Supply and marketing information management system and method thereof

Abstract

The invention provides a supply and marketing information management system and method, and the system comprises an updating module which is used for updating a supply and marketing information base; the monitoring module is used for monitoring supply and marketing abnormal events based on the supply and marketing information base; the decision-making module is used for performing processing decision-making on the supply and marketing abnormal events; the first execution module is used for executing a processing strategy of the processing decision; the generation module is used for generating a visual monitoring model executed by the processing strategy in the process of executing the processing strategy; the auxiliary module is used for assisting the user to optimize the processing strategy based on the visual monitoring model; and the second execution module is used for relay execution of the optimized processing strategy. According to the method, the supply and marketing abnormal events are automatically monitored, the supply and marketing abnormal events are processed and decided, the processing strategy of the processing decision is executed, the labor cost is reduced, and the system efficiency is improved; and generating a visual monitoring module for processing strategy execution, and supporting a worker to monitor and optimize the execution of the processing strategy.

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, used 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, the generation module generates a visual monitoring model of the execution of the processing strategy during the execution of the processing strategy, including:

[0013] Serialize the processing strategy to obtain the execution item sequence;

[0014] 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; wherein the absolute value of i minus j is greater than 0 and less than or equal to a preset threshold;

[0015] Acquire an initial visualization model; wherein the initial visualization model includes a main region, a sub-region, and a linkage region; and, when i is greater than j, the area of ​​the main region that is larger than the sub-region is positively correlated with the absolute value of i minus j; and, when i is less than j, the area of ​​the main region that is smaller than the sub-region is positively correlated with the absolute value of i minus j;

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

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

[0018] Map the associated prompt information corresponding to the associated conditions of the execution items into the linkage area;

[0019] The current initial visualization model is used as the visualization 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 kth execution item in the execution item sequence matches the trigger type; where k is between i and j;

[0024] or,

[0025] There is 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 strategy based on the visual monitoring model, including:

[0027] When the user's sight point in the first historical time period first falls into the main area for more than the first time length, then falls into the linkage area for more than the second time length, and then falls into the secondary area for more than the third time length, the behavior information generated by the user in the second historical time period is extracted to obtain the first corpus; wherein the third time length is much longer than the first time length and the second time length; 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;

[0028] Determining an indicator value corresponding to the first corpus from an indicator value library;

[0029] 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;

[0030] Determining optimization knowledge corresponding to the second corpus from the optimization knowledge base;

[0031] Serialize the optimization knowledge to obtain sub-knowledge sequence;

[0032] 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;

[0033] 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;

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

[0035] An embodiment of the present invention provides a supply and marketing information management method, comprising:

[0036] Update the supply and marketing information database;

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

[0038] Make decisions on handling abnormal supply and marketing events;

[0039] Processing strategies to implement processing decisions;

[0040] In the process of executing the processing strategy, a visual monitoring model of the processing strategy execution is generated;

[0041] Based on the visual monitoring model, it helps users optimize the processing strategy;

[0042] Relay execution of optimized processing strategy.

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

[0044] Serialize the processing strategy to obtain the execution item sequence;

[0045] 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; wherein the absolute value of i minus j is greater than 0 and less than or equal to a preset threshold;

[0046] Acquire an initial visualization model; wherein the initial visualization model includes a main region, a sub-region, and a linkage region; and, when i is greater than j, the area of ​​the main region that is larger than the sub-region is positively correlated with the absolute value of i minus j; and, when i is less than j, the area of ​​the main region that is smaller than the sub-region is positively correlated with the absolute value of i minus j;

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

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

[0049] Map the associated prompt information corresponding to the associated conditions of the execution items into the linkage area;

[0050] The current initial visualization model is used as the visualization 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 kth execution item in the execution item sequence matches the trigger type; where k is between i and j;

[0055] or,

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

[0057] Optionally, the visual monitoring model is used to assist the user in optimizing the processing strategy, including:

[0058] When the user's sight point in the first historical time period first falls into the main area for more than the first time length, then falls into the linkage area for more than the second time length, and then falls into the secondary area for more than the third time length, the behavior information generated by the user in the second historical time period is extracted to obtain the first corpus; wherein the third time length is much longer than the first time length and the second time length; 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;

[0059] Determining an indicator value corresponding to the first corpus from an indicator value library;

[0060] 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;

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

[0062] Serialize the optimization knowledge to obtain sub-knowledge sequence;

[0063] 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;

[0064] 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;

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

[0066] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0067] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF 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 of the present invention. In the accompanying drawings:

[0069] Figure 1 A schematic diagram of a supply and marketing information management system according to 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 DESCRIPTION

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

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

[0073] Update module 1, used for updating the supply and marketing information database;

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

[0075] Decision module 3, used to make decisions on abnormal supply and marketing events;

[0076] A first execution module 4, used for executing a processing strategy of a processing decision;

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

[0078] Auxiliary module 6, used to assist users in optimizing processing strategies based on a visual monitoring model;

[0079] The second execution module 7 is used to execute the optimized processing strategy in relay.

[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. Supply and marketing abnormal events include inventory shortage events, inventory surplus events, supply chain terminal events, product quality problem events, etc.; processing decisions are made for supply and marketing abnormal events. When making processing decisions, the processing strategies corresponding to the supply and marketing abnormal events in the preset processing strategy library can be queried; after executing the processing strategy, the system starts to automatically process the supply and marketing abnormal events; in the process of executing the processing strategy, a visual monitoring model of the processing strategy execution is generated for users to monitor the execution of the processing strategy based on the visual monitoring model, and assist users to optimize the processing strategy. After the processing strategy optimization is completed, the optimized processing strategy is executed in relay.

[0081] This application automation is based on the supply and marketing information database, monitors abnormal supply and marketing events, 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 processing strategy execution is generated to support staff in monitoring and optimizing the execution of the processing strategy.

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

[0083] The processing strategy is serialized to obtain an execution item sequence; the execution item sequence includes execution items arranged in sequence according to the execution order, and the execution of the processing strategy is completed after each execution item in the execution item sequence is executed in sequence;

[0084] When the i-th execution item in the execution item sequence is executed, the j-th execution item that has the execution item association condition with the i-th execution item is determined from the execution item sequence; wherein the absolute value of i minus j is greater than 0 and less than or equal to a preset threshold; the preset threshold may be 3; if the j-th execution item has the execution item association condition with the i-th execution item, then the first execution status information of the i-th execution item, the second execution status 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 it by viewing this information; the first execution status information is the execution item information of the execution item. The second execution status 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. of the i-th execution item; when i is greater than j, the j-th execution item has been executed before the i-th execution item is executed, and the second execution status 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, and the second execution status information is the object to be executed, 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 sub-area, and a linkage area; and, when i is greater than j, the area of ​​the main area that is larger than the sub-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 that is smaller than the sub-area is positively correlated with the absolute value of i minus j; when i is greater than j, the jth execution item has been executed before the ith execution item is executed, and the user is less likely to optimize the jth execution item, then the area of ​​the main area is larger than the sub-area, and the greater the absolute value of i minus j, the greater the area of ​​the main area that is larger than the sub-area, which is convenient for users to focus on the ith execution item currently executed; when i is less than j, the jth execution item is executed after the ith execution item is executed, and the user is less likely to optimize the ith execution item, then the area of ​​the main area is smaller than the sub-area, and the greater the absolute value of i minus j, the greater the area of ​​the main area that is smaller than the sub-area, which is convenient for users to focus on the jth execution item that is about to be executed; the rationality of the construction of the visualization monitoring model is greatly improved, which is very user-friendly and also improves the applicability of the system;

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

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

[0088] Map the associated prompt information corresponding to the associated conditions of the execution items into the linkage area;

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

[0090] The execution item association conditions at least include:

[0091] The i-th execution item is the same as the j-th execution item or is of the same type; the i-th execution item is the same as the j-th execution item or is of the same type, which indicates that the execution items are repeatedly executed or the same type of execution items are executed multiple times, and the user needs to pay attention to it; the associated prompt information corresponding to the execution item association condition can be the same execution items or the same type;

