A big data-based power supply supervision system for fresh tobacco leaf baking houses

Abstract

The present invention relates to the technical field of power monitoring and management, and specifically provides a power supply supervision system for the fresh tobacco leaf baking house area based on big data. The system is provided with an intelligent weight marking module, a sensing information capture module, a block information scoring module, a threshold comparison and adjustment module, and a technology ranking and evaluation module, which can dynamically adjust the power supply level of service nodes to always maintain the optimal state of the comprehensive power supply efficiency, and intelligently adapt to different service requirements and environmental changes; the chain structure information combination can clearly record the source of the block scoring information, facilitating the traceability and tracking of its historical trajectory for optimizing the key traceability of power supply mode adjustment, realizing the multi-dimensional comprehensive evaluation of power supply efficiency, improving the matching degree of power supply efficiency of each business segment, and the accuracy of the comprehensive control decision-making for power supply supervision in the fresh tobacco leaf baking house area.

Description

Technical Field

[0001] The present invention relates to the technical field of power monitoring and management, and specifically to a power supply supervision system for fresh tobacco leaf baking rooms 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 and baking effect of the tobacco leaves. Power supply supervision can monitor the real-time power consumption of baking-related equipment, promptly detect abnormal situations and give alarms and handle them to prevent problems such as equipment failures, deterioration of tobacco leaf quality, and low processing efficiency caused by insufficient or unstable power supply. Existing power supply supervision systems mostly use sensors and monitoring devices to collect various parameters of each device in the area of the fresh tobacco leaf baking room in real time, directly analyze various parameters through data processing technology, and at the same time, through advanced information collection technologies such as cloud computing and wireless communication, achieve efficient information transmission for power supply adjustment decisions, and realize the real-time and coordinated power supply supervision and adjustment in the area of the fresh tobacco leaf baking room.

[0003] However, there are still some deficiencies in the existing regional power supply supervision systems, 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 an important indicator to measure the operation effect of the power supply system, power supply efficiency is an important parameter for power supply monitoring and power supply adjustment based on the energy utilization efficiency and cost-benefit 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 influence degree of each business node on the power supply efficiency of the corresponding business segment in the current power supply mode, making it difficult to promptly discover and solve problems such as low efficiency or waste in the power supply system, and 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, only focusing on improving the power supply efficiency in the current power supply mode alone while ignoring the matching degree of the power supply efficiency of each segment in the overall power supply mode. On the other hand, segmentation makes it difficult to determine the traceability relationship, that is, it is difficult to integrate the judgment of whether there is a need for power supply mode adjustment in the current power supply system and the specific regulation of the power supply mode, making it difficult to achieve the integration of monitoring and control of the power supply supervision system, and it is difficult to grasp the key points of the subsequent regulation and optimization of the power supply mode, increasing the time cost of meeting the power supply demand and affecting the timeliness of the comprehensive understanding and regulation decision-making of the power supply system.

[0005] In summary, the traditional power supply supervision system for tobacco leaf baking houses often has supervision shortcomings such as limited data processing capacity, inability to accurately give efficiency evaluation, high system integration difficulty, and lack of synergy. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides a power supply supervision system for fresh tobacco leaf baking house areas based on big data, which can dynamically adjust the power supply level of business nodes to keep it always in the optimal state of comprehensive power supply efficiency, and can intelligently adapt to different business needs and environmental changes.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: A power supply supervision system for fresh tobacco leaf baking house areas based on big data, comprising:

[0008] An intelligent weight marking module for determining the power supply weight coefficients of each power supply business node in the fresh tobacco leaf baking house area;

[0009] A sensing information capture module for obtaining the power supply amounts of each power supply business node in the fresh tobacco leaf baking house area;

[0010] A block information scoring module for statistically analyzing the power supply parameters of each power supply business segment based on the power supply amounts of each power supply business node in the fresh tobacco leaf baking house area and the corresponding power supply weight coefficients of each power supply business node. Each power supply business segment includes multiple power supply business nodes, and performs block information scoring on the power supply parameters of each power supply business segment based on the set business segment power supply correction factors of each power supply business segment. The business segments include tobacco leaf screening, baking and processing, tobacco leaf quality inspection, and packaging and storage;

[0011] A threshold comparison and adjustment module 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, and adjusting the power supply weight parameters of each power supply business node in the fresh tobacco leaf baking house area based on the deviation rate;

[0012] A technology ranking and evaluation module for integrating block information to form a chained structure information combination with reference to the business process direction, ranking the block information scores of each power supply business segment based on the chained structure information combination, analyzing to obtain a comprehensive score, and giving an early warning prompt for the power supply business segment based on the comprehensive score.

