Information processing method and device, equipment and storage medium
By performing gridding and clustering processing on geographic images, the central location of resource supply modules such as ATMs can be identified, solving the problem of inaccurate ATM site selection and enabling more efficient deployment of resource supply modules.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- INDUSTRIAL AND COMMERCIAL BANK OF CHINA
- Filing Date
- 2023-03-20
- Publication Date
- 2026-05-15
AI Technical Summary
ATMs are facing a shrinking market due to reduced demand for cash payments, and need to optimize site selection to reduce operating costs and improve deployment accuracy.
By processing the preset geographic image into a grid, determining the weight of each grid image unit, and using a clustering algorithm to identify the central grid image unit, the location of the resource supply object can be accurately set.
This improved the deployment accuracy of resource supply modules, reduced the number of resource supply modules such as ATMs, and lowered deployment costs.
Smart Images
Figure CN116303860B_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the fields of computer technology and financial technology, and more particularly to an information processing method, apparatus, device, medium, and program product. Background Technology
[0002] With the rapid development of technology, the digital transformation of banking services is showing increasing results, and various services are gradually moving from offline to online.
[0003] As more and more customers use electronic payments, the demand for cash payments has been impacted, leading to a shrinking market for ATMs whose primary function is cash deposit and withdrawal. To reduce operating costs, it is necessary to optimize the location of ATMs, reduce the number of ATMs deployed, and determine their geographical locations to lower deployment costs while achieving better deployment accuracy. Summary of the Invention
[0004] In view of the above problems, this disclosure provides information processing methods, apparatus, equipment, media and program products.
[0005] According to a first aspect of this disclosure, an information processing method is provided, comprising:
[0006] A preset geographic image is processed into a grid to obtain grid image units, wherein the preset geographic image contains resource requirement marked objects;
[0007] Based on the deployment condition attribute information corresponding to the above-mentioned grid image unit, the grid weight of the above-mentioned grid image unit is determined, and the above-mentioned deployment condition attribute information represents the constraint conditions of the deployment resource supply module.
[0008] Based on the grid weights of the grid image units, the grid image units are clustered to obtain the central grid image unit.
[0009] Based on the clustering algorithm, the resource demand marker objects corresponding to the aforementioned central grid image units are processed to obtain the target resource demand marker objects; and
[0010] Based on the target resource demand marking objects mentioned above, determine the target resource supply objects applicable to characterizing the above resource supply modules.
[0011] According to embodiments of this disclosure, the above-mentioned clustering process of the grid image units based on their respective grid weights includes:
[0012] The affinity matrix is determined based on the grid weights of the aforementioned grid image units.
[0013] The affinity matrix is processed using the nearest neighbor propagation clustering algorithm to obtain the central grid image unit.
[0014] According to embodiments of this disclosure, the above-mentioned processing of the resource requirement marker object corresponding to the above-mentioned central grid image unit according to the clustering algorithm includes:
[0015] If the resource requirement marker objects contained in the above-mentioned central grid image unit meet the preset conditions, the above-mentioned central grid image unit will be determined as a candidate central grid image unit.
[0016] The resource requirement labeling objects contained in the above candidate center grid image units are identified as candidate resource requirement labeling objects;
[0017] The candidate resource requirement tags are processed according to the clustering algorithm described above.
[0018] According to embodiments of this disclosure, the aforementioned preset conditions include:
[0019] The number of the aforementioned resource requirement markers exceeds a preset marker count threshold; or
[0020] The number of the aforementioned resource requirement markers is greater than a preset marker number threshold, and the distance between multiple of the aforementioned resource requirement markers is less than or equal to a preset distance threshold.
[0021] According to embodiments of this disclosure, determining the target resource supply object suitable for characterizing the resource supply module based on the target resource demand marker object includes:
[0022] Based on the target resource demand marked objects, a first resource supply object is generated in the preset geographic image.
[0023] If the number of resource demand marker objects contained in the aforementioned central grid image unit is less than or equal to the aforementioned preset marker object number threshold, a second resource supply object is generated in the aforementioned central grid image unit; and
[0024] Based on the aforementioned first resource supply target and the aforementioned second resource supply target, the aforementioned target resource supply target is determined.
