Efficient projection equipment sales strategy optimization method and system

Abstract

The invention relates to the field of information technology and multimedia application, and discloses a high-efficiency projection equipment sales strategy optimization method and system, and the method comprises the steps: firstly obtaining the delivery data and real-time distribution spectrum of target projection equipment, analyzing different channel delivery demand curves according to the delivery data and real-time distribution spectrum, constructing an online and offline distribution cooperation network, and carrying out the real-time distribution of the target projection equipment; analyzing channel response characteristics, querying a user conversion value, calculating a user coupling matching degree of parallel distribution, simulating a user distribution graph under distribution fluctuation, constructing a self-adaptive logic rule, integrating the rule and a promotion compensation mechanism, generating a dynamic regulation and control strategy, calculating a regulation and control attenuation degree, determining a distribution state, analyzing distribution dimensions, and finally, obtaining a distribution result. And constructing a sales optimization scheme based on the distribution dimension. According to the invention, the accuracy of the sales strategy can be improved.

Description

Technical Field

[0001] The present invention relates to a method and system for optimizing the sales strategy of an efficient projection device, belonging to the fields of information technology and multimedia applications. Background Art

[0002] In the fields of information technology and multimedia applications, projection devices, as the core tools for realizing high-quality image display, conference presentations, and education and training, have a continuously growing market demand. With the continuous improvement of the performance requirements of commercial, educational, and household users for projection devices, the sales strategy of projection devices directly affects the market share and brand competitiveness.

[0003] Currently, there are still many deficiencies in the traditional projection device sales strategy in terms of market demand analysis and resource allocation. On the one hand, the traditional sales strategy often adopts a fixed market promotion model and cannot be dynamically adjusted according to the actual needs of different regions and different user groups, resulting in serious waste of market resources and low sales efficiency. On the other hand, the flexibility and accuracy of the sales strategy are insufficient, and it is easy to cause the sales target to be difficult to achieve due to changes in market demand or fluctuations in the competitive environment. Therefore, an efficient method for optimizing the sales strategy of projection devices is needed to improve the accuracy of the sales strategy. Summary of the Invention

[0004] The present invention provides a method and system for optimizing the sales strategy of an efficient projection device, and its main purpose is to improve the accuracy of the sales strategy.

[0005] To achieve the above object, an efficient method for optimizing the sales strategy of a projection device provided by the present invention includes:

[0006] Obtain the device placement data corresponding to the target projection device, collect the real-time distribution spectrum corresponding to the device placement data, based on the real-time distribution spectrum, analyze the placement demand curve of the target projection device under different channels, and according to the placement demand curve, construct a distribution collaboration network corresponding to the target projection device, where the distribution collaboration network includes an online distribution network and an offline distribution network;

[0007] Analyze the channel response characteristics of the distribution collaboration network, query the user conversion value in the channel response characteristics, and based on the user conversion value, calculate the user coupling matching degree of the target projection device during parallel distribution;

[0008] Based on the user coupling matching degree, simulate the user distribution map of the target projection device in a distribution fluctuation environment, and according to the user distribution map, construct an adaptive logic rule corresponding to the target projection device;

[0009] Integrate the adaptive logic rules with the preset promotion compensation mechanism to generate the dynamic regulation strategy corresponding to the target projection device, extract the key execution parameters in the regulation strategy, and calculate the regulation attenuation degree corresponding to the target projection device based on the key execution parameters;

[0010] Based on the regulation attenuation degree, determine the distribution status corresponding to the target projection device, analyze the distribution dimension of the target projection device under the corresponding distribution status, and construct a sales optimization plan corresponding to the target projection device based on the distribution dimension.

[0011] Optionally, constructing the distribution collaboration network corresponding to the target projection device according to the placement demand curve includes:

[0012] Analyze the time period rule corresponding to the placement demand curve;

[0013] Based on the time period rule, determine the demand adaptation information of the target projection device in each time period;

[0014] Query the resource bearing data in the demand adaptation information;

[0015] Based on the resource bearing data, determine the collaboration complementary index corresponding to the target projection device;

[0016] Based on the collaboration complementary index, construct the distribution collaboration network corresponding to the target projection device.

[0017] Optionally, analyzing the channel response characteristics of the distribution collaboration network includes:

[0018] Query the all-channel data stream in the distribution collaboration network;

[0019] Collect the original response data set in the all-channel data stream;

[0020] Extract the response high-frequency signal in the original response data set;

[0021] Generate the response classification label corresponding to the response high-frequency signal;

[0022] Based on the response classification label, analyze the channel response characteristics of the distribution collaboration network.

[0023] Optionally, calculating the user coupling matching degree of the target projection device during parallel distribution based on the user conversion value includes:

[0024] Use the following formula to calculate the user coupling matching degree of the target projection device during parallel distribution:

[0025]

[0026] Among them, Yp represents the user coupling matching degree of the target projection device during parallel distribution, n represents the number of different distribution channels, i represents the index of the number of different distribution channels, and w i represents the channel weight of the i-th distribution channel, and R i represents the user conversion value of the i-th distribution channel, and C i represents the fitting degree coefficient between the user characteristics of the i-th distribution channel and the corresponding target user characteristics of the target projection device, m represents the number of influencing factors, j represents the index of the number of influencing factors, and D j represents the interference degree value corresponding to the j-th influencing factor.

[0027] Optionally, simulating the user distribution map of the target projection device in a distribution fluctuation environment based on the user coupling matching degree includes:

[0028] Extracting the historical coupling data corresponding to the user coupling matching degree;

[0029] Mining the user fluctuation factors in the historical coupling data;

[0030] Analyzing the potential association rules between the user fluctuation factors and the distribution fluctuation environment;

[0031] Based on the potential association rules, simulating the user distribution map of the target projection device in a distribution fluctuation environment.

[0032] Optionally, constructing the adaptive logic rule corresponding to the target projection device according to the user distribution map includes:

[0033] Querying the user density characteristics in the user distribution map;

[0034] Based on the user density characteristics, determining the market demand priority of the target projection device in different regions;

[0035] Based on the market demand priority, analyzing the channel allocation weight corresponding to the target projection device;

[0036] According to the channel allocation weight, formulating the placement strategy of the target projection device under different channels;

[0037] Verifying the execution effect of the placement strategy in the simulated market environment;

[0038] Based on the execution effect, constructing the adaptive logic rule corresponding to the target projection device.

[0039] Optionally, integrating the adaptive logic rule with a preset promotion compensation mechanism to generate the dynamic regulation strategy corresponding to the target projection device includes:

[0040] Query the key control parameters in the adaptive logic rules;

[0041] Based on the key control parameters, determine the trigger conditions corresponding to the promotion compensation mechanism;

[0042] Based on the trigger conditions, analyze the compensation effects of the promotion compensation mechanism in different market environments;

[0043] According to the compensation effects, formulate the collaborative control process corresponding to the promotion compensation mechanism and the adaptive logic rules;

[0044] Based on the collaborative control process, generate the dynamic control strategy corresponding to the target projection device.

[0045] Optionally, the calculating the control attenuation degree corresponding to the target projection device based on the key execution parameters includes:

[0046] Calculate the control attenuation degree corresponding to the target projection device by using the following formula:

[0047]

[0048] where DA represents the control attenuation degree corresponding to the target projection device, p represents the total number of categories of the key execution parameters, k represents the category index corresponding to the key execution parameters, Q k represents the basic influence factor corresponding to the k-th category of key execution parameters, R k represents the correction coefficient corresponding to the k-th category of key execution parameters, c represents the number of specific execution factors in the key execution parameters, v represents the number index of the specific execution factors, E kv represents the current value of the v-th specific execution factor in the k-th category of key execution parameters, F kv represents the weight coefficient of the v-th specific execution factor in the k-th category of key execution parameters.

[0049] Optionally, the determining the distribution status corresponding to the target projection device based on the control attenuation degree includes:

[0050] Query the attenuation characteristic range corresponding to the control attenuation degree;

[0051] Based on the attenuation characteristic range, determine the distribution division criteria corresponding to the control attenuation degree;

[0052] Based on the distribution division criteria, analyze the distribution performance of the target projection device in different attenuation intervals;

[0053] According to the distribution performance, determine the distribution status corresponding to the target projection device.

