Tobacco leaf raw material warehouse energy consumption optimization method based on alcoholization progress prediction

Abstract

The invention belongs to the technical field of tobacco leaf raw material storage, and discloses a tobacco leaf raw material warehouse energy consumption optimization method based on alcoholization progress prediction, which comprises the following steps: establishing a sensory quality evaluation function of each grade of tobacco leaves according to historical effective accumulated temperature data and sensory quality evaluation scores of stock tobacco leaves, establishing a tobacco leaf raw material alcoholization progress prediction model based on the sensory quality evaluation function of each grade of tobacco leaves; establishing an overall alcoholization quality difference index function based on the tobacco raw material alcoholization progress prediction model, and establishing a tobacco raw material warehouse energy consumption optimization model based on the production demand vacancy rate index function, the overall alcoholization quality difference index function and the warehouse overall energy consumption level index function; and solving the tobacco leaf raw material warehouse energy consumption optimization model by adopting a genetic algorithm, and adjusting the storage stack position of the tobacco leaf raw materials and the set environment temperature of the warehouse based on the solving result. The problems that the intelligent degree of tobacco leaf raw material storage alcoholization progress regulation and control is low, and warehouse energy consumption optimization is difficult are solved.

Description

Technical Field

[0001] The invention belongs to the technical field of tobacco raw material storage, and in particular relates to a method for optimizing energy consumption of a tobacco raw material warehouse based on alcoholization progress prediction. Background Art

[0002] The in-warehouse aging of tobacco raw materials is a crucial link in the tobacco logistics process, which is closely related to the quantity supply and aging quality of tobacco raw materials required for cigarette production and processing. Warehouse managers need to adjust the temperature settings of each warehouse area and regulate the aging rate of tobacco raw materials according to the aging progress of tobacco raw materials to meet the quantity of raw materials supplied for cigarette production. The in-warehouse adjustment of tobacco raw materials needs to consider indicators such as the completion rate of production tasks, the aging quality of tobacco leaves shipped out of the warehouse, and the energy consumption of the warehouse. According to future production plan requirements, the warehouse temperature is adjusted to regulate the aging progress of tobacco raw materials, which is of great significance to the supply of tobacco raw materials and the economic cost of the warehouse.

[0003] There have been a lot of studies in the academic community on the issue of adjusting the cargo positions in warehouses. However, the existing public research results are usually based on traditional commercial logistics scenarios, and the characteristics and evaluation indicators of the incoming goods involved in these logistics scenarios are relatively similar, unlike the tobacco leaf raw material supply, which has such a variety of influencing factors. Therefore, for different storage layouts and tobacco-specific evaluation indicators, it is difficult for the classic model to cover all scenarios, and it cannot be effectively applied in the more characteristic aging adjustment and energy consumption optimization scenarios. At present, the allocation of cargo positions, temperature settings, and scheduling decisions in the storage and aging of tobacco leaf raw materials mainly rely on manual operations and historical experience, with a low degree of intelligence, and it is difficult to optimize warehouse energy consumption efficiently. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and to provide a method for optimizing energy consumption of a tobacco raw material warehouse based on aging progress prediction, so as to solve the problems of low intelligence level in regulating the aging progress of tobacco raw material warehouses and difficulty in optimizing warehouse energy consumption.

[0005] To solve the above technical problems, the present invention is implemented by adopting the following solutions: The present invention provides a method for optimizing energy consumption of a tobacco raw material warehouse based on alcoholization progress prediction, comprising: According to the historical effective accumulated temperature data and sensory quality evaluation scores of tobacco leaves in stock, the sensory quality evaluation function of tobacco leaves of various grades is established, and based on the sensory quality evaluation function of tobacco leaves of various grades, the prediction model of the aging progress of tobacco leaves raw materials is established; Based on the prediction model of tobacco leaf raw material aging progress, the overall aging quality difference index function is established. Based on the production demand shortage rate index function, the overall aging quality difference index function and the overall energy consumption level index function of the warehouse, the tobacco leaf raw material warehouse energy consumption optimization model is established. Genetic algorithm is used to solve the energy consumption optimization model of tobacco raw material warehouse, and the storage stacks of tobacco raw materials and the ambient temperature of the warehouse are adjusted based on the solution results.

