Production environment monitoring method and system based on interference analysis

Abstract

The invention discloses a production environment monitoring method and system based on interference analysis, and relates to the technical field of environment monitoring, and the method comprises the steps: dividing a target production workshop, and obtaining node production workshops; collecting production task information to obtain a node production task; performing production environment interference prediction on the node production workshop, and establishing an interference production environment space; collecting real-time production environment information of a node production workshop to obtain a workshop environment monitoring data set; traversing the workshop environment monitoring data set for interference analysis and compensation to obtain a workshop environment monitoring result; performing production interference prediction on the workshop environment monitoring result to obtain an environment production interference prediction coefficient; and loading an environment production interference threshold value, and carrying out production environment early warning on the production workshop in combination with the environment production interference prediction coefficient. According to the invention, the technical problem of low monitoring precision caused by weak comprehensive analysis capability during production environment monitoring in the prior art is solved, and the technical effect of improving the monitoring precision of production environment monitoring is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and particularly to a production environment monitoring method and system based on interference analysis. Background Art

[0002] With the continuous development of industrial production, the influence of interference factors in the production environment on the manufacturing process has become increasingly complex and diverse. Traditional production environment monitoring methods mainly rely on the monitoring of a single parameter, such as temperature or humidity. However, with the expansion of production scale and the increasing complexity of the process flow, these methods are unable to cope with multiple environmental interference factors. Traditional monitoring methods usually have weak comprehensive analysis capabilities and are difficult to meet the requirements of efficient, accurate, and stable control in modern industrial production. Summary of the Invention

[0003] This application provides a production environment monitoring method and system based on interference analysis, which is used to solve the technical problem that the existing technology has weak comprehensive analysis capabilities during production environment monitoring, resulting in low monitoring accuracy.

[0004] In view of the above problems, this application provides a production environment monitoring method and system based on interference analysis.

[0005] In the first aspect of this application, a production environment monitoring method based on interference analysis is provided. The method includes: Dividing a target production workshop based on the production process flow to obtain multiple node production workshops; collecting production task information of the multiple node production workshops to obtain multiple node production tasks; predicting production environment interference for the multiple node production workshops based on the multiple node production tasks to establish multiple interference production environment spaces; collecting real-time production environment information of the multiple node production workshops according to the sensing monitoring array of the target production workshop to obtain multiple workshop environment monitoring data sets; traversing the multiple workshop environment monitoring data sets for interference analysis and compensation to obtain multiple workshop environment monitoring results; predicting production interference based on the multiple workshop environment monitoring results by combining the multiple interference production environment spaces according to a predetermined interference prediction constraint condition to obtain multiple environmental production interference prediction coefficients; loading multiple environmental production interference thresholds corresponding to the multiple node production workshops, and combining the multiple environmental production interference prediction coefficients to give a production environment warning for the multiple node production workshops.

[0006] In the second aspect of this application, a production environment monitoring system based on interference analysis is provided. The system includes: Node division module, which divides the target production workshop based on the production process flow to obtain multiple node production workshops; task information collection module, which collects the production task information of the multiple node production workshops to obtain multiple node production tasks; environmental interference prediction module, which predicts the production environmental interference of the multiple node production workshops based on the multiple node production tasks to establish multiple interference production environment spaces; production environment information collection module, which collects the real-time production environment information of the multiple node production workshops according to the sensing monitoring array of the target production workshop to obtain multiple workshop environment monitoring data sets; interference analysis and compensation module, which traverses the multiple workshop environment monitoring data sets for interference analysis and compensation to obtain multiple workshop environment monitoring results; production interference prediction module, which predicts the production interference of the multiple workshop environment monitoring results based on the predetermined interference prediction constraint conditions and in combination with the multiple interference production environment spaces to obtain multiple environmental production interference prediction coefficients; production environment early warning module, which loads the multiple environmental production interference thresholds corresponding to the multiple node production workshops and performs production environment early warning on the multiple node production workshops in combination with the multiple environmental production interference prediction coefficients.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application divides the target production workshop based on the production process flow to obtain multiple node production workshops; collects the production task information of the multiple node production workshops to obtain multiple node production tasks; predicts the production environmental interference of the multiple node production workshops based on the multiple node production tasks to establish multiple interference production environment spaces; collects the real-time production environment information of the multiple node production workshops according to the sensing monitoring array of the target production workshop to obtain multiple workshop environment monitoring data sets; traverses the multiple workshop environment monitoring data sets for interference analysis and compensation to obtain multiple workshop environment monitoring results; predicts the production interference of the multiple workshop environment monitoring results based on the predetermined interference prediction constraint conditions and in combination with the multiple interference production environment spaces to obtain multiple environmental production interference prediction coefficients; loads the multiple environmental production interference thresholds corresponding to the multiple node production workshops and performs production environment early warning on the multiple node production workshops in combination with the multiple environmental production interference prediction coefficients. This invention solves the technical problem that the prior art has weak comprehensive analysis ability in production environment monitoring, resulting in low monitoring accuracy. Through the division of the production process flow, the collection of production task information and real-time environmental monitoring, multiple interference production environment spaces are established, and through interference analysis and compensation technologies, the environmental monitoring data is processed and predicted. Combining the predetermined interference prediction constraint conditions and thresholds, real-time early warning of the production environment is carried out, achieving the technical effect of improving the monitoring accuracy of the production environment. Brief Description of the Drawings

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0009] Figure 1 It is a schematic flow chart of the production environment monitoring method based on interference analysis provided by the embodiments of the present application; Figure 2 It is a schematic structural diagram of the production environment monitoring system based on interference analysis provided by the embodiments of the present application.