[0054] To solve the above problems, the present invention also provides an optimization system for the sales strategy of an efficient projection device, and the system includes:

[0055] A network construction module, configured to obtain device placement data corresponding to a target projection device, collect a real-time distribution spectrum corresponding to the device placement data, analyze a placement demand curve of the target projection device under different channels based on the real-time distribution spectrum, and construct a distribution collaboration network corresponding to the target projection device according to the placement demand curve, where the distribution collaboration network includes an online distribution network and an offline distribution network;

[0056] A matching degree calculation module, configured to analyze the channel response characteristics of the distribution collaboration network, query the user conversion value in the channel response characteristics, and calculate the user coupling matching degree of the target projection device during parallel distribution based on the user conversion value;

[0057] A rule construction module, configured to simulate a user distribution map of the target projection device in a distribution fluctuation environment based on the user coupling matching degree, and construct an adaptive logic rule corresponding to the target projection device according to the user distribution map;

[0058] An attenuation degree calculation module, configured to integrate the adaptive logic rule with a preset promotion compensation mechanism to generate a dynamic regulation strategy corresponding to the target projection device, extract key execution parameters in the regulation strategy, and calculate the regulation attenuation degree corresponding to the target projection device based on the key execution parameters;

[0059] A solution construction module, configured to determine the distribution status corresponding to the target projection device based on the regulation attenuation degree, analyze the distribution dimension of the target projection device corresponding to the distribution status, and construct a sales optimization solution corresponding to the target projection device based on the distribution dimension.

[0060] Compared with the problems described in the background art, the present invention can achieve precise configuration and dynamic optimization of channel resources by obtaining the device placement data corresponding to the target projection device and collecting the real-time distribution spectrum corresponding to the device placement data, so that the online and offline placement ratios are highly matched with the regional demand characteristics; at the same time, a millisecond-level market response mechanism is constructed to anticipate channel fluctuations in advance and quickly adjust the promotion strategy, ultimately enhancing market competitiveness. By analyzing the channel response characteristics of the distribution collaboration network, the present invention can accurately grasp the response speed and mode of each online and offline channel to market changes, clarify the advantages and limitations of different channels in coping with demand fluctuations, help allocate resources flexibly according to the characteristics of each channel, quickly adjust the sales strategy, and improve the overall operation efficiency. Further, based on the user coupling matching degree, the present invention simulates the user distribution map of the target projection device in the distribution fluctuation environment, which can intuitively present the user flow and aggregation of each channel, help grasp the market change trend, accurately locate high-potential user areas and weak links, rationally allocate resources, and optimize the distribution strategy. Further, by integrating the adaptive logic rules and the preset promotion compensation mechanism, the present invention generates a dynamic regulation strategy corresponding to the target projection device, which can achieve a millisecond-level response of the distribution strategy to market fluctuations, effectively balance the short-term sales sprint and the long-term brand value maintenance, and finally form a full-closed-loop intelligent regulation system covering demand prediction, resource allocation, and effect feedback. Finally, based on the regulation attenuation degree, the present invention determines the distribution state corresponding to the target projection device, which can intuitively show the actual effect of the regulation strategy in the distribution link, timely discover the deviations and problems in the strategy execution, better adapt to market changes, enhance the market competitiveness of the product, help achieve the sales target, and improve the overall distribution efficiency. Therefore, the efficient projection device sales strategy optimization method and system provided by the embodiments of the present invention can improve the accuracy of the sales strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 FIG. is a schematic flow chart of an efficient projection device sales strategy optimization method provided by an embodiment of the present invention;

[0062] Figure 2 FIG. is a schematic module diagram of an efficient projection device sales strategy optimization system provided by an embodiment of the present invention.

[0063] The implementation, functional features, and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0064] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0065] The embodiments of the present application provide a method for optimizing the sales strategy of high - efficiency projection devices. The execution entities of the method for optimizing the sales strategy of high - efficiency projection devices include, but are not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiments of the present application. In other words, the method for optimizing the sales strategy of high - efficiency projection devices can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc.

[0066] Embodiment 1:

[0067] Referring to Figure 1 As shown, it is a flowchart of the method for optimizing the sales strategy of high - efficiency projection devices provided by an embodiment of the present invention. In this embodiment, the method for optimizing the sales strategy of high - efficiency projection devices includes:

[0068] S1. Obtain the device placement data corresponding to the target projection device, collect the real - time distribution spectrum corresponding to the device placement data, based on the real - time distribution spectrum, analyze the placement demand curve of the target projection device under different channels, and according to the placement demand curve, construct a distribution collaboration network corresponding to the target projection device, where the distribution collaboration network includes an online distribution network and an offline distribution network.

[0069] By obtaining the device placement data corresponding to the target projection device and collecting the real - time distribution spectrum corresponding to the device placement data, the present invention can achieve precise allocation and dynamic optimization of channel resources, making the online - offline placement ratio highly match the regional demand characteristics; at the same time, constructing a millisecond - level market response mechanism, predicting channel fluctuations in advance and quickly adjusting the promotion strategy, and finally enhancing the market competitiveness.

[0070] Among them, the target projection device refers to a projection product with specific technical parameters and functional positioning. Its design focuses on meeting the differentiated market demands. Such devices form segmented solutions for scenarios such as education, business, home use, and engineering through the combination of technical indicators (such as brightness, resolution, projection ratio) and functional characteristics (such as intelligent systems, portability). For example, educational devices emphasize eye protection functions and support for interactive teaching, business models highlight wireless screen mirroring and high reliability, home products focus on picture quality and sound effects, and engineering projectors pursue high brightness and adaptability to complex environments; the device delivery data refers to a systematic data set that records the quantitative performance and user behavior characteristics of projection devices in the market circulation process through multi-dimensional monitoring and analysis. Its core components cover sales circulation indicators such as sales volume distribution, unit price per customer, inventory turnover rate, and promotion activity response rate in various channels (online / offline). At the same time, a demand adaptation model is constructed by combining user behavior data such as device activation rate, frequency of function use, and fault repair rate, and geographical information system (GIS) is integrated to analyze regional demand density, consumption stratification, and policy impacts. Finally, a dynamic database is formed by real-time collection through docking with e-commerce platform APIs, offline POS systems, and Internet of Things sensors; the real-time distribution spectrum refers to a comprehensive state map that dynamically presents the projection device in the multi-channel distribution process through algorithm models and visualization technologies. Its core functions include monitoring the traffic conversion efficiency of online platforms and offline networks, order fluctuation rules, and cross-channel synergy effects, generating a demand density heat map based on GIS to identify potential markets and weak links, analyzing time-periodic sales peaks, periodic fluctuations, and promotion response curves, and real-time tracking the inventory level of distribution centers, cross-regional transfer efficiency, and warning thresholds for slow-moving products. Finally, millisecond-level market change perception is achieved through machine learning algorithms. Optionally, the acquisition of device delivery data corresponding to the target projection device can be realized through the docking of the sales system API. For example, by real-time capturing order data through the open interfaces of e-commerce platforms (such as JD.com, Tmall) and combining the outbound records of the enterprise's internal ERP system, the sales volume distribution, unit price per customer, and inventory turnover rate of the device in different channels can be obtained, and finally the device delivery data can be obtained; the collection of the real-time distribution spectrum corresponding to the device delivery data can be realized through network analysis algorithms. For example, the PageRank algorithm is used to evaluate the importance of dealer network nodes, and the layout of offline experience stores is optimized by combining the regional demand heat map, and the online traffic conversion path (such as the conversion rate from official website advertisements to offline experience stores) is monitored synchronously, and finally a visual distribution synergy spectrum is output.

[0071] Furthermore, based on the real-time distribution spectrum, the present invention analyzes the delivery demand curves of the target projection device in different channels, can dynamically identify the demand peaks and fluctuation rules of each channel, and enables the resource allocation to be matched with the actual market demand at the millisecond level; by accurately capturing the differentiated demand characteristics of scenarios such as education and business, the delivery combination strategy of online and offline channels can be optimized, and the waste of ineffective resources can be reduced.

