Method and device for determining product promotion area, electronic equipment and medium

Abstract

The embodiment of the invention provides a method and device for determining a product promotion area, electronic equipment and a medium, and the method comprises the steps: determining at least one effective promotion area in a target area, and determining an area promotion attribute according to the area density and surrounding environment data; inputting historical transaction information and product attribute information corresponding to the to-be-promoted product, obtained user behavior data and regional promotion attributes of the to-be-promoted product and time sequence data corresponding to the historical transaction information into a plurality of promotion attribute evaluation models obtained through pre-training, and outputting corresponding to-be-used evaluation attributes; determining a target evaluation attribute corresponding to the effective promotion area according to the to-be-used evaluation attribute, the spatial simulation attribute corresponding to the effective promotion area and the weight value corresponding to each evaluation attribute; and determining a target promotion area according to the target evaluation attribute. According to the technical scheme, multiple evaluation parameters are adopted, consideration is given from the global perspective, and the accuracy of product popularization area evaluation is improved.

Description

Technical Field

[0001] This invention relates to the field of data processing and information recommendation technology, and in particular to a method, apparatus, electronic device, and medium for determining product promotion areas. Background Technology

[0002] Currently, product promotion is often determined by the geographical location, transportation information, and regional information of the promotion area.

[0003] However, the above approach has a problem: when considering only the three dimensions mentioned above, there are too few factors to take into account. For example, users in a certain region may never have heard of the product, but the product still has certain promotional value for that region, which leads to the problem of failing to accurately and effectively determine the promotion area. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and medium for determining a product promotion area, so as to achieve the effect of effectively determining the promotion area.

[0005] In a first aspect, embodiments of the present invention provide a method for determining a product promotion area, including:

[0006] Identify at least one effective promotion area within the target area, and determine the regional promotion attributes of the effective promotion area based on the regional density of the effective promotion area and the surrounding environmental data of the effective promotion area;

[0007] For at least one effective promotion area, the historical transaction information, product attribute information, user behavior data of the product to be promoted, regional promotion attributes, and time series data corresponding to the historical transaction information are input into multiple pre-trained promotion attribute evaluation models so that each promotion attribute evaluation model outputs the corresponding evaluation attributes to be used.

[0008] Based on the evaluation attributes to be used, the spatial simulation attributes corresponding to the effective promotion areas, and the weight values ​​corresponding to each evaluation attribute, the target evaluation attributes corresponding to the effective promotion areas are determined; among them, the spatial simulation attributes are used to characterize the effectiveness data of the effective promotion areas.

[0009] Based on the target evaluation attributes corresponding to at least one effective promotion area, determine at least one target promotion area corresponding to the product to be promoted.

[0010] Secondly, embodiments of the present invention also provide an apparatus for determining a product promotion area, the apparatus comprising:

[0011] The regional promotion attribute determination module is used to determine at least one effective promotion area in the target area, and to determine the regional promotion attribute of the effective promotion area based on the regional density of the effective promotion area and the surrounding environmental data of the effective promotion area.

[0012] The evaluation attribute output module is used to input the historical transaction information, product attribute information, user behavior data of the product to be promoted, regional promotion attributes, and time series data corresponding to the historical transaction information of the product to be promoted into multiple pre-trained promotion attribute evaluation models for at least one effective promotion area, so that each promotion attribute evaluation model outputs the corresponding evaluation attribute to be used.

[0013] The target evaluation attribute determination module is used to determine the target evaluation attributes corresponding to the effective promotion area based on the evaluation attributes to be used, the spatial simulation attributes corresponding to the effective promotion area, and the weight value corresponding to each evaluation attribute; wherein, the spatial simulation attributes are used to characterize the effectiveness data of the effective promotion area;

[0014] The target promotion area determination module is used to determine at least one target promotion area corresponding to the product to be promoted, based on the target evaluation attributes corresponding to at least one effective promotion area.

[0015] Thirdly, embodiments of the present invention also provide an electronic device, comprising:

[0016] At least one processor; and

[0017] A memory that is communicatively connected to at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor can perform a method for determining a product promotion area as provided in any embodiment of the present invention.

[0019] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer instructions for causing a processor to execute a method for determining a product promotion area as provided in any embodiment of the present invention.

[0020] This invention identifies at least one effective promotion area within a target region and determines its regional promotion attributes based on the area density and surrounding environmental data of the effective promotion area. This approach combines multiple reference factors to determine whether a potential promotion area is effective and its corresponding regional promotion attributes, overcoming the limitations of existing technologies that only consider geography, transportation, and product sales areas. Furthermore, for at least one effective promotion area, historical transaction information, product attribute information, user behavior data of the product to be promoted, regional promotion attributes, and time-series data corresponding to historical transaction information are input into multiple pre-trained promotion attribute evaluation models. Each model outputs a corresponding evaluation attribute to be used. Based on the evaluation attribute to be used, the spatial simulation attribute corresponding to the effective promotion area, and the weight value of each evaluation attribute, the target evaluation attribute corresponding to the effective promotion area is determined. By employing multiple evaluation parameters, the promotional value of the product is considered from multiple dimensions, increasing the accuracy of product promotion area evaluation.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A flowchart illustrating a method for determining a product promotion area according to an embodiment of the present invention;

[0024] Figure 2 This is a schematic diagram of a target region selection method provided in an embodiment of the present invention;

[0025] Figure 3 A flowchart illustrating a method for determining a product promotion area according to an embodiment of the present invention;

[0026] Figure 4 A flowchart illustrating a method for determining a product promotion area according to an embodiment of the present invention;

[0027] Figure 5 This is a schematic diagram of a device for determining a product promotion area according to an embodiment of the present invention;

[0028] Figure 6 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] Figure 1 This invention provides a flowchart of a method for determining a product promotion area, applicable to scenarios where the promotion area corresponding to a product to be promoted is determined. The method can be executed by a device for determining a product promotion area, which can be implemented in hardware and / or software and can be configured in a computing device.

[0032] like Figure 1 As shown, the method includes:

[0033] S110. Determine at least one effective promotion area in the target area, and determine the regional promotion attributes of the effective promotion area based on the regional density of the effective promotion area and the surrounding environmental data of the effective promotion area.

[0034] The target area is a pre-defined region that is intended to be used as a potential promotional area. Typically, based on experience, a regional center, such as a core business district or landmark, is selected, and a preset regional range is set. The target area is selected based on the location of the regional center and the preset regional range. For example, Figure 2 This is a schematic diagram illustrating a target region selection method provided in an embodiment of the present invention. Figure 2As shown, first, the central point for the planned product promotion is selected, and a preset area range value, such as 5KM, is set. Based on the geographical coordinates of the area center and the preset area range value, the target area for the promotion activity is finally determined through geospatial calculation.

