Intelligent environment emergency early warning decision-making system

Abstract

The invention relates to the technical field of environment monitoring, in particular to an intelligent environment emergency early warning decision-making system which can improve the control efficiency of environment quality, save the operation cost and ensure the health and safety of operators. The system is applied to environment monitoring and comprises an information acquisition module used for acquiring a processing operation type information set of a future decision date and sending the processing operation type information set; the information processing module is used for receiving the processing operation type information set, generating an operation Gantt chart according to the processing operation type information set and sending the operation Gantt chart; the time division module is used for receiving the job Gantt chart, acquiring starting timestamps and ending timestamps of all task strips in the job Gantt chart, sequencing according to a time sequence, generating a job time sequence, dividing a time period between two adjacent timestamps into job time windows according to the job time sequence, and sending the job time windows to the job Gantt chart; a plurality of operation time windows are obtained and sent; and the feature extraction module is used for receiving the operation time window.

Description

Technical Field

[0001] This invention relates to the technical field of environmental monitoring, and in particular to an intelligent environmental emergency early warning and decision-making system. Background Technology

[0002] In machining workshops, environmental quality control is crucial. To ensure the health and safety of operators, these workshops are typically equipped with effective environmentally friendly ventilation equipment. This equipment effectively reduces harmful substances such as dust, smoke, and exhaust fumes, maintaining fresh air. The operating costs of this equipment include electricity consumption, equipment maintenance, and regular filter replacement.

[0003] However, in the use of environmental ventilation equipment, it is often operated all day long or according to the processing operation. This results in the situation where the environmental ventilation equipment is still running even when the workshop environmental quality meets the standards, which increases the operating cost. Therefore, there is an urgent need for a smart environmental emergency early warning decision system that can accurately control the start and stop of environmental ventilation equipment according to the workshop production operation. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides an intelligent environmental emergency early warning and decision-making system that can improve the control efficiency of environmental quality, save operating costs, and ensure the health and safety of operators.

[0005] In a first aspect, the present invention provides an intelligent environmental emergency early warning and decision-making system, the system comprising:

[0006] The information acquisition module is used to acquire a set of processing operation type information for future decision dates and send it.

[0007] The information processing module is used to receive a set of processing job type information, generate a job Gantt chart based on the set of processing job type information, and send it.

[0008] The time segmentation module is used to receive the job Gantt chart, obtain the start and end timestamps of all task bars in the job Gantt chart, sort them in chronological order, generate a job time sequence, divide the time period between two adjacent timestamps into job time windows according to the job time sequence, obtain several job time windows, and send them.

[0009] The feature extraction module is used to receive the job time window, count the processing job types within each job time window, perform data formatting and conversion on the processing job types, generate a job type feature vector corresponding to each job time window, and send it.

[0010] The environmental quality prediction module is used to receive the feature vector of each job type, input the feature vector of each job type into the pre-built workshop environmental quality prediction model, obtain the workshop environmental quality index for each job time window, and send it.

[0011] The early warning and decision-making module is used to receive the environmental quality index of each workshop, compare the environmental quality index of each workshop with the preset environmental quality threshold, and formulate emergency measures for environmental improvement based on the comparison results.

[0012] Furthermore, the processing operation type information set includes processing operation types and the start and end timestamps corresponding to each processing operation type.

[0013] Furthermore, the set of processing operation type information is obtained through a production planning system, and the planning method of the production planning system for the processing operation type information includes:

[0014] Understand production needs, objectives, and constraints; clarify the goals and requirements of the production plan.

[0015] Plan available resources on site, including equipment, materials, and human resources; analyze, evaluate, and optimize the allocation and utilization of resources.

[0016] Collect and manage production order information, including order quantity, delivery date, and product specifications, and develop plans based on order priority and time requirements;

[0017] Based on the results of demand analysis and resource management, a production task scheduling plan is generated, orders are allocated to production lines or equipment, and the start and end times of equipment operations are scheduled.

[0018] The system monitors the actual situation during the production process, tracks the completion of production tasks, production efficiency, and material consumption, and compares and analyzes these with the plan.

