Unit operation state self-diagnosis optimization system based on consumption difference analysis

Abstract

The invention discloses a unit operation state self-diagnosis optimization system based on consumption difference analysis, relates to the technical field of unit operation management, and aims to solve the problem that corresponding adjustment and performance assessment are not performed according to the actual condition of the unit operation state in the prior art. The influence of each parameter on the unit economy can be accurately reflected through the consumption difference analysis method, so that the interference of subjective factors in the traditional performance assessment is avoided, and the scoring mechanism can stimulate the working unit personnel or departments to actively improve the unit operation performance, so that the overall working efficiency is improved, and the economic benefit is improved. Performance scores are combined with work performance of working crew personnel or departments, not only are performance indexes of unit operation considered, but also working attitudes and attendance conditions of personnel are considered, comprehensiveness of performance evaluation is achieved, consumption difference data are combined with performance assessment, and the operation personnel can be stimulated to actively improve operation skills and energy-saving awareness.

Description

Technical Field

[0001] The present invention relates to the technical field of unit operation management, and in particular to a self-diagnosis and optimization system for unit operation status based on differential analysis. Background Art

[0002] Unit operation management refers to the effective monitoring, control, maintenance, and optimization of various units used in the industrial production process to ensure the safe, stable, and efficient operation of the units.

[0003] Chinese Patent with Publication No. CN117028194A discloses a unit operation monitoring and management control system based on the Internet of Things. It mainly monitors and statistically analyzes the external environmental water quality during the operation of unit equipment and conducts data analysis. It can not only intuitively obtain the impact of the external environment during the operation of unit equipment but also provide reliable local data support for the adaptive dynamic adjustment of subsequent unit equipment maintenance, improving the diversity of data analysis and utilization in unit equipment operation monitoring. It monitors and evaluates and classifies the current operation sound and operation amplitude aspects using the operation sound data and operation vibration data during the historical operation faults of unit equipment, so as to dynamically alarm and prompt the real-time operation status of the unit according to the evaluation results, improving the reliability and diversity of abnormal monitoring and alarm prompt during the unit operation process. It evaluates and classifies the maintenance status of unit equipment during the operation monitoring period, which can not only expand the utilization of the operation status data of some unit equipment in the early stage but also dynamically adjust the maintenance of unit equipment to improve the overall operation effect, realizing the association and expanded utilization of operation analysis data of unit equipment in different periods. Although the above patent solves the problem of unit operation management, there are still the following problems in actual operation:

[0004] 1. There is no effective data processing and data storage during the unit data collection process and after the data collection is completed, resulting in poor accuracy of data collection.

[0005] 2. There is no more accurate data analysis and analysis data optimization for unit operation data, resulting in the inability to effectively adjust unit equipment according to the actual situation.

[0006] 3. There is no more accurate formulation of adjustment plans according to the actual situation of unit operation compared with the normal situation, and there is no corresponding performance evaluation according to the adjustment plan, resulting in poor effects of the unit operation status adjustment plan. Summary of the Invention

[0007] The object of the present invention is to provide a self-diagnosis and optimization system for the operating state of a unit based on exergy loss analysis. The exergy loss analysis method can accurately reflect the impact of various parameters on the economy of the unit, thereby avoiding the interference of subjective factors in traditional performance appraisals. The scoring mechanism can motivate the operating personnel or departments of the unit to actively improve the operating performance of the unit, thereby improving the overall work efficiency. Combining the performance score with the work performance of the operating personnel or departments of the unit not only considers the performance indicators of the unit operation but also considers the work attitude and attendance of the personnel, realizing the comprehensiveness of performance evaluation. Combining the exergy loss data with the performance appraisal can motivate the operating personnel to actively improve their operation skills and energy-saving awareness, and can solve the problems in the prior art.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A self-diagnosis and optimization system for the operating state of a unit based on exergy loss analysis, comprising:

[0010] A unit data acquisition unit, used for;

[0011] Retrieving the unit operation data from the database, and obtaining the to-be-processed unit data after the retrieval is completed;

[0012] A data preprocessing unit for the acquired data, used for:

[0013] Performing data preprocessing on the to-be-processed unit data, and performing unique coding and labeling after the data preprocessing is completed, and obtaining the target unit data after the unique coding and labeling is completed;

[0014] A preprocessed data storage and backup unit, used for:

[0015] First performing data backup on the target unit data, and respectively performing data storage according to the type of each data after the data backup is completed, and obtaining the backup unit data after the data storage is completed;

[0016] A unit data exergy loss analysis unit, used for:

[0017] Performing exergy loss calculation on the backup unit data and the standard operation management data, and obtaining the actual unit exergy loss data after the exergy loss calculation is completed;

[0018] An exergy loss data analysis and optimization unit, used for:

[0019] Performing abnormal analysis of the operation data on the actual unit exergy loss data, judging the degree of abnormality of the analyzed abnormal operation data according to the abnormal analysis result, generating an operation data optimization plan according to the degree of abnormality of the operation data, and obtaining the to-be-executed unit operation data after the operation data optimization plan is generated;

[0020] An optimization plan generation unit, used for:

[0021] Generate an execution plan based on the operating data of the unit to be executed, and send the generated execution plan to the corresponding operating crew according to the optimized unit type in the plan;

[0022] The optimization plan implementation monitoring unit is used for:

[0023] The operating crew adjusts the operation equipment parameters according to the execution plan. After the parameter adjustment is completed, real-time parameter monitoring is carried out on the adjusted operating equipment, and the monitored real-time parameters are marked as adjusted monitoring parameter data;

[0024] The implementation plan performance assessment unit is used for:

[0025] Compare the difference between the adjusted monitoring parameter data and the standard operation management data, confirm the performance change data according to the comparison result, conduct performance correlation analysis according to the performance change situation, and finally visually transform and present the performance correlation analysis result.

