Data processing method and device for dynamic simulation optimization system of marketing strategy
By constructing a market dynamic twin engine, combining market demand, competitive landscape, and production capacity models, dynamic simulation of marketing strategies is conducted, solving the problems of lagging and inefficiency in traditional marketing strategies, and achieving global optimization and long-term marketing strategy optimization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-09
- Publication Date
- 2026-07-07
AI Technical Summary
Traditional marketing strategy formulation relies on experience-based judgment, which makes it difficult to accurately adapt to the dynamically changing market environment, resulting in strategy lag and resource waste. Existing technologies cannot achieve dynamic simulation and global optimization of marketing strategies, leading to low data processing efficiency.
By acquiring industry-specific data to establish a market demand forecasting model, and combining historical market data and capacity models to construct a market dynamic twin engine, we can conduct dynamic simulation of marketing strategies, optimize market competition and capacity, and achieve dynamic simulation and full-cycle prediction of the optimal marketing strategy.
It enables accurate prediction of market demand throughout its entire lifecycle and clear definition of competitive positioning. By solving long-term optimal marketing strategies through multi-scenario simulations, it improves the data processing efficiency of marketing strategies and solves the problems of lag and reliance on experience in traditional marketing strategies.
Smart Images
Figure CN122347440A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of data processing technology, and in particular to a data processing method and apparatus for a dynamic simulation optimization system for marketing strategies. Background Technology
[0002] Currently, amidst increasingly fierce market competition and fluctuating demand across industries, traditional marketing strategies rely heavily on experience-based judgment, making it difficult to accurately adapt to the dynamically changing market environment. This often leads to problems such as outdated strategies, wasted resources, or lower-than-expected returns. Existing technologies for determining marketing strategies only consider the current market situation, making it difficult to dynamically simulate marketing strategies. This prevents the overall optimization of marketing strategies from a holistic perspective, resulting in low data processing efficiency for current marketing strategies. Summary of the Invention
[0003] The purpose of this invention is to provide a data processing method and apparatus for a dynamic simulation optimization system for marketing strategies, so as to solve the technical problem of low data processing efficiency in current marketing strategies.
[0004] In a first aspect, this application provides a data processing method for a dynamic simulation optimization system for marketing strategies, the method comprising: Acquire industry characteristic data and establish an industry market demand forecasting model for a specified period based on the industry characteristic data; the industry corresponding to the industry characteristic data and the industry corresponding to the industry market demand forecasting model are the same target industry. Historical market data of the target industry is obtained, a market competition situation and market influence model is established based on the historical market data, and the competition type of each market participant in the target industry in the market competition structure is determined through the market competition situation and market influence model. A capacity and energy storage model is established based on the production process characteristics data, capacity limit data, and inventory turnover level data of the target industry. A market dynamic twin engine is constructed based on the aforementioned capacity and energy storage model, the aforementioned market competition situation and market influence model, and the aforementioned industry market demand forecasting model. Based on the specified optimization target data, the marketing strategies corresponding to different competition types are dynamically simulated using the market dynamic twin engine, so as to obtain the optimal marketing strategy data for the specified period based on the comprehensive data of the marketing strategies of each competitor's competition type corresponding to its own competition type.
[0005] In one possible implementation, the industry characteristic data includes industry supply and demand characteristic data and seasonal fluctuation pattern data; the specified period includes a specified short-term and a specified long-term, wherein the specified short-term period is shorter than the specified long-term period; the step of establishing an industry market demand forecasting model for the specified period based on the industry characteristic data includes: Based on the industry supply and demand characteristics data and the seasonal fluctuation pattern data, the specified short-term market demand forecasting model is constructed using time series analysis. Based on the specified short-term market demand forecasting model, long-term variables are integrated, and the specified long-term market demand forecasting model is constructed through machine learning to make full-cycle predictions of market demand; wherein, the long-term variables include industry development trend variables and / or policy guidance variables.
