Main body information display method and computer program product

Abstract

The invention discloses a subject information display method and a program product. The method comprises the following steps: acquiring subject identification information, first resource information and first space information of a target subject, and determining a space-time fusion feature according to the subject identification information, the first resource information and the first space information; determining subject change features according to the space-time fusion features and a time sequence model, and determining subject attribute information of the target subject according to the subject change features and an attribute prediction model; and displaying the target main body on an electronic map of an area where the target main body is located according to the main body attribute information and the spatial position information of the target main body. Therefore, more accurate subject attribute information of the target subject can be determined according to the multi-dimensional data information of the target subject, and the attribute information of the target subject is visually displayed, so that the determination efficiency and the selection success rate of the target subject are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a subject information display method and a computer program product. BACKGROUND

[0002] In the era of rapid development of informatization and intelligentization, subject attribute recognition technology has important application value in many fields such as city management, public security, business analysis, and resource scheduling.

[0003] Although the prior art has achieved a certain degree of attribute recognition in specific scenarios, there are still the following key technical defects in complex dynamic environments, resulting in insufficient recognition accuracy and reliability. SUMMARY

[0004] The present application provides a subject information display method and a computer program product to solve the technical problem of inaccurate target store attribute recognition results in related technologies.

[0005] According to an aspect of the present application, a subject information display method is provided, which comprises:

[0006] Obtaining subject identification information, first resource information and first spatial information of a target subject, determining spatio-temporal fusion features according to the subject identification information, the first resource information and the first spatial information;

[0007] Determining subject change features according to the spatio-temporal fusion features and a time series model, and determining subject attribute information of the target subject according to the subject change features and an attribute prediction model;

[0008] Displaying the target subject on an electronic map of the region where the target subject is located according to the subject attribute information and spatial location information of the target subject.

[0009] According to another aspect of the present application, a subject information display device is provided, which comprises:

[0010] A multi-dimensional data fusion module for obtaining subject identification information, first resource information and first spatial information of a target subject, and determining spatio-temporal fusion features according to the subject identification information, the first resource information and the first spatial information;

[0011] A model prediction module for determining subject change features according to the spatio-temporal fusion features and a time series model, and determining subject attribute information of the target subject according to the subject change features and an attribute prediction model;

[0012] An attribute information display module for displaying the target subject on an electronic map of the region where the target subject is located according to the subject attribute information and spatial location information of the target subject.

[0013] According to another aspect of the present application, there is provided an electronic device comprising:

[0014] at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the subject information display method according to any one of the embodiments of the present application.

[0015] According to another aspect of the present application, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the subject information display method according to any one of the embodiments of the present application when executed by the processor.

[0016] According to another aspect of the present application, the embodiments of the present disclosure also provide a computer program product comprising a computer program which, when executed by a processor, implements the subject information display method according to any one of the embodiments of the present disclosure.

[0017] The technical solution of the embodiments of the present application, by acquiring the subject identifier information, the first resource information and the first space information of the target subject, determining the space-time fusion feature according to the subject identifier information, the first resource information and the first space information; thereby constructing the multi-dimensional feature data, which effectively improves the quality of the data and is conducive to improving the accuracy of the target subject attribute recognition result. Then, by determining the subject change feature according to the space-time fusion feature and the time series model, and determining the subject attribute information of the target subject according to the subject change feature and the attribute prediction model; thereby combining the advantages of different models in data processing, the output subject attribute information can be more comprehensive, real and accurate. Finally, according to the subject attribute information and the spatial location information of the target subject, the target subject is displayed on the electronic map of the region where the target subject is located, so that the user can intuitively and conveniently understand the subject attribute information evaluation result of different target subjects, and can understand the development trend corresponding to different target subjects, so that the user can more conveniently, accurately and efficiently select the target subject, thereby improving the work efficiency.

[0018] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort based on these drawings.

[0020] Figure 1 is a flow chart of a subject information display method according to an embodiment of the present application;

[0021] Figure 2 is a flow chart of a subject information display method according to an embodiment of the present application;

[0022] Figure 3A is a schematic diagram of specific data content of basic attribute information and business qualification information in a subject information display method according to an embodiment of the present application;

[0023] Figure 3B is a schematic diagram of specific data content of transaction information in a subject information display method according to an embodiment of the present application;

[0024] Figure 3C is a schematic diagram of specific data content of geographic information data in a subject information display method according to an embodiment of the present application;

[0025] Figure 3D is a schematic diagram of a spatiotemporal fusion feature construction flow of a subject information display method according to an embodiment of the present application;

[0026] Figure 3E is a schematic diagram of a spatiotemporal cube data processing flow of a subject information display method according to an embodiment of the present application;

[0027] Figure 4 is a structural schematic diagram of a subject information display device according to an embodiment of the present application;

[0028] Figure 5 is a structural schematic diagram of an electronic device implementing a subject information display method according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, so that those skilled in the art can better understand the technical solutions of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of the present application.