[0010] Description of the reference numerals: Node division module 11, task information acquisition module 12, environmental interference prediction module 13, production environment information acquisition module 14, interference analysis and compensation module 15, production interference prediction module 16, production environment early warning module 17. Detailed Embodiments

[0011] By providing a production environment monitoring method and system based on interference analysis, the present application aims to solve the technical problem that the prior art has weak comprehensive analysis ability in production environment monitoring, resulting in low monitoring accuracy. Through the division of the production process flow, the collection of production task information and real-time environmental monitoring, multiple interference production environment spaces are established, and through interference analysis and compensation technologies, the environmental monitoring data is processed and predicted. Combining the predetermined interference prediction constraint conditions and thresholds, real-time early warning of the production environment is carried out, achieving the technical effect of improving the monitoring accuracy of the production environment.

[0012] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0013] It should be noted that any variations of the terms "comprising" and "having" are intended to cover non-exclusive inclusion. For example, a process, method, system, product or server that includes a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0014] Embodiment 1, as Figure 1As shown in the figure, the present application provides a production environment monitoring method based on interference analysis, and the method includes: Step S100: Divide the target production workshop based on the production process flow to obtain multiple node production workshops.

[0015] In the embodiment of the present application, the production process flow is fixed, and the process steps of each stage have been predefined in advance. Then, the user inputs the workshop information corresponding to each stage according to the production process flow. Each workshop is responsible for a specific production task. For example, a certain workshop is responsible for primary processing, and another workshop is responsible for assembly, etc. Finally, according to the workshop information input by the user, each production process step is corresponding to a specific workshop, so as to divide multiple node production workshops. Each node production workshop represents an independent production unit and is responsible for completing the production task of a certain specific stage.

[0016] Step S200: Collect the production task information of the multiple node production workshops to obtain multiple node production tasks.

[0017] In the embodiment of the present application, first, through the workshop management system or the industrial Internet of Things platform, the data related to production tasks is obtained in real time from each node production workshop. These data include information such as production plans, task instructions, equipment status, and material requirements. Then, the collected data is sorted and standardized, and converted into structured production task information, so as to generate clear node production tasks.

[0018] Through the above process, multiple node production tasks are obtained.

[0019] Step S300: Perform production environment interference prediction on the multiple node production workshops based on the multiple node production tasks, and establish multiple interference production environment spaces.

[0020] In the embodiment of the present application, first, according to the multiple node production tasks, a retrospective analysis is performed on the historical production data and environmental data of each node production workshop. By correlating the historical data, the environmental interference factors that may occur in each workshop during the previous production process are identified, and the influence range of these interference factors is determined, so as to obtain the environmental interference domain of each workshop.

[0021] Next, multiple node production workshops with similar production processes or using the same equipment are classified and associated to form clusters of similar workshops. These workshop clusters are grouped according to factors such as the similarity of production tasks, equipment types, and production processes between workshops. Then, based on the clusters of similar workshops, environmental interference retrospective association is performed again to analyze the environmental interference factors jointly faced by these similar workshops during the production process, and further obtain the environmental interference domain of the similar workshops.

[0022] Finally, combine the environmental interference domains of similar workshops with those of each node's production workshop to supplement and improve the interference domain of a single workshop, and finally generate multiple interference production environment spaces.

[0023] Further, in the method provided by the application embodiment, based on the multiple node production tasks, perform production environment interference prediction on the multiple node production workshops, and establish multiple interference production environment spaces, further including: Perform environmental interference backtracking association on the multiple node production workshops according to the multiple node production tasks to obtain multiple workshop environmental interference domains; perform similar workshop association on the multiple node production workshops respectively to determine multiple node similar workshop clusters; based on the multiple node production tasks, perform environmental interference backtracking association on the multiple node similar workshop clusters respectively to obtain multiple similar workshop environmental interference domains; based on the multiple similar workshop environmental interference domains, perform interference supplementation on the multiple workshop environmental interference domains to generate the multiple interference production environment spaces.

[0024] In the embodiment of the present application, environmental interference backtracking association is performed through a big data analysis platform and data mining technology. Specifically, first extract the historical data of each node production workshop from the production management system and the historical environment monitoring system. These data include production tasks, equipment operation records, environmental parameters, etc. Then use data mining technology, such as association rule mining, to analyze these historical data to identify environmental interference factors related to production tasks, such as temperature changes, equipment vibration conditions, air quality changes, etc. under specific tasks. Based on these analysis results, generate the environmental interference domain of each workshop to obtain multiple workshop environmental interference domains.

[0025] Next, use a clustering algorithm to perform similar workshop association on the multiple node production workshops. By considering the similarity of production tasks, equipment types, and production process flows of the workshops, use algorithms such as K-means or hierarchical clustering to divide these workshops into different clusters to form similar workshop clusters and determine multiple node similar workshop clusters.

[0026] On this basis, once again use big data analysis and data mining technology to perform environmental interference backtracking association on each similar workshop cluster. By mining the historical data of the workshops within these clusters again, identify the environmental interference factors they commonly face under similar production tasks. For example, it is found that when certain equipment is used simultaneously in different workshops, the heat generated has a similar impact on the environmental temperature, or a certain production process is prone to causing air pollution problems. Finally, obtain multiple similar workshop environmental interference domains.

[0027] Finally, interference supplementation is carried out through environment model integration and simulation technology, and finally an interference production environment space is generated. Specifically, the interference domains of the same type of workshop environment obtained previously are integrated with the environment interference domains of each node production workshop. Through the environment model integration technology, the common and individual environment interference information is fused together. Then, technologies such as finite element analysis or discrete event simulation are used to further supplement and improve the interference model of each workshop, simulate various possible production scenarios, and predict potential environment interferences. Finally, multiple interference production environment spaces are generated.