[0019] Based on the progress tracking results, for processing plans that need optimization, tasks are reallocated, scheduling plans are adjusted, and human resources are rearranged to meet production targets;

[0020] Generate reports and analysis results to monitor production performance and evaluate the effectiveness of production plans.

[0021] Furthermore, the method for drawing the job Gantt chart includes:

[0022] Receive a set of processing operation type information;

[0023] Determine the start and end times of the time axis based on the time span of the task Gantt chart;

[0024] Determine the vertical task bar columns based on each processing operation type, and draw the corresponding task bars on the operation Gantt chart based on the start and end timestamps corresponding to each processing operation type.

[0025] After generating the complete job Gantt chart, send it to the subsequent modules.

[0026] Furthermore, the method for dividing the job time window includes:

[0027] Receive the job Gantt chart and sort the task bars in the job Gantt chart according to the start and end timestamps of the task bars in the job Gantt chart in chronological order;

[0028] The task list is used as input, and the task time window is divided according to time order. The task time window is a continuous time period, which includes the time range between two adjacent timestamps.

[0029] If the time interval is less than or equal to the preset threshold, then the two timestamps will be assigned to the same job time window;

[0030] If the time interval is greater than the preset threshold, the previous timestamp will be used as the end timestamp of a complete job time window, and the next timestamp will be used as the start timestamp of the next job time window.

[0031] Furthermore, the task type feature vector transformation step includes:

[0032] Job reception time window;

[0033] The types of processing operations within a statistical time window are determined by analyzing the start and end timestamps of the task bars in the Gantt chart.

[0034] Convert the processing operation type into a specific value;

[0035] Based on the statistics of processing job types and the data formatting conversion results for each job time window, a corresponding job type feature vector is generated;

[0036] The generated job type feature vector is sent to the subsequent environmental quality prediction module.

[0037] Furthermore, the method for constructing the workshop environmental quality prediction model includes:

[0038] Collect environmental quality data, which includes monitoring data on dust concentration, smoke concentration, and exhaust gas emissions in the workshop, as well as a set of processing operation type information associated with the detection data;

[0039] Feature engineering is performed on environmental quality data, including data cleaning, missing value handling, outlier handling, feature selection, and feature transformation.

[0040] Selecting a machine learning model, the workshop environmental quality prediction model takes the type of processing operation as input and the monitoring data of dust concentration, smoke concentration and exhaust gas emission in the workshop as output.

[0041] The environmental quality data is divided into K subsets of equal size. For each subset i, i = 1, 2, ..., K, the remaining K-1 subsets are used as the training set.

[0042] Use subset i as the validation set to evaluate model performance;

[0043] Repeat the above two steps until each subset has been used as the validation set;

[0044] The final model evaluation result is obtained by averaging the results of the K evaluations.

[0045] The trained model is deployed into the intelligent environmental emergency early warning and decision-making system and continuously monitored.

[0046] Furthermore, the preset environmental quality threshold setting elements include:

[0047] Formulate legal requirements and indicators for environmental quality in accordance with local laws, regulations, and relevant industry standards;

[0048] According to occupational safety and health requirements, including permissible exhaust gas concentrations, dust exposure limits, and noise exposure limits;

[0049] Depending on the type of work and the risk assessment, different types of processing operations will generate different degrees of environmental impact and health risks. Appropriate environmental quality thresholds need to be determined based on the type of work and the relevant risk assessment results.

[0050] On the other hand, this application provides an electronic device including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor. The transceiver, the memory, and the processor are connected via the bus, and the computer program, when executed by the processor, implements the steps of any of the above-described systems.

[0051] Thirdly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in any of the above-described systems.

[0052] Compared with the prior art, the beneficial effects of the present invention are: it can accurately control the start and stop of environmental protection ventilation equipment according to the actual workshop production operation, avoid unnecessary operation, and improve the efficiency of environmental quality control; it can obtain the environmental quality status of each time window in advance and make emergency warnings and decisions based on the prediction results;

[0053] The intelligent environmental emergency early warning decision system controls the start and stop of environmental protection ventilation equipment according to the actual processing operation, avoiding the situation of starting all day or unnecessary start-up, reducing operating time and power consumption, and reducing the operating costs of equipment maintenance and filter replacement, etc.

