Fresh tobacco leaf baking room area power supply supervision system based on big data

Abstract

The invention relates to the technical field of power monitoring and management, in particular to a fresh tobacco leaf baking room area power supply supervision system based on big data, which is provided with an intelligent weight marking module, a sensing information capturing module, a block information scoring module, a threshold comparison and adjustment module and a technical sorting evaluation module. The power supply level of the service node can be dynamically adjusted, so that the service node is always kept in a power supply efficiency comprehensive optimal state, and different service requirements and environment changes are intelligently adapted; the chain structure information combination can clearly record the source of the block scoring information, so that the source can be traced and the historical track can be tracked to carry out the optimized key tracing of the power supply mode adjustment, and the multi-dimensional comprehensive evaluation of the power supply efficiency can be realized. And the matching degree of the power supply efficiency of each business plate and the comprehensive regulation decision accuracy for the power supply supervision of the fresh tobacco leaf baking room area are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power monitoring and management, and in particular to a regional power supply monitoring system for a fresh tobacco leaf curing room based on big data. Background Art

[0002] Tobacco leaf baking is an important link in the tobacco production process, and the power supply supervision system of the fresh tobacco leaf baking room is the key link to ensure the stability and safety of power supply during the baking process. During the tobacco leaf baking process, the baking room requires a large amount of electricity to maintain a constant temperature and humidity to ensure the quality of the tobacco leaves and the baking effect. Power supply supervision can monitor the real-time power consumption of baking-related equipment, discover abnormal conditions in time and issue alarms and handle them to prevent equipment failure or tobacco leaf quality degradation, low processing efficiency and other problems caused by insufficient or unstable power supply. The existing power supply supervision system mostly uses sensors and monitoring equipment to monitor and collect various parameters of various equipment in the fresh tobacco leaf baking room area in real time, and directly analyzes various parameters through data processing technology. At the same time, through advanced information collection technologies, such as cloud computing and wireless communication, efficient information transmission of power supply regulation decisions is realized, and the real-time and coordinated power supply supervision and regulation in the fresh tobacco leaf baking room area are realized.

[0003] However, the existing regional power supply supervision system still has some shortcomings, which are specifically reflected in the following aspects: At present, the supervision perspective of the regional power supply supervision system is relatively limited, mainly focusing on monitoring the basic operation and safety of the power supply system, while ignoring the accurate expression of the power supply efficiency of each business segment. As one of the important indicators to measure the operation effect of the power supply system, power supply efficiency is an important parameter for the power supply system to monitor power supply and implement power supply regulation based on the energy utilization efficiency and cost-effectiveness in the process of providing power services. The lack of accurate expression of the power supply efficiency of each segment affects the evaluation of the degree of influence of each business node on the power supply efficiency of the corresponding business segment under the current power supply mode, making it difficult to timely discover and solve the inefficiency or waste problems in the power supply system, reducing the overall operation efficiency and economy of the power supply system.

[0004] The regional power supply monitoring in the regional power supply supervision system is too segmented. On the one hand, the evaluation mechanism of the power supply supervision system for the current power supply mode is too segmented, lacking a multi-dimensional comprehensive evaluation of power supply efficiency. It only pays attention to improving the power supply efficiency under the current power supply mode and ignores the matching degree of power supply efficiency of each segment under the overall power supply mode. On the other hand, segmentation makes it difficult to determine the traceability relationship, that is, to judge whether there is a demand for power supply mode adjustment in the current power supply system and the specific regulation of the power supply mode are separated, making it difficult to realize the integrated monitoring and control of the power supply supervision system. It is difficult to grasp the key points of regulation and optimization of subsequent power supply modes, which increases the time cost of meeting power supply needs and affects the comprehensive grasp of the power supply system and the timeliness of regulation decisions.

[0005] In summary, the traditional tobacco drying room power supply supervision system often has shortcomings such as limited data processing capabilities, inability to accurately evaluate efficiency, difficulty in system integration, and lack of coordination. Summary of the invention

[0006] In view of the shortcomings of the existing technology, the present invention provides a fresh tobacco leaf curing room area power supply supervision system based on big data, which can dynamically adjust the power supply level of the business node so that it always maintains the comprehensive optimal state of power supply efficiency, and can intelligently adapt to different business needs and environmental changes.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a regional power supply monitoring system for fresh tobacco leaf curing room based on big data, comprising: Intelligent weight marking module, used to determine the power supply weight coefficient of each power supply business node in the fresh tobacco curing room area; The sensor information capture module is used to obtain the power supply of each power supply business node in the fresh tobacco baking room area; The block information scoring module is used to collect statistics on the power supply parameters of each power supply business block based on the power supply of each power supply business node in the fresh tobacco leaf curing room area and the power supply weight coefficient corresponding to each power supply business node. Each power supply business block includes multiple power supply business nodes, and the power supply parameters of each power supply business block are scored based on the set business block power supply correction factor of each power supply business block. The business blocks include tobacco leaf screening, curing processing, tobacco leaf quality inspection and packaging storage; A threshold comparison and adjustment module is used to generate a deviation rate between the power supply parameters of each power supply business segment and the power supply parameter threshold stored in the database, and adjust the power supply weight parameters of each power supply business node in the fresh tobacco leaf curing room area based on the deviation rate; The technical ranking and evaluation module is used to integrate block information in accordance with the business process direction to form a chain structure information combination, sort the block information scores of each power supply business segment based on the chain structure information combination, analyze the comprehensive score, and issue early warning prompts to the power supply business segment based on the comprehensive score.