[0006] Optionally, according to the historical effective accumulated temperature data and sensory quality evaluation scores of the stocked tobacco leaves, a sensory quality evaluation function of each grade of tobacco leaves is established, and based on the sensory quality evaluation function of each grade of tobacco leaves, a tobacco leaf raw material aging progress prediction model is established, including: Step a: During the tobacco leaf aging process, the effective accumulated temperature of the tobacco leaves is measured and stored in real time, and the tobacco leaves are sampled at equal intervals for sensory quality evaluation to obtain sensory quality evaluation results; Step b: combining the sensory quality evaluation results with the effective accumulated temperature of the tobacco leaves at the sampling time point to form an effective accumulated temperature-sensory quality score data point; Step c: for multiple groups of effective accumulated temperature-sensory quality score data points of tobacco leaves of the same grade, using the effective accumulated temperature as the independent variable and the sensory quality score as the dependent variable, a cubic polynomial fitting is performed using the least squares method to obtain a sensory quality evaluation function of the tobacco leaves of this grade; Step d: implementing step c on tobacco leaves of various grades to obtain sensory quality evaluation functions of tobacco leaves of various grades, and establishing a prediction model for the aging progress of tobacco leaf raw materials based on the sensory quality evaluation functions of tobacco leaves of various grades.

[0007] Optionally, the production demand shortage rate indicator function is used to evaluate the degree of completion of the quantity of tobacco leaf raw materials shipped out of the warehouse to the production task requirements, and the production demand shortage rate indicator function is:

[0008] in, It is the production demand shortage rate indicator; An index of tobacco leaf raw material types; is the index of the time period; The maximum time for tobacco leaf storage; is the total number of types of tobacco leaf raw materials; For After time The estimated total outbound quantity of these tobacco leaf raw materials; For After time The total demand for tobacco leaf raw materials.

[0009] Optionally, the overall aging quality difference index function is used to evaluate the overall aging quality of the tobacco leaf raw materials shipped out of the warehouse, and the overall aging quality difference index function is:

[0010]

[0011]

[0012] in, It is the overall alcoholization quality difference indicator; For Stored in the warehouse within the time period The effective accumulated temperature increment of tobacco leaves; For Warehouse area within the time period The set temperature; The index of the warehouse area; is the index of the time period; is the non-alcoholization temperature of tobacco leaves; For the The effective accumulated temperature value of the tobacco leaf raw materials at the estimated time of shipment; For the The effective accumulated temperature value of the tobacco leaf raw material at the initial time; An index of tobacco leaf raw material types; The total number of available storage areas in the warehouse; For the The estimated time for the tobacco leaf raw materials to be shipped out of the warehouse; For the Tobacco leaf raw materials Stored in the warehouse within the time period If stored in the warehouse area If yes, it is 1, otherwise it is 0; is the total number of types of tobacco leaf raw materials; For the Sensory quality evaluation function of tobacco leaf raw materials.

[0013] Optionally, the overall energy consumption level index function of the warehouse is used to evaluate the overall energy consumption level of the warehouse when the tobacco raw materials are aged in the warehouse. The overall energy consumption level index function of the warehouse is:

[0014] in, It is an indicator of the overall energy consumption level of the warehouse; The maximum time for tobacco leaf storage; is the index of the time period; The total number of available storage areas in the warehouse; The index of the warehouse area; For Warehouse area within the time period The set temperature; For The average natural temperature of the local area during the time period.

[0015] Optionally, the genetic algorithm adopts a hybrid genetic taboo search algorithm, and the genetic algorithm is used to solve the tobacco raw material warehouse energy consumption optimization model, including: Using the tobacco storage area selection sequence and the storage area temperature setting sequence as decision variables, the planned storage areas of various tobacco leaves and the planned set temperatures of each storage area in each time period are encoded respectively, and the initial population is randomly generated; The production demand shortage rate index function, the overall alcoholization quality difference index function and the warehouse overall energy consumption level index function are used as the fitness function of the genetic algorithm. The selection operation is realized by using the ternary tournament. During the crossover operation, the tobacco storage area selection sequence and the storage area temperature setting sequence are respectively adopted by single-point crossover and two-point crossover. The uniform mutation method with conflict detection is adopted to realize the mutation operation. Combined with the taboo search algorithm, the decision variables corresponding to the optimal strategy are solved.