[0030] It should be noted that the terms "first", "second", "target" and the like in the description, claims, and drawings of the present application are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] It should be noted that the modification of "one" or "multiple" mentioned in the present disclosure is illustrative but not restrictive, and those skilled in the art should understand that unless otherwise explicitly indicated in the context, it should be understood as "one or more".

[0032] The names of the messages or information exchanged between the devices in the embodiments of the present disclosure are only for illustrative purposes, and are not intended to limit the scope of the messages or information.

[0033] It can be understood that the above-mentioned notification and user authorization process are only illustrative and do not limit the implementation of the present disclosure, and other ways that meet the relevant laws and regulations can also be applied to the implementation of the present disclosure.

[0034] It can be understood that the data involved in the technical solutions (including but not limited to the data itself, the acquisition or use of the data) should comply with the requirements of the relevant laws and regulations and relevant provisions.

[0035] Embodiment one

[0036] Figure 1A flowchart for providing main body information display of an embodiment of the present application is provided. The embodiment can be applicable to a scenario of identifying the attributes of a shop through attribute data possessed by the shop and data of the area where the shop is located, and in particular, can be applicable to analyzing high-value shops with relatively high values in an area. The method can be executed by a main body information display device, which can be implemented in the form of hardware and / or software. Optionally, the main body information display device is implemented through an electronic device, which can be a mobile terminal, a PC terminal, a server, or the like. As shown in FIG. 8, the method can specifically include the following steps. Figure 1

[0037] S110, obtaining main body identification information, first resource information, and first space information of a target main body, and determining a space-time fusion feature according to the main body identification information, the first resource information, and the first space information.

[0038] The target main body can be understood as an object for attribute identification. The target main body can be set according to actual needs, and the present application does not limit this. For example, the target main body can be a shop or the like. The main body identification information can include at least one of preset main body identification, a main body belonging category, location description information, and main body composition information. The first resource information can include at least one of resource transfer information, resource transfer type, and resource transfer mode. The first space information can include space location information and / or location analysis data of the target main body. The location analysis data can be determined according to area environment information of an area where the target main body is located. The area environment data can include at least one of road distribution data, main body distribution data, interactive object distribution data of the target main body, and area planning data. The space-time fusion feature can be understood as a three-dimensional space-time cube data that is obtained by fusing a resource change state and space information change of the target main body, and has time dynamics, space correlation, and resource dependence. The space-time fusion feature can be used to reveal the coupling rules of behavior patterns, state evolution, and environmental interaction of the target main body in continuous space-time dimensions.

[0039] ​Specifically, first, the subject identification information corresponding to the target subject, the first resource information and the first space information are collected. Then, by preprocessing the subject identification information and the first space information, the explicit space position expression of the target subject is determined, and the space positions of all subjects are converted into latitude and longitude coordinates, so as to take latitude and longitude coordinates as a unified mark in the space dimension, and take latitude and longitude coordinates as X, Y coordinates of a three-dimensional coordinate system; the first resource information is time-stamped calibrated according to the time dimension, so that different types of resource information are arranged according to a unified time reference, and the calibrated time reference is taken as the Z coordinate of the three-dimensional coordinate system, thereby constructing the initial space-time data. Finally, by mapping the calibrated first resource information into the initial space-time data, the data coupling is performed according to the data in different dimensions after the unified reference, and the resource change characteristic information containing the time dimension and the space dimension is formed, so as to determine the space-time fusion feature. In the technical solution, the inherent information (information not changing with time) of the target subject, the resource transfer information and the position information are deeply fused, so that more accurate space-time fusion features representing the characteristic information of the target subject can be determined according to the space information of the target subject in different regions, so as to more accurately evaluate the value result and the value change of different target subjects.

[0040] In one embodiment, the space-time fusion feature is determined according to the subject identification information, the first resource information and the first space information, including: constructing a subject time feature according to the subject identification information and the first resource information, constructing a first space feature according to the first space information, and determining a space-time fusion feature according to the subject time feature and the first space feature.