[0054] The system compares predefined environmental quality thresholds with predicted environmental quality indices, formulates emergency measures for environmental improvement based on the comparison results, and through automated decision-making, can quickly respond to abnormal environmental situations and take corresponding measures to ensure the health and safety of operators.

[0055] In summary, the intelligent environmental emergency early warning and decision-making system can improve the efficiency of environmental quality control, save operating costs, and ensure the health and safety of operators by precisely controlling the start and stop of environmental protection ventilation equipment, predicting environmental quality indices, and making intelligent decisions. Attached Figure Description

[0056] Figure 1 This is a structural diagram of the present invention;

[0057] Figure 2 This is a flowchart of the production planning system planning method;

[0058] Figure 3 This is a flowchart of the method for drawing a Gantt chart for a task;

[0059] Figure 4 This is a flowchart illustrating the construction method of a workshop environmental quality prediction model;

[0060] Figure 5 This is a schematic diagram of a Gantt chart for a task. Detailed Implementation

[0061] As will be apparent to those skilled in the art from the description of this application, this application can be implemented as a method, apparatus, electronic device, and computer-readable storage medium. Therefore, this application can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software. Furthermore, in some embodiments, this application can also be implemented as a computer program product contained in one or more computer-readable storage media, which includes computer program code.

[0062] The aforementioned computer-readable storage medium may be any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, flash memory, optical fiber, optical disc read-only memory, optical storage devices, magnetic storage devices, or any combination thereof. In this application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0063] The acquisition, storage, use, and processing of data in this application all comply with relevant national laws and regulations.

[0064] This application describes the provided methods, apparatus, and electronic devices using flowcharts and / or block diagrams.

[0065] It should be understood that each block of a flowchart and / or block diagram, as well as combinations of blocks in a flowchart and / or block diagram, can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine that, when executed by a computer or other programmable data processing apparatus, creates means for implementing the functions / operations specified in the blocks of the flowchart and / or block diagram.

[0066] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to function in a particular manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction apparatus product that includes the functions / operations specified in the blocks of a flowchart and / or block diagram.

[0067] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer-implemented process, such that the instructions that execute on the computer or other programmable data processing apparatus provide a process for implementing the functions / operations specified in the blocks of a flowchart and / or block diagram.

[0068] This application will now be described with reference to the accompanying drawings.

[0069] Example 1

[0070] like Figures 1 to 5 As shown, the intelligent environmental emergency early warning decision-making method of the present invention specifically includes the following steps;

[0071] S1. Obtain a set of processing operation type information for the future decision date;

[0072] The processing operation type information set includes the processing operation type and the start and end timestamps corresponding to each processing operation type. This information set is directly obtained from the factory's production planning system. The production planning system is a tool used to manage and optimize the factory's production process. The planning methods for processing operation type information in the production planning system include:

[0073] S11. Demand analysis: Understand production needs, objectives, and constraints; clarify the goals and requirements of the production plan.

[0074] S12. Resource management: planning available resources on site, including equipment, materials, and human resources; analyzing, evaluating, and optimizing the allocation and utilization of resources to ensure the feasibility and efficiency of production plans.

[0075] S13. Order Management: Collect and manage all production order information, including order quantity, delivery date, product specifications, etc., and formulate plans based on order priority and time requirements.

[0076] S14. Scheduling plan: Based on the results of demand analysis and resource management, generate a scheduling plan for production tasks, allocate orders to production lines or equipment, and arrange the start and end times of equipment operations to ensure the rationality and sequence of production progress.

[0077] S15. Progress tracking: The system monitors the actual situation during the production process, tracks the completion of production tasks, production efficiency, material consumption, and other data, and compares and analyzes them with the plan.

[0078] S16. Optimize and adjust: Based on the progress tracking results, for processing plans that need optimization, reallocate tasks, adjust scheduling plans, and rearrange human resources to meet production targets.

