A supply and marketing information management system and its method

Abstract

The present invention provides a supply and marketing information management system and a method thereof. The system includes: an update module for updating a supply and marketing information database; a monitoring module for monitoring supply and marketing abnormal events based on the supply and marketing information database; a decision-making module for making a processing decision on the supply and marketing abnormal events; a first execution module for executing a processing strategy of the processing decision; a generation module for generating a visual monitoring model for the execution of the processing strategy during the execution of the processing strategy; an auxiliary module for assisting a user in optimizing the processing strategy based on the visual monitoring model; and a second execution module for successively executing the optimized processing strategy. The present invention automatically monitors supply and marketing abnormal events, makes a processing decision on the supply and marketing abnormal events and executes the processing strategy of the processing decision, reducing the labor cost and improving the system efficiency; generates a visual monitoring model for the execution of the processing strategy, supporting the monitoring and optimization of the execution of the processing strategy by staff members.

Description

Technical Field

[0001] The present invention relates to the technical field of computer data processing, and in particular to a supply and marketing information management system and method thereof. Background Art

[0002] In today's rapidly changing market environment, companies are facing complex supply chain and sales challenges. The acceleration of globalization and digitalization has made the supply chain increasingly complex, involving multiple links and stakeholders. Effective supply chain management requires refined management of raw material procurement, production, inventory and logistics, and real-time tracking of a large amount of supply and marketing information. On this basis, analyzing supply and marketing abnormal events and making processing decisions usually rely on manual operations, resulting in high labor costs and low efficiency. Therefore, there is an urgent need for an automated technical solution to analyze abnormal events based on supply and marketing information and make processing decisions. In addition, in order to ensure the rationality of the processing strategy, especially when facing complex supply and marketing abnormal events, staff are required to monitor and optimize the strategy in a timely manner. Therefore, there is also an urgent need for a technical solution to support staff in monitoring and optimizing the execution of processing strategies. Summary of the invention

[0003] One of the purposes of the present invention is to provide a supply and marketing information management system and method thereof, which automatically monitors abnormal supply and marketing events based on a supply and marketing information database, makes processing decisions on abnormal supply and marketing events and executes processing strategies for the processing decisions, thereby reducing labor costs and improving system efficiency; in addition, in the process of executing the processing strategy, a visual monitoring model of the execution of the processing strategy is generated to support staff in monitoring and optimizing the execution of the processing strategy.

[0004] An embodiment of the present invention provides a supply and marketing information management system, including:

[0005] Update module, used to update the supply and marketing information database;

[0006] Monitoring module, used to monitor abnormal supply and marketing events based on the supply and marketing information database;

[0007] Decision-making module, used to make decisions on abnormal supply and marketing events;

[0008] A first execution module, for executing a processing strategy of a processing decision;

[0009] A generation module, used for generating a visual monitoring model of the execution of the processing strategy during the execution of the processing strategy;

[0010] Auxiliary module, used to assist users in optimizing processing strategies based on visual monitoring models;

[0011] The second execution module is used to relay the execution of the optimized processing strategy.

[0012] Optionally, during the execution of the processing policy, the generation module generates a visual monitoring model for the execution of the processing policy, including:

[0013] Perform serialization processing on the processing policy to obtain an execution item sequence;

[0014] When the i-th execution item in the execution item sequence is executed, determine the j-th execution item in the execution item sequence that satisfies the execution item association condition with the i-th execution item; wherein, the absolute value of i minus j is greater than 0 and less than or equal to a preset threshold;

[0015] Obtain an initial visual model; wherein, the initial visual model includes a main area, a secondary area, and an associated area; and, when i is greater than j, the area of the main area is positively correlated with the absolute value of i minus j; and, when i is less than j, the area of the main area is negatively correlated with the absolute value of i minus j;

[0016] Map the first execution situation information of the i-th execution item into the main area;

[0017] Map the second execution situation information of the j-th execution item into the secondary area;

[0018] Map the association prompt information corresponding to the execution item association condition into the associated area;

[0019] Use the current initial visual model as the visual monitoring model.

