Methods, apparatus, equipment and media for selecting agricultural regions for business promotion

Abstract

This application discloses a method, apparatus, equipment, and medium for selecting agricultural regions for business promotion, belonging to the field of data processing. The method includes: generating a promotion influence factor matrix based on multiple business promotion influence factors for various agricultural regions; obtaining an optimal influence factor vector and a worst influence factor vector based on the promotion influence factor matrix and the advantageous and disadvantageous change directions of the business promotion influence factors; obtaining business promotion adaptation parameters based on the promotion influence factor vector, optimal influence factor vector, and worst influence factor vector for each agricultural region; and selecting the Q agricultural regions with the highest business promotion adaptation degree represented by the business promotion adaptation parameters as business promotion regions. The embodiments of this application improve the accuracy of selecting agricultural regions for business promotion.

Description

Technical Field

[0001] This application belongs to the field of data processing, and in particular relates to a method, apparatus, equipment and medium for selecting agricultural business promotion areas. Background Technology

[0002] With the promotion of the rural revitalization strategy, there is an urgent need to develop agriculture-related financial services in predominantly agricultural areas to advance the strategy. To promote these services to suitable regions, preliminary research is necessary. Specifically, professional personnel can be dispatched to conduct on-site visits to gather information relevant to the promotion. Based on this information, a manual evaluation of the source region is then conducted to determine if it is a suitable area for the promotion. However, due to the significant subjective element of manual visits and evaluations, the personal factors of the staff involved can greatly influence the selection of agricultural regions for promotion, resulting in lower accuracy in this selection process. Summary of the Invention

[0003] This application provides a method, apparatus, equipment, and medium for selecting agricultural areas for business promotion, which can improve the accuracy of selecting agricultural areas for business promotion.

[0004] In a first aspect, embodiments of this application provide a method for selecting agricultural regions for business promotion, comprising: generating a promotion influence factor matrix based on multiple business promotion influence factors for multiple agricultural regions, wherein the promotion influence factor matrix includes multiple first elements, each first element representing a business promotion influence factor for an agricultural region; obtaining an optimal influence factor vector and a worst influence factor vector based on the promotion influence factor matrix and the advantageous and disadvantageous change directions of the business promotion influence factors, wherein the optimal influence factor vector includes the first element of each business promotion influence factor in multiple agricultural regions that best matches the advantageous change direction, and the worst influence factor vector includes the first element of each business promotion influence factor in multiple agricultural regions that best matches the disadvantageous change direction; obtaining business promotion adaptation parameters based on the promotion influence factor vector, optimal influence factor vector, and worst influence factor vector of each agricultural region, wherein the promotion influence factor vector of an agricultural region includes the first element corresponding to each business promotion influence factor in the agricultural region; and selecting the Q agricultural regions with the highest business promotion adaptation degree represented by the business promotion adaptation parameters as business promotion regions, where Q is a positive integer.

[0005] Secondly, embodiments of this application provide a device for selecting agricultural regions for business promotion, comprising: a matrix generation module, used to generate a promotion influence factor matrix based on multiple business promotion influence factors of multiple agricultural regions, the promotion influence factor matrix including multiple first elements, each first element representing a business promotion influence factor of an agricultural region; a target vector generation module, used to obtain an optimal influence factor vector and a worst influence factor vector based on the promotion influence factor matrix and the advantageous and disadvantageous change directions of the business promotion influence factors, the optimal influence factor vector including the first element of each business promotion influence factor of multiple agricultural regions that best conforms to the advantageous change direction, and the worst influence factor vector including the first element of each business promotion influence factor of multiple agricultural regions that best conforms to the disadvantageous change direction; an adaptation parameter calculation module, used to obtain business promotion adaptation parameters based on the promotion influence factor vector, optimal influence factor vector, and worst influence factor vector of each agricultural region, the promotion influence factor vector of the agricultural region including the first element corresponding to each business promotion influence factor of the agricultural region; and a region selection module, used to select the Q agricultural regions with the highest business promotion adaptation degree represented by the business promotion adaptation parameters as business promotion regions, where Q is a positive integer.

[0006] Thirdly, embodiments of this application provide a device for selecting agricultural promotion areas, including: a processor and a memory storing computer program instructions; the processor executes the computer program instructions to implement the method for selecting agricultural promotion areas in the first aspect.

[0007] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the method for selecting agricultural promotion areas in the first aspect.

[0008] This application provides a method, apparatus, equipment, and medium for selecting agricultural regions for business promotion. Based on the promotion influence factor matrix and the advantageous and disadvantageous change directions of the business promotion influence factors, the optimal influence factor vector and the worst influence factor vector can be obtained. The optimal influence factor vector is the vector of business promotion influence factors most suitable for the promotion business, and the worst influence factor vector is the vector of business promotion influence factors least suitable for the promotion business. Through the promotion influence factor vector, optimal influence factor vector, and worst influence factor vector for each agricultural region, a business promotion suitability parameter representing the business promotion suitability of each agricultural region is obtained. Agricultural regions with higher business promotion suitability are selected from multiple agricultural regions as business promotion regions. Since the business promotion suitability parameter representing the business promotion suitability is obtained based on the optimal and worst influence factor vectors, taking into account both the suitability and unsuitability of the promotion business, the business promotion suitability parameter is more accurate, thereby improving the accuracy of selecting agricultural regions for the promotion business. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A flowchart is provided for one embodiment of this application regarding a method for selecting agricultural areas for business promotion;

[0011] Figure 2 A flowchart illustrating a method for selecting agricultural promotion areas according to another embodiment of this application;

[0012] Figure 3 A flowchart illustrating a method for selecting agricultural promotion areas in accordance with yet another embodiment of this application;

[0013] Figure 4 A schematic diagram of the structure of a device for selecting agricultural promotion areas according to an embodiment of this application;

[0014] Figure 5 A schematic diagram of the structure of a business promotion agricultural area selection device provided in an embodiment of this application. Detailed Implementation

[0015] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0016] With the promotion of the rural revitalization strategy, there is an urgent need to develop agriculture-related financial services in predominantly agricultural areas to advance the strategy. To promote these services to suitable regions, preliminary research is necessary. Professional personnel can be dispatched to conduct on-site visits to gather information relevant to the promotion. Based on this information, the personnel should manually evaluate whether the source region is suitable for the promotion. However, due to the significant subjective element of manual visits and evaluations, the personal factors of the staff involved can greatly influence the selection of agricultural regions for promotion, resulting in low accuracy. Furthermore, because predominantly agricultural regions are widely distributed, manual visits and evaluations are very time-consuming, further reducing the efficiency of selecting suitable agricultural regions.