[0026] Preferably, the unit data acquisition unit is further used for:

[0027] The unit operating data retrieved from the database includes process parameter data, equipment status data, energy consumption data, environmental parameter data, and product quality data;

[0028] The unit operating data is collected through sensors, data collectors, controllers, intelligent meters, and monitoring systems respectively;

[0029] Before the sensors, data collectors, controllers, intelligent meters, and monitoring systems collect data, first conduct collection equipment detection, and after all detections are qualified, conduct unit operating data collection;

[0030] Import the collected unit operating data into the database, retrieve the unit operating data in the database, and obtain the unit data to be processed after retrieval.

[0031] Preferably, the collected data preprocessing unit is further used for:

[0032] Preprocess the unit data to be processed;

[0033] Data preprocessing is to first clean the unit data to be processed. Data cleaning includes removing irrelevant data, filling in missing values, and handling outliers;

[0034] After data cleaning, data conversion is carried out. Data conversion includes unit conversion and format unification;

[0035] After data conversion, data standardization is carried out. Data standardization includes normalization and standardization processing;

[0036] After data standardization, data validation is performed, which includes range validation and logical validation;

[0037] After data validation, data integration is performed, which includes merging data sets and data association.

[0038] Preferably, the collected data preprocessing unit is further configured to:

[0039] Perform unique coding and labeling on the to-be-processed unit data after data preprocessing;

[0040] Before performing unique coding and labeling, first formulate the coding rules;

[0041] The coding rules include timestamp, unit number, serial number, and data type;

[0042] Generate unique coding for the to-be-processed unit data according to the formulated coding rules;

[0043] Perform data verification on the generated unique coding, and label the to-be-processed unit data with the unique coding as target unit data according to the verification result.

[0044] Preferably, the preprocessed data storage and backup unit is further configured to:

[0045] Before backing up the target unit data, first formulate the backup strategy;

[0046] The backup strategy includes backup type, backup frequency, and backup location. The backup type includes full backup, incremental backup, or differential backup; the backup frequency is the backup time and backup task; the backup location includes local server, external hard drive, network storage device, or cloud storage service;

[0047] Perform data encryption and compression on the target unit data;

[0048] After data encryption and compression, back up the target unit data according to the backup strategy;

[0049] After the target unit data backup is completed, perform data verification. After the data verification is completed, record the backup log, and the backup log includes backup time, data volume, backup location, and backup result;

[0050] Perform data type identification on the target unit data after data backup, and store it according to the identified data type;

[0051] Before data storage, first formulate the storage strategy, and the storage strategy includes storage location and storage format;

[0052] According to the formulated storage strategy, store the target unit data after data type identification in the corresponding location according to the specified format;

[0053] After the storage is completed, the backup unit data is obtained.

[0054] Preferably, the unit data consumption difference analysis unit is further configured to:

[0055] Retrieve the standard operation management data from the database, and compare each item of the retrieved standard operation management data with the backup unit data item by item;

[0056] After the item-by-item comparison is completed, the consumption difference data between the standard operation management data and the backup unit data is obtained;

[0057] Among them, the consumption difference data includes absolute consumption difference data and relative consumption difference data;

[0058] The absolute consumption difference data is the difference between the data of the same type in the standard operation management data and the backup unit data. The calculation formula is as follows:

[0059] P = T - Z

[0060] P represents the absolute consumption difference value; T represents the value of a certain data in the backup unit data; Z represents the value of a certain data in the standard operation management data;

[0061] The relative consumption difference data is the ratio of the consumption difference value between the standard operation management data and the backup unit data. The calculation formula is as follows:

[0062] D = P / Z * 100%

[0063] D represents the relative consumption difference value;

[0064] Use the weighted average algorithm to calculate the overall consumption difference for the absolute consumption difference data and the relative consumption difference data;

[0065] After the overall consumption difference calculation is completed, the actual unit consumption difference data is obtained.

[0066] Preferably, the consumption difference data analysis and optimization unit is further configured to:

[0067] Perform anomaly analysis on the actual unit consumption difference data according to the anomaly analysis rules;

[0068] The anomaly analysis rules are retrieved from the database. When performing anomaly analysis, if the threshold value of the actual unit consumption difference data exceeds the normal operation threshold value in the anomaly analysis rules, the actual unit consumption difference data is abnormal data;

[0069] Judge the anomaly degree of the actual unit consumption difference data that is abnormal data;

[0070] The anomaly degree judgment is to divide the anomaly degree according to the anomaly value of the abnormal data of the actual unit consumption difference data;

[0071] The degree of abnormality is divided into slight abnormality, moderate abnormality, and severe abnormality;

[0072] Analyze the causes of the abnormal data in each degree of abnormality. The causes of abnormality include equipment failure, improper operation, environmental factor changes, and improper process parameter settings;

[0073] Formulate different optimization adjustment plans according to the causes of abnormality and the degree of abnormality;

[0074] After the optimization adjustment plan is formulated, the operation data of the unit to be executed is obtained.

[0075] Preferably, the optimization plan generation unit is further configured to:

[0076] Generate a plan for the operation data of the unit to be executed by using a plan conversion tool;

[0077] Approve the generated plan, and distribute it to the corresponding working crew members after the plan approval is completed;

[0078] Among them, before the plan is distributed, confirm the unit equipment to be adjusted in the plan;

[0079] Determine the corresponding working crew members according to the unit equipment confirmation;

[0080] After the corresponding working crew members are completed, send the operation data of the unit to be executed with the plan approval completed through a communication channel;

[0081] The communication channels include the unit display terminal and the working crew member meeting terminal.