[0072] Among them, the delivery demand curve refers to a quantitative curve that dynamically depicts the change in demand intensity of the target projection equipment in different channels (such as online e-commerce and offline experience stores) over time through real-time distribution spectrum analysis. The curve uses core indicators such as sales volume, conversion rate, and average order value as coordinate axes, combined with influencing factors such as promotional activities, seasonal fluctuations, and regional policies, to accurately reflect the demand peak and attenuation laws of segmented scenarios such as education, business, and home use, and provide a visual decision-making basis for resource allocation, inventory scheduling, and promotion strategy formulation, ultimately achieving millisecond-level matching of channel resources and market demand. Optionally, the analysis of the delivery demand curve of the target projection equipment in different channels can be achieved through curve generation tools, such as: Tableau, PowerBI and other tools.

[0073] Furthermore, the present invention constructs a distribution collaboration network corresponding to the target projection device according to the delivery demand curve, which can realize dynamic optimization configuration of channel resources, accurately match differentiated demand characteristics of scenarios such as education and business, and enhance the synergy effect of online and offline channels; by identifying regional demand peaks and inventory distribution patterns, it can effectively reduce the risk of unsalable products and shorten the replenishment cycle.

[0074] Among them, the distribution collaborative network refers to the organic integration of online distribution networks (such as major e-commerce platforms, official websites, etc.) and offline distribution networks (such as physical stores, dealers, etc.) in order to achieve efficient sales of target projection equipment. It forms a network system of mutual cooperation and resource sharing. The network is constructed based on the collaborative complementarity index, and aims to adapt information according to demand in different time periods, dynamically optimize channel resource allocation, and accurately meet market demand.

[0075] As an embodiment of the present invention, constructing a distribution collaborative network corresponding to the target projection device according to the delivery demand curve includes: analyzing the time period pattern corresponding to the delivery demand curve; determining the demand adaptation information of the target projection device in each time period based on the time period pattern; querying the resource carrying data in the demand adaptation information; determining the collaborative complementarity index corresponding to the target projection device based on the resource carrying data; and constructing the distribution collaborative network corresponding to the target projection device based on the collaborative complementarity index.

[0076] Among them, the time-period law refers to the repetitive and periodic change characteristics presented by the demand curve of the target projection device in the time dimension, which reflects the fluctuations in demand for the projection device in different time periods and can be related to various factors such as seasonal factors, holidays, and industry activity cycles; the demand adaptation information refers to the specific information determined based on the time-period law and describes the matching of the target projection device with the market demand in each time period. It comprehensively considers factors such as the market demand characteristics, consumer preferences, and functional characteristics of the projection device in different time periods to ensure that the supply of the device can accurately meet the market demand; the resource-carrying data refers to a part of the demand adaptation information, which mainly refers to the data related to the resource-carrying capacity of each distribution channel in the process of meeting the market demand of the target projection device. These resources include but are not limited to inventory quantity, logistics distribution capacity, after-sales service capacity, and capital reserve; the collaborative complementarity index is a quantitative indicator determined based on the resource-carrying data and is used to measure the degree of collaborative cooperation and mutual complementarity between different distribution channels in terms of resources, functions, etc. A higher collaborative complementarity index means that each distribution channel can effectively integrate resources, give play to its respective advantages, and achieve better sales results.

[0077] Further, the analysis of the time - period law corresponding to the placement demand curve can be achieved through seasonal decomposition algorithms. For example, time - series data can be decomposed into three parts: trend, seasonality, and residuals, thus clearly showing the time - period law. The determination of the demand adaptation information of the target projection device in each time period can be achieved through association rule mining algorithms. For example, it is found that consumers are more inclined to outdoor portable projection devices in summer, while the demand for high - brightness indoor projection devices is higher in winter. Combining the functional characteristics of projection devices, the demand adaptation information in each time period is determined. For example, projection devices with waterproof and portable functions are adapted in summer, and projection devices with high lumen and high resolution are adapted in winter. The query of the resource - bearing data in the demand adaptation information can be achieved through SQL query algorithms. For example, using the SELECT statement of SQL combined with aggregation functions, etc., to query the resource - bearing data from the database tables storing demand adaptation information and resource data. The determination of the distribution collaboration index corresponding to the target projection device can be achieved through the analytic hierarchy process (AHP) combined with the entropy weight method. For example, taking the inventory complementarity, logistics collaboration, service collaboration, etc. of distribution channels as indicators, calculating the weights of each indicator through AHP and the entropy weight method, then scoring the performance of each channel on these indicators, and calculating the collaboration and complementarity index through weighted calculation. The construction of the distribution collaboration network corresponding to the target projection device can be achieved through graph - theory modeling and optimization methods. For example, taking each online and offline distribution channel as a node, and the connection and resource - flow relationship between channels as an edge, using the NetworkX library to construct a graph model, and then using the Dijkstra algorithm to find the optimal path from the production end to each sales terminal, determining the resource allocation and collaboration method, and constructing the distribution collaboration network.

[0078] Specifically, the distribution collaboration network includes an online distribution network and an offline distribution network. Among them, the online distribution network refers to the system that realizes product sales and distribution through Internet platforms and digital channels, covering e - commerce platforms (such as comprehensive platforms like JD.com, Tmall, and vertical platforms like Suning.com), brand official websites (for product display and direct sales), social media (such as WeChat, Douyin for community operation and content marketing), and live - streaming platforms (combining live - streaming sales by hosts and scenario - based demonstrations). Through data - driven precision marketing and real - time transaction processing, it achieves cross - regional and all - weather market coverage. The offline distribution network refers to the system centered on physical channels, including brand exclusive stores (providing immersive experiences and professional services), comprehensive electrical appliance stores (displaying multiple brands concentratedly for comparison and selection), dealer and agent networks (covering regional markets and providing customized solutions), and offline activities and exhibitions (strengthening brand awareness and channel expansion through scenarios such as industry exhibitions and new product launches). Relying on physical touchpoints to provide product experience, after - sales service, and immediate delivery capabilities, the two dynamically optimize resource allocation through the collaboration and complementarity index, forming an omni - channel integrated distribution system.

[0079] S2. Analyze the channel response characteristics of the distribution collaboration network, query the user conversion value in the channel response characteristics, and calculate the user coupling matching degree of the target projection device during parallel distribution based on the user conversion value.

[0080] By analyzing the channel response characteristics of the distribution collaboration network, the present invention can accurately grasp the reaction speed and mode of each online and offline channel to market changes, clarify the advantages and limitations of different channels in coping with demand fluctuations, help allocate resources flexibly according to the characteristics of each channel, quickly adjust sales strategies, and improve the overall operation efficiency.

[0081] Among them, the channel response characteristics refer to the quantitative indicators such as the response sensitivity (such as the response speed of the e-commerce platform to price promotions is 40% faster than that of the offline), the synergy effect (such as the online advertising exposure drives a 25% increase in the passenger flow of the offline experience store), and the risk threshold (such as the response condition for triggering cross-warehouse transfer when the inventory in a certain area is lower than the safety line), which are obtained by integrating the response classification tags and channel attributes (such as the traffic conversion efficiency of the online platform, the radiation radius of the offline experience store).

[0082] As an embodiment of the present invention, the analysis of the channel response characteristics of the distribution collaboration network includes: querying the full-channel data stream in the distribution collaboration network; collecting the original response data set in the full-channel data stream; extracting the response high-frequency signal in the original response data set; generating the response classification tag corresponding to the response high-frequency signal; and analyzing the channel response characteristics of the distribution collaboration network based on the response classification tag.

