CN113191793A - Artificial intelligence based marketing asset delivery demand planning method and device - Google Patents
Artificial intelligence based marketing asset delivery demand planning method and device Download PDFInfo
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Abstract
The invention discloses an artificial intelligence marketing asset distribution demand planning method, which comprises the following steps: collecting and sorting data related to the distribution of the marketing assets to form a corresponding knowledge base; summarizing the requirement plan declared in the lower level, and describing the requirement plan by using a script language to form a plan template library; analyzing a demand plan in a demand plan template library by using a demand plan interpreter; and reasoning the analysis result of the demand plan based on the knowledge base to obtain a distribution decision, and forming a formal marketing asset distribution demand plan. The invention perfects the system structure of the marketing asset delivery demand plan, enhances the accuracy of the demand plan, effectively manages marketing resources and improves the material resource intensive management level.
Description
Technical Field
The invention belongs to the technical field of electric power system information, and particularly relates to a planning method for distribution demand of assets based on artificial intelligence marketing, and further relates to a planning device for distribution demand of assets based on artificial intelligence marketing.
Background
The power grid marketing assets are marketing professional equipment which is used for electric energy metering, business expansion, customer service and the like and supports high-efficiency operation of marketing business, and are uniformly supervised and guided by metering centers of power companies of various provinces, but the planning, the verification and the execution of a traditional marketing asset distribution demand plan are more dependent on manual judgment and experience, so that the problems of inconsistent demand and reality, inflexible distribution plan making, difficult modification and the like often occur, the overstock or shortage of asset inventory is caused, and the asset distribution is delayed.
The accuracy of the demand plan is enhanced by designing a perfect marketing asset delivery demand plan system structure, dynamic balance among inventory, demand and delivery is realized, marketing resources are effectively managed, and the material resource intensive management level is improved.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, provides a planning method and a planning device based on artificial intelligence marketing asset delivery requirements, and solves the technical problem of low accuracy of artificial auditing in the prior art.
In order to solve the technical problems, the technical scheme of the invention is as follows.
In a first aspect, the invention provides a planning method for distribution demand of assets based on artificial intelligence marketing, which comprises the following steps:
collecting and sorting data related to the distribution of the marketing assets to form a corresponding knowledge base;
summarizing the requirement plan declared in the lower level, and describing the requirement plan by using a script language to form a plan template library;
analyzing a demand plan in a demand plan template library by using a demand plan interpreter;
and reasoning the analysis result of the demand plan based on the knowledge base to obtain a distribution decision, and forming a formal marketing asset distribution demand plan.
Optionally, the related data includes knowledge of the specification of the demand plan, knowledge of the transportation route, knowledge of the decision, knowledge of the vehicle grouping, knowledge of the routing, and the emergency scheduling plan.
Optionally, the knowledge base includes a requirement plan detail specification knowledge base, a transportation route knowledge base, a decision knowledge base, a vehicle dynamic knowledge base, a route arrangement knowledge base and an emergency scheduling plan base.
Optionally, the rule of the transportation route knowledge base is described as follows:
line (origin, destination, path set).
Optionally, the rule of the vehicle dynamic knowledge base is described as:
jc (vehicle number, current position, state, towing mode, direction, number of trailers, running target).
Optionally, the reasoning of the analysis result of the demand plan based on the knowledge base to obtain the decision includes:
and calculating the credibility probability which should be established for the unreal conclusion, and taking the conclusion with the maximum credibility probability value as the conclusion of the inference process.
Optionally, after the reasoning is performed on the analysis result of the demand plan based on the knowledge base to obtain the decision, the method further includes: and manually adjusting the unreasonable demand plan, reasoning the demand plan again, and continuously repeating the process if the reasoning result is incorrect.
In a second aspect, the present invention further provides an artificial intelligence based marketing asset delivery demand planning device, including:
the data collection module is used for collecting and sorting the data related to the distribution of the marketing assets to form a corresponding knowledge base;
the demand summarizing module is used for summarizing the demand plans declared in the lower level and describing the demand plans by using a script language to form a plan template library;
the analysis demand module is used for analyzing the demand plan in the demand plan template library by using a demand plan interpreter;
and the distribution decision module is used for reasoning the analysis result of the demand plan based on the knowledge base to obtain a distribution decision so as to form a formal marketing asset distribution demand plan.
Compared with the prior art, the invention has the following beneficial effects: the invention perfects the system structure of the marketing asset delivery demand plan, enhances the accuracy of the demand plan, effectively manages marketing resources and improves the material resource intensive management level.