[0008] Preferably, the weight coefficient of each business node in the fresh tobacco leaf curing room area is used to characterize the power supply efficiency of the business node in its corresponding business section in the fresh tobacco leaf curing room area. The process of determining the weight coefficient of each business node in the fresh tobacco leaf curing room area is as follows: Obtain the power supply demand parameters of each business node in the fresh tobacco drying room area; The weight coefficient of each business node is obtained based on the power supply demand parameters of each business node in the fresh tobacco leaf curing room area.

[0009] Preferably, the power supply requirement parameters of the service node specifically include: The power consumption of equipment used by business nodes, the power conversion efficiency of equipment used at business nodes, the accumulated power consumption of equipment used at business nodes, the power supply duration per business cycle, and the deviation rate between the power supply parameters of business sections and the power supply parameter thresholds stored in the database.

[0010] Preferably, the calculation formula for the weight coefficient of each business node in the fresh tobacco leaf curing room area is as follows: ; In the formula, j is the number of each business block in the fresh tobacco leaf curing room area, j=1,2,3,…,n, n is the total number of business blocks, i is the number of each business node in each business block, i=1,2,3,…,m, m is the total number of business nodes in each business block, is the power supply weight coefficient of the i-th business node of the j-th business sector in the fresh tobacco leaf curing room area, a ji The electrical power of the equipment used by the business node of the i-th business node of the j-th business segment, b ji is the power conversion efficiency of the business node equipment of the i-th business node of the j-th business segment, c ji is the cumulative power consumption of the business node equipment of the i-th business node of the j-th business segment, d ji The power supply duration per unit business cycle of the i-th business node of the j-th business segment, is the deviation rate between the business block power supply parameter of the i-th business node of the j-th business block and the power supply parameter threshold stored in the database, and e is a natural constant.

[0011] Preferably, the process of determining the power supply parameters of each business segment is as follows: Based on the correspondence between business sectors and business nodes, determine the business nodes corresponding to each business sector; Obtain the weight coefficient of each business node in the fresh tobacco leaf curing room area and the power supply of each business node in the fresh tobacco leaf curing room area; Based on the correspondence between business sectors and business nodes, the weight coefficients of each business node in the fresh tobacco leaf curing room area and the power supply of each business node in the fresh tobacco leaf curing room area are comprehensively obtained to obtain the corresponding power supply parameters of each business sector.

[0012] Preferably, the specific analysis process of generating the deviation rate between the power supply parameters of each power supply business segment and the power supply parameter threshold stored in the database is: Obtain the power supply parameter thresholds stored in the database for each business segment; Determine the difference between the power supply parameters of each business segment and the power supply parameter thresholds stored in the database; The deviation rate is obtained based on a comprehensive analysis of the power supply parameters and differences of each business segment.

[0013] Preferably, the specific analysis process of integrating block information to form a chain structure information combination with reference to the business process direction is: Get the order of business nodes when executing the fresh tobacco leaf baking business process; Integrate and arrange business segments based on the corresponding relationship between business segments and business nodes to form a business segment sequence; Arrange each business segment in order based on the business segment to form a chain structure information combination of the current fresh tobacco leaf drying room power supply area.

[0014] Preferably, the block information score is sorted based on the chain structure information combination, and the analysis process for obtaining the comprehensive score is as follows: Obtain the chain structure information combination and perform block information scoring sorting based on the chain structure information combination to obtain the sorting results of the block information scores of each power supply business segment; Extract the highest value of the block information score in the sorting results, and record the highest value as the optimal value; Extract the lowest value of the block information score in the sorting results, and record the lowest value as the worst value; The best block distance is calculated based on the best value obtained by sorting the block information scores and the scores of each block information, and the worst block distance is calculated based on the worst value obtained by sorting the block information scores and the scores of each block information; A comprehensive score is obtained based on the analysis of the optimal plate distance and the worst plate distance.

[0015] Preferably, the calculation formula of the comprehensive score is as follows: ; In the formula, S is the comprehensive score, ZL is the worst plate distance, and ZY is the best plate distance.

[0016] Preferably, the comprehensive score is used to indicate the degree of concentration of the scores of each block information at the optimal value, characterizing the power supply efficiency of the overall power supply system. The process of providing early warning prompts to the power supply business sector based on the comprehensive score is as follows: The comprehensive score is compared with the reference value of the comprehensive score of the power supply area of ​​the tobacco curing room stored in the database; If the comprehensive score is greater than the reference value of the comprehensive score of the tobacco curing room power supply area stored in the database, no early warning will be issued to the power supply business section; If the comprehensive score is less than or equal to the reference value of the comprehensive score of the power supply area of ​​the tobacco curing room stored in the database, the business segment corresponding to the worst value is traced back based on the correspondence between the block information score and the business segment in the chain structure information combination, and an early warning prompt is issued for the business segment corresponding to the worst value.

[0017] The present invention has the following beneficial effects: The present invention determines the weight coefficient of each business node through intelligent weight marking, thereby realizing more intelligent management and control of the power supply of business nodes, realizing accurate expression of the power supply efficiency of business nodes corresponding to business sectors, and reasonably adjusting the weight coefficient based on the importance of different business nodes relative to the business sectors and the power supply demand situation, optimizing resource allocation, improving resource utilization and system efficiency, and dynamically adjusting the weight coefficient based on real-time data and threshold comparison feedback information to ensure that the power supply level of the business node is always maintained at the comprehensive optimal state of power supply efficiency. By dynamically adjusting and optimizing the weight coefficient of each business node, it is possible to intelligently adapt to different business needs and environmental changes, thereby improving the flexibility and adaptability of the system.