[0083] Among them, the omnichannel data stream refers to a real-time dynamic data set covering all online and offline distribution channels, including multi-dimensional data such as order generation timestamps, inventory level change records, logistics track information, user behavior logs (such as clicks, consultations, returns), and external market events (such as competitor price adjustments, policy subsidies). Millisecond-level data synchronization is achieved through technical means such as Internet of Things sensors, API interfaces, and buried point monitoring. The original response data set refers to the unprocessed basic data directly collected from the omnichannel data stream, including discrete event records in the form of time series (such as the number of orders per minute, inventory turnover rate per hour), and unstructured text data (such as user comments, customer service feedback). Its integrity and real-time nature determine the accuracy of subsequent analysis. The response high-frequency signal refers to the short-term sharp fluctuation characteristics separated from the original data, such as the pulse signal of a sharp increase in the number of orders within 0-2 hours after a promotion event is triggered, or the peak signal of a sudden increase in the passenger flow of an offline experience store in a certain area due to the release of a new product. The response classification label refers to the response pattern label automatically generated by a machine learning classification model (such as Support Vector Machine SVM) based on the high-frequency signal characteristics, including categories such as "explosive", "smooth", "lagged", "fluctuating", etc. Each label corresponds to a specific response speed, duration, and influence range.

[0084] Further, querying the omnichannel data stream in the distribution collaboration network can be achieved through Talend tools. For example, using Talend's transformation components to perform preliminary cleaning and format unification on the data, and finally outputting the integrated data to a centralized data warehouse through the loading component to obtain the omnichannel data stream. Collecting the original response data set in the omnichannel data stream can be achieved through data collection tools such as Kafka and Sqoop. Extracting the response high-frequency signal in the original response data set can be achieved through Origin tools. For example, when using Origin, after importing the data, the filtering function module can be used to select high-pass filtering, set an appropriate cut-off frequency, and perform filtering operations on the original data to obtain the response high-frequency signal. Generating the response classification label corresponding to the response high-frequency signal can be achieved through the clustering analysis method. For example, clustering algorithms such as K-Means can be selected, an appropriate number of clusters can be set, and Weka will automatically cluster the signals and can manually or automatically assign classification labels according to the clustering results. Analyzing the channel response characteristics of the distribution collaboration network can be achieved through the SPSS Modeler tool. For example, data including channel information and response classification labels can be imported into the SPSS Modeler tool, and its association rule mining module can be used to set parameters such as support and confidence to mine the association rules between different channels and response characteristics, thereby analyzing the channel response characteristics.

[0085] By querying the user conversion value in the channel response characteristics, the present invention can specifically improve the traffic reception capacity and user experience, thereby enhancing the overall conversion efficiency. Through correlation analysis in combination with indicators such as response speed and synergy effect, it can also predict channel potential and risks, providing a decision-making basis for building an intelligent distribution network, and ultimately achieving a double improvement in market coverage and sales quality.

[0086] Among them, the user conversion value refers to a quantitative indicator used to measure the ability of a channel to convert potential users into actual consumers in a distribution collaboration network. Specifically, it is the proportion of users who complete purchase behaviors or achieve core business goals (such as placing an order, registering as a member) in the channel traffic within a specific time period. This indicator is accurately calculated through cross-channel data integration and attribution analysis. Its core components include: first, the conversion rate, that is, the conversion ratio from clicking on an advertisement to completing a purchase (such as the average conversion rate of online channels is 2.3%); second, the conversion path efficiency, that is, the average number of steps from the first touch (such as advertisement exposure) to the final conversion (such as the conversion path of offline experience stores is 1.8 steps shorter than that of online channels); third, the conversion quality, that is, the weighted comprehensive value of derivative indicators such as average order value and repurchase rate. Optionally, querying the user conversion value in the channel response characteristics can be achieved through data query tools, such as tools like Google Analytics, Mixpanel, etc.

[0087] Furthermore, based on the user conversion value, the present invention calculates the user coupling matching degree of the target projection device during parallel distribution, which can accurately identify the degree of fit between the user needs of each channel and the functional characteristics of the device, and dynamically optimize the resource allocation strategy; by quantitatively analyzing the overlap degree of cross-channel user behavior trajectories and preferences, it can effectively reduce the waste of resources caused by user competition between channels at the same time.

[0088] Among them, the user coupling matching degree refers to the degree of tight matching between the users of different distribution channels and the target users of the target projection device in the scenario of parallel distribution of the target projection device. The higher the value, the higher the degree of fit between the users reached by each distribution channel and the target user group of the device, which is more conducive to the promotion and sales of products.

[0089] As an embodiment of the present invention, calculating the user coupling matching degree of the target projection device during parallel distribution based on the user conversion value includes:

[0090] Using the following formula to calculate the user coupling matching degree of the target projection device during parallel distribution:

[0091]

[0092] Among them, Yp represents the user coupling matching degree of the target projection device during parallel distribution, n represents the number of different distribution channels, i represents the index of the number of different distribution channels, and w i represents the channel weight of the i-th distribution channel, and R i represents the user conversion value of the i-th distribution channel, and C i represents the matching degree coefficient between the user characteristics of the i-th distribution channel and the target user characteristics of the target projection device. m represents the number of influencing factors, j represents the index of the number of influencing factors, and D j represents the interference degree value corresponding to the j-th influencing factor.

[0093] Specifically, the channel weight refers to the relative importance of each distribution channel in the entire parallel distribution system, which comprehensively considers factors such as the influence of the channel, coverage, historical sales performance, etc.; the user conversion value refers to the ability index of converting potential users into actual purchasers of the target projection device in a specific distribution channel, which reflects the efficiency of the channel in guiding users to complete the purchase behavior. The higher the value, the stronger the ability of the channel to convert users; the matching degree coefficient is used to measure the degree of conformity between the user characteristics (such as age, gender, consumption habits, usage scenario preferences, etc.) reached by each distribution channel and the target user characteristics of the target projection device. The coefficient value ranges from 0 to 1, where 0 means completely non-conforming and 1 means completely conforming; the influencing factors refer to various internal and external conditions that will affect the user coupling matching degree of the target projection device during parallel distribution, excluding the distribution channel itself and user characteristics, such as market competition situation, economic environment change, promotion activity effect, brand popularity, etc.; the interference degree value is a quantitative value that evaluates the degree of interference of each influencing factor on the user coupling matching degree. The larger the value, the stronger the negative impact of the influencing factor on the matching degree between the user and the device.

[0094] S3. Based on the user coupling matching degree, simulate the user distribution map of the target projection device in a distribution fluctuation environment, and construct an adaptive logic rule corresponding to the target projection device according to the user distribution map.

[0095] Based on the user coupling matching degree, the present invention simulates the user distribution map of the target projection device in a distribution fluctuation environment, which can intuitively present the user flow and aggregation situation of each channel, help to grasp the market change trend, accurately locate high-potential user areas and weak links, reasonably allocate resources, and optimize the distribution strategy.

[0096] Among them, the distribution fluctuation environment refers to the situation jointly constituted by various internal and external factors that cause the instability of the distribution state during the distribution process of the target projection device. The internal factors include the enterprise's own strategic adjustments, production and supply changes, and channel cooperation relationship changes; the external factors cover changes in the market competition situation, rapid changes in consumer demand preferences, economic situation fluctuations, policy and regulation adjustments, etc.; the user distribution map refers to a visualization map constructed based on association rules, which can dynamically display the user aggregation trends and flow paths in each region and channel under different distribution fluctuation scenarios (such as the heat map of household users concentrating on highly compatible offline experience stores). Its core value lies in providing decision-making support for resource allocation and strategy optimization by intuitively presenting the dynamic adaptation relationship between users and channels.

[0097] As an embodiment of the present invention, simulating the user distribution map of the target projection device in the distribution fluctuation environment based on the user coupling matching degree includes: extracting the historical coupling data corresponding to the user coupling matching degree; mining the user fluctuation factors in the historical coupling data; analyzing the potential association rules between the user fluctuation factors and the distribution fluctuation environment; and simulating the user distribution map of the target projection device in the distribution fluctuation environment based on the potential association rules.

