A supply chain data information analysis platform system

Abstract

The application relates to the technical field of supply chain management, and discloses a supply chain data information analysis platform system, which comprises a dynamic data acquisition condition constraint module, an information analysis platform demand prediction module, a demand prediction mapping inventory optimization module and an information analysis platform management output module. The first stage of a time series cycle and the second stage of the time series cycle are taken as constraint conditions of a condition constraint layer, the commodity delivery quantity of the first stage and the second stage in the time series cycle is acquired, the demand prediction of supply chain data is completed through a time series prediction model, the difference between the demand prediction result and the current inventory quantity is compared with a preset judgment threshold value, a single-level optimization scheme based on an inventory redundancy risk and a multi-level optimization scheme based on an inventory shortage risk are generated, and an optimization instruction is sent, so that the continuity and stability of the supply chain are ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of supply chain management, and more particularly to a supply chain data information analysis platform system. BACKGROUND

[0002] In the traditional supply chain, the nodes such as suppliers, manufacturers, logistics companies and distributors use independent information systems, resulting in fragmented distribution of full-link data. At the same time, the traditional management mode relying on manual experience and scattered data cannot meet the needs of real-time, accuracy and global optimization, and more accurate demand forecasting is needed to optimize production planning and inventory management. The traditional prediction model is not adaptive to nonlinear and sudden demand changes, and inventory management relies on experience, which is easy to cause inventory accumulation or shortage. Therefore, it has become an industry trend to build a dynamic and intelligent supply chain data analysis platform by combining big data, artificial intelligence, Internet of Things and cloud computing technologies.

[0003] With the progress of big data technologies such as distributed computing, cloud computing and machine learning, it is possible to process and analyze massive transactions, transportation, inventory and other high-frequency and high-complexity data in the supply chain, improving the efficiency of data processing and enhancing the accuracy and real-time nature of data analysis, providing more powerful support for supply chain management.

[0004] However, with the continuous expansion of the supply chain scale and the sharp increase in data volume, the market changes rapidly, and enterprises need to be able to quickly respond to market changes and timely adjust production plans and supply chain strategies. However, the existing supply chain data information analysis platform lacks multi-level inventory structure coordination optimization, information transmission delay, resulting in high inventory costs and slow response speed. In the face of market changes, the traditional supply chain data information analysis platform cannot quickly adjust production plans and inventory strategies, resulting in resource waste or opportunity loss. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, the present application provides a supply chain data information analysis platform system to solve the problems existing in the background art.

[0006] The present application provides the following technical solution: a supply chain data information analysis platform system, comprising: a dynamic data acquisition condition constraint module, an information analysis platform demand prediction module, a demand prediction mapping inventory optimization module and an information analysis platform management output module.

[0007] The dynamic data acquisition condition constraint module comprises a dynamic data acquisition layer and a condition constraint layer, the dynamic data acquisition layer is used for collecting demand prediction parameters in the supply chain in real time, and the condition constraint layer filters and filters the collected data according to the preset rules and strategies;

[0008] The information analysis platform demand prediction module receives the screened and filtered demand prediction parameters, inputs the supply chain data information analysis platform, and the supply chain data information analysis platform completes demand prediction of the supply chain data through a time series prediction model;

[0009] The demand prediction mapping inventory optimization module receives the demand prediction result output by the supply chain data information analysis platform, maps to an inventory optimization analysis and judgment layer, generates an inventory optimization scheme, and the scheme includes single-level optimization and multi-level calling optimization;

[0010] The information analysis platform management output module receives the inventory optimization scheme through the supply chain data information analysis platform, issues an optimization instruction according to the inventory optimization scheme, and outputs the optimization result to a display terminal.

[0011] Preferably, the dynamic data acquisition condition constraint module includes the specific contents of the dynamic data acquisition layer and the condition constraint layer as follows:

[0012] The dynamic data acquisition layer: through the deployment of a lightweight data processing node at the end of the supply chain warehouse, the dynamic data information of the supply chain is obtained;

[0013] The condition constraint layer: including time condition constraint and data condition constraint, the time condition constraint is used for screening the collected data according to the preset time range; the data condition constraint is used for filtering the collected data according to the preset data standard and rule.