[0017] This application provides a method, apparatus, equipment, and medium for selecting agricultural regions for business promotion. It can acquire multiple business promotion influencing factors from multiple agricultural regions and generate a promotion influencing factor matrix. Based on the direction of change of the advantages and disadvantages of the business promotion influencing factors, the business promotion suitability of each agricultural region is obtained. This business promotion suitability is obtained by referring to both the positive and negative aspects of the suitability of the promotion business, resulting in higher accuracy. Business promotion is carried out in the selected agricultural regions with higher business promotion suitability from multiple agricultural regions, improving the accuracy of agricultural region selection for promotion. Furthermore, the selection of agricultural regions for business promotion in this application can reduce or even eliminate manual intervention, significantly improving the efficiency of agricultural region selection for promotion.

[0018] The following sections will explain the methods, devices, equipment, and media for selecting agricultural areas for business promotion.

[0019] This application provides a method for selecting agricultural areas for business promotion, which can be applied to scenarios where agricultural areas are selected for business promotion. This method can be executed by a device or equipment for selecting agricultural areas for business promotion, and is not limited thereto. Figure 1 A flowchart illustrating a method for selecting agricultural promotion areas according to an embodiment of this application is provided. Figure 1As shown, the method for selecting agricultural promotion areas may include steps S101 to S104.

[0020] In step S101, a promotion impact factor matrix is ​​generated based on various business promotion impact factors in multiple agricultural regions.

[0021] Agricultural areas can be divided according to predetermined standards, such as by townships or towns, by area, or by the agricultural products grown; there are no limitations on this. Business promotion influencing factors include factors that affect business promotion. In the embodiments of this application, multiple business promotion influencing factors can be used to comprehensively determine whether an agricultural area is suitable as a business promotion area.

[0022] In some examples, business promotion influencing factors may include one or more of the following categories: historical factors of agricultural crops, agricultural environmental factors, business risk factors, and agricultural transaction factors. Each category of business promotion influencing factors includes at least one business promotion influencing factor. For example, if the business promotion influencing factors include four categories of business promotion influencing factors—historical factors of agricultural crops, agricultural environmental factors, business risk factors, and agricultural transaction factors—and if each category includes two business promotion influencing factors, then there are a total of eight business promotion influencing factors. These eight factors are used to determine whether an agricultural region is suitable as a business promotion region.

[0023] Historical factors for agricultural crops include information reflecting the historical state of crops, such as historical crop yields and historical crop growth, and are not limited here. Agricultural environmental factors include information reflecting the environment of the agricultural region. For example, agricultural environmental factors may include historical natural disasters, average temperature, and precipitation in the agricultural region, and are not limited here. In some examples, historical factors for agricultural crops and agricultural environmental factors can be obtained through satellite remote sensing data acquisition technology, or through other means, and are not limited here. Business risk factors include information reflecting the risks arising from business promotion in the agricultural region. For example, business risk factors may include the insured amount in the agricultural region, continuous insurance information in the agricultural region, and the number of insurance claims in the agricultural region, and are not limited here. Agricultural transaction factors include transaction information reflecting the agricultural region. For example, agricultural transaction factors may include the number of specific types of cards, transaction amounts, and transaction frequency, etc. Specific types of cards can be set according to the scenario of the promotion business, such as a rural revitalization card, and are not limited here.

[0024] The promotion impact factor matrix includes multiple first elements, each representing a specific promotion impact factor for an agricultural region. For example, if there are *m* agricultural regions and *n* promotion impact factors, the generated promotion impact factor matrix will be an *m×n* matrix. The first element corresponding to the *j*th promotion impact factor for the *i*th agricultural region in this matrix can be represented by g. ij The promotion impact factor matrix is ​​shown in equation (1) below:

[0025]

[0026] Where G is the generalization impact factor matrix, g ij This can characterize the j-th business promotion impact factor for the i-th agricultural region. In some examples, g ij This can be the j-th business promotion influencing factor for the i-th agricultural region. In other examples, g ij This can be the information obtained after processing the j-th business promotion impact factor for the i-th agricultural region, and is not limited here. i = 1, ..., m; j = 1, ..., n.

[0027] In step S102, based on the promotion impact factor matrix and the advantageous and disadvantageous change directions of the business promotion impact factors, the optimal impact factor vector and the worst impact factor vector are obtained.

[0028] Different values ​​of the business promotion influencing factor can reflect whether the factor has an advantage or disadvantage in promoting the business. An advantageous change indicates that the value of the business promotion influencing factor changes in a favorable direction, which will have a beneficial impact on the business promotion. A disadvantageous change indicates that the value of the business promotion influencing factor changes in a unfavorable direction, which will have a negative impact on the business promotion.