[0041] The subject time feature can be constructed by the inherent attribute information (data information not changing with time) and the dynamic attribute information (data information changing with time) in the target subject, and is used to represent the data of the state change of the target subject. The first space feature can be understood as data information representing the position of the target subject in the space. For example, the first space feature information can be at least one of latitude and longitude coordinates, geographic grid encoding and administrative division information.

[0042] Specifically, first, the subject identification information and the first resource information are analyzed and processed to extract inherent attribute information and dynamic attribute information, thereby constructing a subject time feature; second, data representing the spatial position information of the target subject in the first spatial information is extracted as a first spatial feature. Finally, the subject time feature and the first spatial feature are fused and spliced to determine a spatio-temporal fusion feature. In the technical solution, multiple dimensional feature data are comprehensively processed to construct feature data mainly in the time dimension and the space dimension, thereby improving the robustness of the model processing, improving the quality of the processed data, and improving the accuracy of the target subject attribute identification.

[0043] S120, determining a subject change feature according to the spatio-temporal fusion feature and a time series model, and determining subject attribute information of the target subject according to the subject change feature and an attribute prediction model.

[0044] The time series model can include at least one of a Prophet time series prediction model, an AutoRegressive Integrated Moving Average (ARIMA) time series prediction model, a Generalized Additive Model (GAMs), and the like. The subject change feature can be understood as a feature data that changes in the time dimension and is analyzed and processed to represent a trend prediction result. For example, the subject change information can include at least one of holiday resource transfer prediction results, seasonal resource transfer prediction results, and resource transfer trend changes. The attribute prediction model can include a gradient boosting model. More specifically, the gradient boosting model can include at least one of a Light Gradient Boosting Machine (LightGBM), an eXtreme Gradient Boosting (XGBoost), and a Categorical Boosting (CatBoost). The subject attribute information can include at least one of a value result, a value change result, and a value prediction result of the target subject.

[0045] In the embodiment of the application, the time series model can be obtained by training a machine learning model according to sample spatio-temporal fusion features. The attribute prediction model can be obtained by training a machine learning model according to sample subject change features and their corresponding expected output attributes.

[0046] Specifically, according to the predetermined time series model, the information of the time dimension in the spatio-temporal fusion feature is analyzed for feature extraction, so as to determine the data variation law of the target subject in different periods through the historical data of the target subject, and then obtain the subject change feature. Further, by inputting the subject change feature into the attribute prediction model, and by analyzing and locking different subject change features and giving different weight values to different subject change feature data, the subject attribute information corresponding to the target subject is calculated and output. The technical solution combines the use of the time series model and the attribute prediction model to process the spatio-temporal fusion feature data, so that the feature data of different dimensions can be fully mined and processed, the advantages of different types of models are complementary, the accuracy, authenticity and reliability of the subject attribute information are enhanced, and the subject attribute information is more in line with the actual change of the target subject.

[0047] In one embodiment, the determination of the subject attribute information of the target subject according to the subject change feature and the attribute prediction model comprises: obtaining second resource information and second spatial information of the target subject, determining a subject resource feature according to the second resource information, and determining a second spatial feature according to the second spatial information; merging the subject change feature, the subject resource feature and the second spatial feature to obtain a subject fusion feature, and inputting the subject fusion feature into the attribute prediction model to obtain the subject attribute information of the target subject.

[0048] The second resource information can include at least one of resource transfer information, resource transfer amount, resource transfer time, resource transfer cost, resource transfer cost deduction mode, resource transfer type and the like. The second resource information can be the same as or different from the first resource information, which is not limited in the technical solution. The second spatial information can include at least one of spatial position information, spatial accommodation object and spatial layout information of the target subject. The second spatial information can be the same as or different from the first spatial information, which is not limited in the technical solution. The subject resource feature mainly refers to the resource information that has a greater impact on the target subject. For example, the subject resource feature mainly includes at least one of resource transfer amount, resource transfer cost and the like. The second spatial feature mainly refers to the position analysis data based on the target subject, which can be used to analyze and predict the development trend of the target subject. The subject fusion feature can be understood as a feature matrix containing dimension information such as time dimension and space dimension.