[0028] Further, in the method provided by the application embodiment, after obtaining multiple workshop environment interference domains by performing environment interference backtracking association on the multiple node production workshops according to the multiple node production tasks, it further includes: According to the multiple node production tasks and the multiple node production workshops, the nth node production task and the nth node production workshop are extracted, where n is a positive integer; according to the nth node production workshop, a retrieval of environment interference records is performed to obtain the nth workshop environment interference record library, where the nth workshop environment interference record library includes multiple environment interference record groups, and each environment interference record group includes the historical node production task corresponding to the nth node production workshop and historical environment interference information; based on the nth node production task, environment interference selection is performed on the nth workshop environment interference record library to determine the nth workshop environment interference information registration set that meets the predetermined task association constraint; according to the nth workshop environment interference information registration set, environment interference dimension identification is performed to obtain a multi-feature environment interference dimension; based on the multi-feature environment interference dimension, trigger interval identification is performed on the nth workshop environment interference information registration set to establish the nth workshop environment interference domain, and the nth workshop environment interference domain is added to the multiple workshop environment interference domains.

[0029] In the embodiment of the present application, first, according to the multiple node production tasks and the corresponding node production workshops, the nth node production task and the nth node production workshop are extracted therefrom, where n is a positive integer. In this step, database query technology is used to extract the production tasks and workshop information related to the nth node from the data warehouse through task identifiers and workshop identifiers.

[0030] Next, according to the information of the nth node production workshop, a retrieval of environment interference records is performed. This step is achieved through time series database and index retrieval technology. Historical environment data related to this workshop is extracted from the time series database, and this data usually includes information collected by sensors such as temperature, humidity, and vibration. These data are organized into the nth workshop environment interference record library, and this record library includes multiple environment interference record groups, and each record group contains the historical node production task related to the nth node production workshop and the corresponding historical environment interference information, such as past temperature, humidity, noise, and vibration data, etc.

[0031] Then, environmental interference selection is performed on the data in the environmental interference record library of the nth workshop. Through rule-based filtering and matching techniques, the data is screened to select records highly relevant to the current production task. This process relies on a predefined set of rules, which include the time range of task execution, the operating status of equipment (such as full load or idle), and specific conditions of environmental parameters (such as temperature exceeding a certain threshold). Through a rule engine, the records that meet these conditions are screened out to form the environmental interference information registration set of the nth workshop.

[0032] Next, multi-dimensional data analysis and feature extraction techniques are used to identify the environmental interference dimensions of the environmental interference information registration set. Through tools such as the pandas library in Python, multi-dimensional analysis of the data is performed to extract the key dimensions affecting the production workshop environment, obtaining multi-feature environmental interference dimensions, which include factors such as temperature, humidity, noise, and vibration.

[0033] After identifying the key environmental interference dimensions, trigger interval identification is further carried out. Through time series analysis and statistical interval identification, the trigger intervals of environmental interference factors are determined, that is, when and under what conditions these interference factors will exceed the preset safety threshold. Using sliding window analysis techniques, rate of change analysis, or anomaly detection techniques, the time periods when environmental parameters change drastically are identified and marked as possible environmental interference trigger intervals. Based on these analysis results, the environmental interference domain of the nth workshop is established, which clarifies the environmental interference risks that the workshop may face under specific conditions. Finally, the environmental interference domain of the nth workshop is integrated into the existing environmental interference domains of multiple workshops.

[0034] Step S400: According to the sensing and monitoring array of the target production workshop, collect the real-time production environment information of the multiple node production workshops to obtain multiple workshop environmental monitoring data sets.

[0035] In the embodiment of the present application, a sensing and monitoring array is deployed in the target production workshop, including sensors such as temperature, humidity, vibration, and noise, for real-time production environment collection of the target production workshop. The environmental information is collected in real time through the sensing and monitoring array, and this information is transmitted to the data acquisition server through a wired or wireless network. The data acquisition system performs preliminary processing on the data to ensure the accuracy and integrity of the data. Then, the preliminarily processed data is transmitted to the central database to form multiple workshop environmental monitoring data sets.

[0036] Step S500: Traverse the multiple workshop environmental monitoring data sets for interference analysis and compensation to obtain multiple workshop environmental monitoring results.

[0037] In the embodiments of the present application, first, each workshop environmental monitoring dataset is traversed, and the first production environmental monitoring parameters, such as temperature, humidity, vibration, etc., are extracted one by one. For each extracted parameter, the device feature tracing of the corresponding sensing monitoring array is performed to determine the first device status information of the monitoring device associated with the parameter. Next, based on the device status information, interference analysis is performed on each monitoring parameter, and a monitoring interference coefficient is calculated. It is judged whether the interference coefficient is greater than or equal to a predetermined monitoring interference threshold. If the monitoring interference coefficient is greater than or equal to the predetermined threshold, the parameter is compensated according to the device status information, so as to calculate the first corrected production environmental monitoring parameter.

[0038] Then, the monitoring dataset is updated using the corrected monitoring parameters, and the mapping update of multiple workshop environmental monitoring datasets is performed. Through the above traversal and compensation processes, multiple workshop environmental monitoring results are finally obtained.

[0039] Further, in the method provided by the application embodiments, when traversing the multiple workshop environmental monitoring datasets for interference analysis and compensation to obtain multiple workshop environmental monitoring results, it further includes: Traverse the multiple workshop environmental monitoring datasets, and extract the first production environmental monitoring parameters; perform monitoring device feature tracing on the sensing monitoring array based on the first production environmental monitoring parameters to determine the first device status information corresponding to the first monitoring device; perform monitoring interference evaluation according to the first device status information to determine the first device monitoring interference coefficient; judge whether the first device monitoring interference coefficient is greater than / equal to a predetermined monitoring interference coefficient; if the first device monitoring interference coefficient is greater than or equal to the predetermined monitoring interference coefficient, compensate the first production environmental monitoring parameter according to the first device status information to determine the first corrected production environmental monitoring parameter; perform mapping update on the multiple workshop environmental monitoring datasets according to the first corrected production environmental monitoring parameter to generate the multiple workshop environmental monitoring results.