[0029] The direction of change in advantages and disadvantages is related to the types of factors influencing business promotion. Different factors may have different directions of change in advantages and disadvantages. For example, factors influencing business promotion include historical crop yields, historical natural disasters in agricultural regions, average temperatures in agricultural regions, and the quantity of specific types of calories. Historical crop yields have a positive direction of change in advantages, meaning that the higher the historical yield, the greater the beneficial impact on promotion. Conversely, historical crop yields have a negative direction of change in disadvantages, meaning that the lower the historical yield, the greater the adverse impact on promotion. Similarly, historical natural disasters in agricultural regions have a negative direction of change in advantages, meaning that fewer historical natural disasters in agricultural regions have a greater beneficial impact on promotion. Conversely, historical natural disasters in agricultural regions have a positive direction of change in disadvantages, meaning that fewer historical natural disasters in agricultural regions have a greater beneficial impact on promotion. The more cards a crop has, the greater its adverse impact on the promotion business. The favorable direction of change in the average temperature of an agricultural region is the median direction; that is, the closer the average temperature of the agricultural region is to the median value, the greater the beneficial impact on the promotion business. The unfavorable direction of change in the average temperature of an agricultural region is the two-sided direction; that is, the greater the difference between the average temperature of the agricultural region and the median value, the greater the adverse impact on the promotion business. The median value can be considered the suitable average temperature for crop growth. The favorable direction of change in the number of specific type cards is the positive direction; that is, the more specific type cards there are, the greater the beneficial impact on the promotion business. The unfavorable direction of change in the number of specific type cards is the opposite direction; that is, the fewer specific type cards there are, the greater the adverse impact on the promotion business.

[0030] The optimal impact factor vector comprises the first element of each business promotion impact factor in multiple agricultural regions that best matches the direction of advantage change. For example, the optimal impact factor vector is shown in equation (2):

[0031] B = [b1, ..., b j ,……,b n ] T (2)

[0032] Among them, b j Let b be the j-th element in the optimal impact factor vector. j The first element that best aligns with the direction of advantage change among multiple agricultural regions is the business promotion influencing factor j.

[0033] In some examples, the advantageous change direction of the business promotion impact factor is positive, meaning that the larger the value of the business promotion impact factor, the greater the beneficial impact on the promotion business. The element with the largest value corresponding to the same business promotion impact factor across multiple agricultural regions can be selected as the element corresponding to this business promotion impact factor in the optimal impact factor vector. If b j The direction of the advantage change is positive, b jIt can be shown in equation (3):

[0034]

[0035] Among them, g ij This refers to the first element corresponding to the business promotion impact factor of the i-th agricultural region in the impact factor matrix.

[0036] In some examples, the advantageous change direction of the business promotion impact factor is inverse; that is, the smaller the value of the business promotion impact factor, the greater the beneficial impact on the promotion business. The element with the smallest value corresponding to the same business promotion impact factor in multiple agricultural regions can be selected as the element corresponding to this business promotion impact factor in the optimal impact factor vector. If b j The direction of the advantage change is in the opposite direction, b j As shown in equation (4):

[0037]

[0038] Among them, g ij This refers to the first element corresponding to the business promotion impact factor of the i-th agricultural region in the impact factor matrix.

[0039] In some examples, the advantageous change direction of the business promotion impact factor is the middle direction. That is, the closer the value of the business promotion impact factor is to the median value, the greater the beneficial impact on the promotion business. The first element whose value of the same business promotion impact factor in multiple agricultural regions is closest to the median value can be selected as the element in the optimal impact factor vector corresponding to this business promotion impact factor.

[0040] The worst-case impact factor vector comprises the first element of each business promotion impact factor in multiple agricultural regions that best matches the direction of disadvantageous change. For example, the worst-case impact factor vector is shown in equation (5):

[0041] W = [w1, ..., w j ,……,w n ] T (5)

[0042] Among them, w j This represents the j-th element in the worst-case impact factor vector. w in the worst-case impact factor vector... j The j-th type of business promotion influencing factor is the first element that best matches the direction of disadvantage change in multiple agricultural regions.

[0043] In some examples, the negative change direction of the business promotion impact factor is positive, meaning that the larger the value of the business promotion impact factor, the greater the adverse impact on the promotion business. The element with the largest value for the same business promotion impact factor across multiple agricultural regions can be selected as the element corresponding to this business promotion impact factor in the worst-case impact factor vector. If w j The direction of the disadvantageous change is positive, w j As shown in equation (6):

[0044]

[0045] Among them, g ij This refers to the first element corresponding to the business promotion impact factor of the i-th agricultural region in the impact factor matrix.

[0046] In some examples, the negative change direction of the business promotion impact factor is inverse; that is, the smaller the value of the business promotion impact factor, the greater the adverse impact on the promotion business. The first element with the smallest value corresponding to the same business promotion impact factor in multiple agricultural regions can be selected as the element corresponding to this business promotion impact factor in the worst impact factor vector. If w j The disadvantage changes in the opposite direction, w j As shown in equation (7):

[0047]

[0048] Among them, g ij This refers to the first element corresponding to the business promotion impact factor of the i-th agricultural region in the impact factor matrix.

[0049] In some examples, the dominant change direction of the business promotion impact factor is in both directions. That is, the greater the difference between the value of the business promotion impact factor and the median value, the greater the adverse impact on the promotion business. The first element with the largest difference between the value of the same business promotion impact factor and the median value in multiple agricultural regions can be selected as the element corresponding to this business promotion impact factor in the worst impact factor vector.

[0050] In step S103, business promotion adaptation parameters are obtained based on the promotion impact factor vector, the optimal impact factor vector, and the worst impact factor vector for each agricultural region.

[0051] The promotion impact factor vector for agricultural regions includes the first element corresponding to each business promotion impact factor in the agricultural region. For example, the promotion impact factor vector g for the i-th agricultural region... i For [g] i1 ,…,g ij ,…,g in ], where g i1Let g be the first element corresponding to the first type of business promotion influencing factor in the i-th agricultural region. ij Let g be the first element corresponding to the j-th business promotion influencing factor in the i-th agricultural region. in This is the first element corresponding to the nth type of business promotion influencing factor in the i-th agricultural region.

[0052] The promotion impact factor vector for each agricultural region reflects the influence of the promotion impact factors on business promotion in that region. The suitability of a region for business promotion can be determined by the difference between its promotion impact factor vector and the optimal impact factor vector; conversely, the missuitability can be determined by the difference between its promotion impact factor vector and the worst impact factor vector. Thus, by considering both suitability and missuitability—the positive and negative aspects of suitability—business promotion suitability parameters that characterize the degree of business promotion suitability can be obtained.