Drawings
FIG. 1 is a logic flow diagram of the present invention;
fig. 2 is a flow chart of the operation of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Example 1
The invention discloses a method for planning distribution demand of assets based on artificial intelligence marketing, which is shown in a figure 1 and a figure 2 and comprises the following processes:
step 1, collecting and sorting data related to marketing asset distribution;
and acquiring data related to marketing asset distribution, such as detailed specifications of annual demand plans of marketing assets of all departments of the power grid, specifications of equipment qualified in a provincial center warehouse, annual addition plans, types, quantities, specifications, line arrangement of equipment demands in the current month, experience of decision-making personnel and the like. Decision-maker experience, including delivery demand rationalization, delivery demand priority order, demand plan detail specification, transportation routes and decisions, is the basis for forming a demand plan system.
Step 2, translating the knowledge collected in the step 1 into a rule which can be recognized and executed by a computer to form an initial knowledge base;
the knowledge comprises requirement plan detail specification knowledge, transportation route knowledge, decision-making knowledge, vehicle marshalling knowledge, route arrangement knowledge, an emergency scheduling plan and the like, and different knowledge is classified according to rules to establish knowledge bases to form corresponding knowledge bases, such as a requirement plan detail specification knowledge base, a transportation route knowledge base, a decision-making knowledge base, a vehicle dynamic knowledge base, a route arrangement knowledge base, an emergency scheduling plan base and the like. The knowledge base is divided into a static knowledge base and a dynamic knowledge base, which are collectively called the knowledge base. The static knowledge base mainly stores inherent information related to scheduling and knowledge followed in the scheduling process. These include knowledge of the requirements plan specifications, knowledge of the transportation routes, knowledge of the decisions, etc.
For example, in order to quickly find a path to a certain loading and unloading point in the decision, a transportation route knowledge base to each loading and unloading point is established. It is defined as:
line (origin, destination, path set);
such as: line (A31, A5, loading, [ A1, L, B4, B5]) shows that loading is carried out from a shunting yard A31 to a destination A5, and the running routes are respectively A1/L/B4/B5.
The dynamic knowledge base refers to the knowledge of the domain knowledge, which changes along with the change of time and space, and is changed by the change of external information. These include vehicle dynamics knowledge, routing knowledge, emergency dispatch protocol knowledge, etc.
For dynamically changing knowledge base content, such as a vehicle dynamic knowledge base, a locomotive travels on a track, its air-space relationship changes continuously, and its attributes (sometimes loading and sometimes unloading) also change continuously. The knowledge base format follows the following rules:
jc (vehicle number, current position, state, towing mode, direction, trailer number, running target);
such as: jc (0018, A31, RUN, CART, DONG, 15, A1), which indicates that the vehicle with the vehicle number 0018 is currently located on the A31 line of the shunting yard, the operation state, the traction mode is CART, DONG, the number of trailers is 15, and the operation destination is A1.
And step 3, summarizing the requirement plans declared in the lower level, and sending the requirement plans to the step 4.
And reporting the demand plan by each level of power supply unit according to the actual demand, summarizing the demand plan library by a head office to uniformly distribute. The demand plan library manages and summarizes the asset equipment classification, equipment code name, category, type, material, equipment demand quantity, equipment forecast quantity and the like in the demand plan.
Step 4, describing the demand plan in the step 3 according to an agreed grammar rule and a script language, and then storing a demand plan template library;
describing the demand plan into a script language, and providing symbolic conventions such as lc (n)/jh (n)/gddw (n)/ksny (n)/sbfl (n)/xqsl (n)/yksl (n)/jsny (n)/zt (n)/zdry (n)/zdrq (n)/hzbz (n) to respectively represent the process number, plan number, power supply unit, starting month, equipment classification, equipment demand quantity, business expansion new quantity, ending month, plan state, formulation personnel, formulation date, summary flag and other information of each demand plan, wherein a demand plan template library exists after the processing is completed, and is only a container for storing the demand plan template for being called by a demand interpreter in step 5.
Step 5, analyzing the demand plan in the demand plan template library in the step 4 by using a demand plan interpreter according to the user-defined grammar rule and the script language;
the requirements plan interpreter is responsible for interpreting the requirements plan. For example, lc (n)/jh (n)/gddw (n)/ksny (n)/sbfl (n)/xqsl (n)/yksl (n)/jsny (n)/zt (n)/zdry (n)/zdrq (n)/hzbz (n) are translated as follows: hs (in)/d (in)/hy (in)/pd (in)/mf (in)/pa (in)/pf (in)/pk (in)/sc (in)/ma (in)/m (in)/o (in) indicates that the meanings are not changed.
Step 6, reasoning the demand plan according to the demand plan analyzed in the step 5 by a reasoning machine according to the information in the knowledge base to obtain a distribution decision;
the inference of the demand plan has complexity and uncertainty, the inferred original data sometimes can not completely meet a certain inference conclusion, and a decision with high credibility is provided by inaccurate inference for reference of an auditor. And calculating the credibility probability P that the conclusion should be established for the unreal conclusion, wherein the greater the value of P, the greater the possibility that the conclusion is established, and finally taking the conclusion with the maximum value of P as the conclusion (such as the route) of the inference process (such as the transportation route inference).