[0006] In one possible implementation, the historical market data includes historical market sales volume data, historical market sales price data, and historical market price adjustment data; the step of establishing a market competition situation and market influence model based on the historical market data, and determining the competition type of each market participant in the target industry within the market competition structure through the market competition situation and market influence model, includes: The time-series data corresponding to the historical market price adjustment data, the historical market sales data, and the historical market sales price data are standardized and preprocessed to obtain the preprocessing result. Market characteristic indicators are extracted based on the preprocessing results, and a market competition situation and market influence model is constructed based on the market characteristic indicators; wherein, the market characteristic indicators include at least one of market share, price sensitivity, and adjustment response speed; The competitive strength and market influence data of each market participant in the target industry are quantified by the market competition situation and market influence model. Based on the competitive strength data and the market influence data, the participants are classified to obtain the competition type of each market participant in the market competition structure. The competition type includes at least one of the following: leader type, competitor type, follower type, and missing element type.
[0007] In one possible implementation, the construction of a market dynamic twin engine based on the capacity and energy storage model, the market competition situation and market influence model, and the industry market demand forecasting model includes: Based on the aforementioned production capacity and energy storage model, market competition situation and market influence model, and industry market demand forecasting model, a market dynamic twin engine is constructed through data association mapping and feature extraction fusion. Based on the input data of the market dynamic twin engine, the market operation status of the target industry and the interaction between multiple key elements are simulated through the market dynamic twin engine. Among them, the multiple key elements include market demand elements, market sales volume elements, historical sales price elements, market competition elements, and production capacity elements.
[0008] In one possible implementation, the step of dynamically simulating marketing strategies corresponding to different competition types using the market dynamic twin engine based on specified optimization target data, to obtain optimal marketing strategy data for the specified period based on comprehensive data of marketing strategies corresponding to each competitor's competition type corresponding to its own competition type, includes: Based on the specified optimization objectives of maximizing corporate profits and increasing market share, the market dynamic twin engine is used to conduct multi-scenario dynamic simulation of price adjustment planning and response strategies for different types of competition, and the simulation results are obtained. Based on the competitor marketing strategies of each competitor in the various competitive types, the optimal marketing strategy feedback data after comprehensive consideration is obtained through comparative analysis and iterative optimization of the simulation results.
[0009] In one possible implementation, the market dynamic twin engine is a dynamic simulation optimization model for marketing strategies. This model is quantitatively expressed through two synergistic objective function sets. These two sets include a first objective function set, which contains different indicator functions. Multiple indicator functions include the first indicator function, which is a full-cycle prediction function set for market simulation. This full-cycle prediction function set includes all implicit functions and control variables for market demand forecasting.
[0010] In one possible implementation, the two synergistic objective function sets further include a second objective function set, the second objective function set containing multiple relational functions of the enterprise marketing strategy, wherein the first relational function among the multiple relational functions represents all implicit functions and control variables of the enterprise marketing strategy; through the synergistic calculation of the first objective function set and the second objective function set, iterative optimization of multi-scenario simulation results is performed to achieve the continuous output of the specified long-term optimal marketing strategy.
[0011] Secondly, this application provides a data processing device for a dynamic simulation and optimization system for marketing strategies, comprising: The first module is used to acquire industry characteristic data and establish an industry market demand forecasting model for a specified period based on the industry characteristic data; the industry corresponding to the industry characteristic data and the industry corresponding to the industry market demand forecasting model are the same target industry. The second module is used to acquire historical market data of the target industry, establish a market competition situation and market influence model based on the historical market data, and determine the competition type of each market participant in the target industry in the market competition structure through the market competition situation and market influence model. The third module is used to establish a capacity and energy storage model based on the production process characteristics data, capacity limit data, and inventory turnover level data of the target industry. The module is used to build a market dynamic twin engine based on the capacity and energy storage model, the market competition situation and market influence model, and the industry market demand forecasting model. The simulation module is used to dynamically simulate marketing strategies corresponding to different competition types based on specified optimization target data through the market dynamic twin engine, so as to obtain the optimal marketing strategy data for the specified period based on the comprehensive data of the marketing strategies of each competitor's competition type corresponding to its own competition type.
[0012] Thirdly, this application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the method described in the first aspect above.
[0013] Fourthly, this application also provides a computer-readable storage medium storing computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method described in the first aspect above.