[0049] Specifically, the second resource information and the second space information contained in the target subject are acquired, and the subject resource characteristics of the target subject are determined according to the second resource information and the second space information respectively. Then, the target feature matrix containing multiple dimensions is formed by combining and splicing the subject change characteristics, the subject resource characteristics and the second space characteristics. The subject attribute information output by the attribute prediction model is obtained by inputting the feature matrix into the attribute prediction model. The subject change characteristics, the subject resource characteristics and the second space characteristics are processed together, so that the attribute prediction model can perform more refined model prediction based on multi-dimensional features, and the subject attribute information with higher prediction accuracy is obtained.

[0050] In another embodiment, the subject information display method further includes: periodically acquiring sample spatio-temporal fusion features of incremental sample subjects, and corresponding expected subject changes and expected subject attributes, and updating a time series model and an attribute prediction model according to the sample spatio-temporal fusion features of the incremental sample subjects and the corresponding expected subject attributes. Specifically, the sample spatio-temporal fusion features are periodically acquired, the sample spatio-temporal fusion features are input into the time series model to obtain target subject change characteristics corresponding to the sample spatio-temporal fusion features, the target subject change characteristics are input into the attribute prediction model to obtain target subject attribute information of the target subject, a change prediction loss is calculated according to the target subject change characteristics and the corresponding expected subject change characteristics, an attribute prediction loss is calculated according to the target subject attribute information and the corresponding expected subject attribute, the time series model is updated according to the change prediction loss, and the attribute prediction model is updated according to the attribute prediction loss.

[0051] The sample spatio-temporal fusion features can be obtained based on periodically collected subject identification information, first resource information and first space information. The target subject change characteristics refer to output results calculated according to the sample spatio-temporal fusion features. The target subject attribute information refers to output results calculated according to the sample subject change characteristics. The periodicity can include at least one of a one-week interval, a one-month interval and a three-month interval.

[0052] In one embodiment, sample spatio-temporal fusion features are generated based on periodically collected data information of the target subject, and are input into a time series model to obtain target subject change characteristics. The loss of the time series model is calculated according to the target subject change characteristics to obtain an updated time series model. Then, the target subject change characteristics output by the updated time series model are input into an attribute prediction model to obtain target subject attribute information. The loss function of the attribute prediction model is calculated according to the target subject attribute information to obtain an updated attribute prediction model. Thus, the periodic incremental training and updating of the model can be realized, so that the attribute recognition result of the target subject can be accurately calculated continuously.

[0053] S130, display the target subject on an electronic map of a region where the target subject is located according to the subject attribute information and the spatial position information of the target subject.

[0054] The spatial position information specifically can include at least one of longitude and latitude coordinates, topological position, and grid data of the target subject in space. The electronic map specifically can include at least one of Google Map, Baidu Map, Gaode Map, and ArcGIS.

[0055] Specifically, according to the spatial position information of the target subject, the subject attribute information contained in the target subject is stored in the electronic map by importing or editing, etc., so as to display the subject attribute information of the target subject in the spatial position corresponding to the target subject, so that the user can intuitively display the subject attribute data information related to the target subject when selecting the target subject, and the user can quickly and efficiently determine the appropriate target subject according to the displayed subject attribute information, so as to facilitate the development of the corresponding marketing plan.

[0056] In one embodiment, the displaying the target subject on an electronic map of a region where the target subject is located according to the subject attribute information and the spatial position information of the target subject comprises: determining a target display mode of the target subject according to the subject attribute information of the target subject, determining a target display position of the target subject in the electronic map of the region where the target subject is located according to the spatial position information of the target subject, and displaying the target subject at the target display position on the electronic map in the target display mode.

[0057] The target display mode can include at least one of a heat map, a point density map, and a region aggregation map. The target display position specifically refers to the position of the target subject displayed in the electronic map.

[0058] Specifically, according to the content of the subject attribute information of the target subject, the target display mode conforming to the content of the subject attribute information is determined; and then, according to the longitude and latitude coordinates contained in the spatial position information of the target subject, the display position of the target subject in the electronic map is determined. Thus, the target subject is displayed according to the target display mode determined in advance. In the technical solution, the best target display mode is determined according to the subject attribute information, so that the user can more intuitively, comprehensively, and conveniently understand the subject attribute information corresponding to the target subject when selecting the target subject.

[0059] In another embodiment, after displaying the target subject on the electronic map of the region where the target subject is located according to the subject attribute information and the spatial position information of the target subject, the method further comprises: in response to an attribute viewing operation input on the electronic map for the target subject, displaying an attribute change curve of the target subject; wherein the attribute change curve can be used to represent the change information of the subject attribute information of the target subject in the time dimension.