[0014] Preferably, the specific contents of the information analysis platform demand prediction module are as follows:

[0015] Step S1: the time series period length is l days, then the first stage of the time series period is 1, 2, 3,..., l, and the second stage of the time series period is l+1, l+2, l+3,..., 2l;

[0016] Step S2: taking the first stage of the time series period as the constraint condition of the condition constraint layer, obtaining the commodity outbound quantity of each day in the first stage of the time series period, and calculating the average outbound quantity of the first stage of the time series period, wherein represents the average outbound quantity of the first stage of the time series period, and l represents the time series period length, wherein i=1, 2, 3,..., l, and i represents the number of each day in the time series period;

[0017] Step S3: taking the second stage of the time series period as the constraint condition of the condition constraint layer, obtaining the commodity outbound quantity of each day in the second stage of the time series period, and calculating the average outbound quantity of the second stage of the time series period, wherein represents the average outbound quantity of the second stage of the time series period;

[0018] Step S4: Incremental analysis is performed on the average outbound quantity of the first stage and the average outbound quantity of the second stage of the time series period, and the average daily increment in the two stages is calculated, wherein represents the average daily increment of the first stage and the second stage in the time series period;

[0019] Step S5: Fluctuation analysis is performed on the average outbound quantity of the first stage and the average outbound quantity of the second stage of the time series period, and the average fluctuation index in the two stages is calculated, and the calculation formula is: , wherein represents the average fluctuation index of the first stage and the second stage in the time series period, represents the outbound quantity of each day in the time series period, represents the unit time change amount of the outbound quantity of the first stage in the time series period, represents the unit time change amount of the outbound quantity of the second stage in the time series period;

[0020] Step S6: The average outbound quantity of the first stage of the time series period, the average outbound quantity of the second stage of the time series period, and the average fluctuation index of the first stage and the second stage in the time series period are input into the time series prediction model, and the demand prediction of the supply chain data is completed, and the calculation formula of the time series prediction model is: , wherein represents the predicted value of the outbound quantity of the next stage based on the previous two stages in the time series period.

[0021] Preferably, the specific content of the demand prediction mapping inventory optimization module is as follows:

[0022] The demand prediction result output by the supply chain data information analysis platform is received and mapped to the inventory optimization analysis and judgment layer to generate an inventory optimization scheme;

[0023] When the difference between the demand prediction result output by the supply chain data information analysis platform and the current inventory quantity is less than a preset judgment threshold, a single-level optimization scheme is generated for inventory redundancy risk; otherwise, when the difference between the demand prediction result output by the supply chain data information analysis platform and the current inventory quantity is greater than or equal to the preset judgment threshold, a multi-level optimization scheme is generated for inventory shortage risk.

[0024] Preferably, the specific content of the single-level optimization scheme is as follows:

[0025] When the demand prediction result output by the supply chain data information analysis platform exceeds twice the current inventory quantity, a promotion clearance strategy is generated, and the discount rate of the inventory goods is automatically calculated, and the calculation formula is: , wherein a discount rate of the inventory commodity, a preset basic discount coefficient, a preset judgment threshold, a current inventory quantity, a predicted value of the next stage of the inventory quantity based on the previous two stages in a time series period;

[0026] Conversely, when the demand prediction result output by the supply chain data information analysis platform does not exceed twice the current inventory quantity, the dynamic replenishment quantity adjustment is performed on the supply chain, and the original planned replenishment quantity is adjusted to , wherein represents the minimum value.

[0027] Preferably, the specific content of the multi-stage optimization scheme is as follows:

[0028] When the inventory shortage risk is determined, the hierarchical optimization model is established, and the hierarchical optimization of the inventory commodity is completed, and the expression of the hierarchical optimization model is: , wherein represents the adjusted replenishment quantity, represents an elastic replenishment coefficient, represents a preset judgment threshold, represents a current inventory quantity, represents a predicted value of the next stage of the inventory quantity based on the previous two stages in a time series period, represents a maximum delivery quantity of the supply chain.

