A method and system based on business visualization

Abstract

The present invention discloses a method and system based on business visualization, which relates to the field of visualization technology. It includes collecting data, formatting the collected data, generating a regional distribution data set; extracting the inventory shortage degree and competition area characteristics, and generating an inventory feature matrix and a competition feature matrix; generating a dynamic heat map with inventory status and competition area identification according to the inventory feature matrix and the competition feature matrix, and distinguishing the inventory shortage degree by color gradient; matching the data of the inventory shortage area in the inventory feature matrix with the distribution path data, generating a logistics bottleneck node set, and displaying the logistics bottleneck in a dynamic marking manner on the dynamic heat map. The present invention significantly improves the response efficiency and accuracy of enterprises in resource allocation, logistics scheduling and market competition, and achieves a comprehensive optimization effect that cannot be achieved by traditional business models.

Description

Technical Field

[0001] The present invention relates to the field of visualization technology, and in particular to a method and system based on business visualization. Background Art

[0002] With the rapid development of big data and artificial intelligence technologies, data-driven business visualization analysis methods are increasingly being used in various industries. Especially in the retail and logistics fields, by visualizing multi-dimensional business data, companies can more intuitively understand market demand, inventory distribution and competitive situation, thereby optimizing resource allocation and improving decision-making efficiency.

[0003] However, existing business visualization methods mainly focus on single-dimensional data processing and static presentation, such as analyzing only inventory status or consumer behavior, and it is difficult to establish correlations between multi-dimensional data. More importantly, as the complexity of the supply chain increases, existing methods are generally unable to effectively handle dynamic interaction problems between regions, such as the dynamic matching of inventory distribution and logistics bottlenecks. This deficiency directly affects the company's ability to respond quickly to sudden changes in demand and market competition, resulting in uneven resource allocation, inefficient transportation, and loss of market share. Summary of the invention

[0004] In view of the problems existing in the above background technology, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to improve the resource allocation efficiency and market response capability of an enterprise.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a business visualization method, which includes: S1: collecting inventory data, user purchase behavior data and public competitor market dynamics data, formatting the collected data, ensuring the time synchronization and geographical correlation of the data source, and generating a regional distribution data set; S2: based on the regional distribution data set, extracting the inventory shortage degree and competition area characteristics, respectively calculating the inventory saturation rate and competition intensity of the goods in the region, and generating an inventory feature matrix and a competition feature matrix; S3: based on the inventory feature matrix and the competition feature matrix, based on the geographical area, generating a dynamic heat map with inventory status and competition area identification, and distinguishing the inventory shortage degree through color gradient, and marking the potential competition area with boundary highlighting; S4: based on the logistics distribution algorithm, matching the data of the inventory shortage area in the inventory feature matrix with the distribution path data, generating a logistics bottleneck node set, and displaying the logistics bottleneck in the form of dynamic marking on the dynamic heat map.

[0008] As a preferred solution of the business visualization method described in the present invention, the inventory saturation rate is calculated as follows: the inventory saturation rate of each region is calculated according to the inventory status characteristic data and the regional area data in the regional distribution data set:

[0009] ;

[0010] in, For the The inventory saturation rate of each region, For the The total inventory in each region, For the The intensity of demand in each region, For the The area of ​​the region; the inventory feature matrix is ​​expressed as:

[0011] ;

[0012] in, is the total number of regions.

[0013] As a preferred solution of the business visualization method described in the present invention, the calculation formula of the competition intensity is as follows:

[0014] ;

[0015] in, For the The intensity of competition in a region is the total number of competitors, For competitors The sales price, For the Regions and competitors The geographical weight, For the Regions and competitors distance.