[0018] The present invention integrates block information to form a chain structure information combination by referring to the business process direction, integrates the block scoring information corresponding to each business segment in a more orderly and efficient manner, reduces the fragmentation and repeated integration of block scoring information, and the chain structure information combination can clearly record the source and flow of information, facilitates tracing and tracking the historical trajectory of information, information access and transmission authority, improves the security and confidentiality of information, and prevents the risk of information leakage and tampering. The chain structure information combination can clearly record the source of block scoring information, facilitates tracing and tracking its historical trajectory to optimize the key tracing of power supply mode adjustment.

[0019] The present invention realizes a comprehensive evaluation of the power supply efficiency of the power supply system based on the mutual relationship of the business sectors through the comprehensive scoring of the power supply system. By considering the matching degree of the power supply mode of the overall power supply system in terms of power supply efficiency, the short-sighted behavior of the power supply supervision system in single pursuit of maximizing power supply efficiency in each business sector is avoided, so as to fully understand the overall power supply status of the system, which is helpful to timely discover the efficiency problems and potential optimization space of the power supply system, and take corresponding measures to improve and optimize it. The comprehensive scoring of the power supply system can provide managers and decision makers with direct and objective data evaluation and power supply control decision-making basis, and take corresponding measures to improve it, thereby improving the overall operating efficiency of the power supply system.

[0020] The present invention involves big data technology in multiple modules, and through close integration with big data technology, a revolutionary improvement in the power supply system in the fresh tobacco leaf curing room area is achieved. By using big data analysis, it is possible to monitor and dynamically adjust the power supply strategy in real time, optimize resource allocation, and improve power supply efficiency and reliability. At the same time, the predictive analysis function can identify potential risks in advance, prevent equipment failures and power supply problems, and thus reduce unexpected power outages. In addition, the application of big data has also enhanced the intelligence level of the system, and through intelligent weight adjustment and business process optimization, it has further improved the accuracy and adaptability of power supply supervision. These advantages not only reduce operating costs, but also support a sustainable power supply model, ultimately providing users with a more stable and efficient service experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of system module connection of the present invention; Figure 2 It is an image of the change of the weight coefficient of the business node in the fresh tobacco leaf curing room area with the deviation rate of the power supply parameter of the business section and the power supply parameter threshold stored in the database; Figure 3 Schematic diagram of block information scoring corresponding to the chain structure information combination. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present invention are clearly and completely described below in conjunction with the accompanying drawings.

[0023] like Figure 1 As shown, a fresh tobacco leaf curing room area power supply supervision system based on big data includes an intelligent weight marking module, a sensor information capture module, a block information scoring module, a threshold comparison and adjustment module and a technology ranking evaluation module.

[0024] The intelligent weight marking module is used to determine the power supply weight coefficient of each power supply business node in the fresh tobacco drying room area.

[0025] Specifically, the weight coefficient of each business node in the fresh tobacco leaf curing room area is used to characterize the power supply efficiency of the business node in its corresponding business segment in the fresh tobacco leaf curing room area. Due to the business relationship between the business segment and the business nodes in the fresh tobacco leaf curing room area, the power supply efficiency of any business segment cannot be obtained based on the simple accumulation of the power supply of each business node in the corresponding fresh tobacco leaf curing room area. Based on the importance of each business node in the fresh tobacco leaf curing room area corresponding to the business segment compared to the entire business segment, the process of determining the weight coefficient of each business node in the fresh tobacco leaf curing room area is as follows: obtaining the power supply demand parameters of each business node in the fresh tobacco leaf curing room area; obtaining the weight coefficient of each business node based on the obtained power supply demand parameters of each business node in the fresh tobacco leaf curing room area.

[0026] The business segments of power supply supervision in the fresh tobacco leaf curing room area include tobacco leaf selection, curing processing, tobacco leaf quality inspection and packaging storage. Furthermore, the tobacco leaf selection business segment includes raw material reception, tobacco leaf sorting and tobacco leaf grading; the curing processing business segment includes cleaning impurities, curing room disinfection, tobacco leaf filling, curing operation and tobacco leaf cooling; the tobacco leaf quality inspection business segment includes sample collection, appearance inspection, chemical analysis and quality assessment; the packaging storage business segment includes packaging operation, labeling management, logistics transportation and storage management.

[0027] Specifically, the power supply demand parameters of the business node include: the power of the equipment used by the business node, the power conversion efficiency of the business node equipment, the cumulative power consumption of the business node equipment, the power supply duration per unit business cycle, and the deviation rate between the business segment power supply parameters and the power supply parameter thresholds stored in the database.

[0028] The power consumption of equipment used by business nodes, the cumulative power consumption of equipment used by business nodes, and the power supply duration per unit business cycle are important indicators of basic power energy consumption of each business node in the fresh tobacco curing room area. They are used to determine the power supply demand of business nodes, reasonably arrange power supply time and power capacity, optimize equipment usage time and power control, and avoid insufficient or excessive power supply. The power consumption of equipment used by business nodes refers to the evaluation of the power energy utilization efficiency of equipment to avoid energy efficiency problems in equipment power demand. Adjusting or updating the power supply demand parameters of business nodes based on the power consumption of equipment used by business nodes can improve energy utilization efficiency and reduce overall energy consumption. The power consumption of equipment used by business nodes can also provide important reference data for power demand matching fault diagnosis. Abnormal power fluctuations or overload conditions may indicate problems with equipment power demand matching. Timely detection and handling of faults can ensure the normal operation of business and improve production efficiency. The deviation rate between the power supply parameters of the business segment and the power supply parameter thresholds stored in the database is used as the data standard for the accuracy of the power supply demand assessment of the business node. By regulating the deviation rate, it is ensured that under reasonable power supply, the business segment can reach the power supply parameter efficiency threshold set for each business segment in the database, thereby ensuring the power supply quality and reducing the waste of electric energy caused by unreasonable assessment of power supply demand.