[0082] Preferably, the optimization plan implementation monitoring unit is further configured to:

[0083] The working crew members view the implementation plan on the communication channel, and prepare the implementation tools and personnel and conduct safety inspections according to the requirements of the implementation plan;

[0084] The implementation personnel adjust the parameters of the unit equipment according to the implementation plan;

[0085] During and after the parameter adjustment, monitor the operation status of the unit equipment in real time;

[0086] If the operation status is normal, collect and record the monitoring parameters by using the data acquisition equipment of the unit equipment;

[0087] Mark the recorded monitoring parameters as adjusted monitoring parameter data.

[0088] Preferably, the implementation plan performance evaluation unit is further configured to:

[0089] Compare the adjusted monitoring parameter data with the standard operation management data for difference comparison;

[0090] Among them, when performing difference comparison, the types of the compared data are the same;

[0091] Confirm the performance change data according to the difference comparison result. The performance change data is confirmed as follows: when the difference exceeds the preset positive threshold, adjust the monitoring parameter data as performance improvement; when the difference is lower than the preset negative threshold, adjust the monitoring parameter data as performance decline; if the difference is between the positive threshold and the negative threshold, adjust the monitoring parameter data as no change;

[0092] Conduct performance index analysis based on the performance change data. The performance index analysis is to calculate the performance score based on the change trend of the performance change data;

[0093] The performance score calculation gives a positive score when the performance improves and a negative score when the performance declines;

[0094] Integrate the total score of the performance score with the work performance of the work crew or department. Among them, the work performance of the work crew or department is retrieved from the attendance terminal;

[0095] After performance integration, obtain the comprehensive performance analysis data for the unit operation optimization management;

[0096] Convert the comprehensive performance analysis data into visual data. After the visual data conversion is completed, display it on the display terminal of the department.

[0097] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0098] 1. A unit operation status self-diagnosis and optimization system based on heat rate analysis provided by the present invention. The formulation of the backup strategy ensures that data can be completely and effectively restored in multiple scenarios. The application of data encryption technology improves the security of data during backup and storage, preventing data from being accessed or tampered with without authorization. The formulation of the storage strategy enables data to be stored in the most effective way in a suitable location, avoiding waste of resources. The automatic generation and recording of backup logs facilitate the tracking and management of data.

[0099] 2. A unit operation status self-diagnosis and optimization system based on heat rate analysis provided by the present invention. By comparing the heat rate data of different operators, their operation levels and responsibilities can be objectively evaluated. Combining the heat rate data with performance appraisal can motivate operators to actively improve their operation skills and energy-saving awareness, thereby further improving the operation efficiency and economy of the unit, and automatically generating targeted optimization adjustment plans, improving the effectiveness and pertinence of the adjustment. This personalized optimization adjustment plan helps to improve the operation efficiency and performance of the unit, and reduce energy consumption and costs.

[0100] 3. The self-diagnosis and optimization system for unit operation status based on exergy loss analysis provided by the present invention. The exergy loss analysis method can accurately reflect the impact of each parameter on the economy of the unit, thereby avoiding the interference of subjective factors in traditional performance appraisals, improving the accuracy and fairness of the appraisal. The scoring mechanism can encourage the working unit personnel or departments to actively improve the unit operation performance, thereby improving the overall work efficiency. Combining the performance score with the work performance of the working unit personnel or departments not only considers the performance indicators of unit operation but also considers the work attitude and attendance of personnel, achieving the comprehensiveness of performance evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] Figure 1 It is a schematic diagram of the unit operation optimization management and performance appraisal unit of the present invention;

[0102] Figure 2 It is a schematic diagram of the unit operation optimization management and performance appraisal process of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0103] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0104] To solve the problem in the prior art that there is no effective data processing and data storage during the unit data acquisition process and after the data acquisition is completed, resulting in poor accuracy of data acquisition, please refer to Figure 1 and Figure 2 , the following technical solutions are provided in this embodiment:

[0105] A self-diagnosis and optimization system for unit operation status based on exergy loss analysis, including:

[0106] A unit data acquisition unit, used for;

[0107] Retrieving the unit operation data from the database, and obtaining the to-be-processed unit data after the retrieval is completed;

[0108] A data preprocessing unit for the acquired data, used for:

[0109] Preprocessing the to-be-processed unit data, and performing unique coding and labeling after the data preprocessing, and obtaining the target unit data after the unique coding and labeling is completed;

[0110] A preprocessed data storage and backup unit, used for:

[0111] First, perform data backup on the target unit data. After the data backup is completed, store the data separately according to the type of each data. After the data storage is completed, the backup unit data is obtained;

[0112] The unit data consumption difference analysis unit is used for:

[0113] Perform consumption difference calculation on the backup unit data and the standard operation management data. After the consumption difference calculation is completed, the actual unit consumption difference data is obtained;

[0114] The consumption difference data analysis and optimization unit is used for:

[0115] Perform abnormal analysis of operation data on the actual unit consumption difference data, judge the abnormal degree of the analyzed abnormal operation data according to the abnormal analysis result, generate an operation data optimization plan according to the abnormal degree of the operation data, and obtain the unit operation data to be executed after the operation data optimization plan is generated;

[0116] The optimization plan generation unit is used for:

[0117] Generate an execution plan according to the unit operation data to be executed, and send the generated execution plan to the corresponding working unit personnel according to the optimized unit type in the plan;

[0118] The optimization plan implementation monitoring unit is used for:

[0119] The working unit personnel adjust the operating equipment parameters according to the execution plan. After the parameter adjustment is completed, real-time parameter monitoring is performed on the adjusted operating equipment, and the monitored real-time parameters are marked as adjusted monitoring parameter data;

[0120] The implementation plan performance assessment unit is used for:

[0121] Compare the adjusted monitoring parameter data with the standard operation management data, confirm the performance change data according to the comparison result, perform performance correlation analysis according to the performance change situation, and finally perform visual conversion and presentation on the performance correlation analysis result.