[0040] In the embodiments of the present application, first, each workshop environmental monitoring dataset is traversed, and the first production environmental monitoring parameters, including temperature, humidity, vibration, noise, etc., which are key indicators reflecting the workshop environmental status, are extracted. Next, based on the extracted first production environmental monitoring parameters, device feature tracing is performed on the relevant sensing monitoring array. This process uses the device management system to identify the monitoring devices used to collect these parameters. By querying the historical calibration records and current operating status of the devices, the device status information of the first monitoring device is obtained, including the working condition, accuracy, possible faults or deviations of the device.

[0041] Subsequently, a monitoring interference evaluation is performed based on the first device status information. Specifically, rule-based anomaly detection and time series analysis methods are used to detect anomalies in the device status information. First, a series of preset rules are applied, which are set according to the normal operating parameters of the device. For example, if the temperature of the device exceeds a specific threshold or the vibration amplitude suddenly increases, these data will be automatically marked as abnormal. At the same time, through time series analysis, the historical trends of the device operation data are analyzed to detect any abnormal fluctuations or deviations from normal behavior. For example, when an abnormal peak suddenly appears in the temperature or vibration data of the device, it indicates that the device may be affected by external interference or internal faults. Then, the abnormal data points identified in the anomaly detection are compared with the historical normal data of the device. By calculating the proportion of abnormal data in the total data, the deviation amplitude of the abnormal values, etc., the current interference degree of the device is quantified. Next, statistical models such as Z-score calculation or Kalman filtering are used to quantify the degree of anomaly. When calculating the monitoring interference coefficient, , through calculation, the first device monitoring interference coefficient is obtained.

[0042] Subsequently, it is determined whether the calculated first device monitoring interference coefficient is greater than or equal to a predetermined monitoring interference threshold. If the interference coefficient exceeds the threshold, it is considered that the data is significantly interfered and data compensation is required. The system uses interpolation algorithms, historical data review, or prediction methods based on physical models to compensate for the interfered first production environment monitoring parameters and calculates the first corrected production environment monitoring parameters.

[0043] Using the compensated first corrected production environment monitoring parameters, the entire environmental monitoring data set is mapped and updated. This step uses data mapping technology to replace the abnormal values in the original data with the corrected parameter values to ensure that all relevant monitoring data is corrected. After completing the traversal, analysis, and compensation of all parameters, all updated monitoring data sets are summarized to generate multiple workshop environmental monitoring results.

[0044] Furthermore, in the method provided by the application embodiment, if the first device monitoring interference coefficient is greater than or equal to the predetermined monitoring interference coefficient, compensating the first production environment monitoring parameters according to the first device status information to determine the first corrected production environment monitoring parameters further includes: Retrieve compensation records according to the first monitoring device to obtain a first device status information record set, a first production environment monitoring parameter record set, and a first calibrated production environment monitoring parameter record set; use the first device status information record set and the first production environment monitoring parameter record set as input data, and the first calibrated production environment monitoring parameter record set as output data to train a first monitoring compensation and calibration model that meets a predetermined compensation and calibration accuracy; based on the first device status information and the first production environment monitoring parameters, obtain the first calibrated production environment monitoring parameters according to the first monitoring compensation and calibration model.

[0045] In the embodiment of the present application, first, through the database management system and SQL query technology, retrieve the compensation records related to the first monitoring device from the historical database. Query in the database through the unique identifier of the device to obtain three types of key data sets: the first device status information record set, the first production environment monitoring parameter record set, and the first calibrated production environment monitoring parameter record set. The first device status information record set contains the operating status information of the device at different time points, such as temperature, vibration, and workload, which are collected and stored by the device's monitoring system or sensors. The first production environment monitoring parameter record set includes the environmental monitoring data of the device under normal and abnormal conditions, such as temperature, humidity, and vibration. The first calibrated production environment monitoring parameter record set stores the monitoring data of the device after compensation processing when an abnormality is detected.

[0046] Next, use data preprocessing and machine learning model training technology to prepare and process these historical data. First, through data cleaning and preprocessing steps, remove outliers, fill in missing values, and perform normalization processing to ensure that the data input into the model is consistent and accurate. Then, perform feature engineering to extract features from the relevant data in the first device status information record set and the first production environment monitoring parameter record set, such as extracting the temperature change rate, vibration spectrum features, etc. These features are used as input data for model training. Subsequently, select a suitable machine learning algorithm, such as support vector machine, random forest, or neural network, to train the first monitoring compensation and calibration model. During the training process, the system continuously adjusts the hyperparameters of the model to optimize the prediction accuracy of the model and ensure that the model can accurately predict and compensate for the errors of the monitoring parameters based on the device status information.

[0047] After the model training is completed, enter the real-time data processing stage, and use real-time data analysis and model inference technology to generate compensation and calibration parameters. Through sensors and the device monitoring system, real-time collect the current first device status information and the first production environment monitoring parameters. Input these real-time data into the trained first monitoring compensation and calibration model, and the model outputs the calibrated first calibrated production environment monitoring parameters according to the current status of the device.

[0048] Step S600: Based on the predetermined interference prediction constraint conditions, combine the multiple interference production environment spaces to perform production interference prediction on the multiple workshop environment monitoring results, and obtain multiple environmental production interference prediction coefficients.

