Industrial equipment online state and configuration state management system

Abstract

The invention relates to the technical field of equipment management, in particular to an industrial equipment on-line state and configuration state management system which comprises an emotion perception regulation and control module for collecting heart rate data and skin electricity data of a working area, analyzing physiological index fluctuation through heart rate variability measurement in combination with skin electricity response, recognizing a working pressure state and obtaining emotion characteristics. According to the invention, by comprehensively collecting and analyzing the physiological and environmental data of the working area, the dynamic adjustment ability of the production environment emotion is improved. By monitoring the heart rate variability and the electrodermal response, the pressure state of the employee is effectively recognized, and emotion data is further supplemented through sound analysis. The data comprehensive analysis not only optimizes the operation parameters of the equipment, such as adjusting speed and temperature, but also realizes the humanization of the production environment, reduces the pressure of employees, and improves the production efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment management, and particularly to an industrial equipment online status and configuration status management system. Background Art

[0002] An industrial equipment online status and configuration status management system is a system specifically used for monitoring and managing the status of industrial equipment. Such a system enables enterprises to understand the operating status and configuration information of their equipment in real time, so as to carry out effective management and decision-making. Its uses include but are not limited to real-time monitoring of equipment operating status, automatic detection and reporting of faults, and optimization of equipment configuration to adapt to different production requirements.

[0003] The prior art mainly focuses on the operating status and fault diagnosis of the equipment itself, and usually ignores the impact of the emotional state of people in the production environment on production efficiency. This single technical focus leads to deficiencies in human-computer interaction and production environment adaptability. For example, the failure to adjust equipment parameters in real time to meet the emotional needs of workers may increase the work pressure and dissatisfaction of employees, thereby reducing production efficiency and increasing the risk of production accidents. The prior art also shows a lack of flexibility in task scheduling, and fails to respond in real time to sudden changes in task priorities during the production process, resulting in unreasonable resource allocation and affecting the overall efficiency and output quality of production. Summary of the Invention

[0004] The purpose of the present invention is to solve the disadvantages existing in the prior art, and to propose an industrial equipment online status and configuration status management system.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions: The industrial equipment online status and configuration status management system includes:

[0006] An emotion perception and regulation module, which collects heart rate data and skin conductance data in the work area, measures heart rate variability, combines skin conductance responses, analyzes fluctuations in physiological indicators, identifies the work pressure state, and obtains emotion characteristics;

[0007] A sound environment analysis module, which uses a sound collection device to record the noise level and voice information in the factory environment, analyzes intonation changes and speech rates, supplements emotion characteristic data, extracts the emotional response of the work environment, and obtains the sound environment emotion analysis result;

[0008] An equipment parameter adjustment module, based on the sound environment emotion analysis result, adjusts the equipment operating speed and temperature, changes the work parameters of the production line, adjusts the machine state to match the emotional needs of the work area, and establishes optimized work environment parameters;

[0009] The task dependency analysis module analyzes the task dependencies and priorities in the production tasks, reconstructs the task queue, sorts it by priority, and generates a task execution strategy.

[0010] The scheduling and feedback module adjusts the production plan based on the task execution strategy and the current device status information, optimizes the task queue in real time, processes urgent and high-priority tasks, and creates a real-time scheduling result.

[0011] Preferably, the steps for obtaining the emotional characteristics are as follows:

[0012] Collect the heart rate data and skin conductance data of the personnel in the work area, record the results of heart rate variability and skin conductance response, and generate a physiological index data set.

[0013] Perform a fluctuation analysis on the physiological index data set using the formula:

[0014]

[0015] Calculate the heart rate variability, where RR i represents the interval between two consecutive heartbeats, is the average heart rate interval, and n is the total number of heartbeats, generating an emotional fluctuation analysis result.

[0016] Based on the emotional fluctuation analysis result and the physiological index data set, perform a composite analysis to identify stress and emotional states, and obtain the emotional characteristics.

[0017] Preferably, the steps for obtaining the noise level are as follows:

[0018] Use a sound collection device to continuously monitor in the factory environment, record the decibel level and frequency distribution of the environmental noise, and generate an original sound data set.