[0122] Specifically, by using the unit data acquisition unit to conduct equipment detection and then collect data, the problem of inaccurate data caused by equipment failures or errors can be minimized. Through the data preprocessing unit for the collection of unit data, the steps of unique coding and data integration make the unit operation data more structured and systematic, facilitating the performance of heat rate analysis and performance appraisal. By adopting incremental backup and differential backup in the preprocessed data storage and backup unit, only the most recent full backup and subsequent incremental or differential backups need to be restored during data recovery. Through the unit for real-time heat rate data analysis of unit data, the operating personnel can dynamically adjust the unit operation parameters to ensure that the unit always maintains an optimal operating state. By means of automation and intelligence in the heat rate data analysis and optimization unit, the process of unit operation optimization management is simplified and the management efficiency is improved. Through continuous analysis and processing of the unit operation data by the optimization plan generation unit, new problems and potential improvement points can be continuously discovered. Through the optimization plan implementation monitoring unit, fine management of various parameters during the unit operation process can be carried out to ensure that the unit always operates in the best state. Through this scoring mechanism in the implementation plan performance appraisal unit, the working unit personnel or departments can be motivated to actively improve the unit operation performance, thereby improving the overall work efficiency.

[0123] The unit data acquisition unit is also used for:

[0124] The unit operation data retrieved from the database includes process parameter data, equipment status data, energy consumption data, environmental parameter data, and product quality data;

[0125] The unit operation data is collected through sensors, data collectors, controllers, intelligent meters, and monitoring systems respectively;

[0126] Before the sensors, data collectors, controllers, intelligent meters, and monitoring systems collect data, equipment detection is first carried out, and after all are detected to be qualified, the unit operation data is collected;

[0127] The collected unit operation data is imported into the database, and the unit operation data in the database is retrieved to obtain the unit data to be processed after retrieval.

[0128] Specifically, by first detecting the acquisition device and then collecting data, the problem of inaccurate data caused by equipment failures or errors can be minimized, improving the accuracy and reliability of the data. The comprehensive application of sensors, data collectors, controllers, intelligent meters, and monitoring systems enables real-time and continuous collection of unit operation data, avoiding the cumbersome process and errors of manual meter reading and manual calculation. Importing the collected data into the database facilitates subsequent data analysis and processing, improving work efficiency. The collected data provides rich materials for heat rate difference analysis. The system can analyze the impact of each parameter on the unit's heat rate, efficiency, coal consumption, etc. based on the difference between the actual operating value and the reference value. Operators can intuitively and prioritize the adjustment of operating parameters according to the heat rate difference analysis results, reducing controllable losses and improving the economy of the unit. Through continuous data analysis and optimization, the unit's power supply coal consumption can be gradually reduced, and key indicators such as turbine heat rate and unit efficiency can be improved, thereby enhancing the economic benefits and competitiveness of the enterprise.

[0129] The data preprocessing unit for the collected data is also used for:

[0130] Perform data preprocessing on the unit data to be processed;

[0131] The data preprocessing first performs data cleaning on the unit data to be processed. Data cleaning includes removing irrelevant data, filling in missing values, and handling outliers;

[0132] After data cleaning is completed, data conversion is performed. Data conversion includes unit conversion and format unification;

[0133] After data conversion is completed, data standardization is performed. Data standardization includes normalization and standardization processing;

[0134] After data standardization is completed, data verification is performed. Data verification includes range verification and logical verification;

[0135] After data verification is completed, data integration is performed. Data integration includes merging data sets and data association.

[0136] Perform unique coding and labeling on the unit data to be processed after data preprocessing is completed;

[0137] Before performing unique coding and labeling, first formulate the coding rules;

[0138] The coding rules include timestamp, unit number, serial number, and data type;

[0139] Generate unique coding for the unit data to be processed according to the formulated coding rules;

[0140] Perform data verification on the generated unique coding, and label the unit data to be processed with the unique coding as target unit data according to the verification result.

[0141] Specifically, through steps such as removing irrelevant data, filling in missing values, and handling outliers, the data cleaning process can significantly improve the accuracy and integrity of the data, providing a reliable basis for subsequent analysis. The data verification process further ensures the rationality and consistency of the data through range verification and logical verification, reducing analysis biases caused by data errors. The unique coding and data integration steps make the unit operation data more structured and systematic, facilitating exergy loss analysis and performance appraisal. By comparing the data of different units and different time periods, the operation efficiency and performance of the units can be evaluated more accurately, providing strong support for performance appraisal. The automation and standardization of the data preprocessing process reduce manual intervention and errors, improving the processing efficiency and accuracy of the system. The formulation and verification of coding rules ensure the uniqueness and accuracy of the data, avoiding analysis mistakes caused by data duplication and errors. The preprocessed and integrated data provides comprehensive and accurate information support for management, helping to make more scientific and reasonable decisions. Through in-depth analysis of the unit operation data, potential problems and improvement points can be discovered, providing a strong basis for the optimized operation and energy conservation and emission reduction of the units.

[0142] The preprocessed data storage and backup unit is also used for:

[0143] Before backing up the data of the target unit, first formulate a backup strategy;

[0144] The backup strategy includes backup type, backup frequency, and backup location. The backup type includes full backup, incremental backup, or differential backup; the backup frequency is the backup time and backup task; the backup location includes local servers, external hard drives, network storage devices, or cloud storage services;

[0145] Encrypt and compress the data of the target unit;

[0146] After the data encryption and compression are completed, back up the data of the target unit according to the backup strategy;

[0147] After the data backup of the target unit is completed, conduct data verification. After the data verification is completed, record the backup log. The backup log includes backup time, data volume, backup location, and backup result;

[0148] Identify the data type of the data of the target unit after the data backup is completed, and store it according to the identified data type;

[0149] Before storing the data, first formulate a storage strategy. The storage strategy includes storage location and storage format;

[0150] According to the formulated storage strategy, store the data of the target unit after the data type is identified in the corresponding location according to the specified format;

[0151] After the storage is completed, the backup unit data is obtained.