[0016] Optionally, the fitness function of the genetic algorithm is:

[0017] In the formula, , , They are the production demand shortage rate index, the overall alcoholization quality difference index, and the overall energy consumption level index of the warehouse; , , are the weight values ​​of the three indicators respectively; Among them, the weight values ​​of the three indicators are calculated as follows: The three indicators are calculated using the priority diagram method , , The score of is normalized by the sum of the indicator scores to obtain , , The weights are , , .

[0018] Optionally, the storage stack position of the tobacco leaf raw material and the ambient temperature set in the warehouse are adjusted based on the solution result, including: According to the decision variables corresponding to the optimal strategy, the tobacco storage area selection sequence and storage area temperature setting sequence are obtained. According to the tobacco leaf storage area selection sequence, the corresponding stacked tobacco leaves are adjusted to the designated storage area within the specified time period; According to the storage area temperature setting sequence, the set temperature of the storage area in the corresponding time period is adjusted to the specified temperature.

[0019] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the aforementioned method for optimizing energy consumption of a tobacco raw material warehouse based on alcoholization progress prediction are implemented.

[0020] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the aforementioned method for optimizing energy consumption of a tobacco raw material warehouse based on alcoholization progress prediction.

[0021] Beneficial Effects 1. Aiming at the specific scenario that tobacco raw materials in warehouse inventory are used as raw materials for long-term production plans, the present invention establishes a tobacco raw material aging progress prediction model, selects optimization indicators according to the characteristics of tobacco raw material aging and warehouse economic benefits, and establishes a tobacco raw material warehouse energy consumption optimization model. The tobacco raw material aging progress prediction model and the tobacco raw material warehouse energy consumption optimization model conform to the characteristics and laws of tobacco raw material storage and logistics, and take into account the different storage requirements and characteristic performances between tobacco raw materials and ordinary commodities, as well as the influence of the storage environment on the preservation and aging quality of tobacco raw materials. The proposed model can effectively and comprehensively guarantee the supply quality of tobacco raw materials. At the same time, by combining the natural average temperature of the warehouse in different time periods, the air-conditioning set temperature of each storage area is adjusted to achieve the energy consumption optimization of the warehouse.

[0022] 2. The present invention adopts a hybrid genetic taboo search algorithm to search for optimization to avoid falling into the local optimum: the storage area of ​​tobacco leaves and the temperature of the storage area are set as chromosome gene points, which improves the calculation efficiency. At the same time, for the situation of infeasible solutions appearing in the iterative process of the warehouse energy consumption optimization solution algorithm, a uniform mutation operator with correction is proposed to ensure that the same elements will not appear repeatedly when selecting the stack position, and at the same time enhance the ability of the algorithm to find the global optimal solution, and after generating a new population, a taboo search algorithm is used to search for neighborhood solutions, which improves the local search ability of the algorithm.

[0023] 3. The present invention can greatly improve the supply quality of tobacco raw materials and reduce the economic cost of warehouses by applying optimization algorithms to formulate optimization strategies for adjusting tobacco raw materials in warehouses. The algorithm used can analyze the aging progress of the tobacco raw materials in stock and predict the aging quality of tobacco after aging for a period of time according to the expected temperature, thereby adjusting the storage strategy of the warehouse, reducing factors of manual intervention and improving work efficiency.

[0024] 4. The energy consumption optimization method of tobacco raw material warehouse based on the prediction of aging progress of the present invention is of great significance for improving the completeness of tobacco raw material supply, supply quality and economic benefits of warehouses, and is worthy of further research and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1It is a schematic diagram of a single-point crossover operator of a hybrid genetic taboo search algorithm in a tobacco raw material warehouse energy consumption optimization method based on aging progress prediction provided in Example 1 of the present invention; Figure 2 It is a schematic diagram of a two-point crossover operator of a hybrid genetic taboo search algorithm in a tobacco raw material warehouse energy consumption optimization method based on aging progress prediction provided in Example 1 of the present invention; Figure 3 It is a schematic diagram of the taboo search neighborhood structure of a hybrid genetic taboo search algorithm in a method for optimizing energy consumption in a tobacco raw material warehouse based on alcoholization progress prediction provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0026] First of all, it should be noted that the present invention relates to database and search algorithm technology, and is an application of computer technology in the field of warehouse management technology. In the process of implementing the present invention, the application of multiple software function modules will be involved. The applicant believes that after carefully reading the application documents and accurately understanding the implementation principle and purpose of the present invention, in combination with the existing known technology, those skilled in the art can fully use their mastered software programming skills to implement the present invention. The aforementioned software function modules include but are not limited to: tobacco leaf raw material alcoholization progress prediction model, tobacco leaf raw material warehouse energy consumption optimization model, etc. All those mentioned in the application documents of the present invention belong to this category, and the applicant will not list them one by one.