[0029] The weight coefficient of each business node in each fresh tobacco leaf curing room area can be solved by mathematical optimization algorithm, combined with business data and constraints, to obtain a suitable weight coefficient, such as linear programming, integer programming, planning algorithm, etc., or the importance score of each feature can be calculated using the random forest model. These scores can reflect the contribution and importance of each business node in the overall model. The contribution and importance evaluation results of multiple models are averaged to obtain a more accurate weight coefficient of each business node in each fresh tobacco leaf curing room area. It can also be obtained by the following calculation method. The calculation formula for the weight coefficient of each business node in each fresh tobacco leaf curing room area is:

[0030] In the formula, j is the number of each business block in the fresh tobacco leaf curing room area, j=1,2,3,…,n, n is the total number of business blocks, i is the number of each business node in each business block, i=1,2,3,…,m, m is the total number of business nodes in each business block, is the power supply weight coefficient of the i-th business node of the j-th business sector in the fresh tobacco leaf curing room area, a ji The electrical power of the equipment used by the business node of the i-th business node of the j-th business segment, b ji is the power conversion efficiency of the business node equipment of the i-th business node of the j-th business segment, c ji is the cumulative power consumption of the business node equipment of the i-th business node of the j-th business segment, d ji The power supply duration per unit business cycle of the i-th business node of the j-th business segment, is the deviation rate between the business block power supply parameter of the i-th business node of the j-th business block and the power supply parameter threshold stored in the database, and e is a natural constant.

[0031] The weight coefficient of each business node in each fresh tobacco leaf curing room area is calculated based on the importance of the power supply of each business node in the fresh tobacco leaf curing room area corresponding to the business sector compared to the power supply efficiency of the entire business sector. The power supply of each business node remains relatively certain as a regulatory object, and its contribution value and importance to the power supply efficiency of the entire business sector depend on the supply demand of the corresponding equipment. At the same time, the electric power of the equipment used by the business node and the electric energy conversion efficiency of the business node equipment determine the energy utilization efficiency of the equipment. The cumulative power consumption of the business node equipment and the power supply duration per unit business cycle determine the total power consumption of the equipment. The deviation rate between the power supply parameters of the business sector and the power supply parameter thresholds stored in the database reflects the gap between the equipment operating status and the expected status and the accuracy of the expression of the power supply efficiency of any business sector. Therefore, the weight coefficient of each business node in the fresh tobacco leaf curing room area can be obtained by calculating the parameters based on the power supply demand parameters of the business node.

[0032] like Figure 2 As shown, this is an image of the change in the weight coefficient of the business nodes in the fresh tobacco leaf curing room area with the deviation rate between the power supply parameters of the business segment and the power supply parameter threshold stored in the database, where the x-axis represents the deviation rate between the power supply parameters of the business segment and the power supply parameter threshold stored in the database, and the y-axis represents the weight coefficient of the business nodes in the fresh tobacco leaf curing room area. It can be intuitively understood how the deviation rate between the power supply parameters of the business segment and the power supply parameter threshold stored in the database affects the weight coefficient of the business nodes in the fresh tobacco leaf curing room area. The higher the deviation rate between the power supply parameters of the business sector and the power supply parameter threshold stored in the database, the smaller the weight coefficient of the business node in the fresh tobacco leaf curing room area, indicating that the power supply of the business node in the fresh tobacco leaf curing room area is less important than the power supply efficiency of the corresponding business sector. At present, the weight coefficient of the business node in the fresh tobacco leaf curing room area is incorrectly estimated and set, and should be adjusted. With the increase of the deviation rate between the power supply parameters of the business sector and the power supply parameter threshold stored in the database, its influence on the weight coefficient of the business node in the fresh tobacco leaf curing room area gradually weakens, and finally stabilizes at a lower value. The power power of the equipment used by the business node is set to 10 unchanged, the power conversion efficiency of the business node equipment is set to 0.5 unchanged, the cumulative power consumption of the business node equipment is set to 20 unchanged, and the power supply time per unit business cycle is set to 10 unchanged. Only the deviation rate between the power supply parameters of the business sector and the power supply parameter threshold stored in the database is changed. The values ​​of the weight coefficient of the business node in the fresh tobacco leaf curing room area are as follows: Table 1: Example values ​​of the deviation rate between the power supply parameters of the business blocks in the business node weight coefficients in the fresh tobacco curing room area and the power supply parameter thresholds stored in the database

[0033] The sensor information capture module is used to obtain the power supply of each power supply business node in the fresh tobacco leaf curing room area.

[0034] In Table 1, is the power supply weight coefficient of the i-th business node of the j-th business sector in the fresh tobacco leaf curing room area, a ji The electrical power of the equipment used by the business node of the i-th business node of the j-th business segment, in kW; b ji is the power conversion efficiency of the business node equipment of the i-th business node of the j-th business segment, in the form of percentage; c ji is the cumulative power consumption of the business node equipment of the i-th business node of the j-th business segment, in kWh; d ji The power supply duration per unit business cycle of the i-th business node of the j-th business segment, in hours; It is the deviation rate between the business block power supply parameter of the i-th business node of the j-th business block and the power supply parameter threshold stored in the database.