[0016] As a preferred solution of the business visualization method described in the present invention, S3 specifically includes the following steps: extracting the inventory feature matrix and the competition distribution matrix The regional data in the image is mapped to the geographic coordinate system to determine the geographic boundaries of each region and construct an initial set of geographic grids to represent the basic distribution unit of the heat map. The data is color mapped, and the color gradient is used to encode the inventory status according to the inventory saturation rate. The value range of the competition distribution matrix The intensity of competition The data is spatially annotated, the boundaries of areas with high competition intensity are extracted, and potential competition areas are highlighted with boundaries. The geographical location overlap analysis is combined to ensure that the annotated boundaries are aligned with the geographic grid areas. The inventory status color gradient and the competition area boundary highlights are superimposed and rendered, the inventory status color transparency is adjusted to retain the recognition of the two types of information, and a heat map is dynamically generated to ensure the independence of the color gradient and boundary annotations.

[0017] As a preferred solution of the business visualization method described in the present invention, S4 includes the following steps: Filter Inventory Saturation Rate Below threshold The area is regarded as the shortage area, and the distribution demand data of the shortage area is extracted; according to the geographical location and logistics distribution path data of the inventory shortage area, the regional characteristics are associated with the distribution path, and the demand intensity and path load on each path are determined; the load balance coefficient of the path is calculated by the logistics distribution algorithm, and the nodes with path load higher than the average level are screened to generate a set of logistics bottleneck nodes; the set of logistics bottleneck nodes is mapped to a dynamic heat map, the bottleneck nodes are dynamically marked on the logistics path, and the path load degree is represented by different marking intensities.

[0018] As a preferred solution of the business visualization method described in the present invention, the calculation of the load balancing coefficient is: Load balancing factor Calculated through the relationship between path demand intensity and path capacity:

[0019] ;

[0020] in, For delivery route The set of areas covered, For delivery route The capacity of each path , if the condition is met , then the node in the marked path is a bottleneck node, where is the average load balancing factor of all paths, is the load threshold factor.

[0021] As a preferred solution of the business visualization method described in the present invention, S1 further includes: aggregating the data according to the geographic location field to generate a regional data set, and adding a regional attribute identifier to each region to distinguish regional characteristics; when performing geographic location matching, the following formula is used to calculate the comprehensive attribute value of each region:

[0022] ;

[0023] in, For the The comprehensive attribute value of a region represents the comprehensive evaluation result of the inventory status and purchasing behavior characteristics of the region. is the total number of data features, For the The weight of the feature, For the The area in The standardized value of the feature, For the The area in The geographical distribution density of each feature is analyzed; the regions are stratified according to the size of the comprehensive attribute value, the regions are divided into different categories, and the stratification results are visually encoded, and the comprehensive attribute values ​​of the regions are displayed in layers using color gradients.

[0024] In a second aspect, the present invention provides a business visualization system, which includes: a data collection module, which is used to collect inventory data, user purchasing behavior data and public competitor market dynamics data, format the collected data, ensure the time synchronization and geographical correlation of the data source, and generate a regional distribution data set; a calculation module, which is used to extract the inventory shortage degree and competition area characteristics based on the regional distribution data set, calculate the inventory saturation rate and competition intensity of the goods in the region respectively, and generate an inventory feature matrix and a competition feature matrix; an identification module, which is used to generate a dynamic heat map with inventory status and competition area identification based on the inventory feature matrix and the competition feature matrix and the geographical area, and distinguish the inventory shortage degree through color gradient, and highlight the potential competition area with the boundary; a logistics marking module, which is used to match the data of the inventory shortage area in the inventory feature matrix with the distribution path data based on the logistics distribution algorithm, generate a logistics bottleneck node set, and display the logistics bottleneck in the form of dynamic marking on the dynamic heat map.

[0025] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the business visualization method based on the first aspect of the present invention are implemented.

[0026] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the business visualization-based method as described in the first aspect of the present invention are implemented.

[0027] The beneficial effects of the present invention are as follows: the present invention constructs an inventory feature matrix and a competition feature matrix to extract regional feature information in a precise and quantitative manner, and generates a dynamic heat map to present multi-dimensional business data in an intuitive form; in particular, the color gradient is used to distinguish the degree of inventory shortage and the boundary highlighting is combined to mark the competition area, which effectively enhances the visualization expression depth and information recognition.