[0029] Preferably, the specific content of the information analysis platform management output module is as follows:

[0030] The inventory optimization scheme is received through the supply chain data information analysis platform, and the inventory optimization scheme includes a single-stage optimization scheme generated based on an inventory redundancy risk and a multi-stage optimization scheme generated based on an inventory shortage risk;

[0031] According to the received inventory optimization scheme, an optimization instruction is issued, an optimization task is executed, and an optimization result is output to a display terminal.

[0032] Technical effects and advantages of the present application:

[0033] The present application realizes comprehensive collection, intelligent prediction and optimization management of supply chain data by being provided with a dynamic data collection condition constraint module, an information analysis platform demand prediction module, a demand prediction mapping inventory optimization module and an information analysis platform management output module.

[0034] The average outbound quantity of the first stage of the time sequence period and the average outbound quantity of the second stage of the time sequence period are subjected to increment analysis and fluctuation analysis, a time sequence prediction model is established, the demand of the supply chain data is accurately predicted, and the accuracy and real-time performance of the prediction are improved.

[0035] The inventory risk is determined according to the difference between the prediction result and the current inventory quantity, a single-level optimization scheme is generated when the inventory redundancy risk is determined, a hierarchical optimization model is established when the inventory shortage risk is determined, the hierarchical optimization of the inventory goods is completed, the targeted optimization processing under different inventory risk conditions is realized, the occurrence of inventory accumulation or out-of-stock phenomenon is effectively avoided, the efficiency and response speed of the supply chain management are improved, and the intelligent level of the supply chain management is further improved. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 It is a structural schematic view of a supply chain data information analysis platform system.

[0037] Figure 2 It is a step schematic view of information analysis platform demand prediction. DETAILED DESCRIPTION

[0038] The technical solutions in the present application will be described clearly and completely in combination with the drawings in the present application, and in addition, the forms of each structure described in the following embodiments are only examples, and the supply chain data information analysis platform system involved in the present application is not limited to each structure described in the following embodiments, all other embodiments obtained by those skilled in the art without making creative efforts belong to the scope of protection of the present application.

[0039] As shown in Figure 1 The present application provides a supply chain data information analysis platform system, comprising: a dynamic data acquisition condition constraint module, an information analysis platform demand prediction module, a demand prediction mapping inventory optimization module and an information analysis platform management output module.

[0040] The dynamic data acquisition condition constraint module comprises a dynamic data acquisition layer and a condition constraint layer, the dynamic data acquisition layer is used for collecting demand prediction parameters in a supply chain in real time, and the condition constraint layer filters and filters the collected data according to preset rules and strategies.

[0041] The demand prediction parameters include the daily commodity outbound quantity in different stages in the time sequence period, the current commodity inventory quantity, the planned commodity replenishment quantity and the maximum delivery quantity of the supply chain.

[0042] The condition constraint layer takes different stages in the time sequence period as constraint conditions of the condition constraint layer, and filters and filters the collected data.

[0043] The information analysis platform demand prediction module receives the screened and filtered demand prediction parameters, inputs the supply chain data information analysis platform, and the supply chain data information analysis platform completes demand prediction of the supply chain data through a time series prediction model;

[0044] The demand prediction mapping inventory optimization module receives the demand prediction result output by the supply chain data information analysis platform, maps to the inventory optimization analysis and judgment layer, generates an inventory optimization scheme, and the scheme includes single-level optimization and multi-level call optimization;

[0045] The information analysis platform management output module receives the inventory optimization scheme through the supply chain data information analysis platform, issues an optimization instruction according to the inventory optimization scheme, and outputs the optimization result to a display terminal.

[0046] In this embodiment, it needs to be specifically pointed out that the dynamic data acquisition condition constraint module includes the specific contents of the dynamic data acquisition layer and the condition constraint layer as follows:

[0047] The dynamic data acquisition layer: through the deployment of a lightweight data processing node at the end of the supply chain warehouse, the supply chain dynamic data information is used to obtain the supply chain dynamic data information, and the supply chain dynamic data information represents the demand prediction parameters, that is, the daily product out-of-warehouse quantity, the current product inventory quantity, the planned product replenishment quantity, and the maximum supply chain shipment quantity in different stages in the time series period;

[0048] The condition constraint layer: including time condition constraint and data condition constraint, the time condition constraint is used to screen the collected data according to the preset time range, to ensure the timeliness of the analyzed data; the data condition constraint is used to filter the collected data according to the preset data standard and rule, to remove abnormal or invalid data and improve the data quality.