[0019] Perform a frequency analysis on the original sound data set, and through filtering, extract the noise level within the frequency band to obtain the noise level data.

[0020] Based on the noise level data, judge the physiological and psychological impacts on the personnel in the work area, and generate an analysis result of the noise level.

[0021] Preferably, the steps for intonation change and speech rate analysis are as follows:

[0022] Use a sound collection device to record the voice information in the factory environment, including conversations and background exchanges, and generate a voice record data set.

[0023] Perform an analysis of the intonation and speech rate on the voice record data set using the formula:

[0024]

[0025] Calculate the average speech rate S, where P i represents the speech rate of each speech sample, is the average value of the speech rate, n is the number of samples, and generate the analysis result of intonation and speech rate characteristics;

[0026] Combine the analysis result of intonation and speech rate characteristics and the analysis result of the noise level, judge the emotional response in the overall acoustic environment, supplement the emotional feature data through the sound information, and obtain the acoustic environment emotional analysis result.

[0027] Preferably, the obtaining step of the optimized working environment parameters is as follows:

[0028] Based on the emotional features and the acoustic environment emotional analysis result, judge the influence on the working area, and generate a comprehensive environment impact assessment result;

[0029] According to the comprehensive environment impact assessment result, adjust the operation speed of the industrial equipment, using the formula:

[0030] S new = S old ·(1 + α·e -δs )

[0031] Calculate the new equipment operation speed S new where S old represents the original speed, α is the adjustment coefficient based on the environmental impact, δ represents the comprehensive score of the emotional features and the acoustic environment, and s is the adjustment sensitivity, and generate an equipment speed adjustment plan;

[0032] Implement the equipment speed adjustment plan, optimize the machine state to match the emotional needs of the working area by changing the working parameters of the production line, and obtain the optimized working environment parameters.

[0033] Preferably, the obtaining step of the comprehensive score of the emotional features and the acoustic environment is as follows:

[0034] Based on the emotional features and the acoustic environment emotional analysis result, standardize the two types of data to eliminate the influence of dimensions, and generate standardized emotional feature data and acoustic environment data;

[0035] Perform weighted fusion on the standardized emotional feature data and acoustic environment data, using the formula:

[0036] δ = w 1 ×E + w 2 ×V

[0037] Calculate the comprehensive score, where E and V respectively represent the standardized values of the emotional feature data and the acoustic environment data, w 1 and w 2For the weights of two types of data, obtain a preliminary comprehensive emotion score;

[0038] Evaluate the effectiveness of the comprehensive emotion score, and adjust the weights w 1 and w 2 by comparing with test or historical data, verify whether the score truly reflects the emotional and acoustic environment impacts in the workspace, and generate a comprehensive score of emotional characteristics and acoustic environment.

[0039] Preferably, the steps for obtaining the task execution strategy are as follows:

[0040] Analyze the dependencies and priorities among each task in the production task, identify the key tasks and the dependency relationships, and generate a task dependency graph;

[0041] Based on the task dependency graph, reconstruct the task queue, apply a priority sorting algorithm to rearrange the tasks, and use the formula:

[0042]

[0043] Calculate the priority P of task i i where x i represents the urgency score of the task, x 0 is the threshold of task urgency, and k is a constant to adjust the steepness of the curve, to obtain the priority sorting result;

[0044] According to the priority sorting result, ensure that high-priority tasks are executed first, and at the same time consider the dependency relationships among tasks, adjust and optimize the overall production process, to obtain the task execution strategy.

[0045] Preferably, the steps for obtaining the real-time scheduling result are as follows:

[0046] Integrate the task execution strategy and the current device status information, analyze the running situation of the device and the executability of the tasks, and generate a device-task matching report;

[0047] Based on the device-task matching report, make real-time adjustments to the production plan, and use the formula:

[0048]

[0049] Calculate the utilization rate R of task i for resources t where P i represents the priority weight of task i, and T i is the estimated completion time of task i, to obtain the optimized scheduling strategy;

[0050] According to the optimized scheduling strategy, implement task scheduling, handle emergency and high-priority tasks, and generate the real-time scheduling result.