[0152] Specifically, the formulation of the backup strategy, including full backup, incremental backup or differential backup, ensures that the data can be completely and effectively restored in various scenarios. The application of data encryption technology improves the security of data during backup and storage, preventing unauthorized access or tampering with the data. By selecting various backup locations such as local servers, external hard drives, network storage devices or cloud storage services, the data risk is dispersed, enhancing the reliability and fault tolerance of the data. The adoption of incremental backup and differential backup enables only the most recent full backup and subsequent incremental or differential backups to be restored during data recovery, greatly shortening the recovery time. The detailed recording of the backup log, including backup time, data volume, backup location and backup result, provides clear guidance for data recovery, improving the recovery efficiency. The application of data compression technology reduces the storage space occupied by the backup data, improving the storage efficiency. The implementation of data type recognition enables different types of data to be optimized for storage according to their characteristics, further improving the storage efficiency. The formulation of the storage strategy, including the selection of storage location and storage format, enables the data to be stored in the most effective way in the appropriate location, avoiding waste of resources. The automatic generation and recording of the backup log facilitates the tracking and management of the data, enabling the administrator to easily understand the backup and storage status of the data. Reliable data backup and storage guarantee the integrity and accuracy of the unit operation data.

[0153] To solve the problem in the prior art that the unit operation data is not analyzed more accurately and the analysis data is not optimized, resulting in the inability to effectively adjust the unit equipment according to the actual situation, please refer to Figure 1 and Figure 2 , this embodiment provides the following technical solutions:

[0154] The unit data heat rate analysis unit is also used for:

[0155] Retrieve the standard operation management data from the database and compare each item of the retrieved standard operation management data with the backup unit data item by item;

[0156] After the item-by-item comparison is completed, the heat rate data between the standard operation management data and the backup unit data is obtained;

[0157] Among them, the heat rate data includes absolute heat rate data and relative heat rate data;

[0158] The absolute heat rate data is the difference between the data of the same type in the standard operation management data and the backup unit data. The calculation formula is as follows:

[0159] P = T - Z

[0160] P represents the absolute consumption difference value; T represents the value of a certain data in the backup unit data; Z represents the value of a certain data in the standard operation management data;

[0161] The relative consumption difference data is the ratio of the consumption difference value between the standard operation management data and the backup unit data. The calculation formula is as follows:

[0162] D = P / Z * 100%

[0163] D represents the relative consumption difference value;

[0164] Use the weighted average algorithm to calculate the overall consumption difference for the absolute consumption difference data and the relative consumption difference data;

[0165] After the overall consumption difference calculation is completed, the actual unit consumption difference data is obtained.

[0166] Specifically, by comparing the standard operation management data and the backup unit data item by item, the differences between the two can be accurately found, thus ensuring the accuracy of the consumption difference data. Using the calculation formulas for the absolute consumption difference data and the relative consumption difference data, the gap between the backup unit data and the standard operation management data can be scientifically quantified, providing a reliable basis for subsequent optimization management and performance appraisal. Not only considering the absolute consumption difference data, but also introducing the relative consumption difference data makes the consumption difference analysis more comprehensive and can more accurately reflect the actual situation of the unit operation. By using the weighted average algorithm to calculate the overall consumption difference for the absolute consumption difference data and the relative consumption difference data, the actual unit consumption difference data can be obtained, providing strong support for the overall optimization of the unit. Through the consumption difference analysis, the problems and deficiencies existing in the unit operation can be found, thus guiding the operators to carry out targeted optimization adjustments to improve the unit operation efficiency and economy. By real-time monitoring and analyzing the unit consumption difference, the operation modes with high energy consumption can be found and corrected in time, thus reducing the unit energy consumption level. The consumption difference data can be used as an important basis for the performance appraisal of the operators. By comparing the consumption difference data of different operators, their operation levels and responsibilities can be objectively evaluated. Combining the consumption difference data with the performance appraisal can motivate the operators to actively improve their operation skills and energy-saving awareness, thus further improving the unit operation efficiency and economy. According to the real-time consumption difference data, the operators can dynamically adjust the unit operation parameters to ensure that the unit always maintains a better operation state.

[0167] The consumption difference data analysis and optimization unit is also used for:

[0168] Conduct anomaly analysis on the actual unit consumption difference data according to the anomaly analysis rules;

[0169] The anomaly analysis rules are retrieved from the database. When conducting anomaly analysis, if the threshold value of the actual unit consumption difference data exceeds the normal operation threshold value in the anomaly analysis rules, the actual unit consumption difference data is abnormal data;

[0170] The abnormal degree of the actual unit heat rate data that is abnormal data will be judged;

[0171] The abnormal degree judgment is to divide the abnormal degree according to the abnormal value of the abnormal data of the actual unit heat rate data;

[0172] The abnormal degree is divided into slight abnormality, moderate abnormality and severe abnormality;

[0173] The abnormal data in each abnormal degree will be analyzed for the abnormal cause. The abnormal causes include equipment failure, improper operation, environmental factor change and improper process parameter setting;

[0174] Different optimization adjustment plans will be formulated according to the abnormal cause and the abnormal degree;

[0175] After the optimization adjustment plan is formulated, the unit operation data to be executed is obtained.