[0014] This application brings the following beneficial effects: This application provides a data processing method and apparatus for a dynamic simulation optimization system of marketing strategies. It can acquire industry characteristic data and establish an industry market demand forecasting model for a specified period based on the industry characteristic data. The industry corresponding to the industry characteristic data and the industry corresponding to the industry market demand forecasting model are both the same target industry. It acquires historical market data of the target industry, establishes a market competition situation and market influence model based on the historical market data, and determines the competition type of each market participant in the target industry within the market competition structure through the market competition situation and market influence model. It establishes a capacity and energy storage model based on the production process characteristic data, capacity ceiling data, and inventory turnover level data of the target industry. It constructs a market dynamic twin engine based on the capacity and energy storage model, the market competition situation and market influence model, and the industry market demand forecasting model. According to the specified optimization target data, it uses the market dynamic twin engine to dynamically simulate marketing strategies corresponding to different competition types, based on the marketing strategies of each competitor's competition type corresponding to its own competition type. The comprehensive data yields the optimal marketing strategy data for the specified period. This solution combines market demand forecasting with key factors such as sales volume, historical sales price data, and market competition trends, leveraging industry characteristics. A market dynamic twin engine is constructed based on market demand, market competition trends, production capacity, and energy storage modeling. This engine is then used for dynamic simulation of marketing strategies. Furthermore, by constructing a multi-dimensional model and a market dynamic twin system, accurate prediction of market demand throughout its entire lifecycle and clear definition of the competitive positioning of each participant are achieved. Multi-scenario dynamic simulation of marketing strategies for different competitive roles is then performed. By simulating the marketing strategies of different competitive roles and solving for the optimal long-term marketing strategy, the solution efficiently obtains the optimal long-term marketing strategy. This not only achieves dynamic simulation and optimization of enterprise marketing strategies but also integrates market demand forecasting with key factors such as market competition trends and production capacity. This allows for overall optimization of marketing strategies from a holistic and long-term perspective, improving the data processing efficiency of current marketing strategies and solving the technical problem of low data processing efficiency in current marketing strategies.
[0015] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0017] Figure 1 A flowchart illustrating the data processing method of the marketing strategy dynamic simulation optimization system provided in this application embodiment; Figure 2 Another flowchart illustrating the data processing method of the marketing strategy dynamic simulation optimization system provided in this application embodiment; Figure 3 A schematic diagram of the structure of a data processing device for a marketing strategy dynamic simulation optimization system provided in this application embodiment; Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation
[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0019] The terms "comprising" and "having," and any variations thereof, used in the embodiments of this application, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.
[0020] Currently, existing technologies cannot achieve holistic and long-term optimization of marketing strategies, resulting in low data processing efficiency for current marketing strategies. Therefore, this application provides a data processing method and apparatus for a dynamic simulation optimization system for marketing strategies. This method can solve the technical problem of low data processing efficiency in current marketing strategies.
[0021] The embodiments of the present invention will be further described below with reference to the accompanying drawings.
[0022] Figure 1 This is a flowchart illustrating a data processing method for a marketing strategy dynamic simulation optimization system, provided as an embodiment of this application. Figure 1 As shown, the method includes: Step S110: Obtain industry characteristic data and establish an industry market demand forecasting model for a specified period based on the industry characteristic data.
[0023] In this case, the industry corresponding to the industry characteristic data and the industry corresponding to the industry market demand forecasting model are both the same target industry. As an example, such as... Figure 2 As shown, in this step, short-term and long-term forecasting models for market demand are established based on industry characteristics.
[0024] For example, the aforementioned industry characteristic data includes industry supply and demand characteristic data and seasonal fluctuation pattern data; the specified period includes a specified short-term and a specified long-term, with the specified short-term period being shorter than the specified long-term period; the aforementioned industry market demand forecasting model based on industry characteristic data for a specified period may specifically include the following steps: Based on industry supply and demand characteristics data and seasonal fluctuation data, a short-term market demand forecasting model is constructed using time series analysis. Based on the short-term market demand forecasting model, long-term variables are integrated, and a long-term market demand forecasting model is constructed using machine learning to predict market demand throughout the entire cycle. The long-term variables include industry development trend variables and / or policy guidance variables.
[0025] For example, by combining key influencing factors such as industry supply and demand characteristics and seasonal fluctuations, a short-term demand forecasting model can be constructed using time series analysis technology. At the same time, by integrating long-term variables such as industry development trends and policy guidance, a long-term demand forecasting model can be built using machine learning technology, thereby achieving a more accurate prediction of market demand throughout its entire lifecycle.