[0035] The business nodes of power supply supervision in the fresh tobacco leaf curing room area include raw material receiving, tobacco leaf sorting, tobacco leaf grading, cleaning impurities, curing room disinfection, tobacco leaf filling, curing operation, tobacco leaf cooling, sample collection, appearance inspection, chemical analysis, quality assessment, packaging operation, labeling management, logistics and transportation, and storage management, which correspond to different business segments respectively; among them, raw material receiving, tobacco leaf sorting, and tobacco leaf grading correspond to the tobacco leaf screening business segment, cleaning impurities, curing room disinfection, tobacco leaf filling, curing operation, and tobacco leaf cooling correspond to the curing processing business segment, sample collection, appearance inspection, chemical analysis, and quality assessment correspond to the tobacco leaf quality inspection business segment, and packaging operation, labeling management, logistics and transportation, and storage management correspond to the packaging and storage business segment.

[0036] By determining the weight coefficient of each business node through intelligent weight marking, more intelligent business node power supply management and control can be achieved, and the power supply efficiency of the business node corresponding to the business segment can be accurately expressed. Based on the importance and power supply demand of different business nodes relative to the business segment, the weight coefficient can be reasonably adjusted to optimize resource allocation, improve resource utilization and system efficiency, and dynamically adjust the weight coefficient based on real-time data and threshold comparison feedback information to ensure that the power supply level of the business node always remains in the comprehensive optimal state of power supply efficiency. By dynamically adjusting and optimizing the weight coefficient of each business node, it can intelligently adapt to different business needs and environmental changes, and improve the flexibility and adaptability of the system.

[0037] The block information scoring module is used to count the power supply parameters of each power supply business block based on the power supply of each power supply business node in the fresh tobacco leaf curing room area and the power supply weight coefficient corresponding to each power supply business node. Each power supply business block includes multiple power supply business nodes, and the power supply parameters of each power supply business block are scored for block information based on the set business block power supply correction factor of each power supply business block.

[0038] The business block power supply correction factors of each power supply business block are extracted from the mapping set in the database. A mapping set of power supply parameters of the power supply business block measured historically and the business block power supply correction factors is established according to historical data to obtain the business block power supply correction factors corresponding to the power supply parameters of the current power supply business block. The block information score corresponding to any power supply business block is the product of the power supply parameters of the power supply business block and the business block power supply correction factors of each power supply business block set in the database, such as Figure 3As shown, the block information score includes the tobacco leaf screening block information score, the baking and processing block information score, the tobacco leaf quality inspection block information score, and the packaging and storage block information score, wherein the tobacco leaf screening block information score is the block information score for the tobacco leaf screening business segment, the baking and processing block information score is the block information score for the baking and processing business segment, the tobacco leaf quality inspection block information score is the block information score for the tobacco leaf quality inspection business segment, and the tobacco packaging and storage block information score is the block information score for the packaging and storage business segment.

[0039] Specifically, the process of determining the power supply parameters of each business segment is as follows: based on the correspondence between business segments and business nodes, determine the business nodes corresponding to each business segment; obtain the weight coefficient of each business node in the fresh tobacco leaf curing room area and the power supply of each business node in the fresh tobacco leaf curing room area; based on the correspondence between business segments and business nodes, comprehensively calculate the weight coefficient of each business node in the fresh tobacco leaf curing room area and the power supply of each business node in the fresh tobacco leaf curing room area to obtain the power supply parameters of each business segment of the corresponding business segment.

[0040] The purpose of block information scoring is as follows: First, the power supply parameters of each business node are too trivial and unstable, and it is difficult to compare them with the power supply parameter thresholds stored in the database. Only by summarizing them into business block power supply parameters for comparison will it have practical work significance. Secondly, business block is a concept in the business field, and block information is a concept in the digital information field. The two correspond but are not equivalent. However, the comprehensive score in the technical ranking evaluation method must be based on a unified standard. The block information score obtained by empowering the business block. The best and worst values ​​of the subsequent technical ranking evaluation module are based on the block information score. The process of obtaining the power supply parameters of any business block is: first, obtain the power supply of each business node corresponding to the business block. Secondly, multiply the power supply of each business node by the weight coefficient of each business node and then add them up. The calculated numerical result is the power supply parameter of the business block.

[0041] By matching business segments with business nodes, the management of power supply parameters can be made clearer and more orderly. Maintenance personnel can quickly locate specific business nodes according to different business segments, and make corresponding parameter adjustments and optimizations, reducing the workload of maintenance personnel for comprehensive inspections of the entire power supply system. Determining the power supply parameters of the tobacco segment based on the correspondence between business segments and business nodes and the weight coefficients of each business node in the fresh tobacco leaf curing room area can improve the effectiveness and adaptability of power supply supervision in the fresh tobacco leaf curing room area on the basis of ensuring the smoothness of the overall business process, and can ensure that important business segments obtain more stable and reliable power supply support, thereby reducing the risk of business interruptions or power supply failures.

[0042] The threshold comparison and adjustment module is used to generate the deviation rate between the power supply parameters of each power supply business segment and the power supply parameter threshold stored in the database, and adjust the power supply weight parameters of each power supply business node in the fresh tobacco baking room area based on the deviation rate.

[0043] Specifically, the specific analysis process for generating the deviation rate between the power supply parameters of each power supply business segment and the power supply parameter threshold stored in the database is: obtain the power supply parameter threshold stored in the database for each business segment; determine the difference between the power supply parameters of each business segment and the power supply parameter threshold stored in the database; and obtain the deviation rate based on a comprehensive analysis of the power supply parameters and the difference of each business segment.