[0035] In this embodiment, an effective promotion area refers to a region suitable for effective product promotion, and a target area may contain multiple effective promotion areas. Area density is the number of effective promotion areas per unit area within the target area, representing the richness of effective promotion areas within the target area. Surrounding environment data includes information on buildings and residents surrounding the effective promotion area, representing the product demand and preferences within that effective promotion area. Area promotion attributes are qualitative descriptions of the duration characteristics within the effective promotion area, defining the demand preferences and consumption scenarios of the main customer group in that area.

[0036] Specifically, at least one effective promotion area is planned within the target region. The area density of the effective promotion area is calculated, and the surrounding environmental data of the effective promotion area is obtained. Based on the area density and the surrounding environmental data, the area promotion attributes of the effective promotion area are output.

[0037] Optionally, at least one effective promotion area within the target region is identified, including:

[0038] Obtain at least one optional promotion area within the target area; for at least one optional promotion area, determine the effectiveness evaluation attributes of the optional promotion area based on the user traffic of the optional promotion area in a first preset time period, the number of product stores selling the product to be promoted in the optional promotion area, the number of store types whose business hours exceed a preset time threshold in a second preset time period, and the plot ratio of the promotion area; determine at least one effective promotion area based on the effectiveness evaluation attributes of at least one optional promotion area.

[0039] The optional promotion area is a set of candidate promotion areas defined by geographical or business rules within the selected target area. These areas have not undergone feasibility assessment but possess promotional potential. The first preset time period is a statistical period used to evaluate regional customer traffic. This pre-set time period represents the customer traffic level of the optional promotion area during this period. The length of this event period can be flexibly defined according to actual analysis needs, such as the last 30 days or the previous quarter. User traffic is a quantitative estimate of the number of consumers entering the optional promotion area within the first preset time period. The number of product stores is the number of stores actually selling the promoted product within the optional promotion area. The second preset time period is a specific period during which customer traffic in the optional promotion area is significantly higher than normal levels. This time period is set based on empirical values ​​and represents the operational quality of the area during peak hours. The preset duration threshold is a pre-set boundary value for operating hours, representing a higher standard for operating hours. Store type refers to the type of store business in the optional promotion area, which may include restaurants, convenience stores, clothing stores, bookstores, etc. The plot ratio is the ratio of the total building area to the land area of ​​an area. In this embodiment, it is used to measure the space utilization intensity within the optional promotion area. The effectiveness assessment attribute is the commercial value assessment value used to determine whether the region is worthwhile to invest in promoting the product to be promoted.

[0040] In this embodiment, after obtaining the effectiveness evaluation attributes of the selectable promotion area, the attributes are judged, and a preset effectiveness evaluation threshold is set. When the effectiveness evaluation attribute of the area is greater than the effectiveness evaluation threshold, the selectable promotion area is considered an effective promotion area.

[0041] Specifically, at least one selectable promotion area within the target region is obtained. A first preset time period, a second preset time period, and a preset duration threshold are pre-set. Based on these values, the user traffic, the number of stores selling the product to be promoted, the number of store types with operating hours exceeding the preset duration threshold, and the plot ratio of the selectable promotion area are retrieved within the first preset time period. Based on the retrieved data related to the selectable promotion areas, the validity of each selectable promotion area is evaluated to determine if it is a valid promotion area for the product to be promoted. At least one validity evaluation attribute for the selectable promotion area is obtained and compared with a preset validity evaluation threshold to ultimately determine the valid promotion area.

[0042] For example, if the first preset time period is a quarter, the second preset time period is a weekend, and the preset duration threshold is 7 hours, then the following data is obtained: user traffic A in the previous quarter is 1000, the number of product stores selling the product to be promoted in the selectable promotion area is B is 50, the number of store types with operating hours exceeding 7 hours on weekends is C is 10, and the plot ratio of the selectable promotion area is D is 2. The effectiveness of this promotion area for the specific product is then evaluated. :

[0043]

[0044] The effectiveness evaluation value of the selectable promotion area is 1000. When the preset effectiveness evaluation threshold is 800, since the effectiveness evaluation attribute of this area is greater than the threshold, the selectable promotion area is finally determined to be an effective promotion area.

[0045] Optionally, based on the regional density of the effective promotion area and the surrounding environmental data of the effective promotion area, the regional promotion attributes of the effective promotion area are determined, including:

[0046] Based on the number of regions in at least one effective promotion area and the area of ​​the target region, the regional density of the target region is determined, and the regional density is used as the regional density corresponding to each effective promotion area in the target region; the user travel trajectory index, regional density, number of buildings corresponding to the effective promotion area, and number of communities located in the effective promotion area are obtained to determine the regional promotion attributes of the effective promotion area.

[0047] The process involves evaluating the effectiveness of all selectable promotion areas within the target region to obtain effectiveness evaluation attributes. Areas with effectiveness evaluation attributes exceeding a preset effectiveness evaluation threshold are designated as effective promotion areas, and the final number of effective promotion areas within the target region is determined as the region count. The region area is the total area of ​​the selected target region. The region density is the ratio of the number of effective promotion areas within the target region to the total area of ​​the target region. This density value describes the spatial concentration of effective promotion areas within the target region.

[0048] Meanwhile, the regional density of the target area is equivalent to the regional density corresponding to each effective promotion area within the target area. The user travel trajectory index represents the traffic level of users surrounding the effective promotion area. This index is derived by analyzing anonymous user location data and characterizes the activity level of the connection between the effective promotion area and surrounding areas, thus reflecting the area's attractiveness and transportation convenience. The number of buildings represents the total number of buildings within the effective promotion area. The higher this value, the more obstructing buildings there are in the area, reflecting the impact of obstructing buildings on the accessibility and visibility of the business district. The number of residential areas represents the number of residential areas within the effective promotion area, representing the level of local consumption demand within the effective promotion area. The higher this value, the higher the potential customer traffic and spending power of the effective promotion area. The regional promotion attribute is a quantitative value assessment value of the effective promotion area derived from multi-dimensional data analysis.

[0049] Specifically, the number of effective promotional areas within the target region and the area of ​​the target region are obtained. This allows for the calculation of the area density of the target region.

[0050]

[0051] Where W is the regional density of the effective promotion area, K is the number of effective promotion areas, and S is the area of ​​the target area.

[0052] This density is considered an overall attribute of the target area and is equally assigned to each effective promotion area within it. Furthermore, based on the user travel trajectory index, area density, number of buildings, and number of residential communities within the effective promotion area, the area promotion attributes of that effective promotion area are calculated:

[0053]

[0054] Where T is the user's travel trajectory index, M is the number of buildings, and N is the number of residential communities.

[0055] For example, if the number of effective promotion areas is 1000, the target area is 2,000,000 square meters, the user travel trajectory index is 100, the number of buildings is 10, and the number of residential communities is 20, the area density of the target area can be calculated as follows:

[0056]

[0057] This value indicates that there are 0.0005 effective promotion areas per square meter within the target area. After obtaining the area density, the regional promotion attributes of the effective promotion areas are further calculated:

[0058]

[0059] This value indicates that, after comprehensively considering regional density, user travel trajectories, number of buildings, and number of residential communities, the regional promotion attribute of the effective promotion area is 0.1.