The P value calculation formula is as follows:
P＝(sum1/x+sum2/n
wherein n represents the total number of conditions required for the conclusion of a certain judgment method F; sum2 represents the number of conditions for the conclusion that this method F actually holds; sum1 represents the number of satisfied conditions included in a set of conditions T for which the method F was not concluded; x represents the rule threshold corresponding to a set of conditions T for which the method F of conclusion does not hold.
And 7, manually adjusting the demand plan by the auditor.
The knowledge base comprises a circuit arrangement knowledge base, a decision knowledge base and the like, all of which contain experience knowledge of dispatching personnel, and when the dispatching personnel find obvious conflict or error of demand plans and vehicle using conditions, the dispatching personnel modify information such as asset parameters, personnel, vehicles and the like manually, so that the optimization of the system is promoted.
And 8, performing computer simulation judgment, namely reasoning the demand plan again by using a reasoning machine, performing step 9 if the reasoning result is correct, and returning to step 7 if the reasoning result is wrong.
And 9, forming a formal marketing asset distribution demand plan after the decision is passed and implementing.
The method can improve the system structure of the marketing asset delivery demand plan, enhance the accuracy of the demand plan, effectively manage marketing resources and improve the intensive management level of material resources.
Example 2
Based on the same inventive concept as that of embodiment 1, the invention provides an artificial intelligence based marketing asset distribution demand planning device, comprising:
the data collection module is used for collecting and sorting the data related to the distribution of the marketing assets to form a corresponding knowledge base;
the demand summarizing module is used for summarizing the demand plans declared in the lower level and describing the demand plans by using a script language to form a plan template library;
the analysis demand module is used for analyzing the demand plan in the demand plan template library by using a demand plan interpreter;
and the distribution decision module is used for reasoning the analysis result of the demand plan based on the knowledge base to obtain a distribution decision so as to form a formal marketing asset distribution demand plan.
The implementation scheme of each module in the device is shown in the treatment process of each step of the method in the embodiment 1.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (8)
1. A planning method for distribution demand of assets based on artificial intelligence marketing is characterized by comprising the following processes:
collecting and sorting data related to the distribution of the marketing assets to form a corresponding knowledge base;
summarizing the requirement plan declared in the lower level, and describing the requirement plan by using a script language to form a plan template library;
analyzing a demand plan in a demand plan template library by using a demand plan interpreter;
and reasoning the analysis result of the demand plan based on the knowledge base to obtain a distribution decision, and forming a formal marketing asset distribution demand plan.
2. The method of claim 1, wherein the related data comprises knowledge of specification of demand plan details, knowledge of transportation routes, decision-making knowledge, knowledge of vehicle grouping, knowledge of routing, and emergency scheduling plans.
3. The method of claim 1, wherein the knowledge base comprises a knowledge base of demand plan detail specification, a knowledge base of transportation route, a knowledge base of decision, a knowledge base of vehicle dynamic, a knowledge base of circuit arrangement and a knowledge base of emergency scheduling plan.
4. The method for planning delivery demand of marketing assets based on artificial intelligence as claimed in claim 3, wherein the rules of the transportation route knowledge base are described as follows:
line (origin, destination, path set).
5. The method for planning on demand for distribution of marketing assets based on artificial intelligence as claimed in claim 3, wherein the rules of the vehicle dynamic knowledge base are described as follows:
jc (vehicle number, current position, state, towing mode, direction, number of trailers, running target).
6. The method for formulating a demand plan based on artificial intelligence marketing asset delivery according to claim 1, wherein the reasoning the analytic result of the demand plan based on the knowledge base to obtain a decision comprises:
and calculating the credibility probability which should be established for the unreal conclusion, and taking the conclusion with the maximum credibility probability value as the conclusion of the inference process.
7. The method for formulating a demand plan based on artificial intelligence marketing asset delivery according to claim 1, wherein after reasoning the analytic result of the demand plan based on the knowledge base to obtain a decision, the method further comprises: and manually adjusting the unreasonable demand plan, reasoning the demand plan again, and continuously repeating the process if the reasoning result is incorrect.
8. The utility model provides a based on artificial intelligence marketing asset delivery demand planning device which characterized by includes:
the data collection module is used for collecting and sorting the data related to the distribution of the marketing assets to form a corresponding knowledge base;
the demand summarizing module is used for summarizing the demand plans declared in the lower level and describing the demand plans by using a script language to form a plan template library;
the analysis demand module is used for analyzing the demand plan in the demand plan template library by using a demand plan interpreter;
and the distribution decision module is used for reasoning the analysis result of the demand plan based on the knowledge base to obtain a distribution decision so as to form a formal marketing asset distribution demand plan.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113822631A (en) * | 2021-09-16 | 2021-12-21 | 江苏方天电力技术有限公司 | Marketing asset distribution method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113822631A (en) * | 2021-09-16 | 2021-12-21 | 江苏方天电力技术有限公司 | Marketing asset distribution method and system |
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