[0051] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0052] In the present invention, by comprehensively collecting and analyzing the physiological and environmental data of the work area, the dynamic adjustment ability of the production environment mood is improved. By monitoring heart rate variability and skin conductance response, the stress state of employees is effectively identified, and voice analysis further supplements the mood data. Such comprehensive data analysis not only optimizes the equipment operation parameters, such as adjusting speed and temperature, but also humanizes the production environment, reduces employee stress, and improves production efficiency. In addition, by reconstructing the task queue and making real-time adjustments, the priority and efficiency of task processing are improved, enabling rapid response to urgent and important tasks, effectively enhancing the utilization efficiency of equipment and human resources, reducing faults and downtime, thereby optimizing production costs and improving the quality of the working environment. Brief Description of the Drawings

[0053] Figure 1 It is a system flowchart of the present invention. Detailed Embodiments

[0054] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0055] Please refer to Figure 1 , the present invention provides a technical solution: The industrial equipment online status and configuration status management system includes:

[0056] An emotion perception and regulation module, which collects heart rate data and skin conductance data in the work area, measures heart rate variability, combines skin conductance response, analyzes the fluctuations of physiological indicators, identifies the work stress state, and obtains emotion characteristics;

[0057] A sound environment analysis module, which uses a sound collection device to record the noise level and voice information in the factory environment, supplements emotion characteristic data through intonation change and speech rate analysis, extracts the emotion response of the work environment, and obtains the sound environment emotion analysis result;

[0058] An equipment parameter adjustment module, based on the sound environment emotion analysis result, adjusts the equipment operation speed and temperature, changes the working parameters of the production line, adjusts the machine state to match the emotion needs of the work area, and establishes optimized work environment parameters;

[0059] A task dependency analysis module, which analyzes the task dependencies and priorities in the production tasks, reconstructs the task queue, sorts it by priority, and generates a task execution strategy;

[0060] The scheduling and feedback module adjusts the production plan based on the task execution strategy and the current device status information, optimizes the task queue in real time, processes urgent and high-priority tasks, and creates a real-time scheduling result.

[0061] The steps for obtaining emotional characteristics are as follows:

[0062] Collect the heart rate data and galvanic skin response data of the personnel in the working area, record the two results of heart rate variability and galvanic skin response, and generate a physiological index dataset;

[0063] Perform a fluctuation analysis on the physiological index dataset, using the formula:

[0064]

[0065] Calculate the heart rate variability, where RR i represents the interval between two consecutive heartbeats, is the average heart rate interval, and n is the total number of heartbeats, generating an emotional fluctuation analysis result;

[0066] According to the emotional fluctuation analysis result and the physiological index dataset, perform a composite analysis to identify the stress and emotional states, and obtain the emotional characteristics.

[0067] Specifically, by monitoring the physiological reactions of the personnel in the working area, the collected heart rate and galvanic skin response data are used to generate the specific data of heart rate variability and galvanic skin response. These data serve as the basic set of physiological indicators, which are obtained in real time through a heart rate monitoring device and a galvanic skin sensor. The heart rate monitoring device regularly records the number of heartbeats of each employee, and the galvanic skin sensor evaluates the changes in sweat gland activity to reflect the stress state. By integrating these physiological indicators, we can evaluate the stress level of employees in a specific working environment and obtain a physiological index dataset.

[0068] Formula The calculation process of:

[0069] Heart rate variability is used to evaluate the activity of the autonomic nervous system by measuring the interval fluctuations of the heart rate. In the formula, RR i represents the interval between two consecutive heartbeats, is the average of these intervals, and n is the total number of heartbeats during the measurement period.

[0070] If the measured heart rate intervals within one minute are 0.8 seconds, 0.9 seconds, 0.85 seconds, 0.88 seconds, and 0.87 seconds, then is equal to (0.8 + 0.9 + 0.85 + 0.88 + 0.87) / 5 = 0.86. Then, calculate the square of the difference between each interval and the average value, sum them up, divide by the result of the total number minus one, and then take the square root to obtain HRV.

