can be carried out in time after other processes are completed to avoid long waiting time for products affecting overall production efficiency.

[0041] S5: Tracing back and analyzing each home furnishing processing link that implements the second process parameter to obtain a second key process feature corresponding to the second process parameter.

[0042] Specifically, after receiving the second process parameter, it is input into the corresponding processing equipment in each home furnishing processing link. During the implementation process, the pressure data, vibration frequency and amplitude data, rotation speed, temperature and position information of each working component of the processing equipment implementing the second process parameter are collected in real time through the sensors of the reconfigurable device Internet of Things deployed in S1 and the work logs of multiple processing equipment. The start and end time of the processing equipment in each processing link and the equipment failure record are obtained from the work logs of multiple processing equipment to obtain real-time load monitoring data; the real-time load monitoring data is analyzed and processed using big data analysis technology to extract the second key process characteristics.

[0043] Furthermore, the second key process feature is extracted as follows: Vibration energy anomaly index: Use fast Fourier transform to perform spectral analysis on the vibration frequency and amplitude data, and convert the vibration signal in the time domain into a frequency domain signal; Calculate the proportion of energy in the 1-2kHz frequency band to the total energy; If the proportion of energy in the 1-2kHz frequency band to the total energy is greater than 15%, then the vibration energy anomaly index exceeds the standard, and abnormal vibration exists in the processing equipment; Temperature gradient exceedance 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 exceedance rate; Load fluctuation coefficient: Calculate the mean and standard deviation of the pressure data of each component of the processing equipment, and calculate the pressure standard deviation and mean. If the ratio of the pressure standard deviation to the mean is greater than 20%, the load fluctuation coefficient exceeds the standard, indicating that the equipment load is unstable and affects the processing quality; process parameter-quality association rules: the real-time load monitoring data and product quality are associated and analyzed through the Apriori algorithm to obtain the association rules between the processing parameters and product quality; process parameter adjustment signal: real-time statistics of the product qualification rate and compare it with the preset target qualification rate; if the actual qualification rate is lower than 2% of the preset target qualification rate, the process parameter adjustment signal is obtained; parameter deviation signal: the deviation between the actual processing parameter and the set second process parameter is calculated, and the deviation is continuously greater than 3 times the standard deviation. The parameter deviation signal of each processing technology is obtained.

[0044] 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 rotation 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 and end time of the processing equipment in each processing link, and equipment failure records; the second key process characteristics include the operation characteristics of the processing equipment, quality-related characteristics, and timing abnormality signals, among which the operation characteristics of the processing equipment include but are not limited to vibration energy, temperature gradient, load pressure, etc.; the quality-related characteristics include but are not limited to "process parameter-quality" association rules, process parameter adjustment signals, etc., and the timing abnormality signals include deviation signals of various processing process parameters.

[0045] S6: Based on the second key process feature, the home furnishing processing production process is analyzed in real time to dynamically generate the third process parameter.

[0046] Specifically, based on the real-time load monitoring data transmitted by S5 and the second key process feature, a dynamic optimization model is constructed. The dynamic optimization model is a three-level optimization architecture, which includes a compensatory adjustment layer, an adaptive strategy layer, and a constraint update layer. The compensatory adjustment layer generates compensation parameters for immediate abnormalities 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, which dynamically adjusts the detection frequency and the processing parameter adjustment plan; the constraint update layer corrects the processing parameter thresholds and priority rules in real time according to changes in the production environment to achieve the optimization of the process parameters, and dynamically generates the optimized process parameters, i.e., the third process parameters, which include compensation parameters, adaptive strategy parameters, and constraint update signals; wherein the compensation parameters include but are not limited to power compensation amount, speed fine-tuning value, tool offset compensation, etc.; the adaptive strategy parameters include but are not limited to dynamic detection frequency, load balancing rules, etc.; the constraint update signals include but are not limited to the maximum allowable vibration energy threshold and emergency task queue jumping rules.

[0047] In a possible implementation, dynamically generating the third process parameter includes: when the vibration energy monitored in the second key process feature is Compared to standard vibration energy When an offset occurs, calculate the power compensation amount , specifically expressed as: ; in, It is represented by the preset vibration energy abnormality correction coefficient; it should be noted that the value of the preset vibration energy abnormality index is set to 0.03 in this embodiment, and the standard vibration energy is the maximum vibration energy that will not cause abnormal conditions stored in the process parameter database; when the temperature gradient monitored in the second key process feature exceeds the standard, the speed fine-tuning value is calculated , specifically expressed as: ; in, Expressed as the preset temperature gradient correction coefficient, It is expressed as the temperature rise rate, that is, the rate of change of temperature over time; it should be noted that the preset temperature gradient correction coefficient is set to ; Calculate tool offset compensation when load pressure in the equipment monitored in the second critical process feature fluctuates , specifically expressed as: ; in, Expressed as the preset load fluctuation correction factor, Expressed as the standard deviation of the pressure of the tool in the cutting device, It is represented by the average pressure of the tool in the cutting device; it should be noted that the preset load fluctuation correction coefficient is set to 0.1 mm in this embodiment.