[0049] In the embodiment of the present application, first traverse the multiple interference production environment spaces and the multiple workshop environment monitoring results, and extract the first interference production environment space and the first workshop environment monitoring result one by one. Then, based on the extracted first interference production environment space, perform interference identification on the first workshop environment monitoring result, compare the current monitoring data with the preset interference production environment space, identify the possible interference factors under the current environmental conditions, and generate the first production environment interference identification result. Subsequently, according to the first production environment interference identification result, perform interference prediction, and calculate the first environmental production interference depth prediction coefficient and the first environmental production interference breadth prediction coefficient. Then, based on the predetermined interference prediction constraint conditions, perform weighted calculation on these two coefficients. After the weighted calculation, generate the first environmental production interference prediction coefficient, which reflects the overall interference risk of the current environment on the production process. Finally, add the first environmental production interference prediction coefficient obtained from each traversal and calculation to the set of environmental production interference prediction coefficients one by one to obtain multiple environmental production interference prediction coefficients.

[0050] Further, in the method provided by the embodiment of the application, combining the multiple interference production environment spaces to perform production interference prediction on the multiple workshop environment monitoring results, and obtaining multiple environmental production interference prediction coefficients, further includes: Traverse the multiple interference production environment spaces and the multiple workshop environment monitoring results, extract the first interference production environment space and the first workshop environment monitoring result; perform interference identification on the first workshop environment monitoring result based on the first interference production environment space to obtain the first production environment interference identification result; perform interference prediction based on the first production environment interference identification result to determine the first environmental production interference depth prediction coefficient and the first environmental production interference breadth prediction coefficient; perform weighted calculation on the first environmental production interference depth prediction coefficient and the first environmental production interference breadth prediction coefficient according to the predetermined interference prediction constraint conditions, and output the first environmental production interference prediction coefficient, where the predetermined interference prediction constraint conditions include a predetermined interference depth weight and a predetermined interference breadth weight; add the first environmental production interference prediction coefficient to the multiple environmental production interference prediction coefficients.

[0051] In the embodiment of the present application, first traverse the multiple interference production environment spaces and the multiple workshop environment monitoring results. During this process, extract each interference production environment space and its corresponding workshop environment monitoring result, and mark each pair of extracted data sets as the first interference production environment space and the first workshop environment monitoring result respectively.

[0052] Next, through pattern recognition and correlation analysis techniques, interference recognition is performed on the extracted first interference production environment space and the first workshop environment monitoring results. Specifically, using pattern recognition techniques, the current monitoring data is matched and analyzed with the historical data patterns in the interference production environment space, and through correlation analysis, such as Pearson correlation coefficient or Spearman correlation coefficient, the similarity between the two is evaluated. In addition, distance measurement techniques, such as Euclidean distance or Mahalanobis distance, are used to quantify the difference between the current monitoring results and the known interference patterns. If the similarity or difference exceeds a predetermined threshold, potential production interference is identified, and a first production environment interference recognition result is generated.

[0053] After the interference recognition is completed, further interference prediction is performed based on the first production environment interference recognition result. By constructing a corresponding prediction model, two key indicators, namely the first environmental production interference depth prediction coefficient and the first environmental production interference breadth prediction coefficient, are calculated.

[0054] Subsequently, according to the predetermined interference prediction constraint conditions, weighted calculations are performed on the first environmental production interference depth prediction coefficient and the first environmental production interference breadth prediction coefficient. The predetermined constraint conditions include an interference depth weight and an interference breadth weight, and these weights are preset according to the importance of different interference factors in the production process. Using the weighted average technique, the interference depth and breadth prediction coefficients are combined according to their weight values to generate a comprehensive first environmental production interference prediction coefficient.

[0055] Finally, the first environmental production interference prediction coefficients obtained from each traversal and calculation are added one by one to the set of multiple environmental production interference prediction coefficients.

[0056] Furthermore, in the method provided by the application embodiment, when performing interference prediction based on the first production environment interference recognition result to determine the first environmental production interference depth prediction coefficient and the first environmental production interference breadth prediction coefficient, it further includes: Retrieve interference feature records according to the node production workshop corresponding to the first production environment interference recognition result, and obtain the first production environment interference recognition result record set, the first environmental production interference depth coefficient record set, and the first environmental production interference breadth coefficient record set; construct a first environmental production interference depth prediction model based on the first production environment interference recognition result record set and the first environmental production interference depth coefficient record set; build a first environmental production interference breadth prediction model based on the first production environment interference recognition result record set and the first environmental production interference breadth coefficient record set; based on the first production environment interference recognition result, output the first environmental production interference depth prediction coefficient according to the first environmental production interference depth prediction model; based on the first production environment interference recognition result, output the first environmental production interference breadth prediction coefficient according to the first environmental production interference breadth prediction model.

[0057] In the embodiment of the present application, first, according to the first production environment interference recognition result, locate the corresponding node production workshop, and use database query technology to extract relevant interference record data from the historical database. Retrieve and collect the first production environment interference recognition result record set, the first environmental production interference depth coefficient record set, and the first environmental production interference breadth coefficient record set through SQL query. The first production environment interference recognition result record set contains detailed records of similar environmental interference recognition in history. The first environmental production interference depth coefficient record set stores the quantified data of the impact on production depth when each interference occurs, while the first environmental production interference breadth coefficient record set records the breadth coefficient of the impact range of the interference on production.

[0058] Next, use regression analysis technology to construct two prediction models. First, take the first production environment interference recognition result record set and the first environmental production interference depth coefficient record set extracted from the database as input data, and use regression analysis methods such as linear regression for model training. Fit these data by the least squares method to construct the first environmental production interference depth prediction model, which can predict the impact on production depth in the current environment based on historical interference recognition results.