[0060] The attribute viewing operation can refer to an operation of displaying the subject attribute information of the target subject selected by the user.

[0061] Specifically, the change information of the subject attribute information of the target subject in the time dimension is displayed in response to the operation request of the user for attribute viewing of the target subject input on the electronic map. Therefore, the user can intuitively understand the value change of the target subject, so that the user can more accurately select the target subject.

[0062] The technical scheme of the embodiment of the application comprises the following steps: obtaining subject identification information, first resource information and first spatial information of a target subject; determining a space-time fusion feature according to the subject identification information, the first resource information and the first spatial information; constructing multi-dimensional feature data; thereby effectively improving the quality of the data and improving the accuracy of the attribute identification result of the target subject; determining a subject change feature according to the space-time fusion feature and a time series model; determining the subject attribute information of the target subject according to the subject change feature and an attribute prediction model; thereby combining the advantages of different models in data processing, so that the output subject attribute information is more comprehensive, real and accurate; and displaying the target subject on an electronic map of the region where the target subject is located according to the subject attribute information and the spatial position information of the target subject, so that the user can intuitively and conveniently understand the attribute information evaluation result of different target subjects and the development trend of different target subjects, thereby enabling the user to more conveniently, accurately and efficiently select the target subject and improving work efficiency.

[0063] Embodiment two

[0064] Figure 2 A flowchart of a subject information display method provided by the second embodiment of the application, the scheme in the embodiment is a refinement of the technical scheme of displaying the target subject on the electronic map of the region where the target subject is located according to the subject attribute information and the spatial position information of the target subject in the above-mentioned embodiments. The specific implementation can be referred to the description of the embodiment. The same or similar technical features as the above-mentioned embodiments are not described again. For example,Figure 2 As shown, the method can specifically include:

[0065] S210, obtaining subject identification information, first resource information and first space information of a target subject, determining a space-time fusion feature according to the subject identification information, the first resource information and the first space information;

[0066] S220, determining a subject change feature according to the space-time fusion feature and a time sequence model, and determining subject attribute information of the target subject according to the subject change feature and an attribute prediction model;

[0067] S230, determining a subject attribute weight of the target subject according to the first space feature of the target subject, determining target attribute information of the target subject according to the subject attribute information and the subject attribute weight, and displaying the target subject on an electronic map of a region where the target subject is located according to the target attribute information and space position information of the target subject.

[0068] The subject attribute weight is used to represent the influence degree of the position where the target subject is located on the attribute identification of the target subject. The target attribute information specifically refers to the subject attribute information after being corrected by the subject attribute weight, which can make the attribute identification result of the target subject more accurate.

[0069] Specifically, the subject attribute weight of the target subject is calculated by analyzing the first space feature data of the target subject. Then, the subject attribute information is adjusted by using the subject attribute weight to determine the target attribute information of the target subject. Finally, the target subject is displayed on the electronic map of the region where the target subject is located according to the space position information and the target attribute information of the target subject. Thus, the attribute identification result of the target subject is corrected and adjusted by the influence factors of the region where the target subject is located, so that the attribute identification of the target subject is more real and accurate.

[0070] The technical scheme of the embodiment of the application determines a target display mode of the target subject according to the subject attribute information of the target subject, determines a target display position of the target subject in an electronic map of a region where the target subject is located according to the space position information of the target subject, and displays the target subject at the target display position on the electronic map in the target display mode. Thus, the model output result can be more finely predicted and adjusted based on the time feature and the space feature, so that it is more consistent with the real value change of the target subject. Also, the target subject is displayed on the electronic map, so that the user can conveniently know the change of the subject attribute information corresponding to the target subject.

[0071] Embodiment three

[0072] Embodiment three of the present application provides a flow chart of a subject information display method. In order to better introduce the technical solutions provided by the present application, the present embodiment takes the identification of the attributes of a shop as an example. The flow chart of the present embodiment is shown in Figure 3A 、 Figure 3B 、 Figure 3C 、 Figure 3D and Figure 3E . In addition, it is emphasized that the shop information obtained in the present example, as contained in Figure 3A 、 Figure 3B 、 Figure 3C , has been authorized by the shop (target subject) for use.