[0027] In addition, the above-mentioned prediction model and optimization model proposed by the present invention in combination with the characteristics and laws of tobacco raw material storage and logistics, and the content of solving the optimization model using genetic algorithm are skills that technical personnel in this field are proficient in. The application of specific technical means such as population initialization, fitness function selection operation, crossover operation, mutation operation, neighborhood structure, taboo table, etc. involved in the solution process should all be attributed to this.

[0028] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention. Example 1

[0029] In this example, the tobacco storage warehouse in Zhuantang, Hangzhou, Zhejiang Province is selected. The average monthly temperature of the warehouse location varies from 5.1℃ to 29.4℃. The warehouse has a usable area of ​​202,000 square meters, with 7 floors in total, each with 8 floors. Each warehouse has 24 stacks, and the available capacity of a single stack ranges from 252 to 510 pieces. The current warehouse involves a storage volume of 75,627 pieces. The following calculation example uses an adjustment plan involving 5 types of tobacco raw materials, with a time span from March to June and a storage range of 4 warehouses.

[0030] This embodiment provides a method for optimizing energy consumption of a tobacco raw material warehouse based on alcoholization progress prediction, comprising the following steps: Step 1: Establish a sensory quality evaluation function for all grades of tobacco leaves based on the historical effective accumulated temperature data and sensory quality evaluation scores of tobacco leaves in stock; further establish a tobacco leaf raw material aging progress prediction model based on the sensory quality evaluation function of each grade of tobacco leaves to obtain the characteristics for evaluating the aging quality of tobacco leaf raw materials leaving the warehouse; specifically, the following steps are included: 1.1 During the whole aging process of the redried tobacco leaves from storage to delivery, the accumulated temperature collection device is used to measure the effective accumulated temperature data during the aging process of the tobacco leaves, and the measured data is transmitted to the host computer for storage and processing; 1.2 During the whole aging process, samples of tobacco leaves are taken at the same time for sensory quality evaluation; the evaluation results are combined with the effective accumulated temperature of tobacco leaves at the sampling time node to form the effective accumulated temperature-sensory quality score data point; 1.3 For multiple groups of effective accumulated temperature-sensory quality score data points of tobacco leaves of the same grade, with effective accumulated temperature as the independent variable and sensory quality score as the dependent variable, the least squares method was used to perform cubic polynomial fitting to obtain the sensory quality evaluation function of the tobacco leaves of this grade; 1.4 Repeat step 1.3 to establish a sensory quality evaluation function for all grades of tobacco leaves, and then establish a prediction model for the aging progress of tobacco leaf raw materials; based on this model, estimate the sensory quality score of tobacco leaves through their grade and effective accumulated temperature value, and predict the future aging progress of tobacco leaves in combination with their aging temperature, thereby reflecting the aging quality of tobacco leaves, which will be used for the subsequent evaluation of the overall aging quality difference index.

[0031] Step 2: According to the characteristics of production demand, aging quality and warehouse energy consumption, the optimization index for adjusting the tobacco raw material warehouse is proposed, and an energy consumption optimization model for the tobacco raw material warehouse is established based on the production demand shortage rate index function, the overall aging quality difference index function and the overall energy consumption level index function of the warehouse.

[0032] The energy consumption optimization model for tobacco raw material warehouse of the present invention includes a production demand shortage rate index function, an overall aging quality difference index function and an overall energy consumption level index function of the warehouse; the contents of each function are as follows: 2.1 According to the production demand shortage rate indicator function shown in the following formula, the degree of completion of the quantity of tobacco leaf raw materials shipped out of the warehouse to the production task demand is evaluated:

[0033] in, It is the production demand shortage rate indicator; An index of tobacco leaf raw material types; is the index of the time period; The maximum time for tobacco leaf storage; is the total number of types of tobacco leaf raw materials; For After time The estimated total outbound quantity of these tobacco leaf raw materials; For After time The total demand for tobacco leaf raw materials; 2.2 According to the overall aging quality difference index function shown in the following formula, the overall aging quality of the tobacco raw materials leaving the warehouse is evaluated:

[0034]

[0035]

[0036] in, It is the overall alcoholization quality difference indicator; For Stored in the warehouse within the time period The effective accumulated temperature increment of tobacco leaves; For Warehouse area within the time period The set temperature; The index of the warehouse area; is the index of the time period; is the non-alcoholization temperature of tobacco leaves; For the The effective accumulated temperature value of the tobacco leaf raw materials at the estimated time of shipment; For the The effective accumulated temperature value of the tobacco leaf raw material at the initial time; An index of tobacco leaf raw material types; The total number of available storage areas in the warehouse; For the The estimated time for the tobacco leaf raw materials to be shipped out of the warehouse; For the Tobacco leaf raw materials Stored in the warehouse within the time period If stored in the warehouse area If yes, it is 1, otherwise it is 0; is the total number of types of tobacco leaf raw materials; For the Sensory quality evaluation function of tobacco leaf raw materials; 2.3 According to the warehouse overall energy consumption level index function shown in the following formula, the overall energy consumption level of the warehouse during the alcoholization of tobacco raw materials is evaluated:

[0037] in, It is an indicator of the overall energy consumption level of the warehouse; The maximum time for tobacco leaf storage; is the index of the time period; The total number of available storage areas in the warehouse; The index of the warehouse area; For Warehouse area within the time period The set temperature; For The average natural temperature of the area during the time period; 2.4 The optimization direction of the three evaluation indicators is minimization. The three evaluation indicators are calculated using the priority diagram method. , , The weight value of the production demand shortage rate indicator; since the supply quantity of raw materials required for cigarette production is the basic task of the warehouse The importance of is higher than the other two indicators, and the overall alcoholization quality difference indicator The overall energy consumption level index of the warehouse are roughly equivalent in importance.

[0038] Step 3: Use genetic algorithm to solve the optimization model.

[0039] The present invention adopts a hybrid genetic taboo search algorithm to solve the energy consumption optimization model of tobacco raw material warehouse to improve the algorithm's optimization ability and search efficiency; 3.1 The decision variables are encoded using multiple sequences, which include a tobacco storage area selection sequence and a storage area temperature setting sequence, which encode the planned storage areas for various tobacco leaves and the planned set temperatures of each storage area in each time period; the initial population is randomly generated to ensure the diversity of the population; 3.2 The objective optimization function of the tobacco raw material warehouse energy consumption optimization model is used as the fitness function of the genetic algorithm:

[0040] In the formula, , , They are the production demand shortage rate index, the overall alcoholization quality difference index, and the overall energy consumption level index of the warehouse; , , are the weight values ​​of the three indicators respectively; The weight values ​​of the three indicators are calculated as follows: First, the priority diagram method is used to calculate the three indicators , , The score is then normalized using the sum of the indicator scores to obtain , , The weights are , , .

[0041] 3.3 The ternary tournament is used to implement the selection operation to ensure the retention and transmission of excellent individuals in each generation; during the crossover operation, single-point crossover and two-point crossover are used for the storage area selection sequence and the storage area temperature setting sequence respectively to avoid generating infeasible solutions; 3.4Use uniform mutation method with conflict detection to realize mutation operation and increase individual diversity and exploration; 3.5 Combined with the taboo search algorithm, the ability to expand the search range to the neighborhood can be improved to improve the local optimization ability; 3.6 Solve through the algorithm and finally obtain the decision variables corresponding to the optimal strategy; 3.1 to 3.5 specifically include the following steps: (1) Population initialization The population in the genetic algorithm is composed of many chromosomes, each of which is a coding sequence, representing a scheduling scheme for the warehouse energy consumption optimization adjustment problem. The initial population determines the genetic diversity of the offspring population to a certain extent. This paper considers the diversity of storage area selection and the search breadth of temperature setting in the warehouse energy consumption optimization adjustment problem, and randomly initializes the initial population. At the same time, in order to avoid conflicts in storage area selection, the storage area selection sequence is checked for outbound status. After the tobacco leaves are out of the warehouse, they will remain in the outbound status and will no longer return to the warehouse to participate in the storage area adjustment.

[0042] (2) Fitness function and selection operation Calculation using the priority diagram method , , The scores of the three indicators are 2.5, 1, and 1 respectively. The sum of the indicator scores is used for normalization to obtain , , The weights are 0.5556, 0.2222, and 0.2222 respectively.