[0059] Similarly, use the first production environment interference recognition result record set and the first environmental production interference breadth coefficient record set, and use the same regression analysis technology to construct the first environmental production interference breadth prediction model. This model evaluates the breadth of the impact range of the current environmental interference on the production process by fitting historical data.

[0060] After the model is constructed, these regression analysis models are applied to generate specific prediction coefficients. First, based on the current first production environment interference recognition result, the trained first environment production interference depth prediction model is used for inference to output the first environment production interference depth prediction coefficient, which quantifies the severity of the current environment interference on the depth of the production process. Subsequently, the first environment production interference breadth prediction model is used to generate the first environment production interference breadth prediction coefficient based on the current interference recognition result, and this coefficient evaluates the influence range of the interference in the production process.

[0061] Through the above process, the first environment production interference depth prediction coefficient and the first environment production interference breadth prediction coefficient are obtained.

[0062] Step S700: Load the multiple environment production interference thresholds corresponding to the multiple node production workshops, and combine the multiple environment production interference prediction coefficients to conduct production environment early warning for the multiple node production workshops.

[0063] In the embodiment of the present application, first, the environment production interference thresholds corresponding to each node production workshop are loaded. These thresholds are the reference values for judging when the production environment may be interfered, and are preset by technical experts. Then, the previously calculated environment production interference prediction coefficients are compared with these thresholds. If the prediction coefficient exceeds the corresponding threshold, a production environment early warning is triggered, and the on-site alarm is used to remind the staff to complete the early warning.

[0064] In the embodiment of the present application, in summary, the embodiment of the present application has at least the following technical effects: This application divides the target production workshop based on the production process flow to obtain multiple node production workshops; collects the production task information of multiple node production workshops to obtain multiple node production tasks; predicts the production environment interference of multiple node production workshops based on multiple node production tasks to establish multiple interference production environment spaces; collects the real-time production environment information of multiple node production workshops according to the sensing monitoring array of the target production workshop to obtain multiple workshop environment monitoring data sets; traverses multiple workshop environment monitoring data sets for interference analysis and compensation to obtain multiple workshop environment monitoring results; predicts the production interference based on multiple workshop environment monitoring results by combining multiple interference production environment spaces according to the predetermined interference prediction constraint conditions to obtain multiple environmental production interference prediction coefficients; loads the multiple environmental production interference thresholds corresponding to multiple node production workshops, and combines multiple environmental production interference prediction coefficients to conduct production environment early warning for multiple node production workshops. The present invention solves the technical problem that the prior art has weak comprehensive analysis ability in production environment monitoring, resulting in low monitoring accuracy. Through the division of the production process flow, the collection of production task information and real-time environment monitoring, multiple interference production environment spaces are established, and through interference analysis and compensation technology, the environmental monitoring data is processed and predicted. Combining the predetermined interference prediction constraint conditions and thresholds, real-time early warning of the production environment is carried out to achieve the technical effect of improving the monitoring accuracy of the production environment.

[0065] Embodiment 2, based on the same inventive concept as the production environment monitoring method based on interference analysis in the foregoing embodiment, as Figure 2 shown, this application provides a production environment monitoring system based on interference analysis. The system in the embodiment of this application and the method embodiment are based on the same inventive concept. Among them, the system includes: Node division module 11, the node division module 11 divides the target production workshop based on the production process flow to obtain multiple node production workshops; task information collection module 12, the task information collection module 12 collects the production task information of the multiple node production workshops to obtain multiple node production tasks; environmental interference prediction module 13, the environmental interference prediction module 13 predicts the production environmental interference of the multiple node production workshops based on the multiple node production tasks to establish multiple interference production environment spaces; production environment information collection module 14, the production environment information collection module 14 collects the real-time production environment information of the multiple node production workshops according to the sensing monitoring array of the target production workshop to obtain multiple workshop environment monitoring data sets; interference analysis and compensation module 15, the interference analysis and compensation module 15 traverses the multiple workshop environment monitoring data sets for interference analysis and compensation to obtain multiple workshop environment monitoring results; production interference prediction module 16, the production interference prediction module 16 predicts the production interference of the multiple workshop environment monitoring results based on the predetermined interference prediction constraint conditions and in combination with the multiple interference production environment spaces to obtain multiple environmental production interference prediction coefficients; production environment early warning module 17, the production environment early warning module 17 loads the multiple environmental production interference thresholds corresponding to the multiple node production workshops, and combines the multiple environmental production interference prediction coefficients to perform production environment early warning on the multiple node production workshops.

[0066] Further, the system is also used to implement the following functions: Perform environmental interference backtracking association on the multiple node production workshops according to the multiple node production tasks to obtain multiple workshop environmental interference domains; respectively perform association of similar workshops on the multiple node production workshops to determine multiple node similar workshop clusters; based on the multiple node production tasks, respectively perform environmental interference backtracking association on the multiple node similar workshop clusters to obtain multiple similar workshop environmental interference domains; based on the multiple similar workshop environmental interference domains, perform interference supplementation on the multiple workshop environmental interference domains to generate the multiple interference production environment spaces.

[0067] Further, the system is also used to implement the following functions: Extract the production task of the nth node and the production workshop of the nth node according to the multiple node production tasks and the multiple node production workshops, where n is a positive integer; retrieve the environmental interference records according to the production workshop of the nth node to obtain the environmental interference record library of the nth workshop, where the environmental interference record library of the nth workshop includes multiple environmental interference record groups, and each environmental interference record group includes the historical node production task corresponding to the production workshop of the nth node and the historical environmental interference information; based on the production task of the nth node, select the environmental interference in the environmental interference record library of the nth workshop to determine the registration set of environmental interference information of the nth workshop that meets the predetermined task association constraint; identify the environmental interference dimension according to the registration set of environmental interference information of the nth workshop to obtain a multi-feature environmental interference dimension; identify the trigger interval based on the multi-feature environmental interference dimension for the registration set of environmental interference information of the nth workshop, establish the environmental interference domain of the nth workshop, and add the environmental interference domain of the nth workshop to the multiple workshop environmental interference domains.