[0013] Preferably, the weight coefficients of each business node in the fresh tobacco leaf baking house area are used to represent the power supply efficiency of the business node in its corresponding business segment in the fresh tobacco leaf baking house area. The determination process of the weight coefficients of each business node in the fresh tobacco leaf baking house area is as follows:

[0014] Obtain the power supply demand parameters of each business node in the fresh tobacco leaf baking house area;

[0015] The weight coefficients of each service node in the fresh tobacco leaf baking house area are obtained based on the power supply demand parameters of each service node in the area.

[0016] Preferably, the power supply demand parameters of the service node specifically include:

[0017] The electric power of the equipment used by the service node, the electric energy conversion efficiency of the service node equipment, the cumulative power consumption of the service node equipment, the power supply duration per unit service cycle, the power supply parameter of the service segment, and the deviation rate of the power supply parameter stored in the database from the power supply parameter threshold.

[0018] Preferably, the calculation formula for the weight coefficients of each service node in the fresh tobacco leaf baking house area is as follows:

[0019] ;

[0020] In the formula, j is the number of each service segment in the fresh tobacco leaf baking house area, j = 1, 2, 3,..., n, n is the total number of service segments, i is the number of each service node in each service segment, i = 1, 2, 3,..., m, m is the total number of service nodes in each service segment, is the power supply weight coefficient of the i-th service node in the j-th service segment in the fresh tobacco leaf baking house area, a ji is the electric power of the equipment used by the i-th service node in the j-th service segment, b ji is the electric energy conversion efficiency of the equipment of the i-th service node in the j-th service segment, c ji is the cumulative power consumption of the equipment of the i-th service node in the j-th service segment, d ji The power supply duration per unit service cycle of the i-th service node in the j-th service segment, is the deviation rate of the power supply parameter of the i-th service node in the j-th service segment from the power supply parameter threshold stored in the database, and e is the natural constant.

[0021] Preferably, the process of determining the power supply parameters of each service segment is as follows:

[0022] Based on the correspondence between the service segment and the service node, determine the service nodes corresponding to each service segment;

[0023] Obtain the weight coefficients of each service node in the fresh tobacco leaf baking house area and the power supply amounts of each service node in the fresh tobacco leaf baking house area;

[0024] Based on the correspondence between the service segment and the service node, comprehensively combine the weight coefficients of each service node in the fresh tobacco leaf baking house area and the power supply amounts of each service node in the fresh tobacco leaf baking house area to obtain the corresponding power supply parameters of each service segment.

[0025] Preferably, 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:

[0026] Obtain the power supply parameter thresholds stored in the database for each business segment;

[0027] Determine the difference between the power supply parameters of each business segment and the power supply parameter thresholds stored in the database;

[0028] Based on the comprehensive analysis of the power supply parameters and differences of each business segment, obtain the deviation rate.

[0029] Preferably, the specific analysis process for integrating block information into a chain - type structure information combination with reference to the business process direction is as follows:

[0030] Obtain the order of business nodes when the fresh tobacco leaf baking business process is executed;

[0031] Based on the correspondence between business segments and business nodes, integrate and arrange the business segments to form the order of business segments;

[0032] Arrange each business segment based on the order of business segments to form the chain - type structure information combination of the power supply area of the current fresh tobacco leaf baking house.

[0033] Preferably, the analysis process for obtaining the comprehensive score based on the scoring of block information sorted by the chain - type structure information combination is as follows:

[0034] Obtain the chain - type structure information combination and perform sorting on the scoring of block information based on the chain - type structure information combination to obtain the sorting result of the scoring of block information for each power supply business segment;

[0035] Extract the highest value of the scoring of block information in the sorting result, and record the highest value as the optimal value;

[0036] Extract the lowest value of the scoring of block information in the sorting result, and record the lowest value as the worst value;

[0037] Calculate the optimal - plate distance based on the optimal value obtained from the sorting of block information scoring and each block information scoring, and calculate the worst - plate distance based on the worst value obtained from the sorting of block information scoring and each block information scoring;

[0038] Based on the optimal - plate distance and the worst - plate distance, obtain the comprehensive score.