[0071] Conduct a composite analysis based on the results of mood fluctuation analysis and skin conductance response data. This step includes further numerical processing of the skin conductance data and data fusion of the mood fluctuation analysis results, comparing and analyzing the heart rate variability data and skin conductance response data, identifying specific stress and emotional states by analyzing the data differences of staff under different working conditions, and using them for further optimization of the working environment and employee health management to obtain mood characteristics.

[0072] The steps for obtaining the noise level are as follows:

[0073] Use a sound acquisition device to continuously monitor in the factory environment, record the decibel level and frequency distribution of the ambient noise, and generate an original sound data set;

[0074] Conduct a frequency analysis on the original sound data set, extract the noise level within the frequency band through filtering, and obtain the noise level data;

[0075] Based on the noise level data, judge the physiological and psychological impacts on the personnel in the work area, and generate the analysis results of the noise level.

[0076] Specifically, the sound acquisition device captures the sound signals in the factory environment, amplifies and initially filters the sound through the built-in signal processing function, records the decibel level and frequency distribution of the ambient noise, the system automatically analyzes the sound spectrum, identifies and records the noise events exceeding the set threshold, details the noise intensity of each frequency band, and these data are stored to provide basic data for subsequent analysis, generating the original sound data set.

[0077] The original sound data set undergoes further frequency analysis. The background noise is removed using high-pass and low-pass filtering functions, only the data in the key frequency bands are retained, the signal intensity within a specific frequency range is increased through signal enhancement processing, and the average noise level of each frequency band is calculated. These comprehensive data form a comprehensive overview of the noise level, obtaining the noise level data.

[0078] Based on the noise level data, evaluate the physiological and psychological impacts on the personnel in the work area, analyze and compare the noise data with the international standard noise threshold for the working environment. The spectral analysis shows that the noise levels exceeding the standard are automatically marked as high-risk time periods, generating the analysis results of the noise level.

[0079] The steps for intonation change and speech rate analysis are as follows:

[0080] Use a sound acquisition device to record the voice information in the factory environment, including conversations and background exchanges, generating a voice record data set;

[0081] Conduct an analysis of the intonation and speech rate on the voice record data set, using the formula:

[0082]

[0083] Calculate the average speech rate S, where P i represents the speech rate of each speech sample, is the average value of the speech rate, n is the number of samples, and generate the analysis result of intonation and speech rate characteristics;

[0084] Combine the analysis result of intonation and speech rate characteristics and the analysis result of noise level to judge the emotional response in the overall acoustic environment, supplement the emotional characteristic data through the sound information, and obtain the acoustic environment emotional analysis result.

[0085] Specifically, the sound collection device records the speech information in the factory environment, including conversations and background communication sounds. Through this information, the device generates a detailed speech record dataset, which contains the timestamps and spectral characteristics of various types of speech. These data are initially classified and labeled to distinguish conversations and background noise, so that subsequent intonation and speech rate analysis can be more accurately targeted at actual communication sounds. The dataset becomes the basic input of the analysis tool, providing the raw materials for further acoustic processing and feature extraction.

[0086] Formula The calculation process of:

[0087] Calculate the average speech rate S. First, determine the number of speech samples n, which is determined by the number of effective speech segments actually collected.

[0088] If 300 speech samples are recorded within one hour, then n = 300. The speech rate Pi of each sample is automatically calculated by speech analysis software. For example, the speech rate values are 100, 105, 95, etc. units (words / minute), and the average speech rate is the arithmetic mean of these speech rate values. Here, for example words / minute. Then calculate the sum of the squares of the differences between the speech rate of each sample and the average speech rate, and finally divide by the total number of samples n to get S. This value provides a quantitative description of the speech rate fluctuation, which is an important indicator for evaluating the speech rate consistency and helps to understand how environmental noise affects people's communication efficiency.

[0089] Combine the analysis result of intonation and speech rate characteristics and the analysis result of noise level to evaluate the emotional response in the overall acoustic environment. Through this comprehensive analysis, obtain an acoustic environment emotional analysis result, which reveals how the acoustic environment affects the emotional state of the staff through changes in speech communication. Especially in a noisy background, how people's speech rate and intonation adapt to environmental changes. Further supplement the emotional characteristic data through the sound information, providing a strategic basis for the management to improve the working environment and enhance the well-being of employees.