[0020] Optionally, the execution item association condition at least includes:

[0021] The i-th execution item is the same as or of the same type as the j-th execution item;

[0022] Or,

[0023] The type of the k-th execution item in the execution item sequence matches the trigger type; wherein, k is between i and j;

[0024] Or,

[0025] There is an interaction between the execution results of the i-th execution item and the j-th execution item.

[0026] Optionally, the auxiliary module assists the user in optimizing the processing policy based on the visual monitoring model, including:

[0027] When the user's line-of-sight landing point in the first historical time period first falls into the main area for more than the first duration, then falls into the associated area for more than the second duration, and then falls into the secondary area for more than the third duration, corpus extraction is performed on the behavior information generated by the user in the second historical time period to obtain the first corpus; wherein, the third duration is much greater than the first duration which is greater than the second duration; the first historical time period is the time period of the most recent first preset time in history; the starting point of the second historical time period is the moment when the user's line-of-sight landing point starts to fall into the associated area, and the ending point is the moment when the user's line-of-sight landing point remains in the secondary area for up to the second preset time;

[0028] Determine the indication value corresponding to the first corpus from the indication value library;

[0029] When the indication value is greater than the indication threshold, corpus extraction is performed on the first execution situation information in the main area, the associated prompt information in the associated area, the second execution situation information in the secondary area, and the behavior information generated by the user in the third historical time period to obtain the second corpus; the starting point of the third historical time period is the moment when the user's line-of-sight landing point starts to fall into the associated area, and the ending point is the current moment;

[0030] Determine the optimization knowledge corresponding to the second corpus from the optimization knowledge library;

[0031] Perform serialization processing on the optimization knowledge to obtain a sub-knowledge sequence;

[0032] Perform corpus extraction on the behavior information generated by the user in the fourth historical time period to obtain the third corpus; the starting point of the fourth historical time period is the moment when the user's line-of-sight landing point starts to fall into the secondary area, and the ending point is the current moment;

[0033] When there is corpus association between the third corpus and each of the first preset number of sub-knowledges in the sub-knowledge sequence or there is corpus association between the third corpus and at least one sub-knowledge with a knowledge weight greater than the weight threshold among the first second preset number of sub-knowledges in the sub-knowledge sequence, display the sub-knowledge sequence to the user;

[0034] When the user inputs an optimization strategy, optimize the processing strategy based on the optimization strategy.

[0035] A supply and marketing information management method provided by an embodiment of the present invention includes:

[0036] Update the supply and marketing information database;

[0037] Based on the supply and marketing information database, monitor supply and marketing abnormal events;

[0038] Make a processing decision on the supply and marketing abnormal events;

[0039] Execute the processing strategy of the processing decision;

[0040] During the execution of the processing strategy, a visual monitoring model for the execution of the processing strategy is generated;

[0041] Based on the visual monitoring model, assist the user to optimize the processing strategy;

[0042] Relay-execute the optimized processing strategy.

[0043] Optionally, the generating a visual monitoring model for the execution of the processing strategy during the execution of the processing strategy includes:

[0044] Perform serialization processing on the processing strategy to obtain an execution item sequence;

[0045] When the i-th execution item in the execution item sequence is executed, determine the j-th execution item in the execution item sequence that satisfies the execution item association condition with the i-th execution item; where, the absolute value of i minus j is greater than 0 and less than or equal to a preset threshold;

[0046] Obtain an initial visual model; where, the initial visual model includes a main area, a secondary area, and a linkage area; and, when i is greater than j, the area of the main area being greater than that of the secondary area is positively correlated with the absolute value of i minus j; and, when i is less than j, the area of the main area being less than that of the secondary area is positively correlated with the absolute value of i minus j;

[0047] Map the first execution situation information of the i-th execution item into the main area;

[0048] Map the second execution situation information of the j-th execution item into the secondary area;

[0049] Map the association prompt information corresponding to the execution item association condition into the linkage area;

[0050] Use the current initial visual model as the visual monitoring model.