[0026] Step S120: Obtain historical market data for the target industry, establish a market competition situation and market influence model based on the historical market data, and determine the competition type of each market participant in the target industry in the market competition structure through the market competition situation and market influence model.
[0027] As one possible implementation method, such as Figure 2 As shown, based on historical data of market sales volume, sales price and price adjustment, a market competition situation and market influence model is established to identify the type and position of each market participant in the market competition structure.
[0028] For example, the aforementioned historical market data includes historical market sales data, historical market sales price data, and historical market price adjustment data; the aforementioned establishment of a market competition landscape and market influence model based on historical market data, and the determination of the competition type of each market participant in the target industry within the market competition structure through the market competition landscape and market influence model, may specifically include the following steps: The time-series data, historical market sales data, and historical market sales price data corresponding to historical market price adjustment data are standardized and preprocessed to obtain preprocessed results. Market characteristic indicators are extracted based on the preprocessed results, and a market competition situation and market influence model is constructed based on the market characteristic indicators. Among them, market characteristic indicators include at least one of market share, price sensitivity, and adjustment response speed. The competitive strength and market power data of each market participant in the target industry are quantified by the market competition situation and market influence model. Based on the competitive strength and market power data, the participants are classified to obtain the competition type of each market participant in the market competition structure. The competition type includes at least one of the following: leader type, competitor type, follower type, and missing element type.
[0029] It should be noted that standardized preprocessing is performed on market sales volume, historical sales price data, and price adjustment time series data to extract characteristic indicators such as market share, price sensitivity, and adjustment response speed. A market competition situation and market influence model is constructed. This model quantifies the competitive strength and market power of each participant and allows for more precise classification and positioning, such as market leaders, competitors, followers, or niche players.
[0030] Step S130: Establish a capacity and energy storage model based on the production process characteristics data, capacity ceiling data, and inventory turnover level data of the target industry.
[0031] In one alternative implementation, such as Figure 2 As shown, based on industry characteristics, a capacity and energy storage model is constructed, that is, based on key factors such as production process characteristics, capacity ceiling, and inventory turnover level, a capacity and energy storage model is constructed.
[0032] Step S140: Construct a market dynamic twin engine based on the capacity and energy storage model, the market competition situation and market influence model, and the industry market demand forecasting model.
[0033] In one possible implementation, such as Figure 2 As shown, in this step, multiple models are integrated based on the market demand model, market competition model, production capacity and energy storage model to construct a market dynamic twin engine.
[0034] As an example, the aforementioned construction of a market dynamic twin engine based on capacity and energy storage models, market competition and market influence models, and industry market demand forecasting models can specifically include the following steps: Based on the capacity and energy storage model, the market competition situation and market influence model, and the industry market demand forecasting model, a market dynamic twin engine is constructed through data association mapping and feature extraction. Based on the input data of the market dynamic twin engine, the market operation status of the target industry and the interaction between multiple key elements are simulated. Among them, multiple key elements include market demand elements, market sales volume elements, historical sales price elements, market competition elements, and capacity elements.
[0035] In practical applications, market demand forecasting models, market competition models, and capacity and energy storage models are integrated through data association mapping and feature extraction to build a market dynamic twin engine, and simulate the market operation status and the interaction relationship of each core element based on input data.
[0036] Step S150: Based on the specified optimization target data, the marketing strategies corresponding to different competition types are dynamically simulated using a market dynamic twin engine to obtain the optimal marketing strategy data for a specified period based on the comprehensive data of the marketing strategies of each competitor's competition type corresponding to its own competition type.
[0037] As an optional implementation method, such as Figure 2 As shown, given the optimization objective, the marketing strategies of different competitors are dynamically simulated using market twins, thereby integrating the marketing strategies of each competitor to output the long-term optimal strategy.
[0038] For example, the above-mentioned dynamic simulation of marketing strategies corresponding to different competition types using a market dynamic twin engine based on specified optimization target data, to obtain optimal marketing strategy data for a specified period based on comprehensive data of marketing strategies corresponding to various competitors' competition types corresponding to its own competition type, may specifically include the following steps: Based on the specified optimization objectives of maximizing corporate profits and increasing market share, a market dynamic twin engine is used to conduct multi-scenario dynamic simulation of price adjustment planning and response strategies for different types of competition, and the simulation results are obtained. Based on the competitor marketing strategies of each competitor in multiple competitive types, the optimal marketing strategy feedback data is obtained after comprehensive consideration over a specified long term through comparative analysis and iterative optimization of simulation results.