[0079] S17. Reporting and Analysis: Generate reports and analysis results to monitor production performance, evaluate the effectiveness of production plans, help understand production status, and identify problems.

[0080] The set of processing operation type information is obtained from the S14 scheduling plan;

[0081] In this step, production needs, resource allocation, and order information are comprehensively considered to plan an efficient and feasible production schedule. Orders are rationally allocated to production lines or equipment, and appropriate working times are arranged to ensure the rationality and sequence of production progress. This avoids changes in equipment working times due to errors in the production plan, ensures the accuracy of processing operation type information, reduces the impact on the intelligent environmental emergency early warning decision-making system, and provides accurate data support for the intelligent environmental emergency early warning decision-making system.

[0082] More specifically, the processing operations include turning, milling, grinding, polishing, spraying, wire cutting, laser cutting, gas welding, and arc welding;

[0083] Turning and milling produce metal chips, dust, and oil mist;

[0084] Grinding and polishing produce grinding powder and fine particles;

[0085] Spraying produces volatile organic compounds and particulate matter;

[0086] Wire cutting and laser cutting produce fumes, gases, and metal dust;

[0087] Gas welding and arc welding produce fumes, hot gases, and metal dust.

[0088] S2. Generate a Gantt chart of operations based on the set of processing operation type information;

[0089] In a job Gantt chart, the horizontal axis represents time, the vertical axis consists of different processing job types, and the task bars in the job Gantt chart represent the job duration for the corresponding processing job type.

[0090] Methods for drawing Gantt charts include:

[0091] S21. Receive a set of processing operation type information;

[0092] S22. Determine the start and end times of the time axis based on the time span of the Gantt chart. The time axis is divided into hours, with each two-hour increment on the time axis.

[0093] S23. Determine the vertical task bar columns according to each processing operation type. Draw the corresponding task bars on the operation Gantt chart according to the start and end timestamps corresponding to each processing operation type. Each task bar occupies a certain length on the time axis, representing the duration of the task.

[0094] S24. After generating the complete job Gantt chart, send it to subsequent modules to provide basic data for subsequent processing and decision-making.

[0095] The task Gantt chart uses time as the horizontal axis and different processing task types as the vertical axis. The task bars represent the duration of the tasks, which can intuitively show the duration of each processing task type and the overall time span of the tasks, making it easy for people to understand and analyze the time arrangement of the tasks. The time axis is divided into two-hour increments, which can display a long time span in a small space, and also make it easy for readers to quickly obtain the time information of the tasks.

[0096] S3. Obtain the start and end timestamps of all task bars in the job Gantt chart, sort them in chronological order, generate a job time sequence, and divide the time period between two adjacent timestamps into job time windows to obtain several job time windows.

[0097] Step S3 improves the efficiency of production environment management and early warning decision-making by dividing the operation time window. Specific methods for dividing the operation time window include:

[0098] S31. Receive the job Gantt chart, and sort the task bars in the job Gantt chart according to the start and end timestamps of the task bars in the job Gantt chart in chronological order to ensure that the generation of the job time window is based on the continuity of time.

[0099] S32. Take the task list as input, divide the task time window according to the time order. The task time window is a continuous time period, which includes the time range between two adjacent timestamps. Set a fixed time window length, group the sorted task bars one by one, and ensure that adjacent task bars are in the same time window.

[0100] S33. If the time interval is less than or equal to the preset threshold, then these two timestamps will be divided into the same job time window.

[0101] S34. If the time interval is greater than the preset threshold, the previous timestamp is used as the end timestamp of a complete job time window, and the next timestamp is used as the start timestamp of the next job time window.

[0102] Through the above steps, the entire job time series is divided into several job time windows, each representing a continuous job time range;

[0103] In this step, by dividing the job time series into job time windows, the production environment can be managed and monitored. After generating job time windows, each time window can be monitored and evaluated. If an anomaly or delay occurs within a certain time window, timely warnings can be issued and corresponding decisions can be made, helping managers to identify problems in a timely manner and take measures to avoid production interruptions or delays. By setting a fixed time window length and preset thresholds, adjustments and optimizations can be made according to actual needs. Smaller time window lengths can improve the fine-grained management of tasks, while larger time window lengths can reduce the complexity of management.