[0090] The steps to obtain the optimized working environment parameters are as follows:

[0091] Based on the emotional characteristics and the emotional analysis results of the acoustic environment, judge the impact on the working area, and generate a comprehensive environmental impact assessment result;

[0092] According to the comprehensive environmental impact assessment result, adjust the operating speed of industrial equipment, using the formula:

[0093] S new =S old ·(1 + α·e -δs )

[0094] Calculate the new equipment operating speed S new , where S old represents the original speed, α is the adjustment coefficient based on the environmental impact, δ represents the comprehensive score of the emotional characteristics and the acoustic environment, s is the adjustment sensitivity, and generate an equipment speed adjustment plan;

[0095] Implement the equipment speed adjustment plan, optimize the machine state to match the emotional needs of the working area by changing the working parameters of the production line, and obtain the optimized working environment parameters.

[0096] Specifically, based on the emotional characteristics and the emotional analysis results of the acoustic environment, conduct a detailed emotional impact assessment, use the heart rate and skin conductance response datasets to measure the stress level, combine the noise level and speech rate change data for emotional state analysis, evaluate how these physiological and environmental factors jointly affect the performance of employees in the working area, thereby establishing a comprehensive environmental impact assessment model. This model can reveal the specific impact of environmental factors on employee efficiency, thus providing a scientific basis for equipment parameter adjustment. The result indicates that the comprehensive impact of the environment needs to be compensated by adjusting the production line speed.

[0097] The calculation process of the formula S new =S old ·(1 + α·e -δs ):

[0098] The formula is used to calculate the new equipment operating speed S new , where S old represents the original speed, which is the actual speed obtained by collecting data from the equipment operation records. For example, S old =100 units / hour; α is the adjustment coefficient based on the environmental impact, which is set by analyzing the impact of emotional changes on production efficiency in historical data. For example, it is 0.05; δ is the comprehensive score, which is obtained through the statistical analysis of the emotional characteristic data and the acoustic environment analysis results and is set to 0.3; s is the adjustment sensitivity, which is set according to how quickly the equipment responds to emotional changes. For example, it is 10.

[0099] Insert the values for calculation:

[0100] S new= 100·(1 + 0.05·e -0.3×10 ) ≈ 100.05 units per hour, which indicates that a slight adjustment to the equipment operation speed is made to better adapt to the emotional needs of employees, thereby optimizing production efficiency.

[0101] According to the adjusted equipment operation speed, re - set the working parameters of the production line. By changing the speed settings of the machines to match the emotional needs of employees, optimize production efficiency. Adjust the equipment parameters through monitoring and analyzing actual operation data to ensure that the adjusted speed can achieve the expected improvement in production efficiency. Consider the durability and energy efficiency of the equipment during the adjustment process to ensure stability and reliability during long - term operation. Finally, establish an optimized working environment parameter that meets the actual working environment requirements. This refined adjustment helps to improve employees' job satisfaction and the output quality of the overall production line.

[0102] The steps to obtain the comprehensive score of emotional characteristics and acoustic environment are as follows:

[0103] Based on the emotional characteristics and the results of acoustic environment emotional analysis, standardize the two types of data to eliminate the influence of dimensions, generating standardized emotional characteristic data and acoustic environment data;

[0104] Perform weighted fusion on the standardized emotional characteristic data and acoustic environment data, using the formula:

[0105] δ = w 1 ×E + w 2 ×V

[0106] Calculate the comprehensive score, where E and V respectively represent the standardized values of emotional characteristic data and acoustic environment data, and w 1 and w 2 are the weights of the two types of data, obtaining a preliminary comprehensive emotional score;

[0107] Evaluate the effectiveness of the comprehensive emotional score. Through testing or comparison with historical data, adjust the weights w 1 and w 2 , verify whether the score truly reflects the emotional and acoustic environment impact of the work area, and generate the comprehensive score of emotional characteristics and acoustic environment.