[0039] For example, by combining optimization goals such as maximizing corporate profits and increasing market share, and based on a market dynamic twin engine, multi-scenario dynamic simulations can be conducted on marketing strategies such as price adjustment plans and response strategies for different competitors. Through comparative analysis and iterative optimization of simulation results, feedback on the long-term optimal marketing strategy can be achieved by comprehensively considering the marketing strategies of various competitors.
[0040] In some embodiments, the aforementioned market dynamic twin engine is a dynamic simulation optimization model for marketing strategies. The dynamic simulation optimization model for marketing strategies is quantitatively expressed through two synergistic objective function sets. The two synergistic objective function sets include a first objective function set. The first objective function set contains different indicator functions. Multiple indicator functions include the first indicator function. The first indicator function is a full-cycle prediction function set for market simulation. The full-cycle prediction function set for market simulation includes all implicit functions and control variables for market demand forecasting.
[0041] For example, the dynamic simulation optimization model of enterprise marketing strategy is quantitatively expressed through two synergistic function sets. Assume that the market simulation model is a function set M, which contains different indicator functions [M1, M2, M3...Mn]. Among them, the indicator function M1 is the prediction function set for the entire market simulation cycle, which includes all implicit functions and control variables for market demand prediction.
[0042] In some embodiments, the two objective function sets mentioned above for synergy further include a second objective function set. The second objective function set contains multiple relational functions of the enterprise marketing strategy. The first relational function among the multiple relational functions represents all implicit functions and control variables of the enterprise marketing strategy. The iterative optimization of multi-scenario simulation results is performed through the synergistic calculation of the first objective function set and the second objective function set to achieve the continuous output of the specified long-term optimal marketing strategy.
[0043] For example, another function set L is introduced, which contains relational functions of the enterprise's marketing strategy [L1, L2, L3…Ln], where L1 represents all implicit functions and control variables of the enterprise's marketing strategy. By conducting collaborative calculations of function sets M and L, and iteratively optimizing the results of multi-scenario simulations, the long-term optimal marketing strategy can be continuously output.
[0044] In this embodiment, market demand forecasting is combined with various key elements such as sales volume, historical sales price data, and market competition status, leveraging industry characteristics. A market dynamic twin engine is constructed based on market demand, market competition status, production capacity, and energy storage modeling. This engine is then used for dynamic simulation of marketing strategies. By building a multi-dimensional model and a market dynamic twin system, accurate prediction of market demand throughout its entire lifecycle and clear definition of the competitive positioning of each participant are achieved. Furthermore, multi-scenario dynamic simulation of marketing strategies for different competitive roles is conducted. By simulating the marketing strategies of different competitive roles and solving for the optimal long-term marketing strategy, the optimal long-term marketing strategy is efficiently obtained. This not only achieves dynamic simulation and optimization of enterprise marketing strategies but also integrates market demand forecasting with various key elements such as market competition status and production capacity. This enables overall optimization of marketing strategies from a global and long-term perspective, improving the data processing efficiency of current marketing strategies and solving the problems of experience dependence and lag in traditional marketing decision-making. It provides enterprises with scientific and accurate marketing decision support, which is of significant practical importance for enhancing enterprise market competitiveness and long-term profitability.
[0045] Figure 3 A schematic diagram of the data processing device for a dynamic simulation and optimization system for marketing strategies is provided. (For example...) Figure 3 As shown, the data processing device 300 of the marketing strategy dynamic simulation optimization system includes: The first module 301 is used to acquire industry characteristic data and establish an industry market demand forecasting model for a specified period based on the industry characteristic data; the industry corresponding to the industry characteristic data and the industry corresponding to the industry market demand forecasting model are the same target industry. The second module 302 is used to acquire historical market data of the target industry, establish a market competition situation and market influence model based on the historical market data, and determine the competition type of each market participant in the target industry in the market competition structure through the market competition situation and market influence model. The third module 303 is used to establish a capacity and energy storage model based on the production process characteristics data, capacity limit data and inventory turnover level data of the target industry. Module 304 is used to build a market dynamic twin engine based on the capacity and energy storage model, the market competition situation and market influence model and the industry market demand forecasting model; The simulation module 305 is used to dynamically simulate marketing strategies corresponding to different competition types through the market dynamic twin engine based on the specified optimization target data, so as to obtain the optimal marketing strategy data for the specified period based on the comprehensive data of the marketing strategies of each competitor's competition type corresponding to its own competition type.