[0104] S4. Calculate the processing job types within each job time window, and perform data formatting and transformation on the processing job types to generate a job type feature vector corresponding to each job time window.

[0105] The steps for transforming the feature vector of job type include:

[0106] S41, Job Receive Time Window;

[0107] S42. Analyze the types of processing operations within the statistical time window. By analyzing the start and end timestamps of the task bars in the Gantt chart, determine the specific types of processing operations within each time window.

[0108] S43. Convert the processing operation type into a specific value for subsequent processing and analysis;

[0109] S44. Based on the statistics of processing job types and the data formatting conversion results of each job time window, generate the corresponding job type feature vector. The job type feature vector is used to describe the vector representation of the processing job type characteristics within the time window.

[0110] S45. Send the generated job type feature vector to the subsequent environmental quality prediction module to provide input data for predicting the workshop environmental quality index.

[0111] This module transforms raw data into specific numerical values, providing more convenient and consistent input for subsequent data processing, modeling, and prediction. It has higher expressive power and dimensional information, helping to provide more comprehensive and richer input information. This module can efficiently and accurately extract job type features and provide key input information for the subsequent environmental quality prediction module, enhancing the accuracy and reliability of the prediction model.

[0112] S5. Input the feature vector of each job type into the pre-built workshop environmental quality prediction model to obtain the workshop environmental quality index for each job time window;

[0113] Step S5 involves substituting the task type feature vector into the workshop environmental quality prediction model to transform it into a workshop environmental quality index. This is a crucial step in the intelligent environmental emergency early warning decision-making system. By analyzing the workshop environmental quality index, environmentally friendly ventilation equipment can be used rationally according to actual needs, avoiding its activation when unnecessary and thus reducing operating costs. In this step, the workshop environmental quality prediction model completes the transformation of the task type feature vector. The construction method of the workshop environmental quality prediction model includes:

[0114] S51. Collect environmental quality data, which includes monitoring data such as dust concentration, smoke concentration, and exhaust gas emissions in the workshop. By installing dust monitoring sensors, smoke monitoring sensors, and exhaust gas monitoring sensors in the workshop, environmental parameters in the workshop are monitored in real time to obtain environmental quality data. The environmental quality data also includes a set of processing operation type information related to environmental parameters.

[0115] S52. Perform feature engineering processing on environmental quality data, including data cleaning, missing value handling, outlier handling, feature selection, and feature transformation.

[0116] S53. Select a machine learning model. The workshop environmental quality prediction model takes the processing operation type as input and the dust concentration, smoke concentration and exhaust gas emission monitoring data in the workshop as output.

[0117] S54. Divide the environmental quality data into K subsets of equal size. For each subset i (i = 1, 2, ..., K), use the remaining K-1 subsets as the training set.

[0118] S55. Use subset i as the validation set to evaluate model performance;

[0119] S56. Repeat steps S54 and S55 until each subset is used as the validation set;

[0120] S57. Average the K evaluation results to obtain the final model evaluation result;

[0121] S58. Deploy the trained model into the intelligent environmental emergency early warning decision-making system and conduct continuous monitoring;

[0122] In this step, by analyzing the workshop environmental quality index, we can rationally use environmentally friendly ventilation equipment according to actual needs, avoiding the need to start these devices when not in use, thereby reducing operating costs and enabling the workshop to take timely measures to ensure that the workshop environment is always maintained at a safe and healthy level. By constructing a workshop environmental quality prediction model, we can provide the system with real-time environmental quality prediction and assessment capabilities, thereby supporting the system's emergency early warning and decision-making. Using cross-validation to evaluate the model can reduce the variance of the evaluation results, provide a more reliable model performance evaluation, and deploy the trained model into the intelligent environmental emergency early warning and decision-making system for continuous monitoring, which can update the prediction results in real time and maintain the model's accuracy and practicality.

[0123] S6. Compare the environmental quality index of each workshop with the preset environmental quality threshold, and formulate emergency measures for environmental improvement based on the comparison results.