[0044] The power supply parameter thresholds stored in the reference database for each business segment are intended to ensure that the contribution of the power supply of each power supply business node in the fresh tobacco leaf curing room area to the power supply efficiency of the corresponding business segment under the current power supply mode is reasonable and accurate, that is, the power supply weight parameters of each power supply business node in the fresh tobacco leaf curing room area used to calculate the power supply parameters of the business segment are reasonable and accurate. If they are inconsistent and deviations occur, the deviation rate is obtained based on a comprehensive analysis of the power supply parameters and differences of each business segment, which is used as a standard for adjusting the power supply weight parameters of each power supply business node in the fresh tobacco leaf curing room area, so that the contribution value of power supply resources to each power supply business node in the fresh tobacco leaf curing room area can be more reasonably evaluated during the power supply supervision process, resource utilization efficiency can be improved, key support can be given to important business segments, their normal operation can be ensured, and waste of resources and excessive costs can be avoided.

[0045] The technical ranking and evaluation module is used to integrate block information with reference to the business process direction to form a chain structure information combination, sort the block information score based on the chain structure information combination, analyze to obtain a comprehensive score, and issue early warning prompts to the power supply business nodes in the power supply business segment based on the comprehensive score.

[0046] Specifically, the specific analysis process of integrating block information to form a chain structure information combination with reference to the direction of the business process is as follows: obtaining the order of business nodes when the fresh tobacco leaf baking business process is executed, and the business sectors include tobacco leaf screening, baking processing, tobacco leaf quality inspection and packaging storage; integrating and arranging the business sectors based on the correspondence between the business sectors and the business nodes to form a business sector sequence; arranging each business sector based on the business sector sequence to form a chain structure information combination of the current fresh tobacco leaf baking room power supply area.

[0047] By integrating block information with reference to the direction of the business process to form a chain structure information combination, the connection and sequence between each link can be clearly presented, which can help regulatory agencies fully understand the process and links of the power supply business, and help regulate the operation of the entire power supply system. In addition, the logical relationship between the chain structure information combination and the business process facilitates the adjustment of the business segment and business node corresponding to the comprehensive score traceability during the power supply supervision process. By integrating block information with reference to the direction of the business process to form a chain structure information combination, the block scoring information corresponding to each business segment can be integrated more orderly and efficiently, reducing the fragmentation and repeated integration of block scoring information. The chain structure information combination can clearly record the source and flow of information, which is convenient for tracing and tracking the historical trajectory of information. Information access and transmission authority, improve the security and confidentiality of information, and prevent the risk of information leakage and tampering. The chain structure information combination can clearly record the source of the block scoring information, which is convenient for tracing and tracking its historical trajectory to optimize the key tracing of power supply mode adjustment.

[0048] Specifically, the block information scores are sorted based on the chain structure information combination, and the analysis process for obtaining the comprehensive score is as follows: obtain the chain structure information combination and sort the block information scores based on the chain structure information combination, and obtain the sorting results of the block information scores of each obligation block; extract the highest value of the block information score in the sorting results, and record the highest value as the optimal value; extract the lowest value of the block information score in the sorting results, and record the lowest value as the worst value; calculate the best block distance based on the best value obtained from the block information score sorting and each block information score, and calculate the worst block distance based on the worst value obtained from the block information score sorting and each block information score; and obtain the comprehensive score based on the analysis of the best block distance and the worst block distance.

[0049] The comprehensive score is used to indicate the degree of concentration of the information scores of each block at the optimal value. It takes into account the power supply efficiency and mutual relationships of all business segments, and overcomes the assessment shortcoming of waste of power resources caused by high power supply efficiency of a single business segment but mismatched overall power supply efficiency. The comprehensive score reflects the stability of the power supply efficiency of the overall power supply system and the matching degree of power supply efficiency of each business segment in the business process in the fresh tobacco baking area.

[0050] The comprehensive score can be obtained by ensemble learning through gradient boosting tree, initializing the regression tree or classification tree to calculate the residual between the predicted value and the actual value of the model, training the next tree based on the residual to fit these residuals, and adjusting the predicted value of the model by minimizing the gradient of the loss function. Repeating the fitting makes the model more accurate in the residual direction to obtain the comprehensive score. It can also be obtained by the following calculation method. The calculation formula of the comprehensive score is: ; In the formula, S is the comprehensive score, ZL is the worst plate distance, and ZY is the best plate distance.

[0051] ZL is the worst plate distance, and its calculation formula is: ; Where, j=1,2,3...,x,.,y,...,n, x is the business segment number corresponding to the worst value, y is the business segment number corresponding to the best value, is the business segment correction factor of the jth business segment, w ji The business node power supply for the i-th business node of the j-th business segment, is the business segment correction factor of the business segment corresponding to the worst value, is the power supply weight coefficient of the ith business node of the business segment corresponding to the worst value, w xi The business node power supply of the i-th business node of the business segment corresponding to the worst value.

[0052] ZY is the optimal plate distance, and its calculation formula is: ; is the business segment correction factor of the business segment corresponding to the optimal value, is the power supply weight coefficient of the i-th business node of the business sector corresponding to the optimal value, w yi The business node power supply of the i-th business node of the business segment corresponding to the optimal value.

[0053] The comprehensive score takes into account the matching relationship of the power supply efficiency of all business segments, and considers the distribution trend between the block information scores of all business segments based on the optimal value and the worst value. It is used to indicate the concentration degree of each block information score at the optimal value, and intuitively sees the difference in power supply efficiency between business segments. While improving the power supply efficiency of business segments and block information scores, it ensures the stability and matching degree of power supply efficiency, provides a more comprehensive and accurate evaluation, and helps guide regulatory decisions, discover potential optimization opportunities, and achieve more effective resource allocation.