[0046] The marketing strategy dynamic simulation optimization system data processing device provided in this application embodiment has the same technical features as the marketing strategy dynamic simulation optimization system data processing method provided in the above embodiment, so it can also solve the same technical problems and achieve the same technical effects.
[0047] An electronic device provided in this application embodiment, such as Figure 4 As shown, the electronic device 400 includes a processor 402 and a memory 401. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the steps of the method provided in the above embodiments.
[0048] See Figure 4 The electronic device also includes a bus 403 and a communication interface 404. The processor 402, the communication interface 404 and the memory 401 are connected via the bus 403. The processor 402 is used to execute executable modules, such as computer programs, stored in the memory 401.
[0049] The memory 401 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 404 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network.
[0050] Bus 403 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.
[0051] The memory 401 is used to store programs. After receiving an execution instruction, the processor 402 executes the program. The method executed by the apparatus defined by the process disclosed in any of the preceding embodiments of this application can be applied to the processor 402 or implemented by the processor 402.
[0052] Processor 402 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 402 or by instructions in software form. The processor 402 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 401, and processor 402 reads the information from memory 401 and, in conjunction with its hardware, completes the steps of the above method.
[0053] Corresponding to the above-mentioned marketing strategy dynamic simulation optimization system data processing method, this application embodiment also provides a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are called and run by a processor, the computer-executable instructions cause the processor to perform the steps of the above-mentioned marketing strategy dynamic simulation optimization system data processing method.
[0054] The marketing strategy dynamic simulation optimization system data processing device provided in this application embodiment can be specific hardware on the device or software or firmware installed on the device. The device provided in this application embodiment has the same implementation principle and technical effects as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can all be referred to the corresponding processes in the above method embodiments, and will not be repeated here.
[0055] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.
[0056] For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.
[0057] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0058] In addition, the functional units in the embodiments provided in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.
[0059] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the marketing strategy dynamic simulation optimization system data processing method described in the various embodiments of this application. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.
[0060] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.
[0061] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.
Claims
1. A data processing method for a dynamic simulation optimization system for marketing strategies, characterized in that, The method includes: Acquire industry characteristic data and establish an industry market demand forecasting model for a specified period based on the industry characteristic data; the industry corresponding to the industry characteristic data and the industry corresponding to the industry market demand forecasting model are the same target industry. Historical market data of the target industry is obtained, a market competition situation and market influence model is established based on the historical market data, and the competition type of each market participant in the target industry in the market competition structure is determined through the market competition situation and market influence model. A capacity and energy storage model is established based on the production process characteristics data, capacity limit data, and inventory turnover level data of the target industry. A market dynamic twin engine is constructed based on the aforementioned capacity and energy storage model, the aforementioned market competition situation and market influence model, and the aforementioned industry market demand forecasting model. Based on the specified optimization target data, the marketing strategies corresponding to different competition types are dynamically simulated using the market dynamic twin engine, so as to obtain the optimal marketing strategy data for the specified period based on the comprehensive data of the marketing strategies of each competitor's competition type corresponding to its own competition type.
2. The method according to claim 1, characterized in that, The industry characteristic data includes industry supply and demand characteristic data and seasonal fluctuation pattern data; the specified period includes specified short-term and specified long-term, and the specified short-term period is shorter than the specified long-term period. The process of establishing an industry market demand forecasting model for a specified period based on the industry characteristic data includes: Based on the industry supply and demand characteristics data and the seasonal fluctuation pattern data, the specified short-term market demand forecasting model is constructed using time series analysis. Based on the specified short-term market demand forecasting model, long-term variables are integrated, and the specified long-term market demand forecasting model is constructed through machine learning to make full-cycle predictions of market demand; wherein, the long-term variables include industry development trend variables and / or policy guidance variables.