[0025] According to embodiments of this disclosure, the above-mentioned processing of the candidate resource demand marker objects according to the above-mentioned clustering algorithm includes:
[0026] Based on the candidate marker object attribute information, the candidate marker object weights of the aforementioned candidate resource demand marker objects are determined, wherein the aforementioned candidate marker object attribute information represents the deployment condition attribute information corresponding to the aforementioned candidate resource demand marker objects;
[0027] The candidate resource demand labels are processed using the nearest neighbor propagation clustering algorithm, and the weights of the candidate labels corresponding to each candidate resource demand label are determined.
[0028] According to embodiments of this disclosure, the above-mentioned gridding process of the preset geographic image to obtain grid image units includes:
[0029] The preset geographic image is gridded according to the first grid cell to obtain the initial grid image cell;
[0030] If the deployment condition attribute information corresponding to the above initial grid image unit is greater than the preset resource requirement attribute threshold, the above initial grid image unit is gridded according to the second grid unit to obtain the above grid image unit.
[0031] The area of the second grid cell is smaller than the area of the first grid cell.
[0032] According to embodiments of this disclosure, the above-mentioned deployment condition attribute information includes at least one of the following:
[0033] User traffic attribute information, user age structure attribute information, financial consumption attribute information, and the number of resource demand marker objects.
[0034] According to embodiments of this disclosure, the resource supply module includes at least one of the following:
[0035] ATMs and business transaction outlets.
[0036] A second aspect of this disclosure provides an information processing apparatus, comprising: a first obtaining module, configured to perform gridding processing on a preset geographic image to obtain grid image units, wherein the preset geographic image includes resource demand marker objects; a first determining module, configured to determine the grid weight of the grid image units based on deployment condition attribute information corresponding to the grid image units, wherein the deployment condition attribute information characterizes the constraints of deploying a resource supply module; a second obtaining module, configured to perform clustering processing on the grid image units based on their respective grid weights to obtain a central grid image unit; a third obtaining module, configured to process the resource demand marker objects corresponding to the central grid image units according to a clustering algorithm to obtain target resource demand marker objects; and a second determining module, configured to determine, based on the target resource demand marker objects, a target resource supply object suitable for characterizing the resource supply module.
[0037] A third aspect of this disclosure provides an electronic device comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors perform the method described above.
[0038] A fourth aspect of this disclosure also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the methods described above.
[0039] The fifth aspect of this disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.
[0040] According to the information processing methods, apparatus, equipment, media, and program products provided in this disclosure, by performing gridding processing on a preset geographic image containing resource demand markers, grid image units are obtained. The grid weight of each grid image unit is determined based on its corresponding deployment condition attribute information. The grid weight can be used to characterize the degree of resource demand within a grid image unit. Therefore, clustering the grid image units based on their grid weights allows the resulting central grid image unit to initially satisfy the resource demands of other grid image units within the same cluster. Processing the resource demand markers corresponding to the central grid image unit using a clustering algorithm further refines the target resource demand markers, enabling them to more accurately represent the central location of resource demand across different geographic areas (i.e., within clusters) in the preset geographic image. Thus, determining the target resource supply object based on the target resource demand marker allows resource supply modules set up according to the target resource supply object to accurately meet resource demands over a larger geographic area, improving the planning efficiency and accuracy of the deployment of resource supply modules, while at least partially reducing the number of resource supply modules such as ATMs and lowering deployment costs. Attached Figure Description
[0041] The foregoing contents, as well as other objects, features, and advantages of this disclosure, will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:
[0042] Figure 1 The illustrations depict application scenarios of information processing methods, apparatuses, devices, media, and program products according to embodiments of the present disclosure.
[0043] Figure 2 A flowchart illustrating an information processing method according to an embodiment of the present disclosure is shown schematically.
[0044] Figure 3 A flowchart illustrating a method for clustering grid image units according to an embodiment of the present disclosure is shown schematically.