[0048] It should be noted that, based on real-time load monitoring data, real-time correction of processing parameter thresholds and priority rules in the embodiment include: when the product defect rate exceeds 3% for three consecutive shifts, the maximum allowable vibration energy threshold is reduced 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, the emergency task queue-jumping rule is triggered, allowing the emergency task to occupy equipment resources for production first, and the corrected processing parameter thresholds and priority rules are used as constraint condition update signal output.

[0049] S7: constructing a furniture processing technology control strategy based on the third process parameter, and dynamically updating the process parameter database.

[0050] Specifically, based on the third process parameter, the home furnishing processing technology control strategy is implemented on the processing equipment. The home furnishing processing technology control strategy realizes the dynamic regulation of the processing equipment and the dynamic iteration of the process knowledge base. The equipment adjustment instruction is composed of the processing equipment control instruction and the process parameter database iteration instruction; the processing equipment control instruction is to convert the third process parameter into a control signal of an execution device such as a servo motor and a laser calibrator; the iteration instructions of the process parameter database include but are not limited to the updating of the quality indicators of each processing procedure, the updating of the influence coefficient of the procedure on the product quality, the updating of the characteristic data of raw materials of home furnishing products, the updating of various types of processing technology parameter data, the updating of home furnishing processing production quality feedback data, the updating of material characteristics and tool life of processing equipment, and the updating of processing constraints.

[0051] As attached Figure 2 The home furnishing processing technology management system based on the Internet of Things shown 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 tracing module, a process parameter dynamic optimization module, and a real-time home furnishing processing technology control strategy generation module.

[0052] A reconfigurable deployment module: used to obtain a first process parameter through a plurality of wireless sensor nodes pre-deployed on the home processing equipment; Pre-screening module: used for performing a pre-screening operation on the first process parameter to obtain a first key process feature corresponding to the first process parameter; Process parameter database construction module: used to store the process parameters corresponding to the processing equipment at each stage of the home furnishing processing and production process and the product quality feedback data corresponding to the combination of multiple process parameters, and to build a process parameter database; 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; Production process data tracing module: used to trace and analyze each furniture processing link implementing the second process parameter to obtain the second key process feature corresponding to the second process parameter; Process parameter dynamic optimization module: Based on the second key process characteristics, the furniture processing production process is analyzed in real time to dynamically generate the third process parameters; Real-time furniture processing technology control strategy generation module: used to build furniture processing technology control strategy based on the third process parameter 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 text output by each module in real time. The system central processing module is used for the information text instructions output by each module in the central control method, and the user information terminal is an information output device for receiving the home processing technology management system.

[0053] In this embodiment, the second key process characteristics in the production process data traceability module include processing equipment operation characteristics, quality-related characteristics, and timing abnormal signals.

[0054] In this embodiment, an electronic device is also disclosed, referring to Figure 3 The 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.

[0055] Among them, the system central processor 301 is the core operation and control unit of the entire home furnishing process management system; it includes one or more processing cores, and connects various parts of the entire system by using various interfaces and lines; it runs or executes instructions, programs, code sets or instruction sets stored in the system operation database, and can call the data stored therein, so as to perform various functions of the home furnishing process management system, including real-time reception of data from the real-time load monitoring module, and using these data and the preset algorithm model for calculation, so as to dynamically adjust the production parameters of the home furnishing process. At the same time, the system central processor 301 is also responsible for efficient information interaction with the communication bus, the user information terminal and the system operation database to ensure the smooth operation of the entire home furnishing process management system.

[0056] The communication bus 302 is used to realize the connection and communication between components.

[0057] Among them, the system operation database 303 is used to save a large amount of data related to home furnishing processing technology management, including raw material information, processing flow parameters, historical production records, equipment status monitoring data, and production goals and preference settings set by users, as well as storing a large amount of historical operation data; when the system central processor performs various functions, it will frequently call these data from the system operation database for comparative analysis, formulation of control strategies and other operations, so as to achieve precise control and efficient management of home furnishing processing technology management.

[0058] The user information terminal 304 is connected to external devices such as a display screen and a camera through a standard wired interface or a wireless interface to provide an interface for users to interact with the system.

[0059] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other; Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A home furnishing processing technology management method based on the Internet of Things, characterized in that: include: S1: Acquire a first process parameter, where the first process parameter is acquired through a plurality of pre-deployed wireless sensor nodes; S2: performing a pre-screening operation on the first process parameter to obtain a first key process feature corresponding to the first process parameter; S3: Store the process parameters corresponding to the processing equipment at each stage and the product quality feedback data of multiple process parameter combinations to build a process parameter database; S4: obtaining a dynamic resource allocation model, and inputting the first key process feature into the dynamic resource allocation model to obtain a second process parameter; S5: tracing back and analyzing each furniture processing link implementing the second process parameter to obtain a second key process feature corresponding to the second process parameter; S6: Based on the second key process feature, the furniture processing production process is analyzed in real time to dynamically generate the third process parameter; S7: constructing a furniture processing technology control strategy based on the third process parameter, and dynamically updating the process parameter database.