[0049] As shown in Figure 2 In this embodiment, it needs to be specifically pointed out that the specific content of the information analysis platform demand prediction module is as follows:

[0050] Step S1: The time series period length is l days, the first stage of the time series period is 1, 2, 3,..., l, the second stage of the time series period is l+1, l+2, l+3,..., 2l, and so on, and the period length of different stages in the time series is l days;

[0051] Step S2: the first stage of the time series period is taken as the constraint condition of the condition constraint layer to obtain the daily product out-of-warehouse quantity in the first stage of the time series period, and the average out-of-warehouse quantity of the first stage of the time series period is calculated, and the calculation formula is: , wherein represents the average outbound quantity of the first stage of the time series cycle, and l represents the length of the time series cycle, wherein i = 1, 2, 3,..., l, and i represents the number of each day in the time series cycle;

[0052] Step S3: obtaining the outbound quantity of each day in the second stage of the time series cycle and calculating the average outbound quantity of the second stage of the time series cycle as the constraint condition of the conditional constraint layer, the calculation formula is: , wherein represents the average outbound quantity of the second stage of the time series cycle, and l represents the length of the time series cycle, wherein i = 1, 2, 3,..., l, and i represents the number of each day in the time series cycle;

[0053] Step S4: performing incremental analysis on the average outbound quantity of the first stage of the time series cycle and the average outbound quantity of the second stage of the time series cycle, and calculating the average daily increment in the two stages, the calculation formula is: , wherein represents the average daily increment of the first stage and the second stage in the time series cycle;

[0054] Step S5: performing fluctuation analysis on the average outbound quantity of the first stage of the time series cycle and the average outbound quantity of the second stage of the time series cycle, and calculating the average fluctuation index in the two stages, the calculation formula is: , wherein represents the average fluctuation index of the first stage and the second stage in the time series cycle, represents the outbound quantity of each day in the time series cycle, represents the unit time change amount of the outbound quantity of the first stage in the time series cycle, represents the unit time change amount of the outbound quantity of the second stage in the time series cycle;

[0055] Step S6: inputting the average outbound quantity of the first stage of the time series cycle, the average outbound quantity of the second stage of the time series cycle and the average fluctuation index of the first stage and the second stage in the time series cycle into the time series prediction model to complete the demand prediction of the supply chain data, and the calculation formula of the time series prediction model is: , wherein represents the predicted value of the outbound quantity of the next stage based on the previous two stages in the time series cycle.

[0056] In this embodiment, it needs to be specifically pointed out that the specific content of the demand prediction mapping inventory optimization module is as follows:

[0057] receiving the demand prediction result output by the supply chain data information analysis platform, mapping to the inventory optimization analysis and judgment layer, and generating an inventory optimization scheme;

[0058] When the difference between the demand prediction result output by the supply chain data information analysis platform and the current inventory quantity is less than the preset judgment threshold, a single-level optimization scheme is generated for inventory redundancy risk; otherwise, when the difference between the demand prediction result output by the supply chain data information analysis platform and the current inventory quantity is greater than or equal to the preset judgment threshold, a multi-level optimization scheme is generated for inventory shortage risk.

[0059] In this embodiment, it needs to be specifically pointed out that the specific content of the single-level optimization scheme is as follows:

[0060] When the demand prediction result output by the supply chain data information analysis platform exceeds twice the current inventory quantity, a promotion clearance strategy is generated, and the discount rate of the inventory commodity is automatically calculated, and the calculation formula is as follows: , wherein represents the discount rate of the inventory commodity, represents a preset basic discount coefficient, represents a preset judgment threshold, represents the current inventory quantity, represents a predicted value of the next stage of the outbound quantity based on the previous two stages in the time series period;

[0061] Otherwise, when the demand prediction result output by the supply chain data information analysis platform does not exceed twice the current inventory quantity, the original planned replenishment quantity is adjusted to , wherein represents the minimum value.