[0028] In terms of intelligent scheduling, the logistics distribution algorithm dynamically matches inventory shortage data with distribution routes, generates logistics bottleneck nodes and marks them in the heat map, so that the scheduling plan can respond to business needs and market changes in real time. Through full-link data fusion and dynamic optimization, the present invention significantly improves the response efficiency and accuracy of enterprises in resource allocation, logistics scheduling and market competition, and achieves a comprehensive optimization effect that traditional business models cannot achieve. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0030] Figure 1 It is a flowchart based on business visualization method.

[0031] Figure 2 This is a structural diagram based on the business visualization system. DETAILED DESCRIPTION

[0032] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0033] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0034] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0035] Embodiment 1:

[0036] Reference Figure 1~Figure 2, which is the first embodiment of the present invention, and provides a method based on business visualization, including:

[0037] S1: Collect inventory data, user purchase behavior data and public competitor market dynamics data, format the collected data, ensure the time synchronization and geographical relevance of the data source, and generate a regional distribution data set.

[0038] S1.1: Access inventory data, user purchase behavior data, and competitor market dynamics data, add a unique identifier to each set of data based on the data source, use timestamps and geographic location to mark the data, and generate an initial data set.

[0039] Furthermore, the initial data set is cleaned, duplicate data is removed according to unique identification and timestamp rules, and outlier detection algorithms are used to identify outliers in the data, and the outliers are recorded in the outlier data set, and the cleaned main data set is retained.

[0040] The inventory data, purchase behavior data and market dynamics data in the master data set are aligned according to unified format rules, including geographic location, time dimension and quantity dimension fields, to generate a standardized data set.

[0041] S1.2: Based on the standardized data set, the data is aggregated according to the geographic location field to generate a regional data set, and a regional attribute identifier is added to each region to distinguish regional characteristics.

[0042] Preferably, when performing geographic location matching, the following formula is used to calculate the comprehensive attribute value of each region:

[0043] ;

[0044] in, For the The comprehensive attribute value of a region represents the comprehensive evaluation result of the inventory status and purchasing behavior characteristics of the region. is the total number of data features, For the The weight of the feature, For the The area in The standardized value on a feature indicates the relative strength of a feature in the region. For the The area in The geographic distribution density of each feature is calculated by the ratio of the number of data points in the region to the area of ​​the region, which indicates the degree of geographical aggregation of the data.

[0045] Furthermore, according to the size of the comprehensive attribute value, the region is stratified (such as high attribute value, medium attribute value and low attribute value), the region is divided into different categories, and the stratification results are visually encoded, and the comprehensive attribute values ​​of the region are displayed in layers using color gradients, where:

[0046] like , it is classified as a high-attribute area;

[0047] like , it is classified as a medium attribute area;

[0048] like , it is classified as a low-attribute area;

[0049] in, is the average value of the comprehensive attribute values ​​of all regions, and Attribute stratification threshold;

[0050] High-attribute areas are displayed with a stronger color gradient in the heat map to indicate the importance of the area; medium-attribute areas maintain medium color intensity, and low-attribute areas have a weaker color intensity. The color gradient of the comprehensive attribute value serves as the background of the heat map to reflect the importance of the area.

[0051] Priority will be given to areas with high comprehensive attribute values ​​to guide inventory allocation, logistics route optimization and market competition strategies.

[0052] S1.3: Synchronize the time dimension of the sub-regional data set, complete the missing time node data through linear interpolation method, and verify the consistency of the values ​​within each time node to generate the final regional distribution data set.

[0053] It should be noted that inventory data and market dynamics data are matched through geographic correlation algorithms to form a unified data format with regional attributes to ensure consistency in subsequent display.