[0092] or,

[0093] The type of the kth execution item in the execution item sequence matches the trigger type; wherein k is between i and j; the trigger type may be a type representing a more important execution item, and k is between i and j, then the more important execution item is executed between the ith execution item and the jth execution item, and the user needs to evaluate the execution effect of the kth execution item with a greater degree of importance by viewing the execution status of the ith execution item and the jth execution item; the associated prompt information corresponding to the execution item association condition may be that an important execution item needs attention; k=1, 2, 3, ..., N;

[0094] or,

[0095] There is interaction between the execution results of the i-th execution item and the j-th execution item. Interaction between execution results means that the execution result of one execution item affects the execution effect of another execution item; when there is 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 status of the two execution items at the same time; the associated prompt information corresponding to the execution item association condition can be that there is interaction between execution results.

[0096] By setting the above three execution item association conditions, the j-th execution item can be accurately determined, which further improves the humanization and helps users to more conveniently monitor and optimize the execution of processing strategies based on the visual monitoring model.

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

[0098] When the user's sight 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, 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 much longer than the first duration and 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 the second preset time; the third duration The first time length can be 100 seconds; the first time length can be 30 seconds; the second time length can be 10 seconds; the first preset time can be 300 seconds; the second preset time can be 80 seconds; when the sight point first falls into the main area for more than the first time length, then falls into the linkage area for more than the second time length, and then falls into the secondary area for more than the third time length, it means that the user may want to optimize the jth 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 scope of existing technology and will not be elaborated;

[0099] Determine the indicator value corresponding to the first corpus from the indicator value library; the indicator library has indicator values ​​corresponding to different first corpora, and the indicator value represents the degree of maturity of the time when the user can be assisted. For example, if the first corpus is a behavior type in which the user starts to modify the jth execution item, the corresponding indicator value is 10;

[0100] 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; the indication threshold can be 8; when the indication value is greater than the indication threshold, it means that the time to assist the user is relatively mature, and the four types of information are extracted 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 optimization knowledge corresponding to the second corpus from the optimization knowledge base; the optimization knowledge base contains optimization knowledge corresponding to different second corpora, the second corpora reflect the situation in which the user wants to optimize the execution item, and the optimization knowledge indicates how to optimize the execution item in this situation; the optimization knowledge may be preset experience information for execution optimization, etc.;

[0102] The optimized knowledge is serialized to obtain a sub-knowledge sequence; the sub-knowledge sequence includes sub-knowledge arranged in the order of knowledge prompting, and after all sub-knowledge prompts are completed, the optimized knowledge is used;

[0103] The behavior information generated by the user in the fourth historical time period is extracted 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; the behavior information includes: user speech behavior, operation behavior on the visualization monitoring model, etc.; the third corpus includes: behavior type, behavior generation time, etc.;

[0104] 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 whose knowledge weight is greater than the 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; the first preset number may be 3; the second preset number may be 5; having a corpus association means that the behavior reflected by the third corpus is consistent with the prompt expectation of the sub-knowledge, for example: the third corpus is for the user to modify the delivery time, and the sub-knowledge with which it has a corpus association is to prompt the user to modify the delivery time;

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

[0106] When assisting users to optimize processing strategies based on a visualization monitoring model, the embodiment of the present invention accurately determines the timing of assisting users according to changes in the user's line of sight in the main area, linkage area, and sub-area, introduces an indicator value, and further accurately determines the maturity of the timing for assisting users. Based on the corpus generated by users in different historical time periods, the optimization knowledge is accurately used to appropriately assist users, which greatly improves the convenience of users optimizing processing strategies and is more humane and intelligent.

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

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

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

[0110] S3. Make decisions on handling abnormal supply and marketing events;

[0111] S4, a processing strategy for executing the processing decision;

[0112] S5. In the process of executing the processing strategy, a visual monitoring model of the processing strategy execution is generated;

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

[0114] S7, relay execution of the optimized processing strategy.