[0053] The business promotion adaptation parameter characterizes the business promotion adaptation degree. In some examples, the business promotion adaptation parameter can be positively correlated with the business promotion adaptation degree, that is, the larger the business promotion adaptation parameter, the higher the business promotion adaptation degree; the smaller the business promotion adaptation parameter, the lower the business promotion adaptation degree, but this is not limited here.

[0054] In step S104, the Q agricultural regions with the highest business promotion adaptation degree, represented by the business promotion adaptation parameter, are selected as business promotion regions.

[0055] The higher the business promotion suitability of an agricultural region, the more suitable it is for business promotion. Based on actual needs, the Q agricultural regions with the highest business promotion suitability can be selected as the business promotion regions, i.e., the regions where business promotion will take place. Q is a positive integer and can be set according to actual scenarios, needs, experience, etc., but is not limited here.

[0056] In this embodiment, based on the promotion impact factor matrix and the advantageous and disadvantageous change directions of the business promotion impact factors, the optimal and worst impact factor vectors can be obtained. The optimal impact factor vector is the vector of business promotion impact factors most suitable for the promotion business, and the worst impact factor vector is the vector of business promotion impact factors least suitable for the promotion business. Through the promotion impact factor vector, optimal impact factor vector, and worst impact factor vector for each agricultural region, a business promotion suitability parameter representing the business promotion suitability for each agricultural region is obtained. Agricultural regions with higher business promotion suitability are selected from multiple agricultural regions as business promotion regions. Since the business promotion suitability parameter representing the business promotion suitability is obtained based on the optimal and worst impact factor vectors, taking into account both the suitability and unsuitability of the promotion business, the business promotion suitability parameter is more accurate, thereby improving the accuracy of selecting agricultural regions for the promotion business using this business promotion suitability parameter. In this embodiment of the application, the accuracy of selecting agricultural regions for promotional services by calculating the business promotion adaptation parameters through matrix and vector calculations can reduce or eliminate manual intervention, improve the efficiency of selecting agricultural regions for promotional services, reduce the influence of subjective factors in manual evaluation, and make the selection of agricultural regions for promotional services more stable and robust.

[0057] In some embodiments, the suitability of business promotion in agricultural regions can be determined by calculating the distance between the promotion impact factor vector of an agricultural region and the optimal impact factor vector, and the unsuitability of business promotion in agricultural regions can be determined by calculating the distance between the promotion impact factor vector of an agricultural region and the worst impact factor vector. Figure 2 A flowchart illustrating a method for selecting agricultural promotion areas according to another embodiment of this application. Figure 2 and Figure 1 The difference is that, Figure 1 Step S103 can be further refined as follows: Figure 2 Steps S1031 and S1032 in the process.

[0058] In step S1031, the first distance between the promotion impact factor vector and the optimal impact factor vector of each agricultural region, and the second distance between the promotion impact factor vector and the worst impact factor vector of each agricultural region are calculated.

[0059] The first distance characterizes the suitability of business promotion for agricultural regions. The second distance characterizes the unsuitability of business promotion for agricultural regions. Vector distance algorithms such as Euclidean distance, Mahalanobis distance, cosine distance, and cosine similarity can be used to calculate the first and second distances; there is no limitation here. However, the vector distance algorithm used to calculate the first and second distances can be selected based on the specific vectors that can be generated in the promotion impact factor matrix. For example, if there is a linear correlation between different types of business promotion impact factors, the first elements corresponding to multiple business promotion impact factors cannot form a basis that can measure the vector space of multiple agricultural regions. The accuracy of the first and second distances calculated using the Euclidean distance algorithm will decrease, and vector distance algorithms such as Mahalanobis distance, cosine distance, and cosine similarity can be used instead. As another example, if the number of business promotion impact factors is greater than the number of agricultural regions, the accuracy of the first and second distances calculated using the Mahalanobis distance algorithm will decrease, and vector distance algorithms such as Euclidean distance, cosine distance, and cosine similarity can be used instead. When the dimensions of the factors influencing different types of business promotion are inconsistent or there is a certain linear correlation between the factors influencing different types of business promotion, in order to improve the accuracy of the first distance and the second distance, the cosine similarity algorithm can be used to calculate the first distance and the second distance. The cosine similarity algorithm is not affected by the inconsistency of the dimensions of the factors influencing different types of business promotion or the existence of linear correlation, and has stronger applicability.

[0060] In some examples, the differences between the first distances of the promotion impact factor vectors of multiple agricultural regions and the optimal impact factor vectors are small, and the differences between the second distances of the promotion impact factor vectors of multiple agricultural regions and the worst impact factor vectors are also small, making it difficult to reflect the differences in the suitability and unsuitability of business promotion in different agricultural regions. To facilitate the differentiation of the differences between the first distances and the second distances corresponding to different agricultural regions, the promotion impact factor vectors, the optimal impact factor vector, and the worst impact factor vectors can be shifted respectively, and the distances between the shifted vectors can be used as the first and second distances. Specifically, based on the optimal and worst impact factor vectors, a weighted algorithm can be used to calculate the shift vectors; the promotion impact factor vectors, the optimal impact factor vectors, and the worst impact factor vectors of each agricultural region can be shifted according to the shift vectors to obtain the first target vector, the second target vector, and the third target vector; the distance between each first target vector and the second target vector can be calculated using a vector distance algorithm, and the distance between the first target vector and the second target vector can be determined as the first distance; the distance between each first target vector and the third target vector can be calculated using a vector distance algorithm, and the distance between the first target vector and the third target vector can be determined as the second distance.

[0061] The weights of the optimal and worst impact factor vectors can be set, and the sum of their weights is 1. The first product of the optimal and worst impact factor vectors' weights, and the second product of the worst and worst impact factor vectors' weights, can be calculated, and the sum of these two products is used as the shift vector. For example, if the weights of both the optimal and worst impact factor vectors are 1 / 2, the shift vector S can be as shown in equation (8):

[0062] S = [s1, ..., s n ] T = 1 / 2 × [b1 + w1, ..., b n +w n ] T (8)

[0063] Where s1 is the first element in the shift vector, s n b1 is the nth element in the shift vector, b1 is the first element in the optimal impact factor vector, and w1 is the first element in the worst impact factor vector. n w is the nth element in the optimal impact factor vector. n It is the nth element in the worst impact factor vector.