[0124] The preset environmental quality threshold refers to the level of environmental quality considered safe, compliant, or suitable under specific conditions. The threshold is used to determine when emergency environmental improvement measures are needed to maintain or improve environmental quality. The preset environmental quality threshold is set based on the following factors:

[0125] Relevant laws and standards, based on the laws and regulations of the region and relevant industry standards, formulate statutory requirements and indicators for environmental quality, including provisions on air quality, dust concentration, exhaust emissions, noise levels, etc.

[0126] Occupational safety and health requirements take into account workers' occupational safety and health. When determining environmental quality thresholds, relevant occupational safety and health standards need to be referenced. Occupational safety and health standards include permissible exhaust gas concentrations, dust exposure limits, noise exposure limits, etc., to ensure that workers are not excessively exposed to harmful substances in the work environment.

[0127] The type of work and risk assessment: different types of processing work will produce different degrees of environmental impact and health risks. It is necessary to determine appropriate environmental quality thresholds based on the type of work and the relevant risk assessment results. For example, metal cutting will produce more dust, while welding will produce harmful gases. Therefore, when setting thresholds, it is necessary to assess the specific characteristics of the work.

[0128] By monitoring the workshop environmental quality index and comparing it with preset thresholds, the system can understand the environmental quality status in real time. Once the environmental quality falls below the preset threshold, the system can issue an early warning and take corresponding emergency measures to prevent the problem from escalating or causing delays. The preset environmental quality thresholds are based on relevant regulations, standards, and occupational safety and health requirements to ensure that the workshop environmental quality level meets the requirements of laws, regulations, and industry standards, protect workers' occupational safety and health, and prevent excessive exposure to harmful substances. The preset environmental quality thresholds are not only based on regulations and standards, but also consider the technical feasibility and cost-effectiveness of monitoring and improving environmental quality, ensuring that the threshold setting is feasible in practice, can be monitored and controlled using available monitoring technologies, and can also reduce the implementation burden.

[0129] In summary, the S6 early warning and decision-making module can provide timely environmental monitoring and early warning functions to ensure the safety, compliance, and suitability of the working environment. By reasonably setting preset environmental quality thresholds and combining factors such as regulations, standards, occupational safety and health requirements, job types, and technical feasibility, the system can formulate corresponding environmental improvement emergency measures based on the environmental quality index, thereby improving the management efficiency of the workshop environment and the overall quality of the workplace.

[0130] More specifically, emergency measures for environmental improvement include:

[0131] Adjust the operating status of the environmental protection ventilation equipment and determine the start-up and shutdown times of the environmental protection ventilation equipment based on the comparison results between the workshop environmental quality index and the preset threshold.

[0132] Implement source control measures, identify processing operations that may generate high pollutant emissions based on processing operation type information and operation Gantt charts, and take corresponding source control measures, including using environmentally friendly processes or materials and optimizing processing parameters, thereby reducing the generation and emission of pollutants;

[0133] Strengthen cleaning, maintenance, and filter replacement. Regularly clean and maintain environmentally friendly ventilation equipment, filters, and air handling equipment to ensure their normal operation and effective filtration function, guarantee filtration effect, and reduce energy consumption.

[0134] Provide personal protective equipment (PPE) for special processing operations or abnormal environmental conditions. Provide appropriate PPE for operators, including respirators and protective goggles, to reduce direct exposure of operators to pollutants.

[0135] The warning and evacuation system can alert employees to evacuate in time in specific emergency situations, such as fire and gas leaks, through sound and light alarms and emergency broadcasts. It can issue timely warnings to ensure the safety of employees' lives.

[0136] The above environmental improvement emergency measures can reduce exposure to harmful substances, protect the health and safety of employees, reduce the concentration of harmful substances such as dust, smoke, and exhaust gas in the air, and reduce the risk of occupational diseases for employees; intelligent control of ventilation equipment and air conditioning systems can use energy rationally according to actual needs, reduce unnecessary energy consumption, thereby reducing energy costs and carbon emissions; it can quickly detect environmental quality problems and take corresponding measures, shorten emergency response time, and reduce the impact of emergencies on production and employee safety.