[0068] Further, the system is also used to implement the following functions: Traverse the multiple workshop environmental monitoring data sets and extract the first production environment monitoring parameters. Trace the characteristics of the monitoring equipment for the sensing monitoring array based on the first production environment monitoring parameters to determine the first device status information corresponding to the first monitoring device; evaluate the monitoring interference according to the first device status information to determine the first device monitoring interference coefficient; determine whether the first device monitoring interference coefficient is greater than or equal to the predetermined monitoring interference coefficient; if the first device monitoring interference coefficient is greater than or equal to the predetermined monitoring interference coefficient, compensate the first production environment monitoring parameters according to the first device status information to determine the first corrected production environment monitoring parameters; update the mapping of the multiple workshop environmental monitoring data sets according to the first corrected production environment monitoring parameters to generate the multiple workshop environmental monitoring results.

[0069] Further, the system is also used to implement the following functions: Retrieve the compensation records according to the first monitoring device to obtain the first device status information record set, the first production environment monitoring parameter record set, and the first corrected production environment monitoring parameter record set; use the first device status information record set and the first production environment monitoring parameter record set as input data, and the first corrected production environment monitoring parameter record set as output data to train the first monitoring compensation correction model that meets the predetermined compensation correction accuracy; based on the first device status information and the first production environment monitoring parameters, obtain the first corrected production environment monitoring parameters according to the first monitoring compensation correction model.

[0070] Further, the system is also used to implement the following functions: Traverse the multiple interference production environment spaces and the multiple workshop environment monitoring results, extract the first interference production environment space and the first workshop environment monitoring result; based on the first interference production environment space, perform interference identification on the first workshop environment monitoring result to obtain the first production environment interference identification result; based on the first production environment interference identification result, perform interference prediction to determine the first environmental production interference depth prediction coefficient and the first environmental production interference breadth prediction coefficient; according to the predetermined interference prediction constraint conditions, perform weighted calculation on the first environmental production interference depth prediction coefficient and the first environmental production interference breadth prediction coefficient, and output the first environmental production interference prediction coefficient, where the predetermined interference prediction constraint conditions include a predetermined interference depth weight and a predetermined interference breadth weight; add the first environmental production interference prediction coefficient to the multiple environmental production interference prediction coefficients.

[0071] Further, the system is also used to implement the following functions: Retrieve interference feature records according to the node production workshop corresponding to the first production environment interference identification result to obtain the first production environment interference identification result record set, the first environmental production interference depth coefficient record set, and the first environmental production interference breadth coefficient record set; based on the first production environment interference identification result record set and the first environmental production interference depth coefficient record set, construct the first environmental production interference depth prediction model; based on the first production environment interference identification result record set and the first environmental production interference breadth coefficient record set, build the first environmental production interference breadth prediction model; based on the first production environment interference identification result, according to the first environmental production interference depth prediction model, output the first environmental production interference depth prediction coefficient; based on the first production environment interference identification result, according to the first environmental production interference breadth prediction model, output the first environmental production interference breadth prediction coefficient.

[0072] It should be noted that the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above describes specific embodiments of this specification. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0073] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included within the protection scope of the present application.

[0074] This specification and the accompanying drawings are merely exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A production environment monitoring method based on interference analysis, characterized in that: The method comprises: Divide the target production workshop based on the production process flow to obtain multiple node production workshops; Collecting production task information of the multiple node production workshops to obtain multiple node production tasks; Based on the multiple node production tasks, production environment interference prediction is performed on the multiple node production workshops, and multiple interference production environment spaces are established; According to the sensor monitoring array of the target production workshop, real-time production environment information of the multiple node production workshops is collected to obtain multiple workshop environment monitoring data sets; Traversing the plurality of workshop environment monitoring data sets to perform interference analysis and compensation, and obtaining a plurality of workshop environment monitoring results; Based on the predetermined interference prediction constraint conditions, the production interference prediction is performed on the multiple workshop environment monitoring results in combination with the multiple interference production environment spaces to obtain multiple environmental production interference prediction coefficients; Multiple environmental production interference thresholds corresponding to the multiple node production workshops are loaded, and production environment early warnings are performed for the multiple node production workshops in combination with the multiple environmental production interference prediction coefficients.

2. The method according to claim 1, characterized in that Predicting production environment interference for the multiple node production workshops based on the multiple node production tasks, and establishing multiple interference production environment spaces, including: Performing environmental interference backtracking association on the multiple node production workshops according to the multiple node production tasks to obtain multiple workshop environmental interference domains; Associating the multiple node production workshops with the same type of workshops respectively to determine multiple node similar workshop clusters; Based on the multiple node production tasks, environmental interference backtracking association is performed on the multiple node similar workshop clusters respectively to obtain multiple similar workshop environmental interference domains; Based on the multiple similar workshop environment interference domains, interference supplement is performed on the multiple workshop environment interference domains to generate the multiple interference production environment spaces.