[0054] S2: Based on the regional distribution data set, extract the inventory shortage degree and competition area characteristics, calculate the inventory saturation rate and competition intensity of the goods in the region respectively, and generate an inventory feature matrix and a competition feature matrix.

[0055] S2.1: Calculate the inventory saturation rate of each region based on the inventory status feature data and regional area data in the regional distribution data set, and generate an inventory feature matrix using the ratio of the total inventory to the regional demand intensity. , which is used to indicate the inventory adequacy of each region.

[0056] Preferably, the calculation formula for the inventory saturation rate of each region is as follows:

[0057] ;

[0058] in, For the The inventory saturation rate of each region, For the The total inventory in each region, For the The intensity of demand in each region, For the Area of ​​a region.

[0059] The inventory feature matrix is ​​expressed as:

[0060] ;

[0061] in, is the total number of regions.

[0062] S2.2: Generate a competition distribution matrix based on the market dynamic characteristics of competitors in the regional distribution data set, combined with geographic location and sales price data , in order to mark the potential high competition intensity areas in each region, and to associate inventory characteristics with sales characteristics for comprehensive analysis.

[0063] Preferably, the calculation formula of competition intensity is as follows:

[0064] ;

[0065] in, For the The intensity of competition in a region is the total number of competitors, For competitors The sales price, For the Regions and competitors The geographical weight, For the Regions and competitors distance.

[0066] S3: Based on the inventory feature matrix and the competition feature matrix, a dynamic heat map with inventory status and competition area identification is generated based on the geographical area, and the degree of inventory shortage is distinguished by color gradient, and potential competition areas are highlighted with boundaries, which specifically includes the following steps:

[0067] Extracting inventory feature matrix and the competition distribution matrix The regional data in the data is mapped to the geographic coordinate system, the geographic boundaries of each region are determined, and an initial set of geographic grids is constructed to represent the basic distribution unit of the heat map;

[0068] For the inventory feature matrix The data is color mapped, and the color gradient is used to encode the inventory status according to the inventory saturation rate. The inventory shortage degree is displayed in color depth using a numerical range of : an independent color gradient (such as blue) is used to overlay the background layer, where areas with scarce inventory are displayed with higher brightness (such as dark blue) and areas with sufficient inventory are displayed with lower brightness (such as light blue).

[0069] Competition Distribution Matrix The intensity of competition The data is spatially annotated, and the boundaries of areas with high competition intensity are extracted. Potential competition areas are highlighted with boundaries. Geographic location overlap analysis is combined to ensure that the annotated boundaries are aligned with the geographic grid areas. For example, highlight annotations (such as bright red lines) are added to the boundaries of areas with high competition intensity to indicate potential high competition areas.

[0070] The inventory status color gradient and the competition area boundary highlight are overlaid and rendered, and the inventory status color transparency is adjusted to make it appear based on the background comprehensive attribute layering. The recognition of the two types of information is retained, and a heat map is dynamically generated to ensure the independence of the color gradient and boundary markings. At the same time, the recognition of the two types of information is visually enhanced to form a dynamic heat map interface with multi-dimensional information.

[0071] Furthermore, the inventory feature matrix and competition distribution matrix are updated according to the time series, and the color gradient and boundary highlighting of the heat map are dynamically adjusted to form a real-time update mechanism to ensure the timeliness and accuracy of inventory status and competition information.

[0072] It can be seen that through the dual information expression of color and boundary, the visual synchronization of inventory status and competition area is achieved, and the heat map is dynamically updated in combination with time series to ensure the real-time and accuracy of the heat map information. Through the fusion of geographic coordinates with multi-dimensional data of inventory and competition matrix, the generated heat map has spatial logical consistency, ensuring the association and comprehensive expression of multi-dimensional data.

[0073] Moreover, on the basis of the background layer, the color gradient of the inventory status and the boundary highlight of the competition intensity are dynamically superimposed, and the color transparency is adjusted to make the two layers of information (the basic heat map drawing in S1 and the dynamic information superposition in S3) visually separated but semantically complementary.