[0137] Example 2

[0138] like Figure 1 As shown, the intelligent environmental emergency early warning decision system of the present invention specifically includes the following modules;

[0139] Information Acquisition Module: Used to acquire and send a set of processing job type information for future decision dates;

[0140] Information processing module: Used to receive a set of processing job type information, generate a job Gantt chart based on the processing job type information set, and send it;

[0141] Time Division Module: This module receives the job Gantt chart, obtains the start and end timestamps of all task bars in the job Gantt chart, sorts them in chronological order, generates a job time sequence, divides the time interval between two adjacent timestamps into job time windows based on the job time sequence, obtains several job time windows, and sends them.

[0142] Feature extraction module: Used to receive job time windows, count the processing job types within each job time window, perform data formatting and conversion on the processing job types, generate job type feature vectors corresponding to each job time window, and send them;

[0143] Environmental quality prediction module: This module receives feature vectors of different work types, inputs each feature vector into a pre-built workshop environmental quality prediction model, obtains the workshop environmental quality index for each work time window, and then sends it out.

[0144] Early warning and decision-making module: It is used to receive the environmental quality index of each workshop, compare the environmental quality index of each workshop with the preset environmental quality threshold, and formulate emergency measures for environmental improvement based on the comparison results.

[0145] Based on the actual workshop production operations, the system precisely controls the start and stop of environmental protection ventilation equipment to avoid unnecessary operation and improve the efficiency of environmental quality control; it can obtain the environmental quality status of each time window in advance and make emergency warnings and decisions based on the prediction results.

[0146] The intelligent environmental emergency early warning decision system controls the start and stop of environmental protection ventilation equipment according to the actual processing operation, avoiding the situation of starting all day or unnecessary start-up, reducing operating time and power consumption, and reducing the operating costs of equipment maintenance and filter replacement, etc.

[0147] The system compares predefined environmental quality thresholds with predicted environmental quality indices, formulates emergency measures for environmental improvement based on the comparison results, and through automated decision-making, can quickly respond to abnormal environmental situations and take corresponding measures to ensure the health and safety of operators.

[0148] In addition, this application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor. The transceiver, the memory, and the processor are respectively connected via the bus. When the computer program is executed by the processor, it implements the various processes of the above-described method embodiment for controlling output data and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0149] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A smart environmental emergency early warning and decision-making system, characterized in that, The system is an environmentally friendly ventilation system applied in machine processing workshops, and the system includes: The information acquisition module is used to acquire a set of processing operation type information for future decision dates and send it. The information processing module is used to receive a set of processing job type information, generate a job Gantt chart based on the set of processing job type information, and send it. The time segmentation module is used to receive the job Gantt chart, obtain the start and end timestamps of all task bars in the job Gantt chart, sort them in chronological order, generate a job time sequence, divide the time period between two adjacent timestamps into job time windows according to the job time sequence, obtain several job time windows, and send them. The feature extraction module is used to receive the job time window, count the processing job types within each job time window, perform data formatting and conversion on the processing job types, generate a job type feature vector corresponding to each job time window, and send it. The environmental quality prediction module is used to receive the feature vector of each job type, input the feature vector of each job type into the pre-built workshop environmental quality prediction model, obtain the workshop environmental quality index for each job time window, and send it. The early warning and decision-making module is used to receive the environmental quality index of each workshop, compare the environmental quality index of each workshop with the preset environmental quality threshold, and formulate emergency measures for environmental improvement based on the comparison results.

2. The intelligent environmental emergency early warning and decision-making system as described in claim 1, characterized in that, The processing operation type information set includes processing operation types and the start and end timestamps corresponding to each processing operation type.