[0073] Step one: obtain at least one of the following data of the target shop: basic attribute information, business qualification information, transaction information, and geographic location information. Preprocess the obtained data of the target shop to obtain processed data as shop feature data. The basic attribute information can include at least one of the following information: shop ID, legal person name, shop category, and shop name. The business qualification information can include at least one of the following information: qualification certificate, registered address, business address, holding company, and branch. The transaction information can include at least one of the following information: daily / monthly / annual total transaction amount, transaction time, service charge rate, service charge collection method, bank preferential activity amount, transaction type (consumption, refund, cancellation), card swiping, code scanning, and face swiping proportion. The geographic location information can include at least one of the following information: administrative division, road network, population density, per capita income, population structure distribution, population heat data, subway station location, bus station location, parking lot number, road congestion, public transportation planning, and shopping district planning. The preprocessing can include at least one of the following operations: data cleaning, extraction, and filling. Specifically, the basic attribute information and the business qualification information can be as shown in Figure 3A , the transaction information can be as shown in Figure 3B , and the geographic location information can be as shown in Figure 3C .

[0074] Step two: data alignment is performed on the obtained shop feature data. First, timestamp calibration is performed in the time dimension to unify the time reference of data from different data sources and handle data update period differences. Second, spatial position expressions are uniformly converted into latitude and longitude coordinates in the spatial dimension, and address ambiguity is eliminated. Then, spatio-temporal feature extraction is performed. In the time dimension, periodicity index and abnormal monitoring points are constructed according to changes in resource transfer data. In the spatial dimension, the number of competing shops within a radius of 1 km, shop location accessibility index, and the number of bus stations are calculated. Finally, a spatio-temporal cube is constructed by mapping shop resource transfer data to a three-dimensional coordinate system, where the X and Y coordinates are latitude and longitude data, and the Z coordinate is time, forming a spatio-temporal cube (spatio-temporal fusion feature). For example, the process of constructing a spatio-temporal cube can be as shown in Figure 3D .

[0075] Further, in this embodiment, the geographic location information of the target shop can also be analyzed, and a location weight coefficient (subject attribute weight) corresponding to the target shop is determined according to at least one data in the location information, which is used to represent the influence of the location of the target shop on the identification of the shop attribute. For example, the location weight coefficient can be a numerical value.

[0076] Step three: input the spatio-temporal cube data into a preset time series model, extract the time features to identify the trend, periodicity, and seasonal pattern changes in different time scales, and obtain the trend, seasonality, and other features in the prediction results. The obtained prediction results are merged and spliced with the spatial features, resource transfer features, and other data of the shop to obtain a combined feature matrix (subject fusion feature).

[0077] Step four: input the feature matrix and location weight coefficient into the value prediction model. The value prediction model flexibly adjusts the weights of different feature dimensions in the feature matrix according to the input feature matrix, analyzes and locks the core indicators, and can determine the value prediction result and value change trend of the target shop (subject attribute information) by designing a customized weighted objective function and combining the pre-calculated location weight coefficient. This technical solution combines the time series model and the value prediction model for joint data processing, making the target shop value prediction more accurate, flexible, and business interpretable.

[0078] Specifically, the process of data processing of the spatio-temporal cube data can be as shown in Figure 3E .

[0079] Step five: the value prediction result and the value change trend of the target store are displayed in the target electronic map based on a preset display mode, so as to intuitively show the value of the store in the specified area and the real-time change. The preset display mode can include at least one of a heat map, a point density map and a region aggregation map.

[0080] In the technical solution, the preprocessed data files can be periodically extracted and updated in increments to ensure that the training model can dynamically track market changes and guarantee the accuracy of the model output.

[0081] The technical solution in the embodiment of the application can effectively capture the coupling effect of the time periodicity and the spatial distribution of the target store by fusing multi-dimensional feature data, using multiple preset models for data processing and visualizing the output result, thereby improving the robustness of the model through the multi-dimensional feature data, effectively improving the accuracy of the model, and improving the accuracy of the model in identifying the attributes of the target store and the trend change, and visualizing the data result, so that business personnel can more intuitively understand the attribute information of the store according to the display result. The technical solution can provide quantitative data support for store screening and store interaction, and improve the information interaction efficiency and experience with the store.

[0082] Embodiment four

[0083] Figure 4 A structural schematic diagram of a subject information display device provided by the embodiment three of the application is shown in FIG. 4. Figure 4 As shown in the figure, the subject information display device includes a multi-dimensional data fusion module 401, a model prediction module 402 and an attribute information display module 403.