[0064] Shifting each of the promotion impact factor vectors, optimal impact factor vectors, and worst impact factor vectors according to the shift vector is equivalent to moving them to the points corresponding to the shift vectors in the multidimensional space. In other words, the points corresponding to the shift vectors in the multidimensional space are used as the starting points for the promotion impact factor vectors, optimal impact factor vectors, and worst impact factor vectors. The first target vector is the promotion impact factor vector after shifting according to the shift vector; the second target vector is the optimal impact factor vector after shifting according to the shift vector; and the third target vector is the worst impact factor vector after shifting according to the shift vector.

[0065] The following example uses the vector distance algorithm as the cosine similarity algorithm. The calculation of the first distance is shown in equation (9), and the calculation of the second distance is shown in equation (10):

[0066]

[0067]

[0068] Where sim is the cosine value calculated in the cosine similarity calculation; Let g be the first distance between the extension impact factor vector and the optimal impact factor vector for the i-th agricultural region; ijB is the j-th element in the vector of influence factors for the promotion of agricultural regions; B is the optimal influence factor vector; b j s is the j-th element in the optimal impact factor vector; j This is the j-th element in the shift vector; W represents the second distance between the vector of the best and worst impact factors in the i-th agricultural region; W is the vector of the best and worst impact factors. j It is the j-th element in the worst impact factor vector; j = 1, ..., n.

[0069] In step S1032, the business promotion adaptation parameters are calculated based on the first distance and the second distance.

[0070] The business promotion adaptation parameters include either a first percentage or a second percentage. The first percentage is the proportion of the first distance to the sum of the first and second distances, and it is negatively correlated with the business promotion adaptation degree; that is, the larger the first percentage, the lower the business promotion adaptation degree, and vice versa. The second percentage is the proportion of the second distance to the sum of the first and second distances, and it is positively correlated with the business promotion adaptation degree; that is, the larger the second percentage, the higher the business promotion adaptation degree, and vice versa.

[0071] For example, the business promotion adaptation parameters include the first proportion, which can be shown in equation (11):

[0072]

[0073] Among them, J i This represents the first proportion of the i-th agricultural region. Let be the first distance between the extension impact factor vector and the optimal impact factor vector of the i-th agricultural region. It represents the second distance between the vector of the promotion impact factors and the vector of the worst impact factors in the i-th agricultural region.

[0074] For example, the business promotion adaptation parameters include a second percentage, which can be shown in equation (12):

[0075]

[0076] Among them, J i This represents the second proportion of the i-th agricultural region. Let be the first distance between the extension impact factor vector and the optimal impact factor vector of the i-th agricultural region. It represents the second distance between the vector of the promotion impact factors and the vector of the worst impact factors in the i-th agricultural region.

[0077] To further improve the accuracy of business promotion adaptation parameters, the business promotion influencing factors in agricultural regions can be standardized to obtain the weights of various business promotion influencing factors. Based on the standardized business promotion influencing factors and their weights, a promotion influencing factor matrix can be obtained. Figure 3 A flowchart illustrating a method for selecting agricultural promotion areas according to another embodiment of this application. Figure 3 and Figure 1 The difference is that, Figure 1 Step S101 can be further refined as follows: Figure 3 Steps S1011 to S1013 in the process.

[0078] In step S1011, the various business promotion influencing factors of multiple agricultural regions are normalized to obtain a normalized matrix.

[0079] The normalization matrix includes multiple second elements, each of which represents a business promotion impact factor after normalization for an agricultural region. For example, if there are m agricultural regions and n different business promotion impact factors, the normalization matrix can be represented as shown in equation (13).

[0080]

[0081] Where F is the normalized matrix, f ij This represents the j-th type of business promotion impact factor after standardization processing for the i-th agricultural region.

[0082] In some examples, an initial matrix can be generated based on various business promotion influencing factors across multiple agricultural regions. Each element in the initial matrix represents a single business promotion influencing factor for a particular agricultural region. Since different business promotion influencing factors may have different dimensions, orders of magnitude, and units, normalization is performed on the elements of the initial matrix. This ensures that the normalized elements corresponding to different business promotion influencing factors are unaffected by dimensions, orders of magnitude, and units, resulting in a more accurate normalized matrix compared to the initial matrix.

[0083] Normalization is used to ensure that the elements corresponding to different types of business promotion impact factors are standardized, eliminating the influence of different dimensions, orders of magnitude, and units. The specific method of normalization is not limited here. In some examples, the elements in the normalized matrix can be calculated by using the j-th business promotion impact factor of the i-th agricultural region and the sum of the squares of the j-th business promotion impact factors of all agricultural regions. For example, if there are m agricultural regions and n types of business promotion impact factors, the initial matrix can be as shown in equation (14). The relationship between the elements corresponding to the j-th business promotion impact factor of the i-th agricultural region and the j-th business promotion impact factor of the i-th agricultural region in the normalized matrix can satisfy equation (15):

[0084]

[0085]

[0086] Where V is the normalized matrix; v ij f is the business promotion influencing factor for the i-th agricultural region; ij It is the second element corresponding to the j-th business promotion impact factor of the i-th agricultural region in the normalized matrix, that is, the j-th business promotion impact factor of the i-th agricultural region after normalization.

[0087] In step S1012, the weight of each business promotion impact factor is calculated based on the number of agricultural regions and the second element corresponding to each business promotion impact factor.

[0088] Different business promotion influencing factors have varying impacts on the business promotion adaptation parameters calculated in subsequent processes. The weights of the business promotion influencing factors can be obtained by combining the second element of each influencing factor corresponding to each agricultural region. These weights, along with a normalized matrix, yield a more accurate influencing factor matrix.