[0060] S120. For at least one effective promotion area, input the historical transaction information, product attribute information, user behavior data of the product to be promoted, regional promotion attributes, and time series data corresponding to the historical transaction information into multiple pre-trained promotion attribute evaluation models, so that each promotion attribute evaluation model outputs the corresponding evaluation attributes to be used.

[0061] The promotional data includes: **Products to be promoted:** These are specific products that the company aims to increase brand awareness and sales in a targeted region and within a specific timeframe through marketing activities. **Historical transaction information:** This is a collection of past transaction records related to the products to be promoted, including but not limited to sales volume and sales revenue over a period of time. **Product attribute information:** This refers to data on the inherent characteristics of the products to be promoted, including but not limited to price, brand coefficient, and quality coefficient. **User behavior data:** This can be understood as the interaction between users within the effective promotion area and the products to be promoted. This interaction can be offline, such as visiting stores and using the products within the effective promotion area, or online, such as browsing, adding to favorites, or adding items to cart on a webpage within the physical area. **Time series data:** This includes time-related factor coefficients related to the products to be promoted, including but not limited to seasonal factors and holiday coefficients. **Evaluation attributes:** These are specific evaluation results or indicators used for subsequent promotion decisions, calculated by the promotion attribute evaluation model.

[0062] Specifically, acquire relevant information associated with at least one effective promotion area and the product to be promoted, including historical transaction information, product attribute information, user behavior data, regional promotion attributes, and pre-set time series data. Use this data as input training samples, feed it into multiple promotion attribute evaluation models, and output the corresponding evaluation attributes to be used.

[0063] Optionally, historical transaction information includes the historical sales volume and corresponding sales amount of the product to be promoted in the effective promotion area; product attribute information includes at least the product price, brand coefficient, and quality coefficient of the product to be promoted; user behavior data includes the frequency of users acquiring the product to be promoted in the effective promotion area; and time series data includes at least the seasonal coefficient.

[0064] Among these, historical sales volume refers to the actual number of products sold in the effective promotion area within a specific statistical period. Sales revenue refers to the total sales amount generated from historical sales volume within the specific statistical period. Product price is the unit price of the product sold in the effective promotion area. Brand coefficient is a value used to quantify the relative influence of a brand in the effective promotion area; this value is calculated through market research and model extrapolation, and a higher value indicates higher brand awareness. Quality coefficient is a numerical indicator used to quantify the quality level of the product itself; this value can be calculated through product evaluation and user ratings. Acquisition frequency is the average number of times users acquire the product per unit of time within the effective promotion area. Seasonal coefficient quantifies the impact of seasonal factors on product sales, representing the fluctuation rate of product sales volume relative to the average level in specific seasons such as spring, summer, autumn, and winter, or specific periods such as holidays.

[0065] Specifically, for at least one effective promotion area, historical transaction information, product attribute information, user behavior data, regional promotion attributes, and time-series data corresponding to historical transaction information need to be input into the promotion attribute evaluation model as historical sample data. Therefore, it is necessary to retrieve historical sales volume, corresponding sales revenue, product price of the product to be promoted, brand coefficient, quality coefficient, frequency of user acquisition of the product to be promoted, and time-series data such as seasonal coefficients within a specific statistical period in the effective promotion area.

[0066] S130. Based on the evaluation attributes to be used, the spatial simulation attributes corresponding to the effective promotion area, and the weight value corresponding to each evaluation attribute, determine the target evaluation attributes corresponding to the effective promotion area.

[0067] Among them, the spatial simulation attribute is used to characterize the effectiveness data of the effective promotion area.

[0068] In this embodiment, the spatial simulation attributes are calculated using a geospatial model. Weight values ​​are predefined numerical coefficients used to characterize the relative importance of their corresponding evaluation attributes. The target evaluation attribute represents the matching degree of the product to be promoted in the target area; this attribute is a score obtained after weighted fusion and spatial correction.

[0069] Specifically, the system acquires the evaluation attributes to be used from multiple promotion attribute evaluation models, and utilizes spatial dynamic characteristics to output the spatial simulation attributes corresponding to the effective promotion area. Based on the weight value corresponding to each evaluation attribute, the system performs a weighted fusion of each evaluation attribute and the spatial simulation attributes to output the target evaluation attribute corresponding to the effective promotion area.

[0070] For example, based on spatial simulation properties And the predictive adaptation attributes corresponding to the three promotion attribute evaluation models. , as well as Determine the target evaluation attributes :

[0071]

[0072] in, , , as well as For the weight parameters, satisfying .

[0073] S140. Based on the target evaluation attributes corresponding to at least one effective promotion area, determine at least one target promotion area corresponding to the product to be promoted.

[0074] The target promotion area is the region where the product to be promoted will be finally identified.

[0075] Specifically, the target promotion area for the promoted product is determined based on the target evaluation attributes corresponding to at least one effective promotion area obtained.

[0076] Optionally, based on the target evaluation attributes corresponding to at least one effective promotion area, at least one target promotion area corresponding to the product to be promoted is determined, including:

[0077] Based on the target evaluation attributes and preset evaluation attribute thresholds of at least one effective promotion area, at least one target effective promotion area is determined and designated as the target promotion area; or, based on the target evaluation attributes corresponding to at least one effective promotion area and preset promotion quantity thresholds, the target promotion area is determined from at least one effective promotion area.

[0078] The preset evaluation attribute threshold is the minimum standard for determining the effective promotion area of ​​the target, and the target promotion area is the effective promotion area where the product to be promoted will be deployed. The preset promotion quantity threshold is the maximum number of areas that the product to be promoted can cover.

[0079] Specifically, target promotion areas can be selected in two ways. The first method involves setting a preset evaluation attribute threshold. The target evaluation attributes of valid promotion areas within the target area are compared with the preset threshold. Valid promotion areas with target evaluation attributes greater than the preset threshold are selected as target valid promotion areas. There is no limit to the number of target valid promotion areas using this method. The second method involves setting a preset promotion quantity threshold. All valid promotion areas within the target area are sorted according to their target evaluation attributes. The areas with the highest ranking and a quantity equal to the preset promotion quantity threshold are selected as target promotion areas.