[0039] Preferably, the calculation formula for the comprehensive score is as follows:

[0040] ;

[0041] In the formula, S is the comprehensive score, ZL is the worst - plate distance, and ZY is the optimal - plate distance.

[0042] Preferably, the comprehensive score is used to represent the concentration degree of each block information score at the optimal value, and characterize the power supply efficiency of the overall power supply system. The process of giving early warning prompts to the power supply business section based on the comprehensive score is as follows:

[0043] Compare the comprehensive score with the reference value of the comprehensive score of the power supply area of the tobacco leaf baking house stored in the database;

[0044] If the comprehensive score is greater than the reference value of the comprehensive score of the power supply area of the tobacco leaf baking house stored in the database, no early warning prompt is given to the power supply business section;

[0045] 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 leaf baking house stored in the database, trace back to the business section corresponding to the worst value based on the corresponding relationship between the block information score and the business section in the chain structure information combination, and give an early warning prompt to the business section corresponding to the worst value.

[0046] The present invention has the following beneficial effects:

[0047] The present invention determines the weight coefficients of each business node through intelligent weight marking, so as to realize more intelligent management and control of the power supply quantity of business nodes, accurately express the power supply efficiency of the business sections corresponding to the business nodes, reasonably adjust the weight coefficients based on the importance and power supply demand of different business nodes relative to the business sections, optimize resource allocation, improve resource utilization rate and system efficiency, dynamically adjust the weight coefficients based on real-time data and threshold comparison feedback information, ensure that the power supply level of business nodes always remains in the state of overall optimal power supply efficiency, and can intelligently adapt to different business needs and environmental changes by dynamically adjusting and optimizing the weight coefficients of each business node, improving the flexibility and adaptability of the system.

[0048] The present invention integrates block information in the direction of the business process to form a chain structure information combination, more orderly and efficiently integrates the block score information corresponding to each business section, reduces the fragmentation and repeated integration of block score information, the chain structure information combination can clearly record the source and flow of information, is convenient for tracing and tracking the historical track of information, the access and transmission permissions of information, improves the security and confidentiality of information, prevents the risk of information leakage and tampering, and the chain structure information combination can clearly record the source of block score information, which is convenient for tracing and tracking its historical track for optimizing the focus of power supply mode adjustment.

[0049] The present invention realizes a comprehensive evaluation of the power supply efficiency of the power supply system based on the mutual relationship of business segments 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, it avoids the short-sighted behavior of the power supply supervision system that simply pursues the maximization of power supply efficiency in each business segment, thereby comprehensively understanding the overall power supply situation of the system. This helps to timely discover the efficiency problems and potential optimization space existing in the power supply system, take corresponding measures for improvement and optimization. The comprehensive scoring of the power supply system can provide direct and objective data evaluation and a basis for power supply regulation decision-making for managers and decision-makers, and take corresponding measures for improvement, thereby improving the overall operating efficiency of the power supply system.

[0050] The present invention involves big data technology in multiple modules. Through close integration with big data technology, a revolutionary improvement of the power supply system in the area of fresh tobacco leaf baking houses is achieved. By using big data analysis, it is possible to monitor in real time and dynamically adjust the power supply strategy, optimize resource allocation, 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, thereby reducing unexpected power outage events. In addition, the application of big data also enhances the intelligent level of the system. Through intelligent weight adjustment and business process optimization, the accuracy and adaptability of power supply supervision are further improved. These advantages not only reduce the operating cost, but also support a sustainable power supply mode, and ultimately provide users with a more stable and efficient service experience. Brief Description of the Drawings

[0051] Figure 1 is a schematic diagram of the connection of the system modules of the present invention;

[0052] Figure 2 is an image of the change of the weight coefficient of the business node in the area of the fresh tobacco leaf baking house with the deviation rate of the power supply parameters of the business segment from the power supply parameter threshold stored in the database;

[0053] Figure 3 is a schematic diagram of the block information scoring corresponding to the chain structure information combination. Detailed Embodiments

[0054] The following clearly and completely describes the technical solutions in the embodiments of the present invention with reference to the drawings.

[0055] As Figure 1 shown, a power supply supervision system for the area of fresh tobacco leaf baking houses based on big data includes an intelligent weight marking module, a sensing information capture module, a block information scoring module, a threshold comparison and adjustment module, and a technology ranking and evaluation module.