[0176] Specifically, by retrieving the abnormal analysis rules from the database, the automatic abnormal analysis of the actual unit heat rate data is realized, which greatly reduces the manual intervention and improves the analysis efficiency. The abnormal degree judgment and the abnormal cause analysis also adopt intelligent methods, which can accurately divide and diagnose based on the data characteristics, further improving the intelligent level of the system. By setting the normal operation threshold, the system can accurately identify the abnormal data exceeding the threshold, ensuring the accurate monitoring of the unit operation status. The introduction of the abnormal degree division enables the system to more carefully evaluate the severity of the abnormality, providing strong support for the subsequent optimization adjustment. Considering various abnormal causes such as equipment failure, improper operation, environmental factor change and improper process parameter setting, the comprehensiveness of the analysis is ensured. This comprehensive analysis helps to quickly locate the root cause of the problem, providing an important basis for formulating effective optimization adjustment plans. According to the abnormal cause and the abnormal degree, the system can automatically generate targeted optimization adjustment plans, improving the effectiveness and pertinence of the adjustment. This personalized optimization adjustment plan helps to improve the operation efficiency and performance of the unit, reduce energy consumption and costs. Through automatic and intelligent means, the process of unit operation optimization management is simplified and the management efficiency is improved. At the same time, the system can also provide detailed abnormal data and optimization adjustment records, providing an objective basis for performance appraisal, helping to improve the management level and employee performance. Through continuous abnormal analysis and optimization adjustment, the system can continuously accumulate experience and data, providing useful references for the subsequent unit operation optimization.

[0177] To solve the problem in the prior art that there is no more accurate adjustment plan formulated according to the actual situation of the unit operation compared with the normal situation, and there is no corresponding performance appraisal according to the adjustment plan, resulting in poor effect of the unit operation status adjustment plan, please refer to Figure 1 andFigure 2 , this embodiment provides the following technical solutions:

[0178] The optimization plan generation unit is further configured to:

[0179] Generate a plan for the operation data of the unit to be executed by using a plan conversion tool;

[0180] Approve the generated plan, and after the plan approval is completed, distribute it to the corresponding working unit personnel;

[0181] Among them, before the plan is distributed, the unit equipment to be adjusted in the plan is confirmed;

[0182] Confirm the corresponding working unit personnel according to the unit equipment;

[0183] After the corresponding working unit personnel are completed, the operation data of the unit to be executed with the plan approval completed is sent through a communication channel;

[0184] The communication channel includes a unit display terminal and a working unit personnel meeting terminal.

[0185] Specifically, by using a plan conversion tool, the operation data of the unit to be executed is converted into a specific implementation plan. This process is based on heat rate analysis, which can ensure the scientificity and effectiveness of the plan. Before the plan is distributed, confirming the unit equipment to be adjusted can ensure the accuracy of subsequent adjustment work, avoid unnecessary resource waste. Through the system's analysis and processing of the unit operation data, the unit equipment can be configured and adjusted more scientifically, improving the overall operation efficiency. It can record and save the historical data of the unit operation, providing an objective and accurate basis for performance appraisal. Through the performance appraisal management system, employees can clearly understand their work performance and growth space, thus actively seeking improvement and releasing personal potential. Through the approval process, each department can understand the needs of other departments and coordinate resources to achieve common goals. This helps to improve the team collaboration ability and ensure the smooth implementation of the plan. Sending the plan through a communication channel (such as a unit display terminal and a working unit personnel meeting terminal) can ensure the security of information during transmission. Through the approval process, the approval process and results of the plan can be recorded to ensure that every operation is traceable. This helps to trace the cause in a timely manner when problems occur and take corresponding measures for improvement. Through continuous analysis and processing of the unit operation data, new problems and potential improvement points can be continuously discovered. This provides the possibility for the continuous optimization and upgrade of the unit.

[0186] The optimization plan implementation monitoring unit is further configured to:

[0187] The working crew checks the implementation plan on the communication channel, and prepares the implementation tools and personnel and conducts safety inspections according to the requirements of the implementation plan;

[0188] The implementers adjust the parameters of the unit equipment according to the implementation plan;

[0189] During and after the parameter adjustment process, the operating status of the unit equipment is monitored in real time;

[0190] If the operating status is normal, the monitoring parameters are collected and recorded by using the data acquisition equipment of the unit equipment;

[0191] The recorded monitoring parameters are marked as adjusted monitoring parameter data.

[0192] Specifically, the implementers adjust the parameters of the unit equipment according to the implementation plan, can respond in real time to the changes in the operating status of the unit, and ensure that the unit always operates in the best state. Through the differential analysis, the influence of each parameter on the coal consumption (or heat consumption) of the unit can be accurately calculated, so as to guide the implementers to carry out targeted parameter adjustment, reduce the power supply coal consumption of the unit, improve the operating efficiency. During and after the parameter adjustment process, the operating status of the unit equipment is monitored in real time, potential problems can be discovered and processed in time, and the shutdown or efficiency decline caused by unit failures can be avoided. The monitoring parameters are collected and recorded by using the data acquisition equipment of the unit equipment, providing accurate data support for the subsequent differential analysis and unit performance optimization. By recording and analyzing the adjusted monitoring parameter data, the work performance of the operating personnel in the process of unit operation optimization can be objectively evaluated. The differential analysis method can accurately reflect the influence of each parameter on the economy of the unit, thus avoiding the interference of subjective factors in the traditional performance appraisal, improving the accuracy and fairness of the appraisal. Based on the performance appraisal results of the differential analysis, a corresponding incentive mechanism can be established to reward the outstanding operating personnel, stimulating their work enthusiasm and creativity. This incentive mechanism can guide the operating personnel to pay more attention to the economy and safety of the unit operation, promote the continuous improvement of the unit operation optimization management work. The unit operation optimization management system based on the differential analysis can carry out refined management of various parameters in the unit operation process, ensuring that the unit always operates in the best state. Through the analysis and mining of historical data, potential problems and improvement directions in the unit operation can be discovered, providing strong support for the subsequent unit optimization transformation.