[0051] Optionally, the execution item association condition at least includes:

[0052] The i-th execution item is the same as or of the same type as the j-th execution item;

[0053] Or,

[0054] The type of the k-th execution item in the execution item sequence matches the trigger type; where, k is between i and j;

[0055] Or,

[0056] There is an interaction between the execution results of the i-th execution item and the j-th execution item.

[0057] Optionally, the assisting the user to optimize the processing strategy based on the visual monitoring model includes:

[0058] When the user's line-of-sight landing point in the first historical time period first falls into the main area for more than the first duration, then falls into the linkage area for more than the second duration, and then falls into the secondary area for more than the third duration, corpus extraction is performed on the behavior information generated by the user in the second historical time period to obtain the first corpus; wherein, the third duration is much greater than the first duration which is greater than the second duration; the first historical time period is the time period of the most recent first preset time in history; the starting point of the second historical time period is the moment when the user's line-of-sight landing point starts to fall into the linkage area, and the ending point is the moment when the user's line-of-sight landing point remains in the secondary area for up to the second preset time;

[0059] Determine the indication value corresponding to the first corpus from the indication value library;

[0060] When the indication value is greater than the indication threshold, corpus extraction is performed on the first execution situation information in the main area, the associated prompt information in the linkage area, the second execution situation information in the secondary area, and the behavior information generated by the user in the third historical time period to obtain the second corpus; the starting point of the third historical time period is the moment when the user's line-of-sight landing point starts to fall into the linkage area, and the ending point is the current moment;

[0061] Determine the optimization knowledge corresponding to the second corpus from the optimization knowledge base;

[0062] Perform serialization processing on the optimization knowledge to obtain a sub-knowledge sequence;

[0063] Perform corpus extraction on the behavior information generated by the user in the fourth historical time period to obtain the third corpus; the starting point of the fourth historical time period is the moment when the user's line-of-sight landing point starts to fall into the secondary area, and the ending point is the current moment;

[0064] When there is corpus association between the third corpus and each of the first preset number of sub-knowledges in the sub-knowledge sequence, or when there is corpus association between the third corpus and at least one sub-knowledge with a knowledge weight greater than the weight threshold among the first second preset number of sub-knowledges in the sub-knowledge sequence, display the sub-knowledge sequence to the user;

[0065] When the user inputs an optimization strategy, optimize the processing strategy based on the optimization strategy.

[0066] Other features and advantages of the present invention will be described in the subsequent specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained by the structures specifically pointed out in the written specification, claims, and drawings.

[0067] The technical solutions of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0068] The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the accompanying drawings:

[0069] Figure 1 It is a schematic diagram of a supply and marketing information management system in an embodiment of the present invention;

[0070] Figure 2 It is a schematic diagram of a supply and marketing information management method in an embodiment of the present invention. Detailed implementation manners

[0071] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to explain and illustrate the present invention and are not used to limit the present invention.

[0072] An embodiment of the present invention provides a supply and marketing information management system, as Figure 1 shown, including:

[0073] An update module 1, used to update the supply and marketing information database;

[0074] A monitoring module 2, used to monitor supply and marketing abnormal events based on the supply and marketing information database;

[0075] A decision-making module 3, used to make a processing decision on the supply and marketing abnormal events;

[0076] A first execution module 4, used to execute the processing strategy of the processing decision;

[0077] A generation module 5, used to generate a visual monitoring model for the execution of the processing strategy during the execution of the processing strategy;

[0078] An auxiliary module 6, used to assist the user in optimizing the processing strategy based on the visual monitoring model;

[0079] A second execution module 7, used to relay and execute the optimized processing strategy.

[0080] A large amount of supply and marketing information is stored in the supply and marketing information database; after the supply and marketing information database is updated, supply and marketing abnormal events are monitored based on the supply and marketing information database. The supply and marketing abnormal events include inventory shortage events, inventory surplus events, supply chain terminal events, goods quality problem events, etc.; a processing decision is made on the supply and marketing abnormal events. When making a processing decision, the processing strategy corresponding to the supply and marketing abnormal event in the preset processing strategy database can be queried; after the processing strategy is executed, the system automatically starts to process the supply and marketing abnormal events; during the execution of the processing strategy, a visual monitoring model for the execution of the processing strategy is generated for the user to perform execution monitoring on the processing strategy based on the visual monitoring model and assist the user in optimizing the processing strategy. After the processing strategy is optimized, the optimized processing strategy is relayed and executed.