[0080] The technical solution provided by this invention determines at least one effective promotion area within a target region, and determines the regional promotion attributes of the effective promotion area based on its regional density and surrounding environmental data. This approach combines multiple reference factors to determine whether a potential promotion area is effective and its corresponding regional promotion attributes, overcoming the limitations of existing technologies that only consider geography, transportation, and product sales areas. Furthermore, for at least one effective promotion area, historical transaction information, product attribute information, user behavior data of the product to be promoted, regional promotion attributes, and time-series data corresponding to historical transaction information are input into multiple pre-trained promotion attribute evaluation models. Each model outputs a corresponding evaluation attribute to be used. Based on the evaluation attribute to be used, the spatial simulation attribute corresponding to the effective promotion area, and the weight value of each evaluation attribute, the target evaluation attribute corresponding to the effective promotion area is determined. By employing multiple evaluation parameters, the promotional value of the product is considered from multiple dimensions, increasing the accuracy of product promotion area evaluation.

[0081] Figure 3 This is a flowchart illustrating a method for determining a product promotion area according to an embodiment of the present invention. Based on the above embodiments, this embodiment further optimizes the target evaluation attributes after they are obtained. For example... Figure 3 As shown, the method includes:

[0082] S210. For at least one effective promotion area, the current effective promotion area is taken as the main effective promotion area, and the evaluation difference between the main effective promotion area and other effective promotion areas is determined.

[0083] In this context, the primary effective promotional region is the effective promotional region that is the focus in the current calculation round. In this embodiment, the algorithm sequentially sets each effective promotional region as the primary region and performs pairwise comparisons. Other effective promotional regions are all effective promotional regions within the target area other than the primary effective promotional region. The evaluation difference is the numerical value representing the difference between the primary effective promotional region and one of the other effective promotional regions in the optimal evaluation result.

[0084] Specifically, one of the at least one effective promotion area is designated as the primary effective promotion area, and the evaluation difference between the primary effective promotion area and other effective promotion areas is calculated.

[0085] For example, if the target area contains five effective promotion areas A1, A2, A3, A4, and A5, then A1 is designated as the primary effective promotion area, while A2, A3, A4, and A5 are the other effective promotion areas. Furthermore, the evaluation differences between A1 and A2, A3, A4, and A5 need to be calculated separately.

[0086] Optionally, the method also includes:

[0087] Determine the theoretical promotion order value and the inferred promotion order value based on the target evaluation attributes for all effective promotion areas in the target area. Then, based on the theoretical promotion order value and the inferred promotion order value corresponding to the effective promotion areas, determine the evaluation difference of the effective promotion areas.

[0088] The theoretical promotion order value is determined based on one or more of the following: regional attributes corresponding to the effective promotion area, historical transaction information, user behavior data, and time series data.

[0089] In this embodiment, the theoretical promotion order value is derived from prior knowledge and historical sample data corresponding to the effective promotion areas, representing the optimal order of the effective promotion areas. The inferred promotion order value is obtained by sorting the target evaluation attributes in descending order; for example, areas with high target evaluation attributes are inferred to be ranked first.

[0090] Specifically, the theoretical promotion order value of all effective promotion areas within the target area is determined based on information such as the regional attributes, transaction information, user behavior data, and time series data corresponding to the effective promotion areas. After the target attribute determination model calculates and outputs the target evaluation attributes of all effective promotion areas, they are sorted in descending order to obtain the theoretical promotion order value.

[0091] For example, based on the regional attributes and historical transaction information corresponding to the effective promotion areas, the theoretical promotion order of the five effective promotion areas within the target area is determined as A1, A5, A3, A4, and A2. The inferred promotion order determined based on the target evaluation attributes is A1, A2, A3, A4, and A5. Based on the above two promotion order values, the evaluation difference of the effective promotion areas is determined.

[0092] Optionally, the evaluation difference is determined based on the following method:

[0093] For any two effective promotion areas, the evaluation difference of the area combination is determined based on the theoretical promotion order value of the main effective promotion area, the inferred promotion order value of the two effective promotion areas, and the spatial simulation attributes corresponding to the two effective promotion areas.

[0094] The primary effective promotion area is the reference promotion area among the two effective promotion areas.

[0095] In this embodiment, a region combination can be understood as forming a combination of all effective promotional regions within the target region in pairs. The reference promotional region is the effective promotional region selected as the comparison benchmark in a region combination.

[0096] Specifically, the theoretical promotion order value, the inferred promotion order value, and the corresponding spatial simulation attributes of two effective promotion areas are obtained. The inferred promotion order values ​​of the two effective promotion areas are swapped, and the evaluation difference of this combination of areas is calculated.

[0097] For example, if the theoretical promotion order is A1, A5, A3, A4, and A2, and a value is assigned to the ranking position of each effective promotion area, with decreasing scores based on the ranking position, such as A1, ranked first, having a relevance score of 5, and similarly assigning relevance scores of 4, 3, 2, and 1 to the second through fifth ranked areas respectively. Calculate the theoretical promotion order value:

[0098]

[0099] Where k is the number of effective promotion areas. Let be the relevance score corresponding to the i-th effective promotion area. The theoretical promotion order value IdealDCG is calculated using the method described above.

[0100] Furthermore, effective promotional regions are ranked according to target evaluation attributes, such as A1, A2, A3, A4, and A5, and assigned decreasing scores based on their ranking position. That is, the predicted promotional order value for the first-ranked effective promotional region A1 is 5; similarly, the predicted promotional order value for the second-ranked effective promotional region A2 is 4, A3 is 3, A4 is 4, and A5 is 5. Based on the predicted promotional order values ​​of all effective promotional regions, the evaluation difference is calculated after the predicted promotional order values ​​of the main effective promotional region are exchanged with those of other effective promotional regions. :

[0101]

[0102] In this embodiment, The inferred promotion order value after the primary effective promotion area is swapped. This is the inferred promotion order value after exchanging with other effective promotion areas. The spatial simulation attributes corresponding to the main effective promotion area. Z represents the spatial simulation attribute corresponding to other effective promotion areas, where Z is a normalization constant. This can be understood as calculating the evaluation differences between A1 and A2, A3, A4, and A5 after A1 is designated as the primary effective promotion area. , , as well as .

[0103] S220. For any evaluation difference, based on the spatial simulation attributes of the main effective promotion area associated with the evaluation difference, the spatial simulation attributes of other effective promotion areas, and the evaluation difference, determine the first evaluation attribute of the main effective promotion area relative to other effective promotion areas.

[0104] The first evaluation attribute is the weighted attribute value of the target evaluation attribute ranking position of other effective promotion areas and the main effective promotion area from the perspective of the main effective promotion area.

[0105] Specifically, obtain all evaluation differences between the main effective promotion area and other effective promotion areas, as well as the spatial simulation attributes of the main effective promotion area and other effective promotion areas. Based on the above numerical calculations, calculate the first evaluation attribute of the main effective promotion area relative to other effective promotion areas.

[0106] For example, if the spatial simulation attributes of the main effective promotion area and other effective promotion areas are respectively and The evaluation difference after swapping the predicted promotion order values ​​of the main effective promotion area with those of other effective promotion areas. Based on the above data, we obtain The first evaluation attribute is obtained by summing the main effective promotion area with the attributes calculated separately from other effective promotion areas. :

[0107]

[0108] If A1 is the primary effective promotion region, then calculate the attribute values ​​for A1 when its predicted promotion order value is greater than the values ​​of the other four effective promotion regions. , , as well as The four attribute values ​​are then summed to obtain the first evaluation attribute.