[0193] The implementation plan performance appraisal unit is also used for:

[0194] Compare the difference between the adjusted monitoring parameter data and the standard operation management data;

[0195] Among them, when comparing the differences, the types of the data compared are the same;

[0196] Confirm the performance change data based on the difference comparison result. The performance change data is confirmed as follows: when the difference exceeds the preset positive threshold, adjust the monitoring parameter data to performance improvement; when the difference is lower than the preset negative threshold, adjust the monitoring parameter data to performance decline; if the difference is between the positive threshold and the negative threshold, adjust the monitoring parameter data to no change.

[0197] Conduct performance index analysis based on the performance change data. The performance index analysis is to calculate the performance score by analyzing the change trend of the performance change data.

[0198] The performance score calculation gives a positive score when there is performance improvement and a negative score when there is performance decline.

[0199] Integrate the total score of the performance score with the work performance of the work crew or department. Among them, the work performance of the work crew or department is retrieved from the attendance terminal.

[0200] After performance integration, the comprehensive performance analysis data for the optimized management of unit operation is obtained.

[0201] Convert the comprehensive performance analysis data into visual data and display it on the display terminal of the department after the conversion of visual data is completed.

[0202] Specifically, by comparing the difference between the adjusted monitoring parameter data and the standard operation management data, the subtle changes in the unit operation status can be accurately captured. Real-time data comparison and analysis can ensure the timely discovery of problems, provide strong support for rapid response and optimized management. Judging performance improvement, decline or no change according to the preset positive and negative thresholds avoids the interference of subjective judgment, improves the accuracy and objectivity of the evaluation. Incorporating the change trend of the performance change data into the performance index analysis not only considers the current performance status but also the long-term change trend, which helps to comprehensively evaluate the overall performance of unit operation. Giving a positive score when there is performance improvement and a negative score when there is performance decline, this scoring mechanism can motivate the work crew or department to actively improve the unit operation performance, thereby improving the overall work efficiency. Combining the performance score with the work performance of the work crew or department not only considers the performance indicators of unit operation but also the work attitude and attendance of the personnel, realizing the comprehensiveness of performance evaluation. Converting the comprehensive performance analysis data into visualization can intuitively display the optimized management situation and performance score of unit operation on the display terminal, facilitating the management to quickly understand the overall performance status, providing detailed data support and intuitive graphical display for the management, which helps the management to make more scientific and reasonable decisions and improve the effect of unit operation optimized management. Through continuous data monitoring and analysis, problems in unit operation can be discovered and solved in a timely manner.

[0203] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0204] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention.

Claims

1. A self-diagnosis and optimization system for unit operation status based on consumption difference analysis, characterized in that: include: The unit data acquisition unit is used for: The unit operation data is retrieved from the database, and after the retrieval is completed, the unit data to be processed is obtained; The acquisition data preprocessing unit is used to: The data of the unit to be processed is preprocessed, and uniquely coded after the data preprocessing, and the target unit data is obtained after the unique coding is completed; Pre-processing data storage backup unit, used for: The target unit data is backed up first, and after the data backup is completed, the data is stored separately according to the type of each data, and after the data storage is completed, the backup unit data is obtained; The unit data consumption difference analysis unit is used to: The backup unit data and the standard operation management data are calculated for consumption difference, and after the consumption difference calculation is completed, the actual unit consumption difference data is obtained; Consumption difference data analysis and optimization unit, used for: The actual unit consumption difference data is analyzed for abnormal operation data, and the abnormal degree of the abnormal operation data is judged according to the abnormal analysis result, and an operation data optimization plan is generated according to the abnormal degree of the operation data. After the operation data optimization plan is generated, the operation data of the unit to be executed is obtained; The optimization solution generation unit is used to: Generate an execution plan based on the operation data of the unit to be executed, and send the generated execution plan to the corresponding working crew according to the unit type optimized in the plan; The optimization plan implementation monitoring unit is used to: The crew adjusts the parameters of the operating equipment according to the implementation plan. After the parameter adjustment is completed, the real-time parameter monitoring of the adjusted operating equipment is carried out, and the monitored real-time parameters are marked as the adjustment monitoring parameter data; Implement the Implementation Performance Appraisal Unit to: Compare the difference between the adjusted monitoring parameter data and the standard operation management data, confirm the performance change data based on the comparison results, conduct performance correlation analysis based on the performance changes, and finally visualize and present the performance correlation analysis results.

2. According to claim 1, a unit operation status self-diagnosis optimization system based on consumption difference analysis is characterized in that: The unit data acquisition unit is also used for: The unit operation data retrieved from the database include process parameter data, equipment status data, energy consumption data, environmental parameter data and product quality data; The unit operation data is collected through sensors, data collectors, controllers, intelligent instruments and monitoring systems; Before sensors, data collectors, controllers, intelligent instruments and monitoring systems collect data, they should first be tested for data collection. After all the equipment has passed the test, the unit operation data can be collected. The collected unit operation data is imported into the database, and the unit operation data in the database is retrieved to obtain the unit data to be processed.

3. The unit operation status self-diagnosis optimization system based on consumption difference analysis according to claim 2 is characterized in that: The collected data preprocessing unit is further used for: Preprocessing the data of the unit to be processed; Data preprocessing is to clean the data of the unit to be processed first. Data cleaning includes removing irrelevant data, filling missing values ​​and processing outliers. After data cleaning is completed, data conversion is performed, including unit conversion and format unification; After the data conversion is completed, data standardization is performed, which includes normalization and standardization processing; After data standardization is completed, data verification is performed, which includes range verification and logic verification; After data verification is completed, data integration is performed, which includes merging data sets and data association.