[0081] The automation of this application is based on the supply and marketing information database, monitors supply and marketing abnormal events, makes processing decisions on supply and marketing abnormal events, and executes the processing strategy of the processing decision, reducing labor costs and improving system efficiency. In addition, during the execution of the processing strategy, a visual monitoring model for the execution of the processing strategy is generated to support the staff in monitoring and optimizing the execution of the processing strategy.

[0082] In one embodiment, during the execution of the processing strategy, the generating module generates a visual monitoring model for the execution of the processing strategy, including:

[0083] Perform serialization processing on the processing strategy to obtain an execution item sequence; there are execution items arranged in sequence according to the execution order in the execution item sequence, and after each execution item in the execution item sequence is executed in sequence, the execution of the processing strategy is completed;

[0084] When the i-th execution item in the execution item sequence is executed, determine the j-th execution item in the execution item sequence that satisfies the execution item association condition with the i-th execution item; where, the absolute value of i minus j is greater than 0 and less than or equal to a preset threshold; the preset threshold can be 3; if the j-th execution item and the i-th execution item satisfy the execution item association condition, then the first execution situation information of the i-th execution item, the second execution situation information of the j-th execution item, and the execution item association condition between the two need to be displayed to the user together, and the user decides which execution item to optimize and how to optimize by viewing this information; the first execution situation information is the execution object (for example: a certain supplier), execution result (for example: the latest delivery date replied by the supplier), execution progress, etc. when the i-th execution item is executed; when i is greater than j, the j-th execution item has been executed before the i-th execution item is executed, so the second execution situation information is the execution object, execution result, execution progress, etc. of the j-th execution item; when i is less than j, the j-th execution item is executed after the i-th execution item is executed, so the second execution situation information is the to-be-executed object, execution plan, etc. of the j-th execution item; i = 1, 2, 3,..., N; j = 1, 2, 3,..., N; N is the total number of execution items in the execution item sequence;

[0085] Obtain an initial visualization model; wherein, the initial visualization model includes a main area, a secondary area, and an associated area; and, when i is greater than j, the area of the main area being greater than that of the secondary area is positively correlated with the absolute value of i minus j; and, when i is less than j, the area of the main area being less than that of the secondary area is positively correlated with the absolute value of i minus j; when i is greater than j, the j-th execution item has been executed before the i-th execution item is executed, and the possibility for the user to optimize the j-th execution item is relatively small, then the area of the main area is greater than that of the secondary area, and the greater the absolute value of i minus j, the greater the area by which the main area is greater than the secondary area, facilitating the user to focus on the currently executed i-th execution item; when i is less than j, the j-th execution item is executed after the i-th execution item is executed, and the possibility for the user to optimize the i-th execution item is relatively small, then the area of the main area is less than that of the secondary area, and the greater the absolute value of i minus j, the greater the area by which the main area is less than the secondary area, facilitating the user to focus on the upcoming j-th execution item; greatly improving the rationality of constructing the visualization monitoring model, being very user-friendly, and also enhancing the applicability of the system;

[0086] Map the first execution situation information of the i-th execution item into the main area;

[0087] Map the second execution situation information of the j-th execution item into the secondary area;

[0088] Map the associated prompt information corresponding to the execution item association condition into the associated area;

[0089] Use the current initial visualization model as the visualization monitoring model;

[0090] The execution item association condition at least includes:

[0091] The i-th execution item is the same as or of the same type as the j-th execution item; the i-th execution item being the same as or of the same type as the j-th execution item indicates that there is a situation where an execution item is repeatedly executed or multiple execution items of the same type are executed, which requires the user's attention; the associated prompt information corresponding to this execution item association condition can be that the execution items are the same or of the same type;