[0089] In some examples, the proportion of each business promotion impact factor in each agricultural region can be calculated based on the second element corresponding to each business promotion impact factor in multiple agricultural regions; the information content of each business promotion impact factor can be calculated based on the number of agricultural regions and the proportion of each business promotion impact factor in each agricultural region; and the weight of each business promotion impact factor can be calculated based on the information content of each business promotion impact factor.

[0090] Information quantity refers to the amount of information. The information quantity of a business promotion influencing factor can characterize the amount of information contained in that factor. The weight of a business promotion influencing factor can be further inferred from its information quantity. The weight of a business promotion influencing factor reflects the magnitude of its impact on the suitability of the business promotion. The larger the weight of a business promotion influencing factor, the greater its impact on the suitability of the business promotion.

[0091] For example, the proportion of the j-th business promotion influencing factor in the i-th agricultural region can be calculated as shown in equation (16), the information content of the j-th business promotion influencing factor can be calculated as shown in equations (17) and (18), and the weight of the j-th business promotion influencing factor can be calculated as shown in equation (19).

[0092]

[0093]

[0094]

[0095]

[0096] Where, p ij f represents the proportion of the j-th type of business promotion influencing factor in the total business promotion influencing factors for the i-th agricultural region; ij The second element is the business promotion impact factor corresponding to the j-th type in the i-th agricultural region in the normalized matrix; ∈ j θ represents the information content of the business promotion influencing factor of type j; m represents the number of agricultural regions; θ j denoted as the weight of the j-th business promotion influencing factor; n is the number of business promotion influencing factors; i = 1, ..., m; j = 1, ..., n.

[0097] In step S1013, the promotion impact factor matrix is ​​obtained based on the weights and normalized matrix of each business promotion impact factor.

[0098] Combining the weights of business promotion impact factors with a normalized matrix yields a more accurate matrix of promotion impact factors that characterizes the suitability of business promotion for different agricultural regions. In some examples, the first element corresponding to the j-th business promotion impact factor in the i-th agricultural region is the product of the second element corresponding to the j-th business promotion impact factor in the i-th agricultural region and the weight of the j-th business promotion impact factor. i and j are positive integers.

[0099] For example, the promotion impact factor matrix can be shown in equation (1), and the weights of the first element in the promotion impact factor matrix, the second element in the normalized matrix, and the business promotion impact factors can satisfy equation (20):

[0100] g ij =θ j ×f ij (20)

[0101] Among them, g ij To promote the first element corresponding to the business promotion impact factor of the i-th agricultural region in the impact factor matrix; θ j f represents the weight of the influencing factor for the j-th type of business promotion; ij Let be the second element corresponding to the j-th business promotion impact factor of the i-th agricultural region in the normalized matrix; i = 1, ..., m; j = 1, ..., n.

[0102] By combining the normalized matrix and the weights of the business promotion impact factors, the accuracy of the first element in the promotion impact factor matrix representing the business promotion impact factors of agricultural regions can be further improved. That is, the accuracy of the first element is improved, which in turn improves the accuracy of the optimal impact factor vector, the worst impact factor vector obtained based on the first element, and the business promotion fit parameters represented by the business promotion fit parameters obtained based on the optimal impact factor vector and the worst impact factor vector. It also improves the accuracy of ranking agricultural regions based on the business promotion fit.

[0103] This application also provides a device for selecting agricultural areas for business promotion. Figure 4 A schematic diagram of the structure of a device for selecting agricultural promotion areas according to an embodiment of this application. Figure 4 As shown, the agricultural area selection device 200 for this business promotion may include a matrix generation module 201, a target vector generation module 202, an adaptation parameter calculation module 203, and an area selection module 204.

[0104] The matrix generation module 201 can be used to generate a promotion influence factor matrix based on various business promotion influence factors in multiple agricultural regions.

[0105] The promotion impact factor matrix includes multiple first elements, each of which represents a business promotion impact factor for an agricultural region.

[0106] In some examples, business promotion influencing factors include one or more of the following categories: historical factors of agricultural crops, agricultural environmental factors, business risk factors, and agricultural transaction factors.

[0107] Each type of business promotion influencing factor includes at least one business promotion influencing factor.

[0108] The target vector generation module 202 can be used to obtain the optimal and worst impact factor vectors based on the promotion impact factor matrix and the advantageous and disadvantageous change directions of the business promotion impact factors.

[0109] The optimal impact factor vector comprises the first element of each business promotion impact factor in multiple agricultural regions that best aligns with the direction of change in advantages. The worst impact factor vector comprises the first element of each business promotion impact factor in multiple agricultural regions that best aligns with the direction of change in disadvantages.

[0110] The adaptation parameter calculation module 203 can be used to obtain business promotion adaptation parameters based on the promotion impact factor vector, the optimal impact factor vector, and the worst impact factor vector for each agricultural region.

[0111] The vector of promotional impact factors in agricultural regions includes the first element corresponding to each business promotion impact factor in the agricultural region.

[0112] The region selection module 204 can be used to select the Q agricultural regions with the highest business promotion adaptability, as represented by the business promotion adaptability parameters, as business promotion regions.

[0113] Q is a positive integer.

[0114] In this embodiment, based on the promotion impact factor matrix and the advantageous and disadvantageous change directions of the business promotion impact factors, the optimal and worst impact factor vectors can be obtained. The optimal impact factor vector is the vector of business promotion impact factors most suitable for the promotion business, and the worst impact factor vector is the vector of business promotion impact factors least suitable for the promotion business. Through the promotion impact factor vector, optimal impact factor vector, and worst impact factor vector for each agricultural region, a business promotion suitability parameter representing the business promotion suitability for each agricultural region is obtained. Agricultural regions with higher business promotion suitability are selected from multiple agricultural regions as business promotion regions. Since the business promotion suitability parameter representing the business promotion suitability is obtained based on the optimal and worst impact factor vectors, taking into account both the suitability and unsuitability of the promotion business, the business promotion suitability parameter is more accurate, thereby improving the accuracy of selecting agricultural regions for the promotion business using this business promotion suitability parameter. In this embodiment of the application, the accuracy of selecting agricultural regions for promotional services by calculating the business promotion adaptation parameters through matrix and vector calculations can reduce or eliminate manual intervention, improve the efficiency of selecting agricultural regions for promotional services, reduce the influence of subjective factors in manual evaluation, and make the selection of agricultural regions for promotional services more stable and robust.