[0045] Figure 4 The illustration schematically shows a grid image unit for gridding a preset geographic image according to an embodiment of the present disclosure;
[0046] Figure 5 A schematic diagram of a central grid image unit according to an embodiment of the present disclosure is shown;
[0047] Figure 6A A schematic diagram of a target resource requirement object according to an embodiment of the present disclosure is shown;
[0048] Figure 6B A schematic diagram of a target resource supply object according to an embodiment of the present disclosure is shown;
[0049] Figure 6C A schematic diagram of a target resource supply object according to another embodiment of the present disclosure is shown;
[0050] Figure 7 A schematic block diagram of an information processing apparatus according to an embodiment of the present disclosure is shown; and
[0051] Figure 8 A block diagram schematically illustrates an electronic device suitable for implementing an information processing method according to an embodiment of the present disclosure. Detailed Implementation
[0052] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.
[0053] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.
[0054] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.
[0055] When using expressions such as "at least one of A, B, and C", they should generally be interpreted in accordance with the meaning that is commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B, and C, etc.).
[0056] In the technical solutions disclosed herein, the collection, storage, use, processing, transmission, provision, disclosure, and application of data (including but not limited to user personal information) comply with the provisions of relevant laws and regulations, necessary confidentiality measures have been taken, and they do not violate public order and good morals.
[0057] With the rapid development of mobile payments, the increasing use of electronic payments by customers has impacted the demand for cash payments, leading to a shrinking market for ATMs primarily used for cash deposits and withdrawals. Meanwhile, banks' digital transformation is yielding results, with various services gradually moving from offline to online. To reduce operating costs, it is necessary to optimize the location of ATMs to ensure better utilization and more efficient deployment.
[0058] Embodiments of this disclosure provide an information processing method, including: performing gridding processing on a preset geographic image to obtain grid image units, wherein the preset geographic image includes resource demand marker objects; determining the grid weight of the grid image unit according to the deployment condition attribute information corresponding to the grid image unit, wherein the deployment condition attribute information characterizes the constraints of the deployment resource supply module; performing clustering processing on the grid image units according to their respective grid weights to obtain a central grid image unit; processing the resource demand marker objects corresponding to the central grid image unit according to the clustering algorithm to obtain target resource demand marker objects; and determining target resource supply objects suitable for characterizing the resource supply module based on the target resource demand marker objects.
[0059] Figure 1 The diagram illustrates an application scenario of information processing according to an embodiment of the present disclosure.
[0060] like Figure 1 As shown, application scenario 100 according to this embodiment may include an information processing method. Network 104 serves as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.
[0061] Users can interact with server 105 via network 104 using at least one of the first terminal device 101, second terminal device 102, and third terminal device 103 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, second terminal device 102, and third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).
[0062] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.
[0063] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.
[0064] It should be noted that the information processing method provided in this embodiment can generally be executed by server 105. Correspondingly, the information processing device provided in this embodiment can generally be located in server 105. The information processing method provided in this embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the information processing device provided in this embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105.
[0065] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.
[0066] The following will be based on Figure 1 The described scene, through Figure 2 Figure 6 provides a detailed description of the information processing method of the disclosed embodiment.
[0067] Figure 2 A flowchart illustrating an information processing method according to an embodiment of the present disclosure is shown schematically.
[0068] like Figure 2 As shown, the information processing method of this embodiment includes operations S210 to S250.
[0069] In operation S210, the preset geographic image is processed into a grid to obtain grid image units, wherein the preset geographic image contains resource requirement marked objects.
[0070] According to embodiments of this disclosure, a preset geographic image can be gridded by dividing the image into multiple grid cells to form grid image cells. Resource demand markers can represent areas or locations within the preset geographic image that have significant resource demands, such as for business transactions. For example, resource demand markers can represent residential areas, schools, public transportation hubs, and other similar locations. By setting resource demand markers on the preset geographic image, the locations of areas or locations with strong resource demands can be clearly and intuitively represented.
[0071] It should be understood that the resource requirement marker object can include any type, such as icon objects of shapes like circles and triangles, or objects of types like text and artistic drawings to represent the resource requirement marker object. The embodiments of this disclosure do not limit the specific type of the resource requirement marker object.
[0072] It should be noted that a grid image cell may include one or more resource requirement marker objects, or a grid image cell may not contain any resource requirement marker objects.