[0109] S230. For any primary effective promotion area, determine the third evaluation attribute of the primary effective promotion area based on all the first evaluation attributes associated with the current primary effective promotion area, and the second evaluation attributes corresponding to other effective promotion areas when they are associated with the primary effective promotion area.

[0110] The second evaluation attribute is calculated from the perspective of other effective promotion areas, determining whether the predicted promotion order value of the current main effective promotion area is higher than the predicted order value of the previous main effective promotion area. These predicted order values ​​are then summed. In other words, the first evaluation attribute indicates that the predicted promotion order value of the current main effective promotion area is higher than the sum of the evaluation values ​​of other effective promotion areas. The second evaluation attribute indicates that when all other effective promotion areas are considered as main effective promotion areas, the predicted promotion order value is higher than the sum of the evaluation values ​​of the current main effective promotion area. The third evaluation attribute is the final target evaluation attribute of this main effective promotion area. This attribute is calculated from the first and second evaluation attributes.

[0111] Specifically, obtain the calculated first evaluation attribute. And calculate the second evaluation attribute calculated when other effective promotions are used as the main effective promotion area, compared with the current main effective promotion area. The second evaluation attribute is obtained based on the spatial simulation attributes of two effective promotion areas and the evaluation difference. The third evaluation attribute is obtained by subtracting the first and second evaluation attributes. :

[0112]

[0113] For example, if A1 is the primary effective promotion area, after obtaining the first evaluation attribute, other effective promotion areas A2, A3, A4, and A5 are sequentially designated as primary effective promotion areas. That is, the attribute value of A2, A3, A4, and A5 is calculated to be greater than that of A1. , , as well as The above values ​​are then summed to obtain the second evaluation attribute. Furthermore, the difference between the first and second evaluation attributes is calculated to obtain the third evaluation attribute. .

[0114] S240. Based on the third evaluation attribute corresponding to each main effective promotion area, update the target evaluation attribute of the corresponding effective promotion area.

[0115] Specifically, the optimized third evaluation attribute is directly used as the target evaluation attribute for the corresponding effective promotion area. Finally, based on the updated target evaluation attribute, the target promotion area corresponding to the promoted product is determined.

[0116] The technical solution provided by this invention uses the current effective promotion area as the primary effective promotion area and determines the evaluation difference between the primary effective promotion area and other effective promotion areas. For any evaluation difference, a first evaluation attribute of the primary effective promotion area relative to other effective promotion areas is determined based on the spatial simulation attributes of the primary effective promotion area associated with the evaluation difference, the spatial simulation attributes of other effective promotion areas, and the evaluation difference itself. By performing spatial simulation with other effective promotion areas, the correlation between the primary effective promotion area and other effective promotion areas is introduced, improving the evaluation accuracy. Further, a second evaluation attribute is determined corresponding to the primary effective promotion area when other effective promotion areas are used as the primary effective promotion area. For any primary effective promotion area, a third evaluation attribute is determined based on all the first evaluation attributes associated with the current primary effective promotion area and the second evaluation attributes corresponding to the primary effective promotion area when other effective promotion areas are used as the primary effective promotion area. Finally, the target evaluation attribute of the corresponding effective promotion area is updated based on the third evaluation attribute corresponding to each primary effective promotion area. By optimizing the target evaluation attribute, the accuracy and relevance of promotion information are improved, the time for information filtering is reduced, and the efficiency of information acquisition is increased.

[0117] Figure 4 This is a flowchart illustrating a method for determining a product promotion area according to an embodiment of the present invention. Building upon the previous embodiments, this embodiment will now provide a detailed description of how to determine multiple promotion attribute evaluation models and the spatial simulation model used to determine spatial simulation attributes. Figure 4 As shown, the method includes:

[0118] S310. Obtain historical sample data.

[0119] The historical sample data includes sample input data and the theoretical fit attributes of the sample input data relative to the effective promotion area.

[0120] In this embodiment, historical sample data is a standardized set of data records used for training and validating the model, with known results. Each sample contains sample input data and a theoretical fit attribute. The sample input data consists of all feature data of the effective promotion area within a specific period, including but not limited to historical transaction information, product attribute information, user behavior data, and time series data. The theoretical fit attribute is the optimal promotion result corresponding to the sample input data. The predicted fit attribute is the estimated result calculated based on the input sample input data, and this attribute is output by the promotion attribute evaluation model.

[0121] Specifically, obtain historical sample data for training multiple promotion attribute evaluation models. Each sample in the historical sample data must contain sample input data and corresponding theoretical adaptation attributes.

[0122] For example, historical sample data is obtained, which includes sample data x and theoretical fit attribute y.

[0123] S320. For historical sample data, input the sample input data from the historical sample data into each promotion attribute evaluation model so that each promotion attribute evaluation model outputs the prediction adaptation attribute corresponding to the sample input data.

[0124] The predicted fit attribute is the estimated result calculated based on the input sample data, and this attribute is output by the promotion attribute evaluation model. The promotion attribute evaluation model is a set of models used to predict the fit attribute; these evaluation models are of the same type but have different parameter initializations.

[0125] Specifically, the sample input data from the obtained historical sample data is input into the promotion attribute evaluation model, and a predicted adaptation attribute corresponding to the sample input data is output.

[0126] For example, three promotion attribute evaluation models, M1, M2, and M3, are constructed. These three models are of the same type but have different parameter initializations. Historical sample data x is input into M1, M2, and M3 respectively to obtain the predicted fitting attributes. , as well as :

[0127]

[0128]

[0129]

[0130] S330. Based on the predicted fit attribute and theoretical fit attribute output by each promotion attribute evaluation model, determine the residual value of historical sample data under each promotion attribute evaluation model.

[0131] The residual value is the difference between the theoretically fit attribute and the predicted fit attribute during the model training process. This value quantifies the magnitude of the error in the model's current prediction. The larger the value, the less accurate the model's prediction on that sample.

[0132] Specifically, the predicted fit attributes output by the promotion attribute evaluation model are obtained. The difference between these predicted fit attributes and the theoretical fit attributes corresponding to the input data in the historical sample data is calculated to obtain the residual values ​​of the historical sample data under each promotion attribute evaluation model.

[0133] For example, after obtaining the predicted fit attribute, the difference between the predicted fit attribute and the theoretical fit attribute y is calculated to obtain the residual value of the historical sample data under each promotion attribute evaluation model. , as well as :

[0134]

[0135]

[0136]

[0137] S340. Based on the residual value, the objective function corresponding to the effective promotion area in the spatial simulation model, and the spatial attribute information corresponding to the adjacent grids in the spatial grid where the effective promotion area is located, determine the state parameters corresponding to the effective promotion area.