[0098] Among them, the historical coupling data refers to the data set related to the user coupling matching degree accumulated during the past distribution process of the target projection device, covering the coupling degree value records in different time periods, channel types, and market environments (such as the coupling degree difference records of online channels during the new product release period and the promotion period); the user fluctuation factors refer to the key variables that affect the change of user distribution identified from the historical coupling data through data mining techniques (such as dynamic indicators such as channel response speed and user preference migration frequency); the potential association rules refer to the causal relationship patterns between the user fluctuation factors and the distribution fluctuation environment discovered through algorithm analysis (such as the response law of a certain type of user preference change to a specific channel promotion activity).

[0099] Further, the extraction of historical coupling data corresponding to the user coupling matching degree can be achieved through ETL tools, such as Talend, Informatica, etc.; the mining of user fluctuation factors in the historical coupling data can be achieved through association rule mining algorithms, such as Apriori, FP-Growth, etc., to mine the association relationships between data items and find frequent item sets as user fluctuation factors; the analysis of potential association rules between the user fluctuation factors and the distribution fluctuation environment can be achieved through decision tree algorithms, such as (using the C4.5 decision tree algorithm, constructing a decision tree with user fluctuation factors such as user age and purchase frequency and distribution fluctuation environment factors such as market competition intensity and promotion activity intensity as variables, and obtaining potential association rules such as "when the market competition intensity is high and the user age is between 25 and 35 years old, the purchase frequency will increase"); the simulation of the user distribution map of the target projection device in the distribution fluctuation environment can be achieved through data visualization tools, such as using Tableau tool, importing data such as the user's location, purchase channel, purchase time, etc., creating a dashboard by setting different dimensions and metrics, and displaying the user distribution map of the target projection device in different distribution fluctuation environments in various forms such as maps, bar charts, line charts, etc., such as the distribution of user purchase volumes in different regions of a certain city.

[0100] Based on the user distribution map, the present invention constructs an adaptive logic rule corresponding to the target projection device, which can real-time perceive the dynamic distribution of users and the channel matching status, dynamically adjust the resource investment strategy, and by mining the user migration path and preference clustering characteristics in the map, can automatically generate regionalized and scenario-based precision marketing plans.

[0101] Among them, the adaptive logic rule refers to a logical model that dynamically adjusts user density feature analysis, market demand priority division, channel allocation weight calculation, and investment strategy formulation based on execution effect data, ensuring that the sales strategy can be optimized in real time according to market changes and user needs.

[0102] As an embodiment of the present invention, the construction of the adaptive logic rule corresponding to the target projection device according to the user distribution map includes: querying the user density feature in the user distribution map; determining the market demand priority of the target projection device in different regions based on the user density feature; analyzing the channel allocation weight corresponding to the target projection device based on the market demand priority; formulating the investment strategy of the target projection device in different channels according to the channel allocation weight; verifying the execution effect of the investment strategy in the simulated market environment; constructing the adaptive logic rule corresponding to the target projection device based on the execution effect.

[0103] Among them, the user density feature refers to the distribution of the number of users in different regions in the user distribution map, including high-density regions (where users are highly concentrated), medium-density regions (where users are evenly distributed), and low-density regions (where users are sparse), as well as the dynamic trend of the change in user density over time; the market demand priority refers to the quantitative evaluation and ranking of the market demand for the target projection device in each region based on the user density feature, consumption ability, purchase intention, and competition environment in different regions, and determining the grading criteria for high-demand regions, medium-demand regions, and low-demand regions; the channel allocation weight refers to the resource ratio allocated to different sales channels (such as online e-commerce platforms, offline retail stores, agents, etc.) based on the market demand priority, including weight parameters such as capital investment, advertising investment, inventory allocation, and personnel allocation; the placement strategy refers to the specific sales action plan formulated according to the channel allocation weight, including precise advertising placement, social media promotion, search engine optimization for online channels, and strategy combinations such as experience store layout, promotional activities, and user training for offline channels; the execution effect refers to the actual effect data evaluated through key indicators (such as user conversion rate, sales growth rate, channel response time, resource utilization rate, etc.) after the placement strategy runs in the simulated market environment.

[0104] Further, querying the user density feature in the user distribution map can be achieved through Python tools. For example, the GeoPandas library in Python can be used to read the user distribution data, process the data, and draw a user density distribution map to visually present the user density feature; determining the market demand priority of the target projection device in different regions can be achieved through SPSS tools. For example, after importing the data into the SPSS tool, the analytic hierarchy process can be calculated through its analysis function to obtain the market demand priority of each region; analyzing the channel allocation weight corresponding to the target projection device can be achieved through Python tools. For example, the PuLP library in Python can be used to build a linear programming model and optimize the solution in combination with the genetic algorithm to obtain the channel allocation weight; formulating the placement strategy of the target projection device under different channels can be achieved through a strategy formulation tool. For example, the RapidMiner tool can be used to import the channel allocation weight data, analyze it using the decision tree algorithm, and formulate the placement strategy according to the results of the decision tree; verifying the execution effect of the placement strategy in the simulated market environment can be achieved through the simulation modeling method. For example, a simulated market environment model is constructed, and random factors such as user behavior and market competition are set in the model. Through multiple simulation runs, data on key indicators (such as user conversion rate, sales growth rate, etc.) are collected to evaluate the execution effect of the placement strategy; constructing the adaptive logic rule corresponding to the target projection device can be achieved through a rule formulation tool. For example, tools such as TensorFlow and PyTorch.

[0105] S4. Integrate the adaptive logic rules with the preset promotion compensation mechanism to generate a dynamic regulation strategy for the target projection device, extract the key execution parameters in the regulation strategy, and calculate the regulation attenuation degree corresponding to the target projection device based on the key execution parameters.

[0106] By integrating the adaptive logic rules with the preset promotion compensation mechanism, the present invention generates a dynamic regulation strategy for the target projection device, which can achieve a millisecond-level response to the distribution strategy and market fluctuations, effectively balance the short-term sales sprint and long-term brand value maintenance, and finally form a full-closed-loop intelligent regulation system covering demand prediction, resource allocation, and effect feedback.

[0107] Among them, the preset promotion compensation mechanism refers to a set of automated response strategies preset for possible market fluctuations, channel inefficiencies, or user demand changes during the distribution process of the target projection device. This mechanism realizes dynamic intervention in the distribution system by setting trigger conditions (such as the user conversion rate being lower than the threshold, inventory backlog exceeding the safety line), compensation methods (such as time-limited discounts, gift bundling, additional advertising placement), and execution logics (such as phased triggering, regional differential compensation); the dynamic regulation strategy refers to an optimized sales strategy plan generated according to the collaborative regulation process, which can adjust the channel resource allocation, placement strategy, and promotion activities in real time to cope with the changes in the market environment and fluctuations in user demand. Its core lies in its flexibility and accuracy, and through a data-driven decision-making mechanism, it ensures that the sales strategy is always in the optimal state.

[0108] As an embodiment of the present invention, the integration of the adaptive logic rules with the preset promotion compensation mechanism to generate a dynamic regulation strategy for the target projection device includes: querying the key regulation parameters in the adaptive logic rules; determining the trigger conditions corresponding to the promotion compensation mechanism based on the key regulation parameters; analyzing the compensation effects of the promotion compensation mechanism in different market environments based on the trigger conditions; formulating a collaborative regulation process corresponding to the promotion compensation mechanism and the adaptive logic rules according to the compensation effects; and generating a dynamic regulation strategy for the target projection device based on the collaborative regulation process.

[0109] Among them, the key control parameters refer to the core variables used to dynamically adjust the sales strategy, including user density characteristics, market demand priorities, channel allocation weights, the execution effect of the placement strategy, etc. These parameters reflect the changes in the market environment and the dynamic characteristics of user needs, and are the basis for formulating and optimizing the sales strategy; the trigger condition refers to the activation threshold of the promotion compensation mechanism set according to the change range of the key control parameters. For example, when the user conversion rate is lower than the preset threshold, the sales growth rate shows a significant decline, or the utilization rate of channel resources is insufficient, the promotion compensation mechanism is triggered; the compensation effect refers to the adjustment effect of the promotion compensation mechanism on the sales strategy after being triggered, including the increase in the sales growth rate, the improvement of the user conversion rate, the optimization of the utilization rate of channel resources, etc. And through simulating the market environment and analyzing historical data, the performance of the compensation mechanism in different scenarios can be evaluated; the collaborative control process refers to the organic combination of the promotion compensation mechanism and the adaptive logic rules to form a closed-loop process for dynamically adjusting the sales strategy. This process includes the real-time monitoring of the trigger condition, the precise execution of the compensation mechanism, the data feedback of the compensation effect, and the dynamic optimization of the adaptive logic rules.