[0056] The intelligent weight marking module is used to determine the power supply weight coefficients of each power supply business node in the area of the fresh tobacco leaf baking house.

[0057] Specifically, the weight coefficients of each business node in the fresh tobacco leaf baking house area are used to characterize the power supply efficiency of the business node in its corresponding business segment in the fresh tobacco leaf baking house area. Due to the business relationship between the business segments and each business node in the fresh tobacco leaf baking house area, the power supply efficiency of any business segment cannot be obtained simply by adding up the power supply amounts of each business node in the corresponding fresh tobacco leaf baking house area. Based on the importance of each business node in the fresh tobacco leaf baking house area corresponding to the business segment compared to the entire business segment, the process of determining the weight coefficients of each business node in the fresh tobacco leaf baking house area is as follows: Obtain the power supply demand parameters of each business node in the fresh tobacco leaf baking house area; Based on the obtained power supply demand parameters of each business node in the fresh tobacco leaf baking house area, obtain the weight coefficients of each business node.

[0058] The business segments of power supply supervision in the fresh tobacco leaf baking house area include tobacco leaf screening, baking and processing, tobacco leaf quality inspection, and packaging and storage. Further, the tobacco leaf screening business segment includes raw material reception, tobacco leaf sorting, and tobacco leaf grading; the baking and processing business segment includes impurity cleaning, baking house disinfection, tobacco leaf loading, baking operation, and tobacco leaf cooling; the tobacco leaf quality inspection business segment includes sample collection, appearance inspection, chemical analysis, and quality assessment; the packaging and storage business segment includes packaging operation, label management, logistics transportation, and storage management.

[0059] Specifically, the power supply demand parameters of the business node include: the electric power of the equipment used by the business node, the electric energy 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 power supply parameters of the business segment and the power supply parameter threshold stored in the database.

[0060] The electric power of the equipment used by the business node, the cumulative power consumption of the business node equipment, and the power supply duration per unit business cycle are important indicators of the basic power energy consumption of each business node in the fresh tobacco leaf baking house area, which are used to judge the power supply demand of the business node, reasonably arrange the power supply time and power capacity, optimize the equipment usage duration and power control, and avoid the situation of power supply shortage or surplus;

[0061] The electric power of the equipment used by the business node refers to evaluating the power energy utilization efficiency of the equipment, avoiding energy efficiency problems in the power demand of the equipment. Based on the electric power of the equipment used by the business node, adjusting or updating the power supply demand parameters of the business node can improve the energy utilization efficiency and reduce the overall energy consumption. The electric power of the equipment used by the business node can also provide important reference data for power demand matching fault diagnosis. Abnormal power fluctuations or overload situations may indicate problems with power demand matching of the equipment. Discovering and handling faults in a timely manner can ensure the normal operation of the business and improve production efficiency;

[0062] 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 evaluating the accuracy of the power supply demand of the business node. By monitoring the deviation rate, it is ensured that under a reasonable power supply, the business segment can reach the power supply parameter efficiency threshold set for each business segment in the database, guaranteeing the power supply quality and reducing the waste of electric energy caused by unreasonable evaluation of power supply demand.

[0063] For each business node weight coefficient in each fresh tobacco leaf baking room area, mathematical optimization algorithms can be used, combined with business data and constraint conditions, to solve for appropriate weight coefficients, such as linear programming, integer programming, programming algorithms, etc., or use a random forest model to calculate the importance scores of each feature. These scores can reflect the contribution degree and importance of each business node in the overall model. The contribution degrees and importance evaluation results of multiple models are averaged to obtain more accurate weight coefficients for each business node in each fresh tobacco leaf baking room area. It can also be obtained through the following calculation method. The calculation formula for the weight coefficient of each business node in each fresh tobacco leaf baking room area is:

[0064]

[0065] In the formula, j is the number of each business segment in the fresh tobacco leaf baking room area, j = 1, 2, 3,..., n, n is the total number of business segments, i is the number of each business node in each business segment, i = 1, 2, 3,..., m, m is the total number of business nodes in each business segment. is the power supply weight coefficient of the i-th business node of the j-th business segment in the fresh tobacco leaf baking room area, a ji is the electric power of the equipment used by the i-th business node of the j-th business segment, b ji is the power conversion efficiency of the equipment of the i-th business node of the j-th business segment, c ji is the cumulative power consumption of the 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 power supply parameters of the j-th business segment and the power supply parameter thresholds stored in the database for the i-th business node. e is the natural constant.