[0138] The objective function is a mathematical formula used to define the optimization of the spatial simulation model. It is used to update and propagate the predicted adaptation attributes of the business district. The spatial grid is a two-dimensional grid constructed for the effective promotion areas within the target region. This grid represents the entire target region, and each grid cell is defined as an effective promotion area. The number of grid cells is consistent with the number of effective promotion areas in the target region. The spatial attribute information corresponding to a grid cell is the predicted adaptation attribute corresponding to that effective promotion area. Adjacent grids are the grids corresponding to the effective promotion areas adjacent to the target region. The spatial attribute information corresponding to adjacent grids is the spatial attribute information corresponding to other adjacent effective promotion areas. The state parameter characterizes the state of the target region within the spatial grid; this value is calculated from the objective function and the spatial attribute information.

[0139] Specifically, a spatial grid corresponding to the target region is constructed. This spatial grid represents the entire target region, and each grid cell corresponds to an effective promotion region. The spatial attribute information of each grid cell corresponds to the prediction adaptation attribute of the effective promotion region. The spatial attribute information of the spatial grid cell containing the effective promotion region is obtained. Based on the residual value, the target model in the spatial simulation model corresponding to the effective promotion region, and the spatial attribute information of the adjacent grid cells, the state parameters corresponding to the effective promotion region are obtained.

[0140] For example, a spatial grid is constructed based on the target region, and the target region is gridded, dividing each effective promotion area in the target region into a grid. If the target region has 5 effective promotion areas, then there are a total of 5 grids in the spatial grid, and the predicted adaptation attributes corresponding to the effective promotion areas are used as the spatial attribute information of the corresponding grid.

[0141] Furthermore, based on the residual values, the objective function corresponding to the effective promotion area in the spatial simulation model, and the spatial attribute information corresponding to the adjacent grids in the spatial grid where the effective promotion area is located, the state parameters corresponding to the effective promotion area are determined:

[0142]

[0143] Where f is the objective function corresponding to the effective promotion region. This objective function is a local rule function used to define the state of the effective promotion region in the next iteration, and how to calculate it based on its own and the current states of its neighboring grids. For those located in the grid The effective promotion area will be in the next round. The state of the next iteration. This state is the final comprehensive indicator that reflects its spatial properties. This refers to the spatial attribute information of the same effective promotion area at the t-th iteration. as well as These represent the states of the adjacent grids in other directions at the t-th iteration. t is the number of iterations in the spatial simulation. The total number of iterations is determined by the area of ​​the spatial grid; generally, the larger the area, the more iterations are needed to ensure that state changes can propagate stably throughout the entire region.

[0144] This can be understood as assigning initial spatial attribute information to each grid cell in the spatial grid before starting the iteration. Once the total number of iterations T is determined, a loop from t=0 to t=T-1 begins: traversing all grid cells. For each grid cell, the current state values ​​of all adjacent grid cells are collected, such as... as well as Based on the current state of the grid and the current states of its neighboring grids, the objective function is used to calculate the new state of the grid in the next iteration.

[0145] S350. Determine the target prediction attribute based on the state parameters and the prediction fit attribute corresponding to the evaluation model for each promotion attribute.

[0146] The target prediction attribute is the predicted value after correction of the state parameters. This attribute is calculated by comprehensively considering the prediction adaptation attributes and state parameters corresponding to all promotion attribute evaluation models.

[0147] Specifically, obtain the predicted adaptation attribute corresponding to each promotion attribute evaluation model, and determine the optimized target predicted attribute based on the state parameters and the predicted adaptation attribute.

[0148] S360. Based on the target prediction attribute and the theoretical adaptation attribute, the model parameters in the evaluation model of each promotion attribute are modified respectively, and the model parameters obtained when the loss function in each model converges are applied to the model parameters of the evaluation attribute and spatial simulation attribute to be used.

[0149] In the promotion attribute evaluation model, the model parameters are the adjustable weights or coefficients within the model. Each promotion attribute evaluation model continuously refines its parameters by learning from historical data, aiming to make the target predicted attribute output by the model infinitely close to the theoretically suitable attribute. The loss function is a mathematical function used to quantify the overall performance of the promotion attribute evaluation model. Loss function convergence is the process of iteratively refining the model parameters until the value of the loss function decreases to a minimum and stable state.

[0150] Specifically, the model parameters in the model are corrected using the target prediction attributes and theoretical fit attributes. Once the loss function in the model converges and stabilizes, the obtained model parameters are the model parameters of the evaluation attributes and spatial simulation attributes to be used.

[0151] The technical solution provided in this invention involves acquiring historical sample data. For this historical sample data, the sample input data is input into each promotion attribute evaluation model, so that each model outputs a predicted fit attribute corresponding to the sample input data. Based on the predicted fit attribute and theoretical fit attribute output by each model, the residual value of the historical sample data under each model is determined. Further, based on the residual value, the objective function corresponding to the effective promotion area in the spatial simulation model, and the spatial attribute information corresponding to adjacent grids in the spatial grid where the effective promotion area is located, the state parameters corresponding to the effective promotion area are determined. Based on the state parameters and the predicted fit attribute corresponding to each model, the target predicted attribute is determined. Finally, based on the target predicted attribute and theoretical fit attribute, the model parameters in each model are corrected, and the model parameters obtained when the loss function in each model converges are applied to the model parameters for determining the evaluation attribute and spatial simulation attribute to be used. By employing multiple evaluation parameters and considering the overall picture, the accuracy of product promotion area evaluation is increased.

[0152] Figure 5 This is a schematic diagram of a device for determining a product promotion area according to an embodiment of the present invention, as shown below. Figure 5 As shown, the device includes: a regional promotion attribute determination module 410, a target evaluation attribute output module 420, a target evaluation attribute determination module 430, and a target promotion area determination module 440.

[0153] The system includes the following modules: A regional promotion attribute determination module 410, which determines at least one effective promotion area within the target area and determines the regional promotion attribute of the effective promotion area based on its regional density and surrounding environmental data; a pending evaluation attribute output module 420, which, for at least one effective promotion area, inputs historical transaction information, product attribute information, user behavior data for acquiring the product, regional promotion attributes, and time series data corresponding to historical transaction information into multiple pre-trained promotion attribute evaluation models, so that each model outputs a corresponding pending evaluation attribute; a target evaluation attribute determination module 430, which determines the target evaluation attribute corresponding to the effective promotion area based on the pending evaluation attribute, the spatial simulation attribute corresponding to the effective promotion area, and the weight value corresponding to each evaluation attribute; wherein the spatial simulation attribute is used to characterize the effectiveness data of the effective promotion area; and a target promotion area determination module 440, which determines at least one target promotion area corresponding to the product to be promoted based on the target evaluation attribute corresponding to at least one effective promotion area.