3. The intelligent environmental emergency early warning and decision-making system as described in claim 2, characterized in that, The set of processing operation type information is obtained through a production planning system, and the planning method of the production planning system for processing operation type information includes: Understand production needs, objectives, and constraints; clarify the goals and requirements of the production plan. Plan available resources on site, including equipment, materials, and human resources; analyze, evaluate, and optimize the allocation and utilization of resources. Collect and manage production order information, including order quantity, delivery date, and product specifications, and develop plans based on order priority and time requirements; Based on the results of demand analysis and resource management, a production task scheduling plan is generated, orders are allocated to production lines or equipment, and the start and end times of equipment operations are scheduled. The system monitors the actual situation during the production process, tracks the completion of production tasks, production efficiency, and material consumption, and compares and analyzes these with the plan. Based on the progress tracking results, for processing plans that need optimization, tasks are reallocated, scheduling plans are adjusted, and human resources are rearranged to meet production targets; Generate reports and analysis results to monitor production performance and evaluate the effectiveness of production plans.

4. The intelligent environmental emergency early warning and decision-making system as described in claim 2, characterized in that, The method for drawing the job Gantt chart includes: Receive a set of processing operation type information; Determine the start and end times of the time axis based on the time span of the task Gantt chart; Determine the vertical task bar columns based on each processing operation type, and draw the corresponding task bars on the operation Gantt chart based on the start and end timestamps corresponding to each processing operation type. After generating the complete job Gantt chart, send it to the subsequent modules.

5. The intelligent environmental emergency early warning and decision-making system as described in claim 2, characterized in that, The method for dividing the job time window includes: Receive the job Gantt chart and sort the task bars in the job Gantt chart according to the start and end timestamps of the task bars in the job Gantt chart in chronological order; The task list is used as input, and the task time window is divided according to time order. The task time window is a continuous time period, which includes the time range between two adjacent timestamps. If the time interval is less than or equal to the preset threshold, then the two timestamps will be assigned to the same job time window; If the time interval is greater than the preset threshold, the previous timestamp will be used as the end timestamp of a complete job time window, and the next timestamp will be used as the start timestamp of the next job time window.

6. The intelligent environmental emergency early warning and decision-making system as described in claim 2, characterized in that, The job type feature vector transformation steps include: Job reception time window; The types of processing operations within a statistical time window are determined by analyzing the start and end timestamps of the task bars in the Gantt chart. Convert the processing operation type into a specific value; Based on the statistics of processing job types and the data formatting conversion results for each job time window, a corresponding job type feature vector is generated; The generated job type feature vector is sent to the subsequent environmental quality prediction module.

7. The intelligent environmental emergency early warning and decision-making system as described in claim 1, characterized in that, The method for constructing the workshop environmental quality prediction model includes: Collect environmental quality data, which includes monitoring data on dust concentration, smoke concentration, and exhaust gas emissions in the workshop, as well as a set of processing operation type information associated with the detection data; Feature engineering is performed on environmental quality data, including data cleaning, missing value handling, outlier handling, feature selection, and feature transformation. Select a machine learning model; the workshop environmental quality prediction model takes the type of processing operation as input and the monitoring data of dust concentration, smoke concentration and exhaust gas emission in the workshop as output. The environmental quality data is divided into K subsets of equal size. For each subset i, i = 1, 2, ..., K, the remaining K-1 subsets are used as the training set. Use subset i as the validation set to evaluate model performance; Repeat the above two steps until each subset has been used as the validation set; The final model evaluation result is obtained by averaging the results of the K evaluations. The trained model is deployed into the intelligent environmental emergency early warning and decision-making system and continuously monitored.

8. The intelligent environmental emergency early warning and decision-making system as described in claim 1, characterized in that, The preset environmental quality threshold setting elements include: Formulate legal requirements and indicators for environmental quality in accordance with local laws, regulations, and relevant industry standards; According to occupational safety and health requirements, including permissible exhaust gas concentrations, dust exposure limits, and noise exposure limits; Depending on the type of work and the risk assessment, different types of processing operations will generate different degrees of environmental impact and health risks. Appropriate environmental quality thresholds need to be determined based on the type of work and the relevant risk assessment results.

9. An electronic device for a smart environmental emergency early warning decision-making system, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, characterized in that, When the computer program is executed by the processor, it implements the steps in the system as described in any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps in the system as described in any one of claims 1-8.

Images