[0066] The weight coefficients of each business node in the fresh tobacco leaf baking house area are calculated based on the importance of the power supply of each business node in the fresh tobacco leaf baking house area corresponding to the business segment compared to the power supply efficiency of the entire business segment. The power supply of each business node is kept relatively constant as the object of supervision and control. Its contribution value and importance to the power supply efficiency of the entire business segment depend on the supply and demand of the corresponding equipment. At the same time, the electric power of the equipment used by the business node and the power 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 segment and the power supply parameter thresholds stored in the database reflects the gap between the equipment operation state and the expected state and the accuracy of the expression of the power supply efficiency of any business segment. Therefore, based on the power supply demand parameters of the business node, the weight coefficients of each business node in the fresh tobacco leaf baking house area can be calculated from the calculated parameters.

[0067] As Figure 2 shown, it is an image of the change of the weight coefficient of the business node in the fresh tobacco leaf baking house area with the deviation rate between the power supply parameters of the business segment and the power supply parameter thresholds stored in the database. Among them, the x-axis represents the deviation rate between the power supply parameters of the business segment and the power supply parameter thresholds stored in the database, and the y-axis represents the weight coefficient of the business node in the fresh tobacco leaf baking house area. It can be intuitively understood how the deviation rate between the power supply parameters of the business segment and the power supply parameter thresholds stored in the database affects the weight coefficient of the business node in the fresh tobacco leaf baking house area. The higher the deviation rate between the power supply parameters of the business segment and the power supply parameter thresholds stored in the database, the smaller the weight coefficient of the business node in the fresh tobacco leaf baking house area, indicating that the importance of the power supply of this business node in the fresh tobacco leaf baking house area compared to the power supply efficiency of the corresponding business segment is lower. At present, the weight coefficient of this business node in the fresh tobacco leaf baking house area is wrongly estimated and set, and it should be adjusted. As the deviation rate between the power supply parameters of the business segment and the power supply parameter thresholds stored in the database increases, its influence on the weight coefficient of the business node in the fresh tobacco leaf baking house area gradually weakens and finally stabilizes at a lower value. Set the electric power of the equipment used by the business node to be 10 unchanged, the power conversion efficiency of the business node equipment to be 0.5 unchanged, the cumulative power consumption of the business node equipment to be 20 unchanged, and the power supply duration per unit business cycle to be 10 unchanged. Only change the deviation rate between the power supply parameters of the business segment and the power supply parameter thresholds stored in the database. The value of the weight coefficient of the business node in the fresh tobacco leaf baking house area is as follows:

[0068] Table 1: Example values of the deviation rate between the power supply parameters of the business segment and the power supply parameter thresholds stored in the database in the weight coefficient of the business node in the fresh tobacco leaf baking house area

[0069]

[0070] The sensing information capture module is used to obtain the power supply amounts of each power supply service node in the fresh tobacco leaf baking house area.

[0071] In Table 1, is the power supply weight coefficient of the i-th service node in the j-th service segment in the fresh tobacco leaf baking house area, a ji is the electric power of the equipment used by the i-th service node in the j-th service segment, with the unit of kW; b ji is the electric energy conversion efficiency of the equipment of the i-th service node in the j-th service segment, in the form of a percentage; c ji is the cumulative power consumption of the equipment of the i-th service node in the j-th service segment, with the unit of kilowatt-hour; d ji The power supply duration per unit service cycle of the i-th service node in the j-th service segment, with the unit of hour; is the deviation rate between the power supply parameter of the i-th service node in the j-th service segment and the stored power supply parameter threshold in the database.

[0072] The service nodes for power supply supervision in the fresh tobacco leaf baking house area include raw material reception, tobacco leaf sorting, tobacco leaf grading, impurity cleaning, baking house disinfection, tobacco leaf loading, baking operation, tobacco leaf cooling, sample collection, appearance inspection, chemical analysis, quality evaluation, packaging operation, label management, logistics transportation, and storage management, which respectively correspond to different service segments; among them, raw material reception, tobacco leaf sorting, and tobacco leaf grading correspond to the tobacco leaf screening service segment, impurity cleaning, baking house disinfection, tobacco leaf loading, baking operation, and tobacco leaf cooling correspond to the baking and processing service segment, sample collection, appearance inspection, chemical analysis, and quality evaluation correspond to the tobacco leaf quality inspection service segment, and packaging operation, label management, logistics transportation, and storage management correspond to the packaging and storage service segment.