[0054] Specifically, the comprehensive score is used to indicate the degree of concentration of the information scores of each block at the optimal value, characterizing the power supply efficiency of the overall power supply system. The process of issuing early warning prompts to the power supply business nodes in the power supply business segment based on the comprehensive score is as follows: comparing the comprehensive score with the reference value of the comprehensive score of the power supply area of ​​the tobacco curing room stored in the database; if the comprehensive score is greater than the reference value of the comprehensive score of the power supply area of ​​the tobacco curing room stored in the database, no early warning prompt is issued in the power supply business segment; if the comprehensive score is less than or equal to the reference value of the comprehensive score of the power supply area of ​​the tobacco curing room stored in the database, then based on the correspondence between the block information score and the business segment in the chain structure information combination, the business segment corresponding to the worst value is traced back, and an early warning prompt is issued to the business segment corresponding to the worst value.

[0055] The comprehensive score is the decision-making basis for power supply adjustment of the power supply business nodes in the power supply business sector. If the comprehensive score is less than or equal to the reference value of the comprehensive score of the tobacco curing room power supply area stored in the database, the power supply adjustment needs to be carried out according to the standard process of power supply adjustment. The standard process is to trace back to the business sector corresponding to the worst value based on the correspondence between the block information score and the business sector in the chain structure information combination, and based on the correspondence between the business sector and each business node, extract the weight coefficient of each business node corresponding to the business sector corresponding to the worst value, and increase the power supply of the power supply business node corresponding to the maximum value of the business node weight coefficient; Based on the combination of chain structure information, the correspondence between business sectors and each business node, and the business node weight coefficient traceability adjustment of key business nodes can accurately identify the influence relationship and mutual correlation between different business nodes. If the deviation rate between the power supply parameters of the power supply business sector and the power supply parameter threshold stored in the database is less than zero, it means that the power supply efficiency contribution value of the business nodes in the business sector to the business sector is underestimated. If the deviation rate between the power supply parameters of the power supply business sector and the power supply parameter threshold stored in the database is greater than or equal to zero, it means that the power supply efficiency contribution value of the business nodes in the business sector to the business sector is overestimated. The calculation formula of the weight coefficient of each business node in the fresh tobacco leaf curing room can reflect the adjustment direction of the deviation rate for the corresponding business node weight coefficient. By timely adjusting the weight coefficients of these key business nodes, the accurate expression of the power supply efficiency of each business sector is guaranteed. On this basis, rapid regulation and optimization of the power supply system and a new round of comprehensive scoring can be achieved to ensure the reliability and stability of power supply, which is conducive to improving the rationality of power supply supervision and the timeliness of power supply regulation. After the adjustment is completed, the comprehensive score of the power supply area of ​​the tobacco leaf curing room after the power supply adjustment is obtained again can help the power supply supervision to continuously optimize and iterate, and achieve dynamic balance and optimization.

[0056] The standard process of power supply regulation based on comprehensive scoring is different from the power supply weight parameter adjustment of each power supply business node in the fresh tobacco leaf curing room area by the threshold adjustment module. The power supply weight parameter adjustment of each power supply business node by the threshold adjustment module is only to ensure that the contribution value and importance of the business node in any business segment to the power supply efficiency of the business segment are accurately evaluated, so as to achieve accurate expression of the power supply efficiency of the business segment. The block information scoring and further comprehensive scoring are carried out on the basis of the accurately calculated and expressed power supply efficiency of each business segment, which is conducive to improving the data quality of power supply regulation, improving the reliability and accuracy of power supply regulation decision-making, and then realizing the comprehensive evaluation of the power supply efficiency of the power supply system based on the relationship between business segments through the comprehensive scoring of the power supply system; By considering the degree of matching of the power supply mode of the overall power supply system in terms of power supply efficiency, the short-sighted behavior of the power supply supervision system in pursuing the maximization of power supply efficiency in each business segment can be avoided, so as to fully understand the overall power supply status of the system. This will help to timely discover the efficiency problems and potential optimization space in the power supply system, and take corresponding measures to improve and optimize it. The comprehensive score of the power supply system can provide managers and decision makers with direct and objective data evaluation and power supply control decision-making basis, and take corresponding measures to improve it, thereby improving the overall operating efficiency of the power supply system.

Claims

1. A fresh tobacco leaf curing room regional power supply monitoring system based on big data, characterized in that: include: Intelligent weight marking module, used to determine the power supply weight coefficient of each power supply business node in the fresh tobacco curing room area; The sensor information capture module is used to obtain the power supply of each power supply business node in the fresh tobacco leaf curing room area; The block information scoring module is used to collect statistics on the power supply parameters of each power supply business block based on the power supply of each power supply business node in the fresh tobacco leaf curing room area and the power supply weight coefficient corresponding to each power supply business node. Each power supply business block includes multiple power supply business nodes, and the power supply parameters of each power supply business block are scored based on the set business block power supply correction factor of each power supply business block. The business blocks include tobacco leaf screening, curing processing, tobacco leaf quality inspection and packaging storage; A threshold comparison and adjustment module is used to generate a deviation rate between the power supply parameters of each power supply business segment and the power supply parameter threshold stored in the database, and adjust the power supply weight parameters of each power supply business node in the fresh tobacco leaf curing room area based on the deviation rate; The technical ranking and evaluation module is used to integrate block information in accordance with the business process direction to form a chain structure information combination, sort the block information scores of each power supply business segment based on the chain structure information combination, analyze the comprehensive score, and issue early warning prompts to the power supply business segment based on the comprehensive score.