[0043] The fitness function determines the evolutionary direction of the population, and the objective function of optimizing warehouse energy consumption is used as the fitness function of the genetic algorithm.

[0044]

[0045] After calculating the fitness value of each individual in the population, the individuals copied into the next generation population are selected through the selection operation, which can ensure that high-quality individuals survive in iterative evolution with a greater probability, thereby improving the convergence performance and search efficiency of the algorithm. By adopting the selection operation of the ternary tournament, three individuals are randomly sampled from the population, and the individuals with the best fitness are selected to enter the next generation population according to the fitness of each individual, and the rest are returned to the original population. The operation is repeated until the size of the next generation population reaches the size of the parent population. In the competition, a certain population diversity is retained, and the elite individuals in the population are allowed to enter the next generation population.

[0046] (3) Crossover operation Crossover operation is an important way for genetic algorithms to generate new individuals from parent individuals, and it also reflects the global search ability of genetic algorithms. In view of the particularity of tobacco storage area and storage area temperature setting problems in warehouse energy consumption optimization and adjustment, this paper uses single-point crossover operator and two-point crossover operator to perform sequence crossover operation.

[0047] The single-point crossover operator is used to select the sequence of tobacco leaf storage area in the encoding, and a non-zero crossover point is randomly selected in the sequence, such as Figure 1 As shown in the figure, the gene information after the crossover point in the parent individuals P1 and P2 is selected, and the positions of the two groups of genes are exchanged to obtain the offspring individuals C1 and C2. Since the selected crossover points are all non-zero, the outbound state in the offspring after the exchange is at the end of the chromosome, ensuring that the genes in each chromosome of the offspring still meet the outbound state verification.

[0048] A two-point crossover operator is used for the storage area temperature setting sequence in the coding, and two crossover points are randomly selected in the sequence, such as Figure 2 As shown, the genetic information between the two crossover points of the parent individuals P1 and P2 is selected and copied to the corresponding points of the offspring individuals C2 and C1 respectively, and then the genetic information at the remaining positions of the parent individuals P1 and P2 is copied to the corresponding points of the offspring individuals C1 and C2 respectively.

[0049] (4) Mutation operation Mutation operation is an important way to expand the search range and improve local search capabilities in genetic algorithms. By applying small disturbances to chromosomes, the diversity of the population is improved. This paper adopts uniform mutation with conflict detection. For the selection sequence of the tobacco storage area in the coding, a random number is taken from the storage area selection range with a certain mutation probability for each gene point in the sequence to replace it. After generating the random number, the tobacco storage area selection sequence needs to be constrained. If there is a conflict in the outbound state, the random number is regenerated until the constraint check is satisfied to ensure that the decision variable is a feasible solution. For the storage area temperature setting sequence in the coding, a random number is taken from the temperature setting value range with a certain mutation probability for each gene point in the sequence to replace it.

[0050] (5) Neighborhood structure In order to further improve the local optimization ability and search efficiency of the algorithm, the taboo search algorithm is combined to optimize the neighborhood range of the new population in a specific direction. For the reservoir temperature setting sequence, the neighborhood structure commonly used in sequence encoding is used to generate new solutions, such as Figure 3 As shown. Neighborhood structure 1, randomly selects two gene loci in the chromosome sequence and reverses the gene fragments between the loci. Neighborhood structure 2, randomly selects two gene loci in the chromosome sequence and exchanges the gene information at the two loci. Neighborhood structure 3, randomly selects two gene loci in the chromosome sequence and inserts the loci with the later index before the loci with the earlier index, or inserts the loci with the earlier index after the loci with the later index.

[0051] (6) Taboo table In the taboo search algorithm, the taboo table stores objects that are tabooed during the search process. Its purpose is to avoid repeated searches and guide the algorithm to search other better solution space areas. During the algorithm iteration, if the new solution generated by the neighborhood structure perturbing the current solution already exists in the taboo table, the new solution will not be used to update the current solution. The taboo table is a ring table of a specified length. When a new element is added, the oldest element in the table will be overwritten.

[0052] Step 4: Based on the solution of the optimization model, the storage stacks of the tobacco raw materials and the ambient temperature set in the warehouse are adjusted to achieve the regulation of the aging progress of the tobacco raw materials during the inventory process and the optimization of the warehouse energy consumption.