[0073] By determining the weight coefficients of each service node through intelligent weight marking, more intelligent management and control of the power supply amounts of service nodes can be achieved, accurately expressing the power supply efficiency of the service segments corresponding to the service nodes. Based on the importance and power supply demand of different service nodes relative to the service segments, the weight coefficients are reasonably adjusted, resource allocation is optimized, resource utilization rate and system efficiency are improved. Based on real-time data and threshold comparison feedback information, the weight coefficients are dynamically adjusted to ensure that the power supply level of the service nodes always remains in the state of overall optimal power supply efficiency. By dynamically adjusting and optimizing the weight coefficients of each service node, it can intelligently adapt to different service requirements and environmental changes, and improve the flexibility and adaptability of the system.

[0074] The block information scoring module is used to statistically analyze the power supply parameters of each power supply business segment based on the power supply amounts of each power supply service node in the fresh tobacco leaf baking house area and the corresponding power supply weight coefficients of each power supply service node. Each power supply business segment includes multiple power supply service nodes, and the block information scoring of each power supply business segment is performed based on the set business segment power supply correction factors of each power supply business segment.

[0075] The set business segment power supply correction factors of each power supply business segment are extracted from the mapping set in the database. A mapping set of the power supply parameters of the power supply business segments measured historically and the business segment power supply correction factors is established according to historical data to obtain the business segment power supply correction factors corresponding to the power supply parameters of the current power supply business segment. The block information score corresponding to any power supply business segment is the product of the power supply parameters of the power supply business segment and the set business segment power supply correction factors of each power supply business segment in the database. As Figure 3 shown, the block information scoring includes the block information scoring of the tobacco leaf screening block, the block information scoring of the baking and processing block, the block information scoring of the tobacco leaf quality inspection block, and the block information scoring of the packaging and storage block. Among them, the block information scoring of the tobacco leaf screening block is the block information scoring for the tobacco leaf screening business segment, the block information scoring of the baking and processing block is the block information scoring for the baking and processing business segment, the block information scoring of the tobacco leaf quality inspection block is the block information scoring for the tobacco leaf quality inspection business segment, and the block information scoring of the tobacco leaf packaging and storage block is the block information scoring for the packaging and storage business segment.

[0076] Specifically, the process of determining the power supply parameters of each business segment is as follows: Based on the correspondence between the business segment and the service node, determine the service nodes corresponding to each business segment; obtain the weight coefficients of each service node in the fresh tobacco leaf baking house area and the power supply amounts of each service node in the fresh tobacco leaf baking house area; based on the correspondence between the business segment and the service node, comprehensively combine the weight coefficients of each service node in the fresh tobacco leaf baking house area and the power supply amounts of each service node in the fresh tobacco leaf baking house area to obtain the power supply parameters of the corresponding business segment of each business segment.

[0077] The purposes of the block information scoring are as follows: First, the power supply parameters of each business node are too trivial and lack stability, making it difficult to compare them with the power supply parameter thresholds stored in the database. Only by aggregating them into the power supply parameters of the business segment for comparison can there be practical working significance. Second, the business segment is a concept in the commercial field, while the block information is a concept in the digital information field. Although they correspond, they are not equivalent. However, the comprehensive scoring in the technical ranking evaluation method must be based on the block information scoring obtained by weighting the business segments under a unified standard. The best and worst values of the subsequent technical ranking evaluation module are based on the block information scoring. The process for obtaining the power supply parameter of any business segment is as follows: First, obtain the power supply amounts of the business nodes corresponding to the business segment. Second, multiply the power supply amounts of each business node by their respective weight coefficients and then sum them up. The resulting numerical value is the power supply parameter of the business segment.

[0078] By corresponding the business segments with the 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 a comprehensive inspection of the entire power supply system. Determining the power supply parameters of the tobacco leaf segment based on the corresponding relationship between the business segment and the business node and the weight coefficients of each business node in the fresh tobacco leaf baking house area can improve the effectiveness and adaptability of power supply supervision in the fresh tobacco leaf baking house 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 interruption or power supply failure.

[0079] 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 thresholds stored in the database, and adjust the power supply weight parameters of each power supply business node in the fresh tobacco leaf baking house area based on the deviation rate.