3. The method according to claim 2, characterized in that According to the multiple node production tasks, environmental interference backtracking association is performed on the multiple node production workshops to obtain multiple workshop environmental interference domains, including: Extracting the nth node production task and the nth node production workshop according to the multiple node production tasks and the multiple node production workshops, where n is a positive integer; Performing an environmental interference record search according to the n-th node production workshop to obtain an n-th workshop environmental interference record library, wherein the n-th workshop environmental interference record library includes multiple environmental interference record groups, each environmental interference record group includes historical node production tasks and historical environmental interference information corresponding to the n-th node production workshop; Based on the nth node production task, environmental interference selection is performed on the nth workshop environmental interference record library to determine the nth workshop environmental interference information registration set that meets the predetermined task association constraints; Performing environmental interference dimension identification according to the nth workshop environmental interference information registration set to obtain a multi-feature environmental interference dimension; Based on the multi-feature environmental interference dimensions, trigger interval identification is performed on the nth workshop environmental interference information registration set, an nth workshop environmental interference domain is established, and the nth workshop environmental interference domain is added to the multiple workshop environmental interference domains.

4. The method according to claim 1, characterized in that The plurality of workshop environment monitoring data sets are traversed to perform interference analysis and compensation, and a plurality of workshop environment monitoring results are obtained, including: Traversing the plurality of workshop environment monitoring data sets, and extracting a first production environment monitoring parameter; Based on the first production environment monitoring parameter, the sensor monitoring array is traced for monitoring device characteristics to determine first device status information corresponding to the first monitoring device; Performing a monitoring interference evaluation according to the first device state information to determine a first device monitoring interference coefficient; Determining whether the monitoring interference coefficient of the first device is greater than / equal to a predetermined monitoring interference coefficient; If the first equipment monitoring interference coefficient is greater than / equal to the predetermined monitoring interference coefficient, the first production environment monitoring parameter is compensated according to the first equipment status information to determine a first corrected production environment monitoring parameter; The plurality of workshop environment monitoring data sets are mapped and updated according to the first corrected production environment monitoring parameters to generate the plurality of workshop environment monitoring results.

5. The method according to claim 4, characterized in that If the first equipment monitoring interference coefficient is greater than / equal to the predetermined monitoring interference coefficient, compensating the first production environment monitoring parameter according to the first equipment state information to determine a first corrected production environment monitoring parameter, including: Perform compensation record retrieval according to the first monitoring device to obtain a first device status information record set, a first production environment monitoring parameter record set, and a first correction production environment monitoring parameter record set; Using the first device status information record set and the first production environment monitoring parameter record set as input data, and using the first correction production environment monitoring parameter record set as output data, training a first monitoring compensation correction model that meets a predetermined compensation correction accuracy; Based on the first device status information and the first production environment monitoring parameters, the first corrected production environment monitoring parameters are obtained according to the first monitoring compensation correction model.

6. The method according to claim 1, characterized in that Based on the predetermined interference prediction constraint conditions, the production interference prediction is performed on the multiple workshop environment monitoring results in combination with the multiple interference production environment spaces to obtain multiple environmental production interference prediction coefficients, including: Traversing the multiple interfering production environment spaces and the multiple workshop environment monitoring results, extracting a first interfering production environment space and a first workshop environment monitoring result; Performing interference identification on the first workshop environment monitoring result based on the first interfering production environment space to obtain a first production environment interference identification result; Perform interference prediction based on the first production environment interference identification result, and determine a first environment production interference depth prediction coefficient and a first environment production interference breadth prediction coefficient; Performing weighted calculation on the first environment production interference depth prediction coefficient and the first environment production interference breadth prediction coefficient according to the predetermined interference prediction constraint condition, and outputting the first environment production interference prediction coefficient, wherein the predetermined interference prediction constraint condition includes a predetermined interference depth weight and a predetermined interference breadth weight; The first environmental production disturbance prediction coefficient is added to the plurality of environmental production disturbance prediction coefficients.

7. The method according to claim 6, characterized in that Performing interference prediction based on the first production environment interference identification result to determine a first environment production interference depth prediction coefficient and a first environment production interference breadth prediction coefficient includes: Perform interference feature record retrieval according to the node production workshop corresponding to the first production environment interference identification result, and obtain a first production environment interference identification result record set, a first environment production interference depth coefficient record set, and a first environment production interference breadth coefficient record set; Based on the first production environment interference identification result record set and the first environment production interference depth coefficient record set, construct a first environment production interference depth prediction model; Building a first environment production interference breadth prediction model based on the first production environment interference identification result record set and the first environment production interference breadth coefficient record set; Based on the first production environment interference identification result, and according to the first environment production interference depth prediction model, outputting the first environment production interference depth prediction coefficient; Based on the first production environment interference identification result and according to the first environment production interference breadth prediction model, the first environment production interference breadth prediction coefficient is output.

8. The production environment monitoring system based on interference analysis is characterized by: The system comprises: A node division module, wherein the node division module divides the target production workshop based on the production process flow to obtain multiple node production workshops; A task information collection module, wherein the task information collection module collects production task information of the multiple node production workshops to obtain multiple node production tasks; An environmental interference prediction module, which predicts production environment interference for the multiple node production workshops based on the multiple node production tasks and establishes multiple interference production environment spaces; A production environment information collection module, wherein the production environment information collection module collects real-time production environment information of the multiple node production workshops according to the sensor monitoring array of the target production workshop, and obtains multiple workshop environment monitoring data sets; An interference analysis and compensation module, wherein the interference analysis and compensation module traverses the plurality of workshop environment monitoring data sets to perform interference analysis and compensation to obtain a plurality of workshop environment monitoring results; A production interference prediction module, which performs production interference prediction on the multiple workshop environment monitoring results based on predetermined interference prediction constraints and in combination with the multiple interference production environment spaces to obtain multiple environmental production interference prediction coefficients; A production environment early warning module, wherein the production environment early warning module loads multiple environmental production interference thresholds corresponding to the multiple node production workshops, and performs production environment early warning for the multiple node production workshops in combination with the multiple environmental production interference prediction coefficients.

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