[0110] Furthermore, the query of the key control parameters in the adaptive logic rules can be implemented through a data query tool, such as an SQL database query tool, etc. MySQL database can be used to write query statements to extract key control parameters such as user density characteristics, market demand priority, channel allocation weight, and execution effect of the placement strategy from the database table storing the adaptive logic rules, and finally obtain the key control parameters. The determination of the trigger condition corresponding to the promotion compensation mechanism can be implemented through a rule engine, such as the Drools rule engine, etc. The key control parameters are input into the Drools rule engine, and according to the preset rules, when conditions such as the user conversion rate being lower than the preset threshold, a significant decline in the sales growth rate, or insufficient utilization of channel resources are met, the trigger condition is output, and finally the trigger condition is obtained. The analysis of the compensation effect of the promotion compensation mechanism in different market environments can be implemented through a data analysis tool, such as SPSS, etc. SPSS is used to analyze the simulated market environment and historical data, and indicators such as the increase in the sales growth rate, the improvement of the user conversion rate, and the optimization of the channel resource utilization rate are statistically analyzed, and finally the compensation effect is obtained. The formulation of the collaborative control process corresponding to the promotion compensation mechanism and the adaptive logic rules can be implemented through a process modeling tool, such as Bizagi Modeler, etc. Bizagi Modeler is used to draw a process model of the interaction between the promotion compensation mechanism and the adaptive logic rules, clarify links such as real-time monitoring of the trigger condition, precise execution of the compensation mechanism, data feedback of the compensation effect, and dynamic optimization of the adaptive logic rules, and finally obtain the collaborative control process. The generation of the dynamic control strategy corresponding to the target projection device can be implemented through an intelligent optimization algorithm, such as the ant colony algorithm, etc. The relevant data and objectives in the collaborative control process are used as inputs, and the ant colony algorithm is used to continuously search for the optimal solution to generate a dynamic control strategy that can adjust the channel resource allocation, placement strategy, and promotion activities in real time, and finally obtain the dynamic control strategy.

[0111] The present invention effectively reduces resource waste and enhances channel collaboration efficiency by extracting the key execution parameters in the control strategy, and real-time monitoring of parameter fluctuations can identify potential risks in advance and trigger an early warning mechanism to ensure the elasticity of the supply chain and the stability of the strategy, and finally form a data-driven strategy optimization closed loop.

[0112] Among them, the key execution parameters refer to the variables that are crucial for achieving effective sales and market response of the target projection device in the regulation strategy. It covers channel resource allocation parameters, such as the inventory quantity of each sales channel and the advertising investment amount, which determine the investment intensity of resources in different channels; promotion activity parameters, such as promotion forms (buy-one-get-one-free, discount, etc.) and activity duration, which directly affect the promotion effect and users' purchase intention; placement strategy parameters, including the key regions of online and offline promotion, target user group positioning, etc. Optionally, the extraction of the key execution parameters in the regulation strategy can be achieved through the Drools rule engine. For example, by using the pattern matching function of the Drools rule engine to parse the preset regulation strategy logic, automatically identify the quantifiable execution parameters (such as the population density coefficient in the regional priority scoring formula), and finally obtain the key execution parameters.

[0113] Furthermore, based on the key execution parameters, the present invention calculates the regulation attenuation degree corresponding to the target projection device, which can quantify the decreasing trend of the strategy execution effect over time or environment in real time, provide a scientific basis for dynamically adjusting resource allocation, and through analyzing the correlation between the attenuation degree and parameters such as channel weights and promotion responses, can accurately locate the strategy failure links and quickly optimize the compensation plan.

[0114] Among them, the regulation attenuation degree refers to the degree of weakening of the actual regulation effect relative to the expected regulation effect of the target projection device during the regulation process based on the key execution parameters. It reflects the degree value of the regulation strategy not reaching the ideal regulation state due to various factors during the execution process.

[0115] As an embodiment of the present invention, the calculation of the regulation attenuation degree corresponding to the target projection device based on the key execution parameters includes:

[0116] Calculating the regulation attenuation degree corresponding to the target projection device by using the following formula:

[0117]

[0118] Among them, DA represents the regulation attenuation degree corresponding to the target projection device, p represents the total number of categories of the key execution parameters, k represents the category index corresponding to the key execution parameter, Q k represents the basic influence factor corresponding to the kth category of key execution parameters, R k represents the correction coefficient corresponding to the kth category of key execution parameters, c represents the number of specific execution factors in the key execution parameters, v represents the number index of the specific execution factors, E kv represents the current value of the vth specific execution factor in the kth category of key execution parameters, F kvRepresents the weight coefficient of the vth specific execution factor in the kth type of key execution parameters.

[0119] In detail, the basic influencing factor refers to a factor value set for each type of key execution parameter that can reflect the basic effect of this type of parameter on the control attenuation, which represents the inherent influence of this type of parameter itself on the control attenuation in the control process without considering other complex corrections; the correction coefficient refers to a coefficient used to adjust the role of each type of key execution parameter in the calculation of the control attenuation, which comprehensively considers various external and internal factors such as environmental factors and differences in the characteristics of the equipment itself, and corrects the basic influencing factor to make the calculation result more in line with the actual control situation; the specific execution factor refers to the specific elements that constitute each type of key execution parameter, and each type of key execution parameter is composed of multiple specific execution factors, which directly participate in the control process and affect the control effect; the current value refers to the actual value of the specific execution factor at a specific time or control scenario, and this value will change with the advancement of the control process, environmental changes and other factors; the weight coefficient refers to the relative importance of each specific execution factor in the key execution parameters of its category to the control attenuation. The larger the weight coefficient, the greater the influence of the specific execution factor on the control attenuation.

[0120] S5. Based on the regulated attenuation degree, determine the distribution state corresponding to the target projection device, analyze the distribution dimension of the target projection device corresponding to the distribution state, and construct a sales optimization plan corresponding to the target projection device based on the distribution dimension.

[0121] The present invention determines the distribution status corresponding to the target projection device based on the control attenuation degree, can intuitively display the actual effect of the control strategy in the distribution link, timely discover deviations and problems in the strategy execution, can better adapt to market changes, enhance the market competitiveness of products, help achieve sales targets, and improve overall distribution efficiency.

[0122] Among them, the distribution status refers to the overall situation of the product in the distribution system determined after comprehensively considering the distribution performance of the target projection equipment in different attenuation ranges. It is a comprehensive evaluation of the product distribution process, reflecting the product's sales in the market, market competitiveness, customer satisfaction and other aspects of information.

[0123] As an embodiment of the present invention, determining the distribution status corresponding to the target projection device based on the regulation attenuation degree includes: querying the attenuation characteristic range corresponding to the regulation attenuation degree; determining the distribution division criteria corresponding to the regulation attenuation degree based on the attenuation characteristic range; analyzing the distribution performance of the target projection device in different attenuation intervals based on the distribution division criteria; and determining the distribution status corresponding to the target projection device according to the distribution performance.

[0124] Among them, the attenuation characteristic range refers to the numerical change interval of the regulation attenuation degree under different circumstances and the set of characteristics presented by these numerical values. The regulation attenuation degree reflects the degree of gradual weakening of the effect during the regulation process of the target projection device, and its attenuation characteristic range comprehensively considers the influence of various factors on this attenuation degree; the distribution division criteria refer to a set of rules and boundaries for distinguishing different distribution situations based on the attenuation characteristic range of the regulation attenuation degree. Its purpose is to establish a clear corresponding relationship between the regulation attenuation degree and the distribution status of the target projection device, so as to more accurately evaluate and manage the distribution process; the different attenuation intervals refer to several continuous numerical paragraphs divided according to the distribution division criteria within the attenuation characteristic range of the regulation attenuation degree. Each attenuation interval represents a different degree of regulation attenuation and corresponds to different distribution performances of the target projection device; the distribution performance refers to the specific performance of the target projection device in the distribution process under each different attenuation interval, which is measured by a series of indicators. These indicators reflect information such as the sales situation, market share, and customer satisfaction of the product in the market.