[0115] In some embodiments, the adaptation parameter calculation module 203 can be used to: calculate a first distance between the promotion impact factor vector and the optimal impact factor vector of each agricultural region, and a second distance between the promotion impact factor vector and the worst impact factor vector of each agricultural region; and calculate business promotion adaptation parameters based on the first distance and the second distance. The business promotion adaptation parameters include a first proportion or a second proportion. The first proportion is the proportion of the first distance to the sum of the first distance and the second distance, which is negatively correlated with the business promotion adaptation degree. The second proportion is the proportion of the second distance to the sum of the first distance and the second distance, which is positively correlated with the business promotion adaptation degree.

[0116] In some embodiments, the adaptation parameter calculation module 203 can be used to: calculate a shift vector based on the optimal impact factor vector and the worst impact factor vector using a weighted algorithm; shift the promotion impact factor vector, optimal impact factor vector, and worst impact factor vector of each agricultural region according to the shift vector to obtain a first target vector, a second target vector, and a third target vector; calculate the distance between each first target vector and the second target vector using a vector distance algorithm, and determine the distance between the first target vector and the second target vector as the first distance; calculate the distance between each first target vector and the third target vector using a vector distance algorithm, and determine the distance between the first target vector and the third target vector as the second distance.

[0117] In some examples, vector distance algorithms include cosine similarity algorithms.

[0118] In some embodiments, the matrix generation module 201 can be used to: normalize multiple business promotion influencing factors in multiple agricultural regions to obtain a normalized matrix, the normalized matrix including multiple second elements, each second element being a business promotion influencing factor after normalization for an agricultural region; calculate the weight of each business promotion influencing factor based on the number of agricultural regions and the second element corresponding to each business promotion influencing factor; and obtain a promotion influencing factor matrix based on the weight of each business promotion influencing factor and the normalized matrix.

[0119] In some embodiments, the matrix generation module 201 can be used to: calculate the proportion of each business promotion impact factor in each agricultural region to the total business promotion impact factors based on the second element corresponding to each business promotion impact factor in multiple agricultural regions; calculate the information content of each business promotion impact factor based on the number of agricultural regions and the proportion of each business promotion impact factor in each agricultural region to the total business promotion impact factors; and calculate the weight of each business promotion impact factor based on the information content of each business promotion impact factor.

[0120] In some examples, the first element corresponding to the business promotion impact factor of the i-th agricultural region is the product of the second element corresponding to the business promotion impact factor of the i-th agricultural region and the weight of the business promotion impact factor of the j-th agricultural region, where i and j are positive integers.

[0121] This application also provides a device for selecting agricultural areas for business promotion. Figure 5 A schematic diagram of a device for selecting agricultural promotion areas according to an embodiment of this application. Figure 5 As shown, the agricultural area selection device 300 for business promotion includes a memory 301, a processor 302, and a computer program stored on the memory 301 and capable of running on the processor 302.

[0122] In one example, the processor 302 described above may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits that may be configured to implement the embodiments of this application.

[0123] Memory 301 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method for selecting agricultural promotion areas according to embodiments of this application.

[0124] The processor 302 runs a computer program corresponding to the executable program code by reading the executable program code stored in the memory 301, so as to implement the method for selecting agricultural promotion areas in the above embodiments.

[0125] In one example, the agricultural area selection device 300 for business promotion may also include a communication interface 303 and a bus 304. For example, Figure 5 As shown, the memory 301, processor 302, and communication interface 303 are connected through bus 304 and complete communication with each other.

[0126] The communication interface 303 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application. Input devices and / or output devices can also be connected through the communication interface 303.

[0127] Bus 304 includes hardware, software, or both, that couples components of the selected device 300 in the business promotion agricultural area together. For example, and not limitingly, bus 304 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-E) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 304 may include one or more buses. Although specific buses are described and illustrated in the embodiments of this application, this application considers any suitable bus or interconnection.

[0128] This application also provides a computer-readable storage medium storing computer program instructions. When executed by a processor, these computer program instructions can implement the method for selecting agricultural promotion areas described in the above embodiments and achieve the same technical effect. To avoid repetition, further details are omitted here. The aforementioned computer-readable storage medium may include non-transitory computer-readable storage media, such as read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, etc., and is not limited thereto.

[0129] This application also provides a computer program product. When the instructions in this computer program product are executed by the processor of an electronic device, the electronic device performs the method for selecting agricultural promotion areas described in the above embodiments, and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0130] It should be clarified that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. For device embodiments, equipment embodiments, computer-readable storage medium embodiments, and computer program products, relevant parts can be referred to the description section of the method embodiments. This application is not limited to the specific steps and structures described above and shown in the figures. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application. Furthermore, for the sake of brevity, detailed descriptions of known methods and techniques are omitted here.

[0131] The aspects of this application have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0132] Those skilled in the art will understand that the above embodiments are exemplary and not restrictive. Different technical features appearing in different embodiments can be combined to achieve beneficial effects. Based on a study of the drawings, specification, and claims, those skilled in the art should be able to understand and implement other variations of the disclosed embodiments. In the claims, the term "comprising" does not exclude other means or steps; the quantifier "a" does not exclude a plurality; the terms "first" and "second" are used to identify names and not to indicate any particular order. No reference numerals in the claims should be construed as limiting the scope of protection. The functionality of multiple parts appearing in the claims can be implemented by a single hardware or software module. The appearance of certain technical features in different dependent claims does not mean that these technical features cannot be combined to achieve beneficial effects.