[0154] This invention identifies at least one effective promotion area within a target region and determines its regional promotion attributes based on the area density and surrounding environmental data of the effective promotion area. This approach combines multiple reference factors to determine whether a potential promotion area is effective and its corresponding regional promotion attributes, overcoming the limitations of existing technologies that only consider geography, transportation, and product sales areas. Furthermore, for at least one effective promotion area, historical transaction information, product attribute information, user behavior data of the product to be promoted, regional promotion attributes, and time-series data corresponding to historical transaction information are input into multiple pre-trained promotion attribute evaluation models. Each model outputs a corresponding evaluation attribute to be used. Based on the evaluation attribute to be used, the spatial simulation attribute corresponding to the effective promotion area, and the weight value of each evaluation attribute, the target evaluation attribute corresponding to the effective promotion area is determined. By employing multiple evaluation parameters, the promotional value of the product is considered from multiple dimensions, increasing the accuracy of product promotion area evaluation.

[0155] Based on the above technical solutions, the regional promotion attribute determination module includes:

[0156] The optional promotion area acquisition unit is used to acquire at least one optional promotion area within the target area;

[0157] The effectiveness evaluation attribute determination unit is used to determine the effectiveness evaluation attributes of at least one selectable promotion area based on the user traffic of the selectable promotion area in a first preset time period, the number of product stores selling the products to be promoted in the selectable promotion area, the number of store types whose business hours exceed a preset time threshold in a second preset time period, and the plot ratio of the selectable promotion area.

[0158] The effective promotion area determination unit is used to determine at least one effective promotion area based on the effectiveness evaluation attributes of at least one selectable promotion area.

[0159] Based on the above technical solutions, the regional promotion attribute determination module includes:

[0160] The regional density determination unit is used to determine the regional density of the target area based on the number of regions of at least one effective promotion area and the area of ​​the target area, and to use the regional density as the regional density corresponding to each effective promotion area in the target area.

[0161] The regional promotion attribute determination unit is used to obtain the user travel trajectory index, regional density, number of buildings corresponding to the effective promotion area, and number of communities located in the effective promotion area to determine the regional promotion attributes of the effective promotion area.

[0162] Based on the above technical solutions, the evaluation attribute output module to be used includes: historical transaction information, including the historical sales volume and corresponding sales amount of the product to be promoted in the effective promotion area; product attribute information, including at least the product price, brand coefficient, and quality coefficient of the product to be promoted; user behavior data, including the frequency of users obtaining the product to be promoted in the effective promotion area; and time series data, including at least the seasonal coefficient.

[0163] Based on the above technical solutions, the target evaluation attribute determination module includes:

[0164] The historical sample data acquisition unit is used to acquire historical sample data, which includes sample input data and the theoretical adaptation attributes of the sample input data relative to the effective promotion area.

[0165] The prediction and adaptation attribute output unit is used to input the sample input data from the historical sample data into each promotion attribute evaluation model, so that each promotion attribute evaluation model outputs a prediction and adaptation attribute corresponding to the sample input data.

[0166] The residual value determination unit is used to determine the residual value of historical sample data under each promotion attribute evaluation model based on the predicted fit attribute and theoretical fit attribute output by each promotion attribute evaluation model.

[0167] The state parameter determination unit is used to determine the state parameters corresponding to the effective promotion area based on the residual value, the objective function corresponding to the effective promotion area in the spatial simulation model, and the spatial attribute information corresponding to the adjacent grids in the spatial grid where the effective promotion area is located.

[0168] The target prediction attribute determination unit is used to determine the target prediction attribute based on the state parameters and the prediction fit attribute corresponding to the evaluation model for each promotion attribute.

[0169] The model parameter determination unit is used to modify the model parameters in the evaluation model of each generalization attribute according to the target prediction attribute and the theoretical adaptation attribute, and to apply the model parameters obtained when the loss function in each model converges to determine the model parameters of the evaluation attribute and the spatial simulation attribute to be used.

[0170] Based on the above technical solutions, the target evaluation attribute determination module also includes:

[0171] The evaluation difference determination unit is used to determine the evaluation difference between the current effective promotion area and other effective promotion areas for at least one effective promotion area.

[0172] The first evaluation attribute determination unit is used to determine, for any evaluation difference, the first evaluation attribute of the main effective promotion area relative to other effective promotion areas based on the spatial simulation attribute of the main effective promotion area associated with the evaluation difference, the spatial simulation attribute of other effective promotion areas, and the evaluation difference.

[0173] The third evaluation attribute determination unit is used to determine the third evaluation attribute of any main effective promotion area based on all the first evaluation attributes associated with the current main effective promotion area, as well as the second evaluation attributes associated with other effective promotion areas when they are associated with the main effective promotion area.

[0174] The target evaluation attribute update unit is used to update the target evaluation attribute of the corresponding effective promotion area based on the third evaluation attribute corresponding to each main effective promotion area.

[0175] Based on the above technical solutions, the evaluation difference determination unit also includes:

[0176] The evaluation difference determination subunit is used to determine the theoretical promotion order value and the inferred promotion order value based on the target evaluation attributes for all effective promotion areas in the target region. Based on the theoretical and inferred promotion order values ​​corresponding to the effective promotion areas, the evaluation difference of the effective promotion areas is determined. The theoretical promotion order value is determined based on one or more of the following: regional attributes corresponding to the effective promotion areas, historical transaction information, user behavior data, and time series data.

[0177] Based on the above technical solutions, optionally, the evaluation difference is determined in the following way: for any two effective promotion areas, the evaluation difference of the area combination is determined according to the theoretical promotion order value of the main effective promotion area, the inferred promotion order value of the two effective promotion areas, and the spatial simulation attributes corresponding to the two effective promotion areas.

[0178] Based on the above technical solutions, the target promotion area determination module includes:

[0179] The target promotion area determination unit is used to determine at least one target effective promotion area based on the target evaluation attributes and preset evaluation attribute thresholds of the at least one effective promotion area, and to use the target effective promotion area as the target promotion area; or, to determine the target promotion area from the at least one effective promotion area based on the target evaluation attributes and preset promotion quantity thresholds corresponding to the at least one effective promotion area.

[0180] The apparatus for determining a product promotion area provided in the embodiments of the present invention can execute a method for determining a product promotion area provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0181] Figure 6 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0182] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0183] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0184] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for determining product promotion areas.

[0185] In some embodiments, the method for determining a product promotion area may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for determining a product promotion area described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the method for determining a product promotion area by any other suitable means (e.g., by means of firmware).