[0108] Specifically, based on the emotional characteristics and the results of the acoustic environment emotional analysis, the original information of two types of data sources is collected and preliminarily cleaned and screened through dedicated software to remove invalid or abnormal data. This step ensures the preliminary quality and usability of the data. Then, a statistical software package is used for data standardization processing to convert all data to the same dimension for subsequent weighted fusion analysis. In the standardization process, each item of data undergoes mean centering and variance scaling to ensure direct comparability between different data sources, making the data fusion in subsequent steps more accurate and meaningful. Through these processes, standardized emotional characteristic data and acoustic environment data are generated;

[0109] The calculation process of the formula δ = w 1 ×E + w 2 ×V is as follows:

[0110] In the actual working environment, the emotional characteristic data E is set to 0.75, the acoustic environment data V is set to 0.60, and the weights w 1 and w 2 are obtained through historical data analysis as 0.7 and 0.3 respectively. The comprehensive score calculation formula is δ = 0.7×0.75 + 0.3×0.60 = 0.525 + 0.18 = 0.705

[0111] The calculated comprehensive score is 0.705, and this result shows the reference value for the operation adjustment of the equipment after the integration of the current environment and emotional state;

[0112] To evaluate the effectiveness of the comprehensive emotional score, monitoring points are set in the factory, the actual emotional responses and changes in the equipment operation status are compared, relevant data are collected, and the regression analysis method is used to verify the correlation between the comprehensive score and the actual impact. In the regression analysis, the emotional score is used as the independent variable, and the change in the equipment operation efficiency is used as the dependent variable to establish a linear regression model and estimate parameters. In this process, the accuracy of the comprehensive score is verified. If the correlation is significant, the current weight settings continue to be used; if not, the weights w 1 and w 2 are adjusted according to the data feedback. This step ensures that the scoring system can truly and effectively reflect the impact of the environment and emotions on the equipment operation through precise statistical tests. Based on these analyses and adjustments, the comprehensive score of the emotional characteristics and the acoustic environment is finally determined and output.

[0113] The steps to obtain the task execution strategy are as follows:

[0114] Analyze the dependencies and priorities between each task in the production task, identify key tasks and their dependencies, and generate a task dependency graph;

[0115] Based on the task dependency graph, reconstruct the task queue, apply the priority sorting algorithm to rearrange the tasks, and use the formula:

[0116]

[0117] Calculate the priority P of task i i , where x i represents the urgency score of the task, x 0 is the threshold of task urgency, and k is a constant to adjust the steepness of the curve, and obtain the priority sorting result;

[0118] According to the priority sorting result, ensure that high-priority tasks are executed first. At the same time, considering the dependency relationship between tasks, adjust and optimize the overall production process to obtain the task execution strategy.

[0119] Specifically, the process of analyzing dependencies and priorities in production tasks begins with the exhaustive collection and evaluation of the characteristics of each task, such as deadlines, resource requirements, and their relevance to other tasks. Based on this data, a task dependency graph is constructed, which visually shows the sequence and dependency relationships between tasks. For example, if task A must be completed before task B, then task A has a direct precedence dependency on task B. Such a visual representation helps project managers better understand the task execution process and potential bottlenecks. This graph can also be dynamically updated to reflect the actual progress of tasks and any newly emerging dependency relationships. In addition, for critical tasks, that is, those tasks that have the greatest impact on the overall project schedule, special markings will be made to ensure that these tasks receive sufficient attention and resources to prevent any delays from having a chain reaction on the entire project.

[0120] Formula The calculation process of:

[0121] The formula is used to calculate the priority of the task, where P i represents the priority of task i, x i is the urgency score of the task, x 0 is the baseline threshold of urgency, and k is a constant to adjust the steepness of the curve. Set x i = 8, x 0 = 5, k = 1.5. These values are obtained by comparing the task deadlines and resource requirements with historical data and calculating according to the formula:

[0122]

[0123] This calculation result indicates that the execution priority of task i is very high, almost close to 1, which means that this is a task that needs to be processed first.

[0124] After the priority sorting result is generated, the task execution strategy ensures that tasks are executed in the order of priority. At the same time, the system will consider the dependencies between tasks. If a low-priority task is a prerequisite for a high-priority task, the actual execution priority of the low-priority task will be increased. This dynamic adjustment mechanism ensures the efficient execution of the task queue.