[0125] Further, the query of the attenuation characteristic range corresponding to the regulation attenuation degree can be achieved through a historical data analysis tool. For example, by using the Pandas library in Python to read a large amount of data related to the regulation of the target projection device in the past, analyzing the change trend and fluctuation range of the regulation attenuation degree in different time periods and different market environments, and finally obtaining the attenuation characteristic range. The determination of the distribution division standard corresponding to the regulation attenuation degree can be achieved through a multi-factor decision-making algorithm. For example, by applying the Analytic Hierarchy Process (AHP), comprehensively considering factors such as market demand, sales channel cost, and competitor situation, setting reasonable division boundaries for the regulation attenuation degree, and finally obtaining the distribution division standard. The analysis of the distribution performance of the target projection device in different attenuation intervals can be achieved through a Business Intelligence (BI) tool. For example, by using the Tableau tool to perform correlation visualization analysis on different attenuation intervals and distribution data such as sales volume, sales amount, and customer satisfaction, and finally obtaining the distribution performance. The determination of the distribution status corresponding to the target projection device can be achieved through a machine learning classification algorithm. For example, by adopting the Support Vector Machine (SVM) algorithm, using the analyzed distribution performance data as features for input, training the model through the labeled historical distribution status data, and then classifying and predicting the current data, and finally obtaining the distribution status.

[0126] By analyzing the distribution dimensions corresponding to the distribution status of the target projection device, the present invention helps to optimize resource allocation, concentrate human and material resources on the dimensions with great potential, improve sales efficiency. At the same time, by adjusting the strategy according to the analysis results, the market adaptability of the product can be enhanced, better meet the needs of consumers, stabilize and expand the market share, and improve the overall distribution efficiency.

[0127] Among them, the distribution dimension refers to a set of core evaluation indicators for systematically deconstructing and quantitatively analyzing the distribution system of the target projection device from a multi-dimensional perspective. For example, in the channel dimension, the resource ratio and conversion efficiency between the online e-commerce platform and the offline experience store need to be considered. In the customer dimension, the demand differences and purchase decision-making paths between household users and enterprise customers need to be distinguished. In the product dimension, the market acceptance of different models of devices in terms of function configuration and price gradient needs to be analyzed. In the regional dimension, a differentiated placement strategy needs to be formulated in combination with the consumption capacity differences between urban and rural areas. In the time dimension, the impact of promotional nodes and sales off-peak and peak seasons on the distribution effect needs to be dynamically tracked. Optionally, the analysis of the distribution dimensions corresponding to the distribution status of the target projection device can be achieved through a multi-dimensional data analysis method. For example, by comprehensively considering data in multiple dimensions such as channels, customers, products, regions, and time, comprehensively analyzing the distribution situation of the target projection device, and thus obtaining the distribution dimensions.

[0128] Furthermore, based on the distribution dimension, the present invention constructs a sales optimization plan corresponding to the target projection device, enabling dynamic matching of promotional resources and market demand; by analyzing the sales characteristics in different regions and time periods, differential placement strategies can be formulated to significantly improve channel conversion efficiency and user reach accuracy; combined with the correlation analysis of product configuration and user preferences, the product portfolio can be rapidly iterated and the pricing strategy optimized, effectively enhancing market competitiveness.

[0129] Among them, the sales optimization plan refers to a systematic strategy improvement framework formulated for the target projection device based on multi-dimensional distribution data analysis. For example, by analyzing the differences in channel conversion rates between urban and rural markets, the resource ratio of offline experience stores and online e-commerce can be dynamically adjusted; the product recommendation strategy can be optimized in combination with customer portrait data, and differential promotion plans can be designed for enterprise procurement and household users; a stepped inventory allocation plan can be formulated according to the laws of peak and off-peak sales seasons to ensure the supply of goods during key periods; at the same time, intelligent algorithms are introduced to monitor the data fluctuations in each dimension in real time, and strategy response mechanisms such as automatic triggering of channel weight recalculation and promotion threshold adjustment are implemented. Optionally, the construction of the sales optimization plan corresponding to the target projection device can be achieved through plan construction tools, such as tools like Trello and Asana.

[0130] Compared with the problems described in the background art, the present invention can achieve precise configuration and dynamic optimization of channel resources by obtaining the device placement data corresponding to the target projection device and collecting the real-time distribution spectrum corresponding to the device placement data, so that the online and offline placement ratios are highly matched with the regional demand characteristics; at the same time, a millisecond-level market response mechanism is constructed to anticipate channel fluctuations in advance and quickly adjust the promotion strategy, ultimately enhancing the market competitiveness. By analyzing the channel response characteristics of the distribution collaboration network, the present invention can accurately grasp the reaction speed and mode of each online and offline channel to market changes, clarify the advantages and limitations of different channels in coping with demand fluctuations, which helps to flexibly allocate resources according to the characteristics of each channel, quickly adjust the sales strategy, and improve the overall operation efficiency. Further, based on the user coupling matching degree, the present invention simulates the user distribution map of the target projection device in the distribution fluctuation environment, which can intuitively present the user flow and aggregation of each channel, helps to grasp the market change trend, can accurately locate high-potential user areas and weak links, reasonably allocate resources, and optimize the distribution strategy. Further, by integrating the adaptive logic rules and the preset promotion compensation mechanism, the present invention generates a dynamic regulation strategy corresponding to the target projection device, which can achieve a millisecond-level response of the distribution strategy to market fluctuations, effectively balance the short-term sales sprint and the long-term brand value maintenance, and finally form a full-closed-loop intelligent regulation system covering demand prediction, resource allocation, and effect feedback. Finally, based on the regulation attenuation degree, the present invention determines the distribution state corresponding to the target projection device, which can intuitively show the actual effect of the regulation strategy in the distribution link, timely discover the deviations and problems in the strategy execution, better adapt to market changes, enhance the market competitiveness of the product, help to achieve the sales target, and improve the overall distribution efficiency. Therefore, the efficient projection device sales strategy optimization method and system provided by the embodiments of the present invention can improve the accuracy of the sales strategy.

[0131] Embodiment 2:

[0132] As Figure 2 shown, it is a functional module diagram of an efficient projection device sales strategy optimization system of the present invention.

[0133] The efficient projection device sales strategy optimization system 200 of the present invention can be installed in an electronic device. According to the realized functions, the efficient projection device sales strategy optimization system can include a network construction module 201, a matching degree calculation module 202, a rule construction module 203, an attenuation degree calculation module 204, and a solution construction module 205. The modules of the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, and are stored in the memory of the electronic device.

[0134] In the embodiments of the present invention, the functions of each module / unit are as follows:

[0135] The network construction module 201 is configured to obtain device placement data corresponding to a target projection device, collect a real-time distribution spectrum corresponding to the device placement data, analyze a placement demand curve of the target projection device under different channels based on the real-time distribution spectrum, and construct a distribution collaboration network corresponding to the target projection device according to the placement demand curve. The distribution collaboration network includes an online distribution network and an offline distribution network;

[0136] The matching degree calculation module 202 is configured to analyze the channel response characteristics of the distribution collaboration network, query user conversion values in the channel response characteristics, and calculate the user coupling matching degree of the target projection device during parallel distribution based on the user conversion values;

[0137] The rule construction module 203 is configured to simulate a user distribution map of the target projection device in a distribution fluctuation environment based on the user coupling matching degree, and construct an adaptive logic rule corresponding to the target projection device according to the user distribution map;

[0138] The attenuation degree calculation module 204 is configured to integrate the adaptive logic rule with a preset promotion compensation mechanism to generate a dynamic regulation strategy corresponding to the target projection device, extract key execution parameters in the regulation strategy, and calculate the regulation attenuation degree corresponding to the target projection device based on the key execution parameters;

[0139] The solution construction module 205 is configured to determine the distribution status corresponding to the target projection device based on the regulation attenuation degree, analyze the distribution dimensions of the target projection device in the corresponding distribution status, and construct a sales optimization solution corresponding to the target projection device based on the distribution dimensions.