[0073] In operation S220, the grid weight of the grid image unit is determined based on the deployment condition attribute information corresponding to the grid image unit. The deployment condition attribute information represents the constraints of the deployment resource supply module.
[0074] According to embodiments of this disclosure, the resource supply module can be a device, equipment, or location used to provide resources such as business transactions. For example, the resource supply module may include transaction equipment such as ATMs, vending machines, and used goods recycling equipment. However, it is not limited to these; it may also include transaction locations such as convenience stores and bank branches. Embodiments of this disclosure do not limit the specific type of resource supply module; those skilled in the art can select one according to actual needs, as long as it meets the corresponding resource requirements.
[0075] According to embodiments of this disclosure, the constraints represented by the deployment condition attribute information may include restrictions on the deployment resource supply module such as rent level, power supply stability, and transportation convenience, and may also include return conditions for the deployment resource supply module such as expected transaction frequency and expected transaction amount. For example, the deployment condition attribute information can be represented by data related to population density, such as pedestrian flow and fixed number of residents.
[0076] In operation S230, the grid image units are clustered according to their respective grid weights to obtain the central grid image unit.
[0077] According to embodiments of this disclosure, image grid units can be processed based on any type of clustering algorithm. For example, grid image units can be processed based on the K-means clustering algorithm, but it is not limited to this. Clustering processing can also be performed based on other types of clustering algorithms. Embodiments of this disclosure do not limit the specific type of clustering algorithm used for clustering processing.
[0078] According to embodiments of this disclosure, clustering of grid image units can yield one or more clusters, with the central grid image unit serving as the cluster center of a cluster composed of multiple grid image units. Correspondingly, the geographical area corresponding to the central grid image unit can represent a stronger demand for business transactions, and it can be preliminarily determined that the central grid image unit can attract relevant personnel from other grid image units within the same cluster to acquire resources, such as for business transactions like cash withdrawals. Therefore, the central grid image unit can initially narrow down the geographical range suitable for deploying resource supply modules.
[0079] In operation S240, the resource demand marker objects corresponding to the central grid image units are processed according to the clustering algorithm to obtain the target resource demand marker objects.
[0080] According to embodiments of this disclosure, a target resource demand marker object can be obtained by processing the geographical locations of resource demand marker objects corresponding to the central grid image unit using a clustering algorithm, or by processing the deployment condition attribute information of resource demand marker objects corresponding to the central grid image unit. It should be understood that the target resource demand marker object can be the cluster center of multiple resource demand marker objects corresponding to the central grid image unit.
[0081] In operation S250, based on the target resource demand marking object, the target resource supply object suitable for characterizing the resource supply module is determined.
[0082] According to embodiments of this disclosure, the target resource supply object represents the geographical location information of the resource supply module. The target resource supply object can be set at a location that overlaps with the target resource demand marker object, or it can be set at a location within a preset distance from the target resource demand marker object.
[0083] According to embodiments of this disclosure, by performing gridding processing on a preset geographic image containing resource demand markers, grid image units are obtained. The grid weight of each grid image unit is determined based on its corresponding deployment condition attribute information. The grid weight can be used to characterize the degree of resource demand within each grid image unit. Therefore, clustering the grid image units based on their grid weights allows the resulting central grid image unit to initially satisfy the resource demands of other grid image units within the same cluster. Processing the resource demand markers corresponding to the central grid image unit using a clustering algorithm further refines the target resource demand markers, enabling them to more accurately represent the central location of resource demand across different geographic areas (i.e., within clusters) in the preset geographic image. Thus, determining the target resource supply object based on the target resource demand marker allows the resource supply module set up according to the target resource supply object to accurately meet resource demands over a larger geographic area. This improves the planning efficiency and deployment accuracy of the resource supply module's deployment location, while at least partially reducing the number of resource supply modules such as ATMs and lowering deployment costs.
[0084] According to an embodiment of this disclosure, in operation S210, the preset geographic image is gridded to obtain a grid image unit, which includes: the preset geographic image is gridded according to a first grid unit to obtain an initial grid image unit; when the deployment condition attribute information corresponding to the initial grid image unit is greater than a preset resource requirement attribute threshold, the initial grid image unit is gridded according to a second grid unit to obtain a grid image unit; wherein the area of the second grid unit is smaller than the area of the first grid unit.