[0084] The multi-dimensional data fusion module 401 is configured to acquire subject identification information, first resource information and first spatial information of a target subject, and determine spatio-temporal fusion features according to the subject identification information, the first resource information and the first spatial information. The model prediction module 402 is configured to determine subject change features according to the spatio-temporal fusion features and a time series model, and determine subject attribute information of the target subject according to the subject change features and an attribute prediction model. The attribute information display module 403 is configured to display the target subject on an electronic map of a region where the target subject is located according to the subject attribute information and spatial position information of the target subject.

[0085] The technical scheme of the embodiment of the application, the multi-dimensional data fusion module 401 acquires subject identification information, first resource information and first space information of a target subject, determines a space-time fusion feature according to the subject identification information, the first resource information and the first space information, and constructs multi-dimensional feature data, thereby effectively improving the quality of data and improving the accuracy of the attribute recognition result of the target subject. Then, the model prediction module 402 determines a subject change feature according to the space-time fusion feature and a time sequence model, determines subject attribute information of the target subject according to the subject change feature and an attribute prediction model, thereby combining the advantages of different models in data processing, and making the output subject attribute information more comprehensive, real and accurate. Finally, the attribute information display module 403 displays the target subject on an electronic map of a region where the target subject is located according to the subject attribute information and space position information of the target subject, thereby making the user intuitively and conveniently understand the attribute information evaluation result of different target subjects and the development trend corresponding to different target subjects, so that the user can conveniently, accurately and efficiently select a target subject, thereby improving work efficiency.

[0086] On the basis of each optional technical scheme described above, the multi-dimensional data fusion module 401 can also include a feature construction unit, wherein the feature construction unit is configured to construct a subject time feature according to the subject identification information and the first resource information, construct a first space feature according to the first space information, and determine a space-time fusion feature according to the subject time feature and the first space feature.

[0087] On the basis of each optional technical scheme described above, the model prediction module 402 can also include a second information determination unit and a feature fusion unit, wherein the second information determination unit is configured to acquire second resource information and second space information of the target subject, determine a subject resource feature according to the second resource information, and determine a second space feature according to the second space information. The feature fusion unit combines the subject change feature, the subject resource feature and the second space feature to obtain a subject fusion feature, inputs the subject fusion feature into an attribute prediction model, and obtains the subject attribute information of the target subject.

[0088] On the basis of each of the optional technical solutions above, the attribute information display module 403 can further include a first subject display unit. The first subject display unit is configured to determine a subject attribute weight of the target subject according to the first spatial feature of the target subject, determine target attribute information of the target subject according to the subject attribute information and the subject attribute weight, and display the target subject on an electronic map of the region where the target subject is located according to the target attribute information and spatial position information of the target subject.

[0089] On the basis of each of the optional technical solutions above, the attribute information display module 403 can further include a second subject display unit. The second subject display unit is configured to determine a target display mode of the target subject according to the subject attribute information of the target subject, determine a target display position of the target subject in an electronic map of the region where the target subject is located according to spatial position information of the target subject, and display the target subject in the target display mode at the target display position on the electronic map.

[0090] On the basis of each of the optional technical solutions above, the subject information display device can further include an attribute change curve display module. The attribute change curve display module is configured to display an attribute change curve of the target subject in response to an attribute viewing operation input on the electronic map and directed at the target subject. The attribute change curve is used to represent change information of the subject attribute information of the target subject in the time dimension.

[0091] On the basis of each of the optional technical solutions above, the subject identification information includes at least one of a preset subject identification, a subject category to which the subject belongs, position description information, and subject composition information.

[0092] On the basis of each of the optional technical solutions above, the first resource information includes at least one of resource transfer information, a resource transfer type, and a resource transfer mode.

[0093] On the basis of each of the optional technical solutions above, the first spatial information includes spatial position information and / or position analysis data of the target subject. The position analysis data is determined according to regional environment information of the region where the target subject is located. The regional environment data includes at least one of road distribution data, subject distribution data, interactive object distribution data of the target subject, and regional planning data.

[0094] The subject information display device provided by the embodiment of the present application can execute the subject information display method provided by any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of executing the subject information display method. Technical details not described in the embodiment can be found in any of the subject information display methods described in the embodiments of the present application.

[0095] Embodiment five

[0096] Figure 5 A structural schematic diagram of an electronic device 10 that can be used to implement embodiments of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application described and / or claimed in this document.