Claims

1. A method for selecting agricultural regions for business promotion, characterized in that, include: Based on multiple business promotion influencing factors in multiple agricultural regions, a promotion influencing factor matrix is ​​generated. The promotion influencing factor matrix includes multiple first elements, and each first element represents one of the business promotion influencing factors in an agricultural region. Based on the promotion impact factor matrix and the advantageous and disadvantageous change directions of the business promotion impact factors, the optimal impact factor vector and the worst impact factor vector are obtained. The optimal impact factor vector includes the first element of each of the business promotion impact factors in multiple agricultural regions that best conforms to the advantageous change direction, and the worst impact factor vector includes the first element of each of the business promotion impact factors in multiple agricultural regions that best conforms to the disadvantageous change direction. Based on the promotion impact factor vector of each agricultural region, the optimal impact factor vector, and the worst impact factor vector, the business promotion adaptation parameters are obtained. The promotion impact factor vector of the agricultural region includes the first element corresponding to each of the business promotion impact factors of the agricultural region. The Q agricultural regions with the highest business promotion adaptation degree, represented by the business promotion adaptation parameter, are selected as business promotion regions, where Q is a positive integer.

2. The method according to claim 1, characterized in that, The process of obtaining business promotion adaptation parameters based on the promotion impact factor vector, the optimal impact factor vector, and the worst impact factor vector for each agricultural region includes: Calculate the first distance between the promotion impact factor vector of each agricultural region and the optimal impact factor vector, and the second distance between the promotion impact factor vector of each agricultural region and the worst impact factor vector; Based on the first distance and the second distance, the business promotion adaptation parameters are calculated. The business promotion adaptation parameters include a first proportion or a second proportion. The first proportion is the proportion of the first distance in the sum of the first distance and the second distance, which is negatively correlated with the business promotion adaptation degree. The second proportion is the proportion of the second distance in the sum of the first distance and the second distance, which is positively correlated with the business promotion adaptation degree.

3. The method according to claim 2, characterized in that, The calculation of the first distance between the extension impact factor vector of each agricultural region and the optimal impact factor vector, and the second distance between the extension impact factor vector of each agricultural region and the worst impact factor vector, includes: Based on the optimal impact factor vector and the worst impact factor vector, a shift vector is calculated using a weighted algorithm. The promotion impact factor vector, the optimal impact factor vector, and the worst impact factor vector for each agricultural region are shifted according to the shift vector to obtain the first target vector, the second target vector, and the third target vector; Using a vector distance algorithm, the distance between each of the first target vectors and the second target vectors is calculated, and the distance between the first target vector and the second target vector is determined as the first distance; Using the vector distance algorithm, the distance between each of the first target vectors and the third target vector is calculated, and the distance between the first target vector and the third target vector is determined as the second distance.

4. The method according to claim 3, characterized in that, The vector distance algorithm includes the cosine similarity algorithm.

5. The method according to claim 1, characterized in that, The process of generating a promotion impact factor matrix based on multiple business promotion impact factors across multiple agricultural regions includes: The various business promotion influencing factors in multiple agricultural regions are normalized to obtain a normalized matrix. The normalized matrix includes multiple second elements, and each second element is a normalized business promotion influencing factor for an agricultural region. Based on the number of agricultural regions and the second element corresponding to each of the aforementioned business promotion impact factors, the weight of each of the aforementioned business promotion impact factors is calculated. The promotion impact factor matrix is ​​obtained based on the weight of each of the aforementioned business promotion impact factors and the normalized matrix.

6. The method according to claim 5, characterized in that, The weight of each business promotion impact factor is calculated based on the number of agricultural regions and the second element corresponding to each business promotion impact factor, including: Based on the second element corresponding to each of the business promotion impact factors in multiple agricultural regions, calculate the proportion of each of the business promotion impact factors in each agricultural region to the total business promotion impact factors; Based on the number of agricultural regions and the proportion of each of the aforementioned business promotion influencing factors in each agricultural region, the information content of each of the aforementioned business promotion influencing factors is calculated. The weight of each business promotion influencing factor is calculated based on the amount of information in each of the aforementioned business promotion influencing factors.

7. The method according to claim 5, characterized in that, The first element corresponding to the business promotion impact factor of the jth type in the i-th agricultural region is the product of the second element corresponding to the business promotion impact factor of the jth type in the i-th agricultural region and the weight of the business promotion impact factor of the jth type, where i and j are positive integers.

8. The method according to any one of claims 1 to 7, characterized in that, The business promotion influencing factors include one or more of the following categories: Historical factors of agricultural crops, agricultural environmental factors, business risk factors, and agricultural transaction factors; Each of the aforementioned business promotion influencing factors includes at least one of the aforementioned business promotion influencing factors.

9. A device for selecting agricultural areas for business promotion, characterized in that, include: The matrix generation module is used to generate a promotion influence factor matrix based on multiple business promotion influence factors in multiple agricultural regions. The promotion influence factor matrix includes multiple first elements, and each first element represents one of the business promotion influence factors in an agricultural region. The target vector generation module is used to obtain the optimal influence factor vector and the worst influence factor vector based on the promotion influence factor matrix and the direction of advantage and disadvantage change of the business promotion influence factors. The optimal influence factor vector includes the first element of each of the business promotion influence factors in multiple agricultural regions that best matches the direction of advantage change, and the worst influence factor vector includes the first element of each of the business promotion influence factors in multiple agricultural regions that best matches the direction of disadvantage change. The adaptation parameter calculation module is used to obtain business promotion adaptation parameters based on the promotion impact factor vector of each agricultural region, the optimal impact factor vector, and the worst impact factor vector. The promotion impact factor vector of the agricultural region includes the first element corresponding to each of the business promotion impact factors of the agricultural region. The region selection module is used to select the Q agricultural regions with the highest business promotion adaptability, represented by the business promotion adaptability parameter, as business promotion regions, where Q is a positive integer.

10. A device for selecting agricultural areas for business promotion, characterized in that, include: Processor and memory storing computer program instructions; When the processor executes the computer program instructions, it implements the method for selecting agricultural regions for business promotion as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the method for selecting agricultural promotion areas as described in any one of claims 1 to 8.

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