[0080] 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 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; comprehensively analyze the deviation rate based on the power supply parameters and differences of each business segment.

[0081] The power supply parameter thresholds stored in the reference database for each business segment are designed to ensure that the contribution value of the power supply quantity of each power supply service node in the fresh tobacco leaf baking house area under the current power supply mode to the power supply efficiency of the corresponding business segment is reasonable and accurate. That is, the power supply weight parameters of each power supply service node in the fresh tobacco leaf baking house area used to calculate the power supply parameters of the business segment are reasonable and accurate. If they are inconsistent and deviate, the deviation rate is obtained through comprehensive analysis based on the power supply parameters and differences of each business segment, and used as the standard for adjusting the power supply weight parameters of each power supply service node in the fresh tobacco leaf baking house area, so as to more reasonably evaluate the contribution value of power supply resources to each power supply service node in the fresh tobacco leaf baking house area during the power supply supervision process, improve the resource utilization efficiency, focus on supporting important business segments, ensure their normal operation, and avoid resource waste and excessive costs.

[0082] The technical sorting and evaluation module is used to integrate block information in the direction of the business process to form a chain-structured information combination, sort the block information scores based on the chain-structured information combination, obtain the comprehensive score, and give early warning prompts to the power supply service nodes in the power supply business segment based on the comprehensive score.

[0083] Specifically, the specific analysis process of integrating block information in the direction of the business process to form a chain-structured information combination is as follows: Obtain the order of business nodes when the fresh tobacco leaf baking business process is executed. The business segments include tobacco leaf screening, baking and processing, tobacco leaf quality inspection, and packaging and storage; Integrate and arrange the business segments based on the corresponding relationship between the business segments and business nodes to form the business segment order; Arrange each business segment based on the business segment order to form the chain-structured information combination of the current fresh tobacco leaf baking house power supply area.

[0084] By integrating block information in the direction of the business process to form a chain-structured information combination, the connections and order between each link are clearly presented, which can help the regulatory agency comprehensively understand the process and links of the power supply business, and contribute to the supervision of the operation of the entire power supply system. In addition, the logical relationship between the chain-structured information combination and the business process facilitates tracing back to the corresponding business segment and business node for adjustment according to the comprehensive score during the power supply supervision process. By integrating block information in the direction of the business process to form a chain-structured information combination, the block score information corresponding to each business segment is integrated more orderly and efficiently, reducing the fragmentation and repeated integration of block score information. The chain-structured information combination can clearly record the source and flow of information, facilitating the tracing and tracking of the historical trajectory of information. The access and transmission permissions of information are enhanced, improving the security and confidentiality of information, preventing the risk of information leakage and tampering. The chain-structured information combination can clearly record the source of block score information, facilitating tracing and tracking its historical trajectory for optimizing the key traceability of power supply mode adjustment.

[0085] Specifically, the analysis process for obtaining the comprehensive score based on the combined sorting of block information scores in the chain structure is as follows: Obtain the combined chain structure information and perform block information score sorting based on the combined chain structure information to obtain the sorting results of the block information scores for each obligation segment; Extract the highest value of the block information scores in the sorting results and record it as the optimal value; Extract the lowest value of the block information scores in the sorting results and record it as the worst value; Calculate the optimal segment distance based on the optimal value obtained from the block information score sorting and each block information score, and calculate the worst segment distance based on the worst value obtained from the block information score sorting and each block information score; Analyze the comprehensive score based on the optimal segment distance and the worst segment distance.

[0086] The comprehensive score is used to represent the concentration degree of each block information score at the optimal value, taking into account the power supply efficiency and mutual relationships of all business segments, overcoming the evaluation shortcoming of power resource waste 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 the power supply efficiency of each business segment in the business process within the fresh tobacco leaf baking area.

[0087] The comprehensive score can be obtained through integrated learning using gradient boosting trees. Initialize the prediction value and the residual between the actual value of the regression tree or classification tree calculation model, train the next tree based on the residual to fit these residuals, adjust the prediction value of the model by minimizing the gradient of the loss function, and continuously repeat the fitting to make the model obtain the comprehensive score more accurately in the residual direction. It can also be obtained through the following calculation method. The calculation formula for the comprehensive score is:

[0088] ;

[0089] In the formula, S is the comprehensive score, ZL is the worst segment distance, and ZY is the optimal segment distance.