[0092] Or,

[0093] The type of the k-th execution item in the execution item sequence matches the trigger type; where k is between i and j; the trigger type can be a type representing a relatively high importance of the execution item, and since k is between i and j, the execution item with a relatively high importance is executed between the i-th execution item and the j-th execution item, and the user needs to evaluate the execution effect of the k-th execution item with a relatively high importance by checking the execution situations of the i-th execution item and the j-th execution item; the associated prompt information corresponding to this execution item association condition can be that important execution items need attention; k = 1, 2, 3,..., N;

[0094] Or,

[0095] There is an interaction between the execution results of the i-th execution item and the j-th execution item. The existence of an interaction between execution results means that the execution result of one execution item affects the execution effect of another execution item; when there is an interaction between the execution results of the i-th execution item and the j-th execution item, the user needs to pay attention to the execution situations of both execution items simultaneously; the associated prompt information corresponding to the execution item association condition can be that there is an interaction between execution results.

[0096] By setting the above three execution item association conditions, the j-th execution item can be accurately determined, further enhancing the user-friendliness and helping the user more conveniently perform execution monitoring and optimization of the processing strategy based on the visualization monitoring model.

[0097] In one embodiment, the auxiliary module, based on the visualization monitoring model, assists the user in optimizing the processing strategy, including:

[0098] When the user's line-of-sight landing point in the first historical time period first falls into the main area for more than the first duration, then falls into the linkage area for more than the second duration, and then falls into the secondary area for more than the third duration, corpus extraction is performed on the behavior information generated by the user in the second historical time period to obtain the first corpus; where the third duration is much greater than the first duration which is greater than the second duration; the first historical time period is the time period of the most recent first preset time in history; the starting point of the second historical time period is the moment when the user's line-of-sight landing point starts to fall into the linkage area, and the end point is the moment when the user's line-of-sight landing point remains in the secondary area for up to the second preset time; the third duration can be 100 seconds; the first duration can be 30 seconds; the second duration can be 10 seconds; the first preset time can be 300 seconds; the second preset time can be 80 seconds; when the line-of-sight landing point first falls into the main area for more than the first duration, then falls into the linkage area for more than the second duration, and then falls into the secondary area for more than the third duration, it means that the user may want to optimize the j-th execution item in the secondary area; the behavior information includes: user speech behavior, operation behavior on the visualization monitoring model, etc.; the first corpus includes: behavior type, behavior generation time, etc.; the corpus extraction technology belongs to the category of existing technologies and will not be elaborated.

[0099] Determine the indication value corresponding to the first corpus from the indication value library; there are indication values corresponding to different first corpora in the indication library, and the indication value represents the degree of maturity of the timing for assisting the user. For example, if the first corpus is that the behavior type is that the user starts to modify the j-th execution item, the corresponding indication value is 10.

[0100] When the indicated value is greater than the indication threshold, corpus extraction is performed on the first execution situation information in the main area, the associated prompt information in the linkage area, the second execution situation information in the secondary area, and the behavior information generated by the user in the third historical time period to obtain the second corpus; the starting point of the third historical time period is the moment when the user's line of sight begins to fall into the linkage area, and the end point is the current moment; the indication threshold can be 8; when the indicated value is greater than the indication threshold, it means that the timing for assisting the user is relatively mature, and corpus extraction is performed on these four types of information to obtain the second corpus; the second corpus includes: execution situation type, execution situation generation time, behavior type, behavior generation time, etc.

[0101] Determine the optimized knowledge corresponding to the second corpus from the optimized knowledge base; there is optimized knowledge corresponding to different second corpora in the optimized knowledge base. The second corpus reflects the situation where the user wants to optimize the execution item, and the optimized knowledge indicates how to optimize the execution item in this situation; the optimized knowledge can be preset empirical information for performing execution optimization, etc.

[0102] Perform serialization processing on the optimized knowledge to obtain a sub-knowledge sequence; there are sub-knowledges arranged in sequence according to the knowledge prompt order in the sub-knowledge sequence. After all the sub-knowledges are prompted, the optimized knowledge is used up.

[0103] Perform corpus extraction on the behavior information generated by the user in the fourth historical time period to obtain the third corpus; the starting point of the fourth historical time period is the moment when the user's line of sight begins to fall into the secondary area, and the end point is the current moment; the behavior information includes: user speech behavior, operation behavior on the visual monitoring model, etc.; the third corpus includes: behavior type, behavior generation time, etc.