[0062] In this embodiment, it needs to be specifically pointed out that the specific content of the multi-level optimization scheme is as follows:

[0063] When the inventory shortage risk is determined, a hierarchical optimization model is established to complete the hierarchical optimization of the inventory commodity, and the expression of the hierarchical optimization model is as follows: , wherein represents the adjusted replenishment quantity, represents an elastic replenishment coefficient, represents a preset judgment threshold, represents the current inventory quantity, represents a predicted value of the next stage of the outbound quantity based on the previous two stages in the time series period, represents the maximum delivery quantity of the supply chain.

[0064] In this embodiment, it needs to be specifically pointed out that the specific content of the information analysis platform management output module is as follows:

[0065] receive an inventory optimization scheme through the supply chain data information analysis platform, the inventory optimization scheme including a single-level optimization scheme generated based on an inventory redundancy risk and a multi-level optimization scheme generated based on an inventory shortage risk;

[0066] issue an optimization instruction according to the received inventory optimization scheme, execute an optimization task, and output an optimization result to a display terminal.

[0067] In the embodiment, it is specifically pointed out that the demand prediction of the supply chain data in the time series prediction model needs to input the average fluctuation indexes of the two previous stages, i.e., the average fluctuation indexes based on the first stage and the second stage are used to predict the supply chain demand of the third stage, the average fluctuation indexes based on the second stage and the third stage are used to predict the supply chain demand of the fourth stage, and the average fluctuation indexes based on the third stage and the fourth stage are used to predict the supply chain demand of the fifth stage, to complete the demand prediction of the supply chain data.

[0068] In the embodiment, the difference between the embodiment and the prior art mainly lies in that the embodiment is provided with a dynamic data acquisition condition constraint module, an information analysis platform demand prediction module, a demand prediction mapping inventory optimization module, and an information analysis platform management output module, to realize comprehensive acquisition, intelligent prediction, and optimization management of the supply chain data.

[0069] The average outbound quantity of the first stage of the time series period and the average outbound quantity of the second stage of the time series period are subjected to increment analysis and fluctuation analysis, a time series prediction model is established, the demand of the supply chain data is accurately predicted, and the accuracy and real-time performance of the prediction are improved.

[0070] According to the difference between the prediction result and the current inventory quantity, inventory risk is determined, a single-level optimization scheme is generated when the inventory redundancy risk is determined, a multi-level optimization model is established when the inventory shortage risk is determined, the inventory goods are subjected to multi-level optimization, targeted optimization processing under different inventory risk conditions is realized, the occurrence of inventory accumulation or shortage is effectively avoided, the efficiency and response speed of the supply chain management are improved, and the intelligent level of the supply chain management is further improved.

[0071] Finally, the above description is only for the preferred embodiments of the present application and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