[0125] The steps to obtain the real-time scheduling result are as follows:

[0126] Integrate the task execution strategy and the current device status information, analyze the operation of the device and the executability of the tasks, and generate a device-task matching report;

[0127] Based on the device-task matching report, make real-time adjustments to the production plan, using the formula:

[0128]

[0129] Calculate the utilization rate R of task i for resources t , where P i represents the priority weight of task i, and T i is the estimated completion time of task i, and obtain the optimized scheduling strategy;

[0130] According to the optimized scheduling strategy, implement task scheduling, handle emergency and high-priority tasks, and generate real-time scheduling results.

[0131] Specifically, integrate the task execution strategy and the current device status information. In the analysis, use the comparative analysis of device operation data and task requirements to evaluate the matching degree between the requirements of each task for the device and the current state of the device, examine the operation of each device and the executability of the tasks, and conduct a matching analysis based on the operation parameters of the device and the task requirement parameters to explore which tasks can be executed immediately and which need to wait or adjust the device state, so as to generate a device-task matching report.

[0132] Formula The calculation process of:

[0133] In the formula, R t represents the total resource utilization rate, P i is the priority weight of task i, and T i is the estimated completion time of task i. There are three tasks. The priority of task 1 is 3 and the estimated time is 2 hours; the priority of task 2 is 2 and the estimated time is 1 hour; the priority of task 3 is 1 and the estimated time is 4 hours. The calculation process is as follows:

[0134]

[0135] The result shows that the total resource utilization rate is 3.75, which reflects the comprehensive efficiency of each task according to the priority and required time within a given time.

[0136] According to the optimized scheduling strategy, task scheduling is performed. During the process, the urgency and importance of each task are considered, and production resources are reasonably allocated. Through the optimized scheduling strategy, task scheduling is implemented, the operation plan of the production line is adjusted in real time, high-priority and urgent tasks are processed first. By dynamically adjusting production resources and task queues, the production efficiency is maximized, thereby generating real-time scheduling results to ensure the efficient operation of the production process and the timely completion of priority tasks.

Claims

1. Industrial equipment online status and configuration status management system, characterized by: The system comprises: The emotion perception and regulation module collects heart rate data and skin electrical data in the work area, measures heart rate variability, combines skin electrical response, analyzes physiological index fluctuations, identifies work stress status, and obtains emotional characteristics; The acoustic environment analysis module uses sound collection equipment to record the noise level and voice information in the factory environment, and through intonation changes and speech speed analysis, supplements the emotional feature data, extracts the emotional response of the working environment, and obtains the acoustic environment emotional analysis results; The equipment parameter adjustment module adjusts the equipment running speed and temperature based on the acoustic environment emotion analysis results, adjusts the machine state by changing the working parameters of the production line to match the emotional requirements of the work area, and establishes optimized working environment parameters; The task dependency parsing module analyzes the task dependencies and priorities in production tasks, reconstructs the task queue, sorts them by priority, and generates task execution strategies; The scheduling and feedback module adjusts the production plan based on the task execution strategy and current equipment status information, optimizes the task queue in real time, handles urgent and high-priority tasks, and creates real-time scheduling results.

2. The industrial equipment online status and configuration status management system according to claim 1, characterized in that: The steps of acquiring the emotion features are: Collect heart rate data and skin electrical data of people in the work area, record the results of heart rate variability and skin electrical response, and generate a physiological indicator data set; Fluctuation analysis was performed on the physiological indicator data set using the formula: Calculate heart rate variability, where RR i Represents the interval between two consecutive heartbeats. is the average heartbeat interval, n is the total number of heartbeats, and the emotion fluctuation analysis results are generated; A composite analysis is performed based on the emotion fluctuation analysis results and the physiological indicator data set to identify stress and emotion states and obtain emotion characteristics.