[0104] When there is corpus association between the third corpus and each of the first preset number of sub-knowledges in the front of the sub-knowledge sequence, or when there is corpus association between the third corpus and at least one sub-knowledge with a knowledge weight greater than the weight threshold among the first second preset number of sub-knowledges in the front of the sub-knowledge sequence, display the sub-knowledge sequence to the user; the first preset number can be 3; the second preset number can be 5; having corpus association means that the behavior reflected by the third corpus is consistent with the prompt expectation of the sub-knowledge. For example, if the third corpus is that the user modifies the delivery time, the sub-knowledge associated with it is to prompt the user to modify the delivery time.

[0105] When the user inputs an optimization strategy, optimize the processing strategy based on the optimization strategy.

[0106] When the embodiment of the present invention optimizes the processing strategy to assist the user based on the visual monitoring model, it accurately determines the timing of assisting the user according to the change of the user's line-of-sight landing points in the main area, the linked area and the secondary area, introduces an indication value, further accurately determines the maturity of the timing when the user can be assisted, and accurately uses optimization knowledge to appropriately assist the user based on the corpus generated by the user in different historical time periods, greatly improving the convenience for the user to optimize the processing strategy. At the same time, it is also more user-friendly and intelligent.

[0107] The embodiment of the present invention provides a supply and marketing information management method, as Figure 2 shown, including:

[0108] S1. Update the supply and marketing information database;

[0109] S2. Monitor supply and marketing abnormal events based on the supply and marketing information database;

[0110] S3. Make a processing decision on the supply and marketing abnormal events;

[0111] S4. Execute the processing strategy of the processing decision;

[0112] S5. During the execution of the processing strategy, generate a visual monitoring model for the execution of the processing strategy;

[0113] S6. Based on the visual monitoring model, assist the user to optimize the processing strategy;

[0114] S7. Relay and execute the optimized processing strategy.

[0115] The generating a visual monitoring model for the execution of the processing strategy during the execution of the processing strategy includes:

[0116] Perform serialization processing on the processing strategy to obtain an execution item sequence;

[0117] When the i-th execution item in the execution item sequence is executed, determine the j-th execution item in the execution item sequence that has an execution item association condition with the i-th execution item; where, the absolute value of i minus j is greater than 0 and less than or equal to a preset threshold;

[0118] Obtain an initial visual model; where, the initial visual model includes a main area, a secondary area and a linked area; and, when i is greater than j, the area of the main area greater than the secondary area is positively correlated with the absolute value of i minus j; and, when i is less than j, the area of the main area less than the secondary area is positively correlated with the absolute value of i minus j;

[0119] Map the first execution situation information of the i-th execution item into the main area;

[0120] Map the second execution status information of the j-th execution item into the secondary area;

[0121] Map the association prompt information corresponding to the execution item association condition into the linkage area;

[0122] Use the current initial visualization model as the visualization monitoring model.

[0123] The execution item association condition at least includes:

[0124] The i-th execution item is the same as or of the same type as the j-th execution item;

[0125] Or,

[0126] The type of the k-th execution item in the execution item sequence matches the trigger type; where k is between i and j;

[0127] Or,

[0128] There is an interaction between the execution results of the i-th execution item and the j-th execution item.

[0129] Based on the visualization monitoring model, assist the user in optimizing the processing strategy, including:

[0130] When the user's line-of-sight landing point in the first historical time period first falls into the main area for more than the first duration, then falls into the linkage area for more than the second duration, and then falls into the secondary area for more than the third duration, extract the corpus of the user's behavior information generated in the second historical time period to obtain the first corpus; where the third duration is much greater than the first duration which is greater than the second duration; the first historical time period is the time period of the most recent first preset time in history; the starting point of the second historical time period is the moment when the user's line-of-sight landing point starts to fall into the linkage area, and the ending point is the moment when the user's line-of-sight landing point remains in the secondary area until the second preset time;

[0131] Determine the indication value corresponding to the first corpus from the indication value library;