[0186] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0187] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0188] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0189] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0190] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0191] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0192] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0193] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining a product promotion area, characterized in that, include: Identify at least one effective promotion area within the target area, and determine the regional promotion attributes of the effective promotion area based on the regional density of the effective promotion area and the surrounding environmental data of the effective promotion area; For the at least one effective promotion area, the historical transaction information, product attribute information, user behavior data of the product to be promoted, regional promotion attributes, and time series data corresponding to the historical transaction information are input into multiple pre-trained promotion attribute evaluation models, so that each promotion attribute evaluation model outputs the corresponding evaluation attribute to be used. Based on the evaluation attributes to be used, the spatial simulation attributes corresponding to the effective promotion area, and the weight values ​​corresponding to each evaluation attribute, the target evaluation attributes corresponding to the effective promotion area are determined; wherein, the spatial simulation attributes are used to characterize the effectiveness data of the effective promotion area; Based on the target evaluation attributes corresponding to the at least one effective promotion area, at least one target promotion area corresponding to the product to be promoted is determined.

2. The method according to claim 1, characterized in that, The determination of at least one effective promotion area within the target area includes: Obtain at least one selectable promotion area within the target region; For the at least one optional promotion area, the effectiveness evaluation attributes of the optional promotion area are determined based on the user traffic of the optional promotion area in a first preset time period, the number of product stores selling the product to be promoted in the optional promotion area, the number of store types with operating hours exceeding a preset time threshold in a second preset time period, and the plot ratio of the promotion area. Based on the effectiveness evaluation attributes of the at least one selectable promotion area, at least one effective promotion area is determined.

3. The method according to claim 1, characterized in that, The step of determining the regional promotion attributes of the effective promotion area based on the regional density of the effective promotion area and the surrounding environmental data of the effective promotion area includes: Based on the number of regions in the at least one effective promotion region and the area of ​​the target region, the region density of the target region is determined, and the region density is used as the region density corresponding to each effective promotion region in the target region; The user travel trajectory index, the regional density, the number of buildings and the number of residential communities in the effective promotion area are obtained to determine the regional promotion attributes of the effective promotion area.

4. The method according to claim 1, characterized in that, The historical transaction information includes the historical sales volume and corresponding sales amount of the product to be promoted in the effective promotion area; the product attribute information includes at least the product price, brand coefficient, and quality coefficient of the product to be promoted; the user behavior data includes the frequency with which users obtain the product to be promoted in the effective promotion area; and the time series data includes at least the seasonal coefficient.

5. The method according to claim 1, characterized in that, The multiple promotion attribute evaluation models and the spatial simulation model used to determine spatial simulation attributes were determined based on the following method: Obtain historical sample data, wherein the historical sample data includes sample input data and the theoretical adaptation attributes of the sample input data relative to the effective promotion area; For the historical sample data, the sample input data in the historical sample data is respectively input into each promotion attribute evaluation model, so that each promotion attribute evaluation model outputs the predicted adaptation attribute corresponding to the sample input data; Based on the predicted fit attribute and theoretical fit attribute output by each promotion attribute evaluation model, the residual value of the historical sample data under each promotion attribute evaluation model is determined. Based on the residual value, the objective function corresponding to the effective promotion area in the spatial simulation model, and the spatial attribute information corresponding to the adjacent grids in the spatial grid where the effective promotion area is located, the state parameters corresponding to the effective promotion area are determined. Based on the state parameters and the prediction fit attribute corresponding to each promotion attribute evaluation model, the target prediction attribute is determined; Based on the target prediction attribute and the theoretical adaptation attribute, the model parameters in the evaluation model of each promotion attribute are modified respectively, and the model parameters obtained when the loss function in each model converges are applied to the model parameters of the evaluation attribute and spatial simulation attribute to be used.

6. The method according to claim 1, characterized in that, After obtaining the target evaluation attribute, the method further includes: For the at least one effective promotion area, the current effective promotion area is taken as the main effective promotion area, and the evaluation difference of the main effective promotion area relative to other effective promotion areas is determined. For any evaluation difference, a first evaluation attribute of the main effective promotion area relative to other effective promotion areas is determined based on the spatial simulation attribute of the main effective promotion area associated with the evaluation difference, the spatial simulation attribute of other effective promotion areas, and the evaluation difference. For any primary effective promotion area, the third evaluation attribute of the primary effective promotion area is determined based on all the first evaluation attributes associated with the current primary effective promotion area, and the second evaluation attributes corresponding to other effective promotion areas when they are associated with the primary effective promotion area. Based on the third evaluation attribute corresponding to each main effective promotion area, update the target evaluation attribute of the corresponding effective promotion area.

7. The method according to claim 6, characterized in that, The method further includes: Determine the theoretical promotion order value and the inferred promotion order value based on the target evaluation attribute for all effective promotion areas in the target area, and then determine the evaluation difference of the effective promotion area based on the theoretical promotion order value and the inferred promotion order value corresponding to the effective promotion area. The theoretical promotion order value is determined based on one or more of the following: regional attributes, historical transaction information, user behavior data, and time series data corresponding to the effective promotion area.

8. The method according to claim 7, characterized in that, The assessment difference was determined based on the following method: For any two effective promotion areas, the evaluation difference of the area combination is determined based on the theoretical promotion order value of the main effective promotion area, the inferred promotion order value of the two effective promotion areas, and the spatial simulation attributes corresponding to the two effective promotion areas. The primary effective promotion area is the reference promotion area among the two effective promotion areas.

9. The method according to claim 1, characterized in that, The step of determining at least one target promotion area corresponding to the product to be promoted based on the target evaluation attributes corresponding to the at least one effective promotion area includes: Based on the target evaluation attributes and preset evaluation attribute thresholds of the at least one effective promotion area, at least one target effective promotion area is determined, and the target effective promotion area is used as the target promotion area; or, Based on the target evaluation attributes corresponding to the at least one effective promotion area and the preset promotion quantity threshold, the target promotion area is determined from the at least one effective promotion area.

10. An apparatus for determining a product promotion area, characterized in that, include: The regional promotion attribute determination module is used to determine at least one effective promotion area in the target area, and to determine the regional promotion attribute of the effective promotion area based on the regional density of the effective promotion area and the surrounding environmental data of the effective promotion area. The evaluation attribute output module is used to input the historical transaction information, product attribute information, user behavior data of the product to be promoted, regional promotion attributes, and time series data corresponding to the historical transaction information into multiple pre-trained promotion attribute evaluation models for the at least one effective promotion area, so that each promotion attribute evaluation model outputs the corresponding evaluation attribute to be used. The target evaluation attribute determination module is used to determine the target evaluation attribute corresponding to the effective promotion area based on the evaluation attribute to be used, the spatial simulation attribute corresponding to the effective promotion area, and the weight value corresponding to each evaluation attribute; wherein, the spatial simulation attribute is used to characterize the effectiveness data of the effective promotion area; The target promotion area determination module is used to determine at least one target promotion area corresponding to the product to be promoted based on the target evaluation attributes corresponding to the at least one effective promotion area.

11. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, which enables the at least one processor to perform the method for determining a product promotion area as described in any one of claims 1-9.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for determining a product promotion area as described in any one of claims 1-9.

Images