[0097] As shown in Figure 5 The electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11, wherein the memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or loaded into the random access memory (RAM) 13 from the storage unit 18. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0098] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, a speaker, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunications networks.

[0099] The processor 11 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, and the like. The processor 11 performs various methods and processes described above, such as a subject information presentation method.

[0100] In some embodiments, the subject information presentation method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded onto the RAM 13 and executed by the processor 11, one or more steps of the subject information presentation method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the subject information presentation method by any other suitable means, such as by means of firmware.

[0101] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0102] Computer programs used to implement the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed by the processor of the machine, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0103] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0104] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0105] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0106] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0107] In particular, the processes described above with reference to the flow charts can be implemented as computer software programs in accordance with embodiments of the application. For example, embodiments of the application include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program comprising program code for executing the methods illustrated by the flow charts. In such embodiments, the computer program can be downloaded and installed from a network via the communication unit 19, or installed from the storage unit 18, or installed from the ROM 12. When the computer program is executed by the processor 11, the above-described functions defined in the methods of the embodiments of the application are performed.

[0108] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present application. For example, the steps recited in the present application can be performed in parallel, in series, or in a different order, without departing from the desired results of the technical solutions of the present application, and this is not limited herein.

[0109] The above detailed description does not constitute a limitation on the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method of presenting information about a subject, characterized by, The method comprises the following steps: obtaining subject identification information, first resource information and first space information of a target subject, determining spatio-temporal fusion features according to the subject identification information, the first resource information and the first space information; determining subject change features according to the spatio-temporal fusion features and a time sequence model, and determining subject attribute information of the target subject according to the subject change features and an attribute prediction model; displaying the target subject on an electronic map of a region where the target subject is located according to the subject attribute information and spatial position information of the target subject.

2. The method of claim 1, wherein, The step of determining spatio-temporal fusion features according to the subject identification information, the first resource information and the first space information comprises the following steps: constructing subject time features according to the subject identification information and the first resource information, constructing first space features according to the first space information, and determining spatio-temporal fusion features according to the subject time features and the first space features.

3. The method of claim 1, wherein, The step of determining subject attribute information of the target subject according to the subject change features and an attribute prediction model comprises the following steps: obtaining second resource information and second space information of the target subject, determining subject resource features according to the second resource information, and determining second space features according to the second space information; merging the subject change features, the subject resource features and the second space features to obtain subject fusion features, and inputting the subject fusion features into an attribute prediction model to obtain the subject attribute information of the target subject.

4. The method of claim 1, wherein, The step of displaying the target subject on an electronic map of a region where the target subject is located according to the subject attribute information and spatial position information of the target subject comprises the following steps: determining subject attribute weights of the target subject according to the first space features of the target subject, determining target attribute information of the target subject according to the subject attribute information and the subject attribute weights, and displaying the target subject on an electronic map of a region where the target subject is located according to the target attribute information and the spatial position information of the target subject.

5. The method of claim 1, wherein, The step of displaying the target subject on an electronic map of a region where the target subject is located according to the subject attribute information and spatial position information of the target subject comprises the following steps: determining a target display mode of the target subject according to the subject attribute information of the target subject, determining a target display position of the target subject in an electronic map of a region where the target subject is located according to the spatial position information of the target subject, and displaying the target subject in the target display mode at the target display position on the electronic map.

6. The method of claim 1, wherein, After the step of displaying the target subject on an electronic map of a region where the target subject is located according to the subject attribute information and spatial position information of the target subject, the method further comprises the following steps: in response to an attribute viewing operation input on the electronic map and directed at the target subject, displaying an attribute change curve of the target subject; wherein the attribute change curve is used to represent change information of the subject attribute information of the target subject in a time dimension.

7. The method of claim 1, wherein, The subject identification information includes at least one of preset subject identification, subject category to which the subject belongs, location description information, and subject composition information.

8. The method of claim 1, wherein, The first resource information includes at least one of resource transfer information, resource transfer type, and resource transfer mode.

9. The method of claim 1, wherein, The first space information includes spatial position information and / or position analysis data of the target subject; the position analysis data is determined according to regional environment information of an area where the target subject is located; the regional environment data includes at least one of road distribution data, subject distribution data, interaction object distribution data of the target subject, and regional planning data.

10. A computer program product comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the subject information display method according to any one of claims 1-9.

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