[0132] When the indication value is greater than the indication threshold, extract the corpus of the first execution status information in the main area, the association prompt information in the linkage area, the second execution status information in the secondary area, and the user's behavior information generated in the third historical time period to obtain the second corpus; the starting point of the third historical time period is the moment when the user's line-of-sight landing point starts to fall into the linkage area, and the ending point is the current moment;

[0133] Determine the optimization knowledge corresponding to the second corpus from the optimization knowledge base;

[0134] Perform serialization processing on the optimization knowledge to obtain a sub-knowledge sequence;

[0135] Extract the corpus from the behavioral information generated by the user in the fourth historical time period to obtain the third corpus; the starting point of the fourth historical time period is the moment when the user's line of sight starts to fall into the sub-region, and the end point is the current moment;

[0136] When there is corpus association between the third corpus and each of the first preset number of sub-knowledges in the sub-knowledge sequence, or when there is corpus association between the third corpus and at least one sub-knowledge with a knowledge weight greater than the weight threshold among the first second preset number of sub-knowledges in the sub-knowledge sequence, display the sub-knowledge sequence to the user;

[0137] When the user inputs an optimization strategy, optimize the processing strategy based on the optimization strategy.

[0138] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and its equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A supply and marketing information management system, characterized in that: include: Update module, used to update the supply and marketing information database; Monitoring module, used to monitor abnormal supply and marketing events based on the supply and marketing information database; Decision-making module, used to make decisions on abnormal supply and marketing events; A first execution module, used for executing a processing strategy of a processing decision; A generation module, used for generating a visual monitoring model of the execution of the processing strategy during the execution of the processing strategy; Auxiliary module, used to assist users in optimizing processing strategies based on visual monitoring models; The second execution module is used to relay the optimized processing strategy; The steps to generate a visual monitoring model include: Serialize the processing strategy to obtain the execution item sequence; When the i-th execution item in the execution item sequence is executed, the j-th execution item having the execution item association condition with the i-th execution item is determined from the execution item sequence; Obtaining an initial visualization model; wherein the initial visualization model includes a main area, a sub-area, and a linkage area; Map the first execution status information of the i-th execution item into the main area; Map the second execution status information of the j-th execution item into the secondary region; Map the associated prompt information corresponding to the associated conditions of the execution items into the linkage area; Use the current initial visualization model as the visualization monitoring model; Steps to assist the user include: When the user's sight point in the first historical time period first falls into the main area for more than a first duration, then falls into the linkage area for more than a second duration, and then falls into the secondary area for more than a third duration, the behavior information generated by the user in the second historical time period is extracted to obtain the first corpus; wherein the third duration is greater than the first duration, and the first duration is greater than the second duration; the first historical time period is the time period closest to the first preset time in history; the starting point of the second historical time period is the moment when the user's sight point begins to fall into the linkage area, and the end point is the moment when the user's sight point falls into the secondary area and remains there for a second preset time; Determining an indicator value corresponding to the first corpus from an indicator value library; When the indication value is greater than the indication threshold, the first execution situation information in the main area, the associated prompt information in the linkage area, the second execution situation information in the secondary area, and the behavior information generated by the user in the third historical time period are extracted to obtain the second corpus; the starting point of the third historical time period is the moment when the user's sight point begins to fall into the linkage area, and the end point is the current moment; Determining optimization knowledge corresponding to the second corpus from the optimization knowledge base; Serialize the optimization knowledge to obtain sub-knowledge sequence; Extracting the behavior information generated by the user in the fourth historical time period to obtain the third corpus; the starting point of the fourth historical time period is the moment when the user's sight point begins to fall into the secondary area, and the end point is the current moment; When the third corpus has a corpus association with each of the first preset number of sub-knowledges in the sub-knowledge sequence, or the third corpus has a corpus association with at least one sub-knowledge with a knowledge weight greater than a weight threshold among the first second preset number of sub-knowledges in the sub-knowledge sequence, the sub-knowledge sequence is displayed to the user; When the user inputs an optimization strategy, the processing strategy is optimized based on the optimization strategy.