[0115] In the process of executing the processing strategy, generating a visual monitoring model for the execution of the processing strategy includes:

[0116] Serialize the processing strategy to obtain the execution item sequence;

[0117] 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; wherein the absolute value of i minus j is greater than 0 and less than or equal to a preset threshold;

[0118] Acquire an initial visualization model; wherein the initial visualization model includes a main region, a sub-region, and a linkage region; and, when i is greater than j, the area of ​​the main region that is larger than the sub-region is positively correlated with the absolute value of i minus j; and, when i is less than j, the area of ​​the main region that is smaller than the sub-region is positively correlated with the absolute value of i minus j;

[0119] Map the first execution status 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 region;

[0121] Map the associated prompt information corresponding to the associated conditions of the execution items into the linkage area;

[0122] The current initial visualization model is used as the visualization monitoring model.

[0123] The execution item association conditions at least include:

[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 kth execution item in the execution item sequence matches the trigger type; where k is between i and j;

[0127] or,

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

[0129] The visual monitoring model is used to assist users in optimizing the processing strategy, including:

[0130] When the user's sight point in the first historical time period first falls into the main area for more than the first time length, then falls into the linkage area for more than the second time length, and then falls into the secondary area for more than the third time length, the behavior information generated by the user in the second historical time period is extracted to obtain the first corpus; wherein the third time length is much longer than the first time length and the second time length; 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;

[0131] Determining an indicator value corresponding to the first corpus from an indicator value library;

[0132] 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;

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

[0134] Serialize the optimization knowledge to obtain sub-knowledge sequence;

[0135] 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;

[0136] 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;

[0137] When the user inputs an optimization strategy, the processing strategy is optimized 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 their equivalents, 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 execution of the optimized processing strategy.

2. The supply and marketing information management system according to claim 1, characterized in that: The generation module generates a visual monitoring model of the execution of the processing strategy during the execution of the processing strategy, including: 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; wherein the absolute value of i minus j is greater than 0 and less than or equal to a preset threshold; Acquire an initial visualization model; wherein the initial visualization model includes a main region, a sub-region, and a linkage region; and, when i is greater than j, the area of ​​the main region that is larger than the sub-region is positively correlated with the absolute value of i minus j; and, when i is less than j, the area of ​​the main region that is smaller than the sub-region is positively correlated with the absolute value of i minus j; 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; The current initial visualization model is used as the visualization monitoring model.

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. The supply and marketing information management system according to claim 2, characterized in that: The auxiliary module assists the user in optimizing the processing strategy based on the visual monitoring model, including: When the user's sight point in the first historical time period first falls into the main area for more than the first time length, then falls into the linkage area for more than the second time length, and then falls into the secondary area for more than the third time length, the behavior information generated by the user in the second historical time period is extracted to obtain the first corpus; wherein the third time length is much longer than the first time length and the second time length; 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. 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 strategy.

6. The supply and marketing information management method according to claim 5, characterized in that: In the process of executing the processing strategy, generating a visual monitoring model for the execution of the processing strategy includes: 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; wherein the absolute value of i minus j is greater than 0 and less than or equal to a preset threshold; Acquire an initial visualization model; wherein the initial visualization model includes a main region, a sub-region, and a linkage region; and, when i is greater than j, the area of ​​the main region that is larger than the sub-region is positively correlated with the absolute value of i minus j; and, when i is less than j, the area of ​​the main region that is smaller than the sub-region is positively correlated with the absolute value of i minus j; 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; The current initial visualization model is used as the visualization monitoring model.

7. The supply and marketing information management method according to claim 6, 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.

8. The supply and marketing information management method according to claim 6, characterized in that: The visual monitoring model is used to assist users in optimizing the processing strategy, including: When the user's sight point in the first historical time period first falls into the main area for more than the first time length, then falls into the linkage area for more than the second time length, and then falls into the secondary area for more than the third time length, the behavior information generated by the user in the second historical time period is extracted to obtain the first corpus; wherein the third time length is much longer than the first time length and the second time length; 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.

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