4. The unit operation status self-diagnosis optimization system based on consumption difference analysis according to claim 3 is characterized in that: The collected data preprocessing unit is further used for: Uniquely code and label the data of the unit to be processed after data preprocessing; Before making a unique coding label, the coding rules should be formulated first; The encoding rules include timestamp, unit number, serial number and data type; Generate unique codes for the unit data to be processed according to the established coding rules; The generated unique code is subjected to data verification, and the unit data to be processed with the unique code is marked as the target unit data according to the verification result.

5. The unit operation status self-diagnosis optimization system based on consumption difference analysis according to claim 4 is characterized in that: The preprocessing data storage backup unit is also used for: Before backing up the target unit data, a backup strategy should be formulated; The backup strategy includes the backup type, backup frequency, and backup location. The backup type includes full backup, incremental backup, or differential backup. The backup frequency is the backup time and backup task; Backup locations include local servers, external hard drives, network storage devices, or cloud storage services; Encrypt and compress the target unit data; After data encryption and compression are completed, the target unit data is backed up according to the backup strategy; After the target unit data is backed up, data verification is performed. After the data verification is completed, a backup log is recorded. The backup log includes the backup time, data volume, backup location and backup results; Identify the data type of the target unit data that has completed data backup, and store it according to the identified data type; Before storing data, a storage strategy should be formulated, which includes storage location and storage format; According to the established storage strategy, the target unit data whose data type has been identified is stored in the corresponding location according to the specified format; After storage is completed, the backup unit data is obtained.

6. The unit operation status self-diagnosis optimization system based on consumption difference analysis according to claim 5 is characterized in that: The unit data consumption difference analysis unit is also used for: Retrieve standard operation management data from the database, and compare the retrieved standard operation management data with the backup unit data item by item; After the comparison is completed item by item, the consumption difference data between the standard operation management data and the backup unit data is obtained; The consumption difference data includes absolute consumption difference data and relative consumption difference data; The absolute consumption difference data is the difference between the standard operation management data and the backup unit data of the same type. The calculation formula is as follows: P=TZ P represents the absolute consumption difference value; T represents the value of a certain data in the backup unit data; Z represents the value of a certain data in the standard operation management data; The relative consumption difference data is the ratio of the consumption difference between the standard operation management data and the backup unit data. The calculation formula is as follows: D=P / Z*100% D represents the relative consumption difference; The absolute consumption difference data and the relative consumption difference data are used to calculate the overall consumption difference using a weighted average algorithm; After the overall consumption difference calculation is completed, the actual unit consumption difference data is obtained.

7. The unit operation status self-diagnosis optimization system based on consumption difference analysis according to claim 6 is characterized in that: The consumption difference data analysis and optimization unit is further used for: According to the abnormal analysis rules, the actual unit consumption difference data is analyzed for abnormalities; The abnormal analysis rules are retrieved from the database. When performing abnormal analysis, if the threshold of the actual unit consumption difference data exceeds the normal operation threshold in the abnormal analysis rules, the actual unit consumption difference data is abnormal data; The abnormal degree of the actual unit consumption difference data which is abnormal data is judged; The abnormality degree is judged by dividing the abnormality degree according to the abnormal value of the abnormal data of the actual unit consumption difference data; The degree of abnormality is divided into mild abnormality, moderate abnormality and severe abnormality; Analyze the abnormal data in each abnormal degree for abnormal reasons, which include equipment failure, improper operation, changes in environmental factors and improper process parameter settings; Formulate different optimization and adjustment plans based on the abnormal causes and abnormality levels; After the optimization and adjustment plan is formulated, the operating data of the units to be executed are obtained.

8. The unit operation status self-diagnosis optimization system based on consumption difference analysis according to claim 7 is characterized in that: The optimization scheme generating unit is further used for: Generate a plan using the plan conversion tool using the operation data of the units to be executed; The generated plan will be reviewed and approved, and after the plan is approved, it will be distributed to the corresponding working crew members; Among them, the unit equipment to be adjusted in the plan shall be confirmed before the plan is distributed; Confirm the corresponding working crew members according to the crew equipment; After the work crew completes the plan, they will send the operation data of the units to be executed after the plan is approved through the communication channel; The communication channels include crew display terminals and working crew conference terminals.

9. The unit operation status self-diagnosis optimization system based on consumption difference analysis according to claim 8 is characterized in that: The optimization scheme implementation monitoring unit is further used for: The crew will review the implementation plan on the communication channel and carry out preparation and safety inspection of the implementation tools and personnel according to the requirements of the implementation plan; The implementation personnel adjust the parameters of the unit equipment according to the implementation plan; During and after parameter adjustment, real-time monitoring of the operating status of the unit equipment; If the operating status is normal, the data acquisition equipment of the unit equipment is used to collect and record the monitoring parameters; The recorded monitoring parameters are marked as adjusted monitoring parameter data.

10. The unit operation status self-diagnosis optimization system based on consumption difference analysis according to claim 9 is characterized in that: The implementation plan performance appraisal unit is also used to: Compare the difference between the adjusted monitoring parameter data and the standard operation management data; Among them, when performing difference comparison, the types of the compared data are consistent; The performance change data is confirmed based on the difference comparison result. When the difference exceeds the preset positive threshold, the monitoring parameter data is adjusted to improve the performance; when the difference is lower than the preset negative threshold, the monitoring parameter data is adjusted to degrade the performance; if the difference is between the positive threshold and the negative threshold, the monitoring parameter data is adjusted to remain unchanged; Perform performance indicator analysis based on performance change data. Performance indicator analysis is to calculate performance scores based on the change trend of performance change data. Performance scores were calculated by giving positive scores when performance improved and negative scores when performance decreased; The total performance score is combined with the work performance of the crew or department to perform performance integration, wherein the work performance of the crew or department is retrieved from the attendance terminal; After performance integration, comprehensive performance analysis data for unit operation optimization management is obtained; The comprehensive performance analysis data is converted into visual data, and after the visual data conversion is completed, it is displayed on the department's display terminal.

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