2. The method for managing home furnishing processing technology based on the Internet of Things according to claim 1, characterized in that: The S2, 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 procedure, the processing parameters of the processing equipment under normal operating conditions, and the abnormal data mark.

3. The method for managing home furnishing processing technology based on the Internet of Things according to claim 1, characterized in that: S3, constructing a process parameter database, specifically includes: A plurality of process parameter databases are obtained, wherein the plurality of process parameter databases include a target furniture process database, and the target furniture process database is a process parameter database corresponding to the target furniture in the plurality of process parameter databases.

4. The method for managing home furnishing processing technology based on the Internet of Things according to claim 1, characterized in that: The step S4, before obtaining the second process parameter, obtaining the dynamic resource allocation model, specifically includes: Performing a data analysis operation on the first key process feature to obtain a process feature attribute set corresponding to the first key process feature, the process feature attribute set being an equipment operation attribute set corresponding to the first key process feature, the equipment operation attribute set including equipment real-time operation power, equipment operation stability index, and equipment failure frequency; Obtaining a set of influencing factors corresponding to the device operation attribute set; According to the influencing factor set, obtaining the resource demand weight corresponding to the device operation attribute set; A dynamic resource allocation model is constructed according to the resource demand weights.

5. The method for managing home furnishing processing technology based on the Internet of Things according to claim 1, characterized in that: The step S4, obtaining the second process parameter, specifically includes: Based on the first key process feature and the preset constraint condition, and according to the matching relationship between the processing equipment and the processing task, a processing process parameter adjustment plan is obtained, and the processing process parameter adjustment plan includes a mapping relationship between the first key process feature and the second process parameter, the second process parameter, the matching relationship between the optimal processing equipment and the processing task, and the constraint condition updated in real time; The preset constraints include equipment load constraints, task dependency constraints, and quality deviation constraints.

6. The method for managing home furnishing processing technology based on the Internet of Things according to claim 1, characterized in that: The step S6, dynamically generating the third process parameter, specifically includes: Based on real-time load monitoring data and the second key process characteristics, a dynamic optimization model is constructed. The dynamic optimization model is a three-level optimization architecture, which includes a compensatory adjustment layer, an adaptive strategy layer, and a constraint condition update layer. The compensatory adjustment layer generates compensation 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 to dynamically adjust the inspection frequency and processing parameter adjustment plan; The constraint update layer is to modify the processing parameter thresholds and priority rules in real time according to changes in the production environment.

7. The method for managing home furnishing processing technology based on the Internet of Things according to claim 6 is characterized in that: The step S6, generating the compensation parameter in the compensatory adjustment layer, specifically includes: When the vibration energy monitored in the second critical process characteristic Compared to standard vibration energy When an offset occurs, calculate the power compensation amount , specifically expressed as: ； in, It is expressed as a preset vibration energy anomaly correction coefficient; When the temperature gradient monitored in the second key process feature exceeds the limit, calculate the speed fine-tuning value , specifically expressed as: ； in, Expressed as the preset temperature gradient correction coefficient, It is expressed as the rate of temperature rise, which is the rate of change of temperature over time; Calculate tool deflection compensation when load pressure in the equipment monitored in the second critical process feature fluctuates , specifically expressed as: ； in, Expressed as the preset load fluctuation correction factor, Expressed as the standard deviation of the pressure of the tool in the cutting device, Expressed as the mean pressure of the tool in the cutting equipment.

8. The method for managing home furnishing processing technology based on the Internet of Things according to claim 1, characterized in that: S7, constructing a furniture processing technology control strategy, specifically includes: Equipment adjustment instructions consist of processing equipment control instructions and process parameter database iteration instructions.

9. A home processing technology management system based on the Internet of Things, comprising a system operation database, a system central processing module and a user information terminal, adopting a home processing technology management method based on the Internet of Things as claimed in any one of claims 1 to 8, characterized in that: Also includes: A reconfigurable deployment module: used to obtain a first process parameter through a plurality of wireless sensor nodes pre-deployed on the home processing equipment; Pre-screening module: used for performing a pre-screening operation on the first process parameter to obtain a first key process feature corresponding to the first process parameter; Process parameter database construction module: used to store the process parameters corresponding to the processing equipment at each stage of the home furnishing processing and production process and the product quality feedback data corresponding to the combination of multiple process parameters, and to build a process parameter database; 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; Production process data tracing module: used to trace and analyze each furniture processing link implementing the second process parameter to obtain the second key process feature corresponding to the second process parameter; Process parameter dynamic optimization module: Based on the second key process characteristics, the furniture processing production process is analyzed in real time to dynamically generate the third process parameters; Real-time furniture processing technology control strategy generation module: used to build furniture processing technology control strategy based on the third process parameter 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 text output by each module in real time. The system central processing module is used for the information text instructions output by each module in the central control method, and the user information terminal is an information output device for receiving the home processing technology management system.

10. The home processing technology management system based on the Internet of Things according to claim 9 is characterized in that: In the production process data traceability module, the second key process characteristics include processing equipment operation characteristics, quality-related characteristics and timing abnormal signals.

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