[0074] S4: Based on the logistics distribution algorithm, the data of the inventory shortage area in the inventory feature matrix is ​​matched with the distribution path data to generate a set of logistics bottleneck nodes, and the logistics bottleneck is displayed on the dynamic heat map in a dynamic marking manner.

[0075] From the inventory feature matrix Filter Inventory Saturation Rate Below threshold The areas are regarded as shortage areas, and the distribution demand data of these areas are extracted;

[0076] Based on the geographical location and logistics distribution path data of the inventory shortage area, the regional characteristics are associated with the distribution path to determine the demand intensity and path load on each path;

[0077] The path load balance coefficient is calculated through the logistics distribution algorithm, and the nodes with path loads higher than the average level are screened to generate a set of logistics bottleneck nodes;

[0078] The set of logistics bottleneck nodes is mapped to a dynamic heat map, the bottleneck nodes are dynamically marked on the logistics path, and the path load degree is indicated with different marking intensities, providing an intuitive basis for subsequent optimization.

[0079] Furthermore, the delivery path Load balancing factor It can be calculated by the relationship between path demand intensity and path capacity:

[0080] ;

[0081] in, For delivery route The set of areas covered, For delivery route capacity.

[0082] For each path , if the condition is met , then the node in the marked path is a bottleneck node, where is the average load balancing factor of all paths, is the load threshold factor.

[0083] Furthermore, this embodiment also provides a business visualization system, including:

[0084] The data collection module is used to collect inventory data, user purchase behavior data, and public competitor market dynamics data, format the collected data, ensure the time synchronization and geographical relevance of the data source, and generate a regional distribution data set;

[0085] A calculation module, for extracting the inventory shortage degree and competitive regional characteristics based on the regional distribution data set, respectively calculating the inventory saturation rate and competition intensity of the commodities in the region, and generating an inventory characteristic matrix and a competition characteristic matrix;

[0086] An identification module is used to generate a dynamic heat map with inventory status and competition area identification based on the inventory feature matrix and competition feature matrix based on geographical regions, and distinguish the degree of inventory shortage through color gradients, and highlight potential competition areas with boundaries;

[0087] The logistics marking module is used to match the data of the inventory shortage area in the inventory feature matrix with the distribution path data based on the logistics distribution algorithm, generate a set of logistics bottleneck nodes, and display the logistics bottleneck in the form of dynamic marking on the dynamic heat map.

[0088] This embodiment also provides a computer device, which is suitable for the case based on business visualization method, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the business visualization method proposed in the above embodiment.

[0089] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0090] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method based on business visualization as proposed in the above embodiment is implemented.

[0091] In summary, the present invention constructs an inventory feature matrix and a competition feature matrix to extract regional feature information in a precise and quantitative manner, and generates a dynamic heat map to present multi-dimensional business data in an intuitive form; in particular, the color gradient is used to distinguish the degree of inventory shortage and the boundary highlighting is combined to mark the competition area, which effectively enhances the visualization expression depth and information recognition.

[0092] In terms of intelligent scheduling, the logistics distribution algorithm dynamically matches inventory shortage data with distribution routes, generates logistics bottleneck nodes and marks them in the heat map, so that the scheduling plan can respond to business needs and market changes in real time. Through full-link data fusion and dynamic optimization, the present invention significantly improves the response efficiency and accuracy of enterprises in resource allocation, logistics scheduling and market competition, and achieves a comprehensive optimization effect that traditional business models cannot achieve.

[0093] Embodiment 2:

[0094] Referring to Table 1, which is a second embodiment of the present invention, this embodiment provides a method based on business visualization. In order to verify the beneficial effects of the present invention, a scientific demonstration is carried out through simulation experiments.