[0140] Specifically, each module in the high-efficiency projection device sales strategy optimization system 200 in the embodiments of the present invention adopts the same technical means as those in the above Figure 1 and can produce the same technical effects, which will not be elaborated here.

[0141] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0142] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An efficient projection equipment sales strategy optimization method, characterized in that: The method comprises: Acquire device placement data corresponding to a target projection device, and collect real-time distribution spectrum corresponding to the device placement data, analyze the placement demand curve of the target projection device under different channels based on the real-time distribution spectrum, and construct a distribution coordination network corresponding to the target projection device according to the placement demand curve, wherein the distribution coordination network includes an online distribution network and an offline distribution network; Analyzing the channel response characteristics of the distribution collaborative network, querying the user conversion value in the channel response characteristics, and calculating the user coupling matching degree of the target projection device during parallel distribution based on the user conversion value; Based on the user coupling matching degree, simulating the user distribution map of the target projection device in a distribution fluctuation environment, and constructing an adaptive logic rule corresponding to the target projection device according to the user distribution map; Integrate the adaptive logic rules with the preset promotion compensation mechanism to generate a dynamic control strategy corresponding to the target projection device, extract key execution parameters in the control strategy, and calculate the control attenuation corresponding to the target projection device based on the key execution parameters; Based on the regulated attenuation degree, the distribution state corresponding to the target projection device is determined, and the distribution dimension of the target projection device corresponding to the distribution state is analyzed, and based on the distribution dimension, a sales optimization plan corresponding to the target projection device is constructed.

2. The method for optimizing the sales strategy of efficient projection equipment according to claim 1, characterized in that: The step of constructing a distribution collaboration network corresponding to the target projection device according to the delivery demand curve includes: Analyze the time cycle law corresponding to the delivery demand curve; Based on the time period rule, determining the demand adaptation information of the target projection device in each time period; Querying resource bearing data in the demand adaptation information; Based on the resource carrying data, determining a synergy and complementarity index corresponding to the target projection device; Based on the synergy complementarity index, a distribution synergy network corresponding to the target projection device is constructed.

3. The method for optimizing the sales strategy of efficient projection equipment according to claim 1, characterized in that: The analyzing the channel response characteristics of the distribution collaborative network includes: querying omni-channel data flows in the distribution collaboration network; Collecting the original response data set in the omni-channel data stream; Extracting a response high-frequency signal from the original response data set; Generate a response classification label corresponding to the response high-frequency signal; Based on the response classification labels, the channel response characteristics of the distribution collaboration network are analyzed.

4. The method for optimizing the sales strategy of efficient projection equipment according to claim 1, characterized in that: The calculating, based on the user conversion value, the user coupling matching degree of the target projection device during parallel distribution includes: The user coupling matching degree of the target projection device during parallel distribution is calculated using the following formula: Wherein, Yp represents the user coupling matching degree of the target projection device during parallel distribution, n represents the number of different distribution channels, i represents the number index of different distribution channels, and w i represents the channel weight of the i-th distribution channel, R i represents the user conversion value of the i-th distribution channel, C i represents the fit coefficient between the user characteristics of the i-th distribution channel and the target user characteristics corresponding to the target projection device, m represents the number of influencing factors, j represents the number index of influencing factors, and D j Represents the interference value corresponding to the jth influencing factor.

5. The method for optimizing the sales strategy of efficient projection equipment according to claim 1, characterized in that: The simulating the user distribution map of the target projection device in a distribution fluctuation environment based on the user coupling matching degree includes: Extracting historical coupling data corresponding to the user coupling matching degree; Mining user fluctuation factors in the historical coupling data; Analyze the potential association rules between the user fluctuation factors and the distribution fluctuation environment; Based on the potential association rules, a user distribution map of the target projection device in a distribution fluctuation environment is simulated.

6. The method for optimizing the sales strategy of efficient projection equipment according to claim 1, characterized in that: The step of constructing the adaptive logic rule corresponding to the target projection device according to the user distribution map includes: Querying user density characteristics in the user distribution map; Based on the user density characteristics, determining the market demand priorities of the target projection equipment in different regions; Based on the market demand priority, analyzing the channel allocation weight corresponding to the target projection device; Formulate a delivery strategy for the target projection device in different channels according to the channel allocation weights; Verify the execution effect of the delivery strategy in a simulated market environment; Based on the execution effect, an adaptive logic rule corresponding to the target projection device is constructed.

7. The method for optimizing the sales strategy of efficient projection equipment according to claim 1, characterized in that: The step of integrating the adaptive logic rules with the preset promotion compensation mechanism to generate a dynamic control strategy corresponding to the target projection device includes: Querying key control parameters in the adaptive logic rules; Based on the key control parameters, determining the triggering conditions corresponding to the promotion compensation mechanism; Based on the trigger conditions, analyzing the compensation effect of the promotion compensation mechanism in different market environments; According to the compensation effect, formulate a coordinated control process corresponding to the promotion compensation mechanism and the adaptive logic rules; Based on the collaborative control process, a dynamic control strategy corresponding to the target projection device is generated.

8. The method for optimizing the sales strategy of efficient projection equipment according to claim 1, characterized in that: The calculating, based on the key execution parameter, the control attenuation corresponding to the target projection device includes: The control attenuation corresponding to the target projection device is calculated using the following formula: Wherein, DA represents the control attenuation corresponding to the target projection device, p represents the total number of categories of the key execution parameters, k represents the category index corresponding to the key execution parameters, Q k represents the basic impact factor corresponding to the kth key execution parameter, R k represents the correction coefficient corresponding to the kth type of key execution parameter, c represents the number of specific execution factors in the key execution parameter, v represents the number index of the specific execution factor, E kv represents the current value of the vth specific execution factor in the kth key execution parameter, F kv Represents the weight coefficient of the vth specific execution factor in the kth type of key execution parameters.

9. The method for optimizing the sales strategy of efficient projection equipment according to claim 1, characterized in that: The determining, based on the regulated attenuation degree, a distribution state corresponding to the target projection device includes: Querying the attenuation characteristic range corresponding to the regulated attenuation degree; Based on the attenuation characteristic range, determining a distribution division standard corresponding to the regulated attenuation degree; Based on the distribution classification standard, analyzing the distribution performance of the target projection device in different attenuation ranges; According to the distribution performance, a distribution status corresponding to the target projection device is determined.

10. An efficient projection equipment sales strategy optimization system, characterized in that: The system comprises: A network construction module is used to obtain device placement data corresponding to a target projection device, and collect a real-time distribution spectrum corresponding to the device placement data, analyze a placement demand curve of the target projection device under different channels based on the real-time distribution spectrum, and construct a distribution coordination network corresponding to the target projection device according to the placement demand curve, wherein the distribution coordination network includes an online distribution network and an offline distribution network; A matching degree calculation module, used for analyzing the channel response characteristics of the distribution collaborative network, querying the user conversion value in the channel response characteristics, and calculating the user coupling matching degree of the target projection device during parallel distribution based on the user conversion value; A rule construction module, used to simulate the user distribution map of the target projection device in a distribution fluctuation environment based on the user coupling matching degree, and to construct an adaptive logic rule corresponding to the target projection device according to the user distribution map; An attenuation degree calculation module is used to integrate the adaptive logic rules with a preset promotion compensation mechanism, generate a dynamic control strategy corresponding to the target projection device, extract key execution parameters in the control strategy, and calculate the control attenuation degree corresponding to the target projection device based on the key execution parameters; A solution construction module is used to determine the distribution state corresponding to the target projection device based on the regulated attenuation degree, analyze the distribution dimension of the target projection device corresponding to the distribution state, and construct a sales optimization solution corresponding to the target projection device based on the distribution dimension.

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