[0090] ZL is the worst segment distance, and its calculation formula is:

[0091] ;

[0092] Among them, 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 optimal value, is the business segment correction factor of the jth business segment, w ji is the power supply of the ith business node of the jth 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 xiThe power supply of the i-th business node of the business segment corresponding to the worst value.

[0093] ZY is the optimal segment distance, and its calculation formula is:

[0094] ;

[0095] 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 segment corresponding to the optimal value, w yi is the power supply of the i-th business node of the business segment corresponding to the optimal value.

[0096] The comprehensive score considers the matching relationship of the power supply efficiency of all business segments, and based on the optimal value and the worst value, it considers the distribution trend between the block information scores of all business segments. It is used to represent the degree of concentration of each block information score at the optimal value, intuitively showing the difference in power supply efficiency between business segments. While improving the power supply efficiency and block information score of business segments, it ensures the stability and matching degree of power supply efficiency, provides a more comprehensive and accurate evaluation, helps guide regulatory decisions, discovers potential optimization opportunities, and realizes more effective resource allocation.

[0097] Specifically, the comprehensive score is used to represent the degree of concentration of each block information score at the optimal value, characterizing the power supply efficiency of the overall power supply system. The process of giving 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 leaf baking house 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 leaf baking house stored in the database, no early warning prompt is given to 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 leaf baking house stored in the database, then based on the corresponding relationship between the block information score and the business segment in the chain structure information combination, trace back to the business segment corresponding to the worst value, and give an early warning prompt to the business segment corresponding to the worst value.

[0098] The comprehensive score is the decision-making basis for power supply adjustment of the power supply business nodes 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 leaf baking house stored in the database, 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 segment corresponding to the worst value based on the corresponding relationship between the block information score and the business segment in the chain structure information combination, and based on the corresponding relationship between the business segment and each business node, extract the weight coefficients of each business node corresponding to the business segment 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;

[0099] Based on the combination of chain structure information, the corresponding relationship between business segments and each business node, and the retroactive adjustment of the weight coefficient of business nodes, the 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 segment and the power supply parameter threshold stored in the database is less than zero, it indicates that the contribution value of the business nodes within this business segment to the power supply efficiency of the business segment is underestimated. If the deviation rate between the power supply parameters of the power supply business segment and the power supply parameter threshold stored in the database is greater than or equal to zero, it indicates that the contribution value of the business nodes within this business segment to the power supply efficiency of the business segment is overestimated. The deviation rate can reflect the adjustment direction of the weight coefficient of the corresponding business node through the calculation formula of the weight coefficient of each business node in the fresh tobacco leaf baking house. By timely adjusting the weight coefficients of these key business nodes, the accurate expression of the power supply efficiency of each business segment can be ensured. On this basis, rapid regulation and optimization of the power supply system and a new round of comprehensive scoring can be achieved, ensuring the reliability and stability of power supply, helping to improve the rationality of power supply supervision and the timeliness of power supply regulation. After the adjustment is completed, obtaining the comprehensive score of the power supply area of the tobacco leaf baking house after power supply regulation again can help the power supply supervision to continuously optimize and iterate, achieving dynamic balance and optimization.

[0100] The standard process of power supply regulation based on comprehensive scoring is different from the adjustment of the power supply weight parameters of each power supply business node in the fresh tobacco leaf baking house area by the threshold adjustment module. The threshold adjustment module adjusts the power supply weight parameters of each power supply business node only to ensure that the contribution value and importance of the business nodes within any business segment to the power supply efficiency of the business segment are accurately evaluated, and to achieve the accurate expression of the power supply efficiency of the business segment. Based on the accurately calculated and expressed power supply efficiency of each business segment, block information scoring and further comprehensive scoring are carried out, which is beneficial to improving the data quality of power supply regulation, enhancing the reliability and accuracy of power supply regulation decisions, and then comprehensively evaluating the power supply efficiency of the power supply system based on the mutual relationship between business segments through the comprehensive score of the power supply system.

[0101] 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 that simply pursues the maximization of power supply efficiency in each business segment can be avoided, so as to comprehensively understand the overall power supply situation of the system. This helps to timely discover the efficiency problems and potential optimization space existing in the power supply system, and take corresponding measures for improvement and optimization. The comprehensive score of the power supply system can provide direct and objective data evaluation and power supply regulation decision-making basis for managers and decision-makers, and take corresponding measures for improvement, thereby improving the overall operation 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: 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.

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