[0095] In order to verify the effectiveness of the business visualization method, six different geographical regions (regions A to F) were selected as experimental objects. These regions cover a variety of different inventory status, demand intensity and competitive environment. The main steps of the experiment are as follows:

[0096] Data collection and preprocessing: Collect the total inventory, demand intensity, number of competitors and sales price in each region, record the relevant geographical location and timestamp information, and ensure data synchronization and geographical relevance. The collected data was cleaned, duplicate records were removed, and outliers were marked.

[0097] The inventory saturation rate is calculated based on the inventory data. At the same time, the competition intensity is calculated by combining the number of competitors and sales price data.

[0098] After the data is unified and standardized, the inventory feature matrix and the competitive feature matrix are generated.

[0099] The calculated results of inventory saturation rate and competition intensity are mapped to a geographic coordinate system, using a color gradient to distinguish inventory status and highlighting the boundaries of areas with high competition intensity.

[0100] Based on low inventory saturation areas, combined with distribution path load data, bottleneck nodes are dynamically marked. The path load balancing coefficient is used to identify logistics bottlenecks and display them on a dynamic heat map.

[0101] Table 1: Experimental results in different regions

[0102] Test subjects Total inventory (unit) Demand intensity (unit) Inventory saturation rate Number of competitors Sales price (yuan) Competition intensity Region A 500 400 1.25 3 120 0.75 Region B 1200 1000 1.20 5 110 1.20 Region C 800 850 0.94 2 130 0.50 Region D 300 500 0.60 4 115 0.80 Area E 700 650 1.08 3 125 0.90 Region F 1100 950 1.16 6 105 1.10

[0103] From the table data we can see that:

[0104] Regions A and B have higher inventory saturation rates, indicating sufficient inventory; regions C and D have lower inventory saturation rates, indicating they may face inventory shortage risks. Region D has the lowest inventory saturation rate, which is only 0.6.

[0105] Regions B and F have higher competition intensities (1.2 and 1.1, respectively), indicating intense market competition, which may affect sales strategies and inventory allocation;

[0106] Combining inventory saturation rate and competition intensity, regions D and F were marked as priority focus areas due to inventory shortage and high competition intensity, respectively. This assessment directly guided the identification and optimization of subsequent logistics bottleneck nodes.

[0107] Through the above experiments and data analysis, it is proved that the business visualization method not only realizes the synchronous visualization of inventory and competition status, but also optimizes resource allocation and logistics efficiency through data fusion and real-time update. Compared with the existing technology, the method of the present invention has significantly improved the accuracy of data processing, the intuitiveness of information display and the comprehensiveness of decision support.

[0108] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A business visualization method, characterized in that: include: S1: Collect inventory data, user purchase behavior data, and public competitor market dynamics data, format the collected data, ensure the time synchronization and geographical relevance of the data source, and generate a regional distribution data set; S2: Based on the regional distribution data set, extract the inventory shortage degree and competition regional characteristics, calculate the inventory saturation rate and competition intensity of the goods in the region respectively, and generate an inventory feature matrix and a competition feature matrix; S3: Based on the inventory feature matrix and the competition feature matrix, a dynamic heat map with inventory status and competition area identification is generated based on the geographical area, and the degree of inventory shortage is distinguished by color gradient, and potential competition areas are highlighted with boundaries; S4: Based on the logistics distribution algorithm, the data of the inventory shortage area in the inventory feature matrix is ​​matched with the distribution path data to generate a set of logistics bottleneck nodes, and the logistics bottleneck is displayed in a dynamic marking manner on the dynamic heat map; The inventory saturation rate is calculated as follows: According to the inventory status characteristic data and regional area data in the regional distribution data set, the inventory saturation rate of each region is calculated: ； in, For the The inventory saturation rate of each region, For the The total inventory in each region, For the The intensity of demand in each region, For the Area of ​​the region; The inventory feature matrix is ​​expressed as: ； in, is the total number of regions.

2. The business visualization method according to claim 1, characterized in that: The calculation formula of the competition intensity is as follows: ； in, For the The intensity of competition in a region is the total number of competitors, For competitors The sales price, For the Regions and competitors The geographical weight, For the Regions and competitors distance.