3. The industrial equipment online status and configuration status management system according to claim 1, characterized in that: The steps for obtaining the noise level are: Use sound collection equipment to continuously monitor the factory environment, record the decibel level and frequency distribution of environmental noise, and generate an original sound data set; Performing frequency analysis on the original sound data set, extracting the noise level within the frequency band through filtering processing, and obtaining noise level data; Based on the noise level data, the physiological and psychological impacts on personnel in the work area are determined, and an analysis result of the noise level is generated.

4. The industrial equipment online status and configuration status management system according to claim 3, characterized in that: The steps for intonation and speech rate analysis are: Use sound collection equipment to record voice information in the factory environment, including conversations and background communication sounds, to generate a voice recording dataset; The voice recording data set is analyzed for intonation and speech rate using the formula: Calculate the average speaking speed S, where P i Represents the speaking speed of each speech sample, is the average value of speech speed, n is the number of samples, and the analysis results of intonation and speech speed characteristics are generated; The emotional response in the overall sound environment is determined by combining the analysis result of the intonation and speech speed characteristics with the analysis result of the noise level, and the emotional characteristic data is supplemented by sound information to obtain the acoustic environment emotion analysis result.

5. The industrial equipment online status and configuration status management system according to claim 1, characterized in that: The steps for obtaining the optimized working environment parameters are as follows: Based on the emotion characteristics and the acoustic environment emotion analysis results, determine the impact on the work area and generate a comprehensive environmental impact assessment result; According to the comprehensive environmental impact assessment results, the speed of industrial equipment operation is adjusted using the formula: S new =S old ·(1+α·e -δs ) Calculate the new equipment running speed S new , where S old represents the original speed, α is the adjustment coefficient based on environmental influence, δ represents the comprehensive score of emotional characteristics and sound environment, s is the adjustment sensitivity, and the device speed adjustment plan is generated; The equipment speed adjustment scheme is implemented to optimize the machine state to match the emotional needs of the work area by changing the working parameters of the production line, thereby obtaining optimized working environment parameters.

6. The industrial equipment online status and configuration status management system according to claim 5, characterized in that: The steps for obtaining the comprehensive score of the emotional characteristics and the acoustic environment are as follows: Based on the emotion feature and the acoustic environment emotion analysis result, the two types of data are standardized to eliminate the dimension effect, and standardized emotion feature data and acoustic environment data are generated; The standardized emotion feature data and the acoustic environment data are weightedly fused using the formula: δ=w1×E+w2×V Calculate the comprehensive score, where E and V represent the standardized values ​​of the emotional feature data and the acoustic environment data, respectively, and w1 and w2 are the weights of the two types of data, to obtain a preliminary comprehensive emotional score; The effectiveness of the comprehensive emotion score is evaluated, and the weights w1 and w2 are adjusted through testing or historical data comparison to verify whether the score truly reflects the emotion and acoustic environment of the workspace, and generate a comprehensive score of the emotion characteristics and the acoustic environment.

7. The industrial equipment online status and configuration status management system according to claim 1, characterized in that: The steps for obtaining the task execution strategy are as follows: Analyze the dependencies and priorities between each task in the production task, identify key tasks and dependencies, and generate a task dependency graph; Based on the task dependency graph, the task queue is reconstructed and the tasks are rearranged using a priority sorting algorithm using the formula: Calculate the priority P of task i i , where x i represents the urgency score of the task, x0 is the threshold of the task urgency, k is the constant for adjusting the steepness of the curve, and the priority ranking result is obtained; According to the priority sorting results, ensure that high-priority tasks are executed first, and at the same time consider the dependencies between tasks, adjust and optimize the overall production process, and obtain the task execution strategy.

8. The industrial equipment online status and configuration status management system according to claim 1, characterized in that: The steps for obtaining the real-time scheduling result are: Integrate the task execution strategy and current equipment status information, analyze the equipment operation status and task feasibility, and generate a report on equipment and task matching; Based on the equipment and task matching report, the production plan is adjusted in real time using the formula: Calculate the resource utilization R of task i t , where P i represents the priority weight of task i, T i The estimated completion time of task i is used to obtain the optimized scheduling strategy; According to the optimized scheduling strategy, task scheduling is implemented, urgent and high-priority tasks are processed, and real-time scheduling results are generated.

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