2. According to the big data-based fresh tobacco leaf curing room regional power supply monitoring system of claim 1, it is characterized by: The weight coefficient of each business node in the fresh tobacco leaf curing room area is used to characterize the power supply efficiency of the business node in its corresponding business section in the fresh tobacco leaf curing room area. The process of determining the weight coefficient of each business node in the fresh tobacco leaf curing room area is as follows: Obtain the power supply demand parameters of each business node in the fresh tobacco drying room area; The weight coefficient of each business node is obtained based on the power supply demand parameters of each business node in the fresh tobacco leaf curing room area.

3. According to the big data-based fresh tobacco leaf curing room regional power supply monitoring system of claim 2, it is characterized by: The power supply requirement parameters of the service node specifically include: The power consumption of equipment used by business nodes, the power conversion efficiency of equipment used at business nodes, the accumulated power consumption of equipment used at business nodes, the power supply duration per business cycle, and the deviation rate between the power supply parameters of business sections and the power supply parameter thresholds stored in the database.

4. According to the big data-based fresh tobacco leaf curing room regional power supply monitoring system of claim 2, it is characterized by: The calculation formula of the weight coefficient of each business node in the fresh tobacco leaf curing room area is as follows: ； In the formula, j is the number of each business block in the fresh tobacco leaf curing room area, j=1,2,3,…,n, n is the total number of business blocks, i is the number of each business node in each business block, i=1,2,3,…,m, m is the total number of business nodes in each business block, is the power supply weight coefficient of the i-th business node of the j-th business sector in the fresh tobacco leaf curing room area, a ji The electrical power of the equipment used by the business node of the i-th business node of the j-th business segment, b ji is the power conversion efficiency of the business node equipment of the i-th business node of the j-th business segment, c ji is the cumulative power consumption of the business node equipment of the i-th business node of the j-th business segment, d ji The power supply duration per unit business cycle of the i-th business node of the j-th business segment, is the deviation rate between the business block power supply parameter of the i-th business node of the j-th business block and the power supply parameter threshold stored in the database, and e is a natural constant.

5. According to the big data-based fresh tobacco leaf curing room regional power supply monitoring system of claim 1, it is characterized by: The process of determining the power supply parameters for each business segment is as follows: Based on the correspondence between business sectors and business nodes, determine the business nodes corresponding to each business sector; Obtain the weight coefficient of each business node in the fresh tobacco leaf curing room area and the power supply of each business node in the fresh tobacco leaf curing room area; Based on the correspondence between business sectors and business nodes, the weight coefficients of each business node in the fresh tobacco leaf curing room area and the power supply of each business node in the fresh tobacco leaf curing room area are comprehensively obtained to obtain the corresponding power supply parameters of each business sector.

6. According to the big data-based fresh tobacco leaf curing room regional power supply monitoring system of claim 1, it is characterized by: The specific analysis process for generating the deviation rate between the power supply parameters of each power supply business segment and the power supply parameter thresholds stored in the database is as follows: Obtain the power supply parameter thresholds stored in the database for each business segment; Determine the difference between the power supply parameters of each business segment and the power supply parameter thresholds stored in the database; The deviation rate is obtained based on a comprehensive analysis of the power supply parameters and differences of each business segment.

7. The regional power supply monitoring system for fresh tobacco leaf curing room based on big data according to claim 1 is characterized by: The specific analysis process of integrating block information to form a chain structure information combination based on the business process direction is as follows: Get the order of business nodes when executing the fresh tobacco leaf baking business process; Integrate and arrange business segments based on the corresponding relationship between business segments and business nodes to form a business segment sequence; Arrange each business segment in order based on the business segment to form a chain structure information combination of the current fresh tobacco leaf drying room power supply area.

8. The regional power supply monitoring system for fresh tobacco leaf curing room based on big data according to claim 1 is characterized by: Based on the chain structure information combination and sorting block information score, the analysis process of obtaining the comprehensive score is as follows: Obtain the chain structure information combination and perform block information scoring sorting based on the chain structure information combination to obtain the sorting results of the block information scores of each power supply business segment; Extract the highest value of the block information score in the sorting results, and record the highest value as the optimal value; Extract the lowest value of the block information score in the sorting results, and record the lowest value as the worst value; The best block distance is calculated based on the best value obtained by sorting the block information scores and the scores of each block information, and the worst block distance is calculated based on the worst value obtained by sorting the block information scores and the scores of each block information; A comprehensive score is obtained based on the analysis of the optimal plate distance and the worst plate distance.

9. The regional power supply monitoring system for fresh tobacco leaf curing room based on big data according to claim 8 is characterized by: The calculation formula of the comprehensive score is as follows: ； In the formula, S is the comprehensive score, ZL is the worst plate distance, and ZY is the best plate distance.

10. The regional power supply monitoring system for fresh tobacco leaf curing room based on big data according to claim 8 is characterized by: The comprehensive score is used to indicate the degree of concentration of the information scores of each block at the optimal value, and characterizes the power supply efficiency of the overall power supply system. The process of early warning prompts for the power supply business sector based on the comprehensive score is as follows: Compare the comprehensive score with the reference value of the comprehensive score of the power supply area of ​​the tobacco curing room stored in the database; If the comprehensive score is greater than the reference value of the comprehensive score of the tobacco curing room power supply area stored in the database, no early warning will be issued to the power supply business section; If the comprehensive score is less than or equal to the reference value of the comprehensive score of the power supply area of ​​the tobacco curing room stored in the database, the business segment corresponding to the worst value is traced back based on the correspondence between the block information score and the business segment in the chain structure information combination, and an early warning prompt is issued for the business segment corresponding to the worst value.

Images