[0053] 4.1 According to the decision variables corresponding to the calculated optimal strategy, the tobacco leaf storage area selection sequence and the storage area temperature setting sequence are obtained; 4.2 According to the tobacco leaf storage area selection sequence, the corresponding stacked tobacco leaves are adjusted to the designated storage area within the specified time period; specifically, according to the storage area selection sequence, the storage area adjustment plan for the tobacco leaves in the warehouse is sorted out, and the tobacco leaf raw materials are adjusted to the designated storage area within the corresponding time period, such as shown in Table 1.

[0054] Table 1 Allocation results of tobacco storage area

[0055] 4.3 According to the warehouse area temperature setting sequence, the set temperature of the air conditioner in the warehouse area is adjusted to the specified temperature in the corresponding time period; specifically, according to the warehouse area temperature setting sequence, a warehouse area temperature setting plan for optimizing warehouse energy consumption is obtained, and the set temperature of the air conditioner in the warehouse area in the corresponding time period is adjusted to the specified temperature, as shown in Table 2.

[0056] Table 2 Warehouse area temperature setting results

[0057] The method of the present invention was implemented in the warehouse to optimize the management of tobacco storage. After 4 months of operation, by comparing the energy consumption of the same production load in the same period, the present invention was able to reduce the energy consumption level by 6.36% and improve the overall aging quality by 4.21% while meeting the production supply. Example 2

[0058] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the method for optimizing energy consumption of a tobacco raw material warehouse based on alcoholization progress prediction described in Example 1 are implemented. Example 3

[0059] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for optimizing energy consumption of a tobacco raw material warehouse based on alcoholization progress prediction described in Example 1 are implemented.

[0060] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0061] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0062] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0063] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0064] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

[0065] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for optimizing energy consumption of tobacco raw material warehouse based on alcoholization progress prediction, characterized in that: include: According to the historical effective accumulated temperature data and sensory quality evaluation scores of tobacco leaves in stock, the sensory quality evaluation function of tobacco leaves of various grades is established, and based on the sensory quality evaluation function of tobacco leaves of various grades, the prediction model of the aging progress of tobacco leaves raw materials is established; Based on the prediction model of tobacco leaf raw material aging progress, the overall aging quality difference index function is established. Based on the production demand shortage rate index function, the overall aging quality difference index function and the overall energy consumption level index function of the warehouse, the tobacco leaf raw material warehouse energy consumption optimization model is established. Genetic algorithm is used to solve the energy consumption optimization model of tobacco raw material warehouse, and the storage stacks of tobacco raw materials and the ambient temperature of the warehouse are adjusted based on the solution results.

2. The method for optimizing energy consumption of tobacco raw material warehouse based on alcoholization progress prediction according to claim 1, characterized in that: According to the historical effective accumulated temperature data and sensory quality evaluation scores of tobacco leaves in stock, the sensory quality evaluation function of tobacco leaves of various grades is established. Based on the sensory quality evaluation function of tobacco leaves of various grades, a prediction model for the aging progress of tobacco leaf raw materials is established, including: Step a: During the tobacco leaf aging process, the effective accumulated temperature of the tobacco leaves is measured and stored in real time, and the tobacco leaves are sampled at equal intervals for sensory quality evaluation to obtain sensory quality evaluation results; Step b: combining the sensory quality evaluation results with the effective accumulated temperature of the tobacco leaves at the sampling time point to form an effective accumulated temperature-sensory quality score data point; Step c: for multiple groups of effective accumulated temperature-sensory quality score data points of tobacco leaves of the same grade, using the effective accumulated temperature as the independent variable and the sensory quality score as the dependent variable, a cubic polynomial fitting is performed using the least squares method to obtain a sensory quality evaluation function of the tobacco leaves of this grade; Step d: implementing step c on tobacco leaves of various grades to obtain sensory quality evaluation functions of tobacco leaves of various grades, and establishing a prediction model for the aging progress of tobacco leaf raw materials based on the sensory quality evaluation functions of tobacco leaves of various grades.

3. The method for optimizing energy consumption of tobacco raw material warehouse based on alcoholization progress prediction according to claim 1, characterized in that: The production demand shortage rate indicator function is used to evaluate the degree of completion of the quantity of tobacco leaf raw materials shipped out of the warehouse to the production task requirements. The production demand shortage rate indicator function is: ； in, It is the production demand shortage rate indicator; An index of tobacco leaf raw material types; is the index of the time period; The maximum time for tobacco leaf storage; is the total number of types of tobacco leaf raw materials; For After time The estimated total outbound quantity of these tobacco leaf raw materials; For After time The total demand for tobacco leaf raw materials.