[0072] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A supply chain data information analytics platform system, characterized by: The application relates to a dynamic data acquisition condition constraint module, an information analysis platform demand prediction module, a demand prediction mapping inventory optimization module and an information analysis platform management output module. The dynamic data acquisition condition constraint module comprises a dynamic data acquisition layer and a condition constraint layer, the dynamic data acquisition layer is used for collecting demand prediction parameters in a supply chain in real time, and the condition constraint layer filters and screens the collected data according to preset rules and strategies. The information analysis platform demand prediction module receives the screened and filtered demand prediction parameters, inputs supply chain data information analysis platform, and the supply chain data information analysis platform completes demand prediction of supply chain data through a time sequence prediction model. The specific content of the information analysis platform demand prediction module is as follows: Step S1: the time sequence period length is l days, the first stage of the time sequence period is 1, 2, 3,..., l, and the second stage of the time sequence period is l+1, l+2, l+3,..., 2l; The demand prediction mapping inventory optimization module receives the demand prediction result output by the supply chain data information analysis platform, maps to an inventory optimization analysis judgment layer, generates an inventory optimization scheme, and the scheme comprises single-level optimization and multi-level calling optimization. Step S2: obtaining the daily commodity outbound quantity in the first stage of the time series period as the constraint condition of the conditional constraint layer, and calculating the average outbound quantity of the first stage of the time series period, wherein represents the average outbound quantity of the first stage of the time series period, l represents the length of the time series period, i=1, 2, 3, …, l, and i represents the number of each day in the time series period; Step S3: Obtain the daily commodity outbound quantity in the second stage of the time series period, and calculate the average outbound quantity of the second stage of the time series period, as the constraint condition of the conditional constraint layer, wherein represents the average outbound quantity of the second stage of the time series period; Step S4: Incremental analysis is performed on the average outflow quantity of the first stage of the time series period and the average outflow quantity of the second stage of the time series period, and the average daily increment in the two stages is calculated, represents the average daily increment in the first stage and the second stage of the time series period; Step S5: fluctuation analysis is performed on the average outbound quantity of the first stage of the time series period and the average outbound quantity of the second stage of the time series period, average fluctuation indexes in the two stages are calculated, and the calculation formula is: wherein represents the average fluctuation indexes of the first stage and the second stage in the time series period, represents the outbound quantity of each day in the time series period, represents the unit time change amount of the outbound quantity of the first stage in the time series period, represents the unit time change amount of the outbound quantity of the second stage in the time series period; Step S6: input the average outbound quantity of the first stage of the time series period, the average outbound quantity of the second stage of the time series period and the average fluctuation index of the first stage and the second stage in the time series period into the time series prediction model, complete the demand prediction of the supply chain data, and the calculation formula of the time series prediction model is: wherein represents the predicted value of the outbound quantity of the next stage based on the previous two stages in the time series period. The specific content of the demand prediction mapping inventory optimization module is as follows: The demand prediction mapping inventory optimization module receives the demand prediction result output by the supply chain data information analysis platform, maps to an inventory optimization analysis judgment layer, and generates an inventory optimization scheme. When the difference between the demand prediction result output by the supply chain data information analysis platform and the current inventory quantity is less than a preset judgment threshold, a single-level optimization scheme is generated for judging an inventory redundancy risk. Conversely, when the difference between the demand prediction result output by the supply chain data information analysis platform and the current inventory quantity is greater than or equal to the preset judgment threshold, a multi-level optimization scheme is generated for judging an inventory shortage risk. The specific content of the single-level optimization scheme is as follows: The specific content of the multi-level optimization scheme is as follows: When the demand prediction result output by the supply chain data information analysis platform exceeds twice the current inventory quantity, a promotion clearance strategy is generated, and the discount rate of the inventory commodity is automatically calculated, with the calculation formula being: wherein represents the discount rate of the inventory commodity, represents a preset basic discount coefficient, represents a preset judgment threshold, represents the current inventory quantity, represents a predicted value of the next stage of the out-of-stock quantity based on the previous two stages in the time series period. Conversely, when the demand prediction result output by the supply chain data information analysis platform does not exceed twice the current inventory, the dynamic replenishment quantity adjustment is made to the supply chain, and the original planned replenishment quantity is adjusted to , wherein represents the minimum value. The information analysis platform management output module receives the inventory optimization scheme through the supply chain data information analysis platform, issues an optimization instruction according to the inventory optimization scheme, and outputs the optimization result to a display terminal. establish a hierarchical optimization model when determining the inventory shortage risk, and complete the hierarchical optimization of the inventory goods, an expression of the hierarchical optimization model is: wherein represents the adjusted replenishment quantity, represents an elastic replenishment coefficient, represents a preset judgment threshold, represents a current inventory quantity, represents a predicted value of the next stage of the outbound quantity based on the previous two stages in the time series period, represents a maximum delivery quantity of a supply chain; The specific content of the dynamic data acquisition condition constraint module comprising the dynamic data acquisition layer and the condition constraint layer is as follows:

2. The supply chain data information analytics platform system of claim 1, wherein: The dynamic data acquisition layer: a lightweight data processing node is arranged at the supply chain warehouse end, and is used for acquiring supply chain dynamic data information; The condition constraint layer: comprising time condition constraint and data condition constraint, the time condition constraint is used for screening the collected data according to a preset time range; The data condition constraint is used for filtering the collected data according to preset data standards and rules. The specific content of the information analysis platform management output module is as follows:

3. The supply chain data information analysis platform system of claim 1, wherein: The inventory optimization scheme comprises a single-level optimization scheme generated based on an inventory redundancy risk and a multi-level optimization scheme generated based on an inventory shortage risk; According to the received inventory optimization scheme, an optimization instruction is issued, an optimization task is executed, and an optimization result is output to a display terminal. ​

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