3. The business visualization method according to claim 2, characterized in that: The S3 specifically includes the following steps: Extracting inventory feature matrix and the competition distribution matrix The regional data in is mapped to the geographic coordinate system, the geographic boundaries of each region are determined, and an initial set of geographic grids is constructed to represent the basic distribution unit of the heat map; For the inventory feature matrix The data is color mapped, and the color gradient is used to encode the inventory status according to the inventory saturation rate. The value range of the inventory shortage is displayed in shades of color; Competition Distribution Matrix The intensity of competition The data is spatially annotated to extract the boundaries of areas with high competition intensity, highlight potential competition areas with boundaries, and combine geographic location overlap analysis to ensure that the annotated boundaries are aligned with the geographic grid areas; Overlay and render the inventory status color gradient and the competition area boundary highlight, adjust the inventory status color transparency, retain the recognizability of the two types of information, dynamically generate a heat map, and ensure the independence of the color gradient and boundary marking.

4. The business visualization method according to claim 3, characterized in that: The S4 comprises the following steps: From the inventory feature matrix Filter Inventory Saturation Rate Below threshold The area is regarded as a shortage area, and the distribution demand data of the shortage area is extracted; Based on the geographical location and logistics distribution path data of the inventory shortage area, the regional characteristics are associated with the distribution path to determine the demand intensity and path load on each path; The load balance coefficient of the path is calculated through the logistics distribution algorithm, and the nodes with higher-than-average path load are screened to generate a set of logistics bottleneck nodes; The set of logistics bottleneck nodes is mapped to a dynamic heat map, the bottleneck nodes are dynamically marked on the logistics path, and the path load degree is indicated by different marking intensities.

5. The business visualization method according to claim 4, characterized in that: The load balancing factor is calculated as: Delivery route Load balancing factor Calculated through the relationship between path demand intensity and path capacity: ； in, For delivery route The set of areas covered, For delivery route Capacity; For each path , if the condition is met , then the node in the marked path is a bottleneck node, where is the average load balancing factor of all paths, is the load threshold factor.

6. The business visualization method according to claim 5, characterized in that: The S1 further comprises: Aggregate the data according to the geographic location field to generate a regional data set, and add a regional attribute identifier to each region to distinguish regional characteristics; When performing geographic location matching, the following formula is used to calculate the comprehensive attribute value of each area: ； in, For the The comprehensive attribute value of a region represents the comprehensive evaluation result of the inventory status and purchasing behavior characteristics of the region. is the total number of data features, For the The weight of the feature, For the The area in The standardized value of the feature, For the The area in The geographical distribution density of each feature; According to the size of the comprehensive attribute value, the regions are stratified and divided into different categories. The stratification results are visually encoded and the comprehensive attribute values ​​of the regions are displayed in layers using color gradients.

7. A business visualization system based on the business visualization method according to any one of claims 1 to 6, characterized in that: Also includes: The data collection module is used to collect inventory data, user purchase behavior data, and public competitor market dynamics data, format the collected data, ensure the time synchronization and geographical relevance of the data source, and generate a regional distribution data set; A calculation module, for extracting the inventory shortage degree and competitive regional characteristics based on the regional distribution data set, respectively calculating the inventory saturation rate and competition intensity of the commodities in the region, and generating an inventory characteristic matrix and a competition characteristic matrix; An identification module is used to generate a dynamic heat map with inventory status and competition area identification based on the inventory feature matrix and competition feature matrix based on geographical regions, and distinguish the degree of inventory shortage through color gradients, and highlight potential competition areas with boundaries; The logistics marking module is used to match the data of the inventory shortage area in the inventory feature matrix with the distribution path data based on the logistics distribution algorithm, generate a set of logistics bottleneck nodes, and display the logistics bottleneck in the form of dynamic marking on the dynamic heat map.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the business visualization method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the business visualization method according to any one of claims 1 to 6 are implemented.

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