4. The method for optimizing energy consumption of tobacco raw material warehouse based on alcoholization progress prediction according to claim 2, characterized in that: The overall aging quality difference index function is used to evaluate the overall aging quality of the tobacco raw materials shipped out of the warehouse. The overall aging quality difference index function is: ； ； ； in, It is the overall alcoholization quality difference indicator; For Stored in the warehouse within the time period The effective accumulated temperature increment of tobacco leaves; For Warehouse area within the time period The set temperature; The index of the warehouse area; is the index of the time period; is the non-alcoholization temperature of tobacco leaves; For the The effective accumulated temperature value of the tobacco leaf raw materials at the estimated time of shipment; For the The effective accumulated temperature value of the tobacco leaf raw material at the initial time; An index of tobacco leaf raw material types; The total number of available storage areas in the warehouse; For the The estimated time for the tobacco leaf raw materials to be shipped out of the warehouse; For the Tobacco leaf raw materials Stored in the warehouse within the time period If stored in the warehouse area If yes, it is 1, otherwise it is 0; is the total number of types of tobacco leaf raw materials; For the Sensory quality evaluation function of tobacco leaf raw materials.

5. The method for optimizing energy consumption of tobacco raw material warehouse based on alcoholization progress prediction according to claim 1, characterized in that: The overall energy consumption level index function of the warehouse is used to evaluate the overall energy consumption level of the warehouse when the tobacco raw materials are alcoholized in the warehouse. The overall energy consumption level index function of the warehouse is: ； in, It is an indicator of the overall energy consumption level of the warehouse; The maximum time for tobacco leaf storage; is the index of the time period; The total number of available storage areas in the warehouse; The index of the warehouse area; For Warehouse area within the time period The set temperature; For The average natural temperature of the local area during the time period.

6. The method for optimizing energy consumption of tobacco raw material warehouse based on alcoholization progress prediction according to claim 1, characterized in that: The genetic algorithm uses a hybrid genetic taboo search algorithm to solve the energy consumption optimization model of tobacco raw material warehouse, including: Using the tobacco storage area selection sequence and the storage area temperature setting sequence as decision variables, the planned storage areas of various tobacco leaves and the planned set temperatures of each storage area in each time period are encoded respectively, and the initial population is randomly generated; The production demand shortage rate index function, the overall alcoholization quality difference index function and the warehouse overall energy consumption level index function are used as the fitness function of the genetic algorithm. The selection operation is realized by using the ternary tournament. During the crossover operation, the tobacco storage area selection sequence and the storage area temperature setting sequence are respectively adopted by single-point crossover and two-point crossover. The uniform mutation method with conflict detection is adopted to realize the mutation operation. Combined with the taboo search algorithm, the decision variables corresponding to the optimal strategy are solved.

7. The method for optimizing energy consumption of tobacco raw material warehouse based on alcoholization progress prediction according to claim 6, characterized in that: The fitness function of the genetic algorithm is: ； In the formula, , , They are the production demand shortage rate index, the overall alcoholization quality difference index, and the overall energy consumption level index of the warehouse; , , are the weight values ​​of the three indicators respectively; Among them, the calculation method of the weight values ​​of the three indicators is: the three indicators are calculated using the priority diagram method , , The score of is normalized by the sum of the indicator scores to obtain , , The weights are , , .

8. The method for optimizing energy consumption of tobacco raw material warehouse based on alcoholization progress prediction according to claim 7, characterized in that: Based on the solution results, the storage stacks of tobacco raw materials and the ambient temperature of the warehouse are adjusted, including: According to the decision variables corresponding to the optimal strategy, the tobacco storage area selection sequence and storage area temperature setting sequence are obtained. According to the tobacco leaf storage area selection sequence, the corresponding stacked tobacco leaves are adjusted to the designated storage area within the specified time period; According to the storage area temperature setting sequence, the set temperature of the storage area in the corresponding time period is adjusted to the specified temperature.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the method for optimizing energy consumption of a tobacco raw material warehouse based on alcoholization progress prediction as described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing energy consumption of a tobacco raw material warehouse based on alcoholization progress prediction as described in any one of claims 1 to 8 are implemented.

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