3. The method according to claim 1, characterized in that, The historical market data includes historical market sales volume data, historical market sales price data, and historical market price adjustment data; the establishment of a market competition situation and market influence model based on the historical market data, and the determination of the competition type of each market participant in the target industry within the market competition structure through the market competition situation and market influence model, including: The time-series data corresponding to the historical market price adjustment data, the historical market sales data, and the historical market sales price data are standardized and preprocessed to obtain the preprocessing result. Market characteristic indicators are extracted based on the preprocessing results, and a market competition situation and market influence model is constructed based on the market characteristic indicators; wherein, the market characteristic indicators include at least one of market share, price sensitivity, and adjustment response speed; The competitive strength and market influence data of each market participant in the target industry are quantified by the market competition situation and market influence model. Based on the competitive strength data and the market influence data, the participants are classified to obtain the competition type of each market participant in the market competition structure. The competition type includes at least one of the following: leader type, competitor type, follower type, and missing element type.
4. The method according to claim 1, characterized in that, The construction of a market dynamic twin engine based on the aforementioned production capacity and energy storage model, the aforementioned market competition situation and market influence model, and the aforementioned industry market demand forecasting model includes: Based on the aforementioned production capacity and energy storage model, market competition situation and market influence model, and industry market demand forecasting model, a market dynamic twin engine is constructed through data association mapping and feature extraction fusion. Based on the input data of the market dynamic twin engine, the market operation status of the target industry and the interaction between multiple key elements are simulated through the market dynamic twin engine. Among them, the multiple key elements include market demand elements, market sales volume elements, historical sales price elements, market competition elements, and production capacity elements.
5. The method according to claim 2, characterized in that, The step of dynamically simulating marketing strategies corresponding to different competition types using the market dynamic twin engine based on specified optimization target data, and obtaining optimal marketing strategy data for the specified period based on comprehensive data of marketing strategies corresponding to various competitor competition types for its own competition type, includes: Based on the specified optimization objectives of maximizing corporate profits and increasing market share, the market dynamic twin engine is used to conduct multi-scenario dynamic simulation of price adjustment planning and response strategies for different types of competition, and the simulation results are obtained. Based on the competitor marketing strategies of each competitor in the various competitive types, the optimal marketing strategy feedback data after comprehensive consideration is obtained through comparative analysis and iterative optimization of the simulation results.
6. The method according to claim 2, characterized in that, The market dynamic twin engine is a dynamic simulation optimization model for marketing strategies. The dynamic simulation optimization model for marketing strategies is quantitatively expressed through two synergistic objective function sets. The two synergistic objective function sets include a first objective function set. The first objective function set contains different indicator functions. Multiple indicator functions include the first indicator function. The first indicator function is a full-cycle prediction function set for market simulation. The full-cycle prediction function set for market simulation includes all implicit functions and control variables for market demand prediction.
7. The method according to claim 6, characterized in that, The two sets of objective functions for synergy also include a second set of objective functions. The second set of objective functions contains multiple relational functions of the enterprise's marketing strategy. The first relational function among these multiple relational functions represents all implicit functions and control variables of the enterprise's marketing strategy. Through the synergistic calculation of the first set of objective functions and the second set of objective functions, iterative optimization of multi-scenario simulation results is performed to achieve the continuous output of the specified long-term optimal marketing strategy.
8. A data processing device for a dynamic simulation and optimization system for marketing strategies, characterized in that, include: The first module is used to acquire industry characteristic data and establish an industry market demand forecasting model for a specified period based on the industry characteristic data; the industry corresponding to the industry characteristic data and the industry corresponding to the industry market demand forecasting model are the same target industry. The second module is used to acquire historical market data of the target industry, establish a market competition situation and market influence model based on the historical market data, and determine the competition type of each market participant in the target industry in the market competition structure through the market competition situation and market influence model. The third module is used to establish a capacity and energy storage model based on the production process characteristics data, capacity limit data, and inventory turnover level data of the target industry. The module is used to build a market dynamic twin engine based on the capacity and energy storage model, the market competition situation and market influence model, and the industry market demand forecasting model. The simulation module is used to dynamically simulate marketing strategies corresponding to different competition types based on specified optimization target data through the market dynamic twin engine, so as to obtain the optimal marketing strategy data for the specified period based on the comprehensive data of the marketing strategies of each competitor's competition type corresponding to its own competition type.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method according to any one of claims 1 to 7.