2. The supply and marketing information management system according to claim 1, characterized in that: The absolute value of i minus j is greater than 0 and less than or equal to the preset threshold; When i is greater than j, the area of ​​the main region larger than the secondary region is positively correlated with the absolute value of i minus j; when i is less than j, the area of ​​the main region smaller than the secondary region is positively correlated with the absolute value of i minus j.

3. The supply and marketing information management system according to claim 2, characterized in that: The execution item association conditions at least include: The i-th execution item is the same as or of the same type as the j-th execution item; or, The type of the kth execution item in the execution item sequence matches the trigger type; where k is between i and j; or, There is interaction between the execution results of the i-th execution item and the j-th execution item.

4. A supply and marketing information management method, characterized in that: include: Update the supply and marketing information database; Based on the supply and marketing information database, monitor abnormal supply and marketing events; Make decisions on handling abnormal supply and marketing events; Processing strategies to implement processing decisions; In the process of executing the processing strategy, a visual monitoring model of the processing strategy execution is generated; Based on the visual monitoring model, it helps users optimize the processing strategy; Relay execution of optimized processing strategies; The steps to generate a visual monitoring model include: Serialize the processing strategy to obtain the execution item sequence; When the i-th execution item in the execution item sequence is executed, the j-th execution item having the execution item association condition with the i-th execution item is determined from the execution item sequence; Obtaining an initial visualization model; wherein the initial visualization model includes a main area, a sub-area, and a linkage area; Map the first execution status information of the i-th execution item into the main area; Map the second execution status information of the j-th execution item into the secondary region; Map the associated prompt information corresponding to the associated conditions of the execution items into the linkage area; Use the current initial visualization model as the visualization monitoring model; Steps to assist the user include: When the user's sight point in the first historical time period first falls into the main area for more than a first duration, then falls into the linkage area for more than a second duration, and then falls into the secondary area for more than a third duration, the behavior information generated by the user in the second historical time period is extracted to obtain the first corpus; wherein the third duration is greater than the first duration, and the first duration is greater than the second duration; the first historical time period is the time period closest to the first preset time in history; the starting point of the second historical time period is the moment when the user's sight point begins to fall into the linkage area, and the end point is the moment when the user's sight point falls into the secondary area and remains there for a second preset time; Determining an indicator value corresponding to the first corpus from an indicator value library; When the indication value is greater than the indication threshold, the first execution situation information in the main area, the associated prompt information in the linkage area, the second execution situation information in the secondary area, and the behavior information generated by the user in the third historical time period are extracted to obtain the second corpus; the starting point of the third historical time period is the moment when the user's sight point begins to fall into the linkage area, and the end point is the current moment; Determining optimization knowledge corresponding to the second corpus from the optimization knowledge base; Serialize the optimization knowledge to obtain sub-knowledge sequence; Extracting the behavior information generated by the user in the fourth historical time period to obtain the third corpus; the starting point of the fourth historical time period is the moment when the user's sight point begins to fall into the secondary area, and the end point is the current moment; When the third corpus has a corpus association with each of the first preset number of sub-knowledges in the sub-knowledge sequence, or the third corpus has a corpus association with at least one sub-knowledge with a knowledge weight greater than a weight threshold among the first second preset number of sub-knowledges in the sub-knowledge sequence, the sub-knowledge sequence is displayed to the user; When the user inputs an optimization strategy, the processing strategy is optimized based on the optimization strategy.

5. The supply and marketing information management method according to claim 4, characterized in that: The absolute value of i minus j is greater than 0 and less than or equal to the preset threshold; When i is greater than j, the area of ​​the main region larger than the secondary region is positively correlated with the absolute value of i minus j; when i is less than j, the area of ​​the main region smaller than the secondary region is positively correlated with the absolute value of i minus j.

6. The supply and marketing information management method according to claim 5, characterized in that: The execution item association conditions at least include: The i-th execution item is the same as or of the same type as the j-th execution item; or, The type of the kth execution item in the execution item sequence matches the trigger type; where k is between i and j; or, There is interaction between the execution results of the i-th execution item and the j-th execution item.

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