[0085] According to embodiments of this disclosure, a preset geographic image is segmented based on a first grid cell to obtain initial grid image units surrounded by multiple grid cells. For example, the side length of the first grid cell represents 4km, and it is segmented into 4*4 initial grid image units. If the deployment condition attribute information corresponding to the initial grid image unit is greater than a preset resource demand attribute threshold, the initial grid image unit is segmented based on a second grid cell to obtain grid image units. For example, the side length of the second grid cell represents 2km, and it is segmented into 4*4 grid image units. This reduces the deployment condition attribute information corresponding to the grid image units, thereby meeting the deployment requirements of resource supply modules in areas or locations with high resource demand and improving deployment accuracy.
[0086] According to embodiments of this disclosure, the resource supply module includes at least one of the following: ATMs and business transaction outlets.
[0087] According to embodiments of this disclosure, a resource supply module is a device, equipment, or location used to provide resources such as those for business transactions, to meet resource demands such as cash deposit and withdrawal, transfers, and foreign exchange. For example, the resource supply module may include ATMs and business transaction outlets, but is not limited to these; it may also include bank branches, self-service terminal placement areas, etc.
[0088] According to embodiments of this disclosure, the deployment condition attribute information includes at least one of the following: user traffic attribute information, user age structure attribute information, fund consumption attribute information, and the number of resource demand marker objects.
[0089] According to embodiments of this disclosure, deployment condition attribute information characterizes the constraints of deploying the resource supply module. Deployment condition attribute information can be restrictive conditions such as user traffic attribute information like pedestrian flow and population density; it can also be applicable population conditions such as user age structure attribute information like age and proportion of age groups; it can also be capital consumption attribute information such as rental prices and daily operating costs; and it can also be the quantity information of resource demand marker objects such as residential areas, schools, bus stops, or subway stations.
[0090] According to an embodiment of this disclosure, in operation S220, the grid weight of the grid image unit is determined based on the layout condition attribute information corresponding to the grid image unit.
[0091] According to embodiments of this disclosure, the grid weights include weights for user age structure attributes, user traffic attributes, financial consumption attributes, and the number of resource demand markers.
[0092] Specifically, the formula (1) for calculating the weight of the user's age structure attribute information is as follows:
[0093]
[0094] In formula (1), θ age For the age structure attribute information of users, the age weight is λ. age A is the empirical moderating coefficient for the user's age structure attribute information factor. old To determine the number of permanent residents based on the preset user age, A total This refers to the total resident population.
[0095] The formula (2) for calculating the weight of user traffic attribute information is:
[0096]
[0097] In formula (2), θ vol λ represents the weight of user traffic attribute information. vol C is the empirical adjustment coefficient for user traffic attribute information factors. volC represents the average value of user traffic attribute information. inital This is for preset user traffic attribute information.
[0098] The formula (3) for calculating the weight of the capital consumption attribute information is as follows:
[0099]
[0100] In formula (3), θ price λ represents the weight of the fund consumption attribute information. price P is the empirical adjustment coefficient for the information factor of fund consumption attributes. price P represents the mean of the fund consumption attribute information. inital This is to preset the fund consumption attribute information.
[0101] The formula (4) for calculating the weight of the quantity information of the resource demand marker object is as follows:
[0102]
[0103] In formula (4), θ scale Weights, λ, are assigned to the quantity information of objects representing resource requirements. seale The empirical adjustment coefficient s for the quantitative information factor of the resource demand labeling object. scale To mark the quantity information of objects for resource requirements, s inital The quantity information of the objects marked for the preset resource requirements.
[0104] Figure 3 A flowchart illustrating a method for clustering grid image units according to an embodiment of the present disclosure is shown.
[0105] like Figure 3 As shown, the method for clustering grid image units in this embodiment includes operations S310 to S320.
[0106] In operation S310, the affinity matrix is determined based on the grid weights of each grid image cell.
[0107] In operation S320, the affinity matrix is processed based on the neighbor propagation clustering algorithm to obtain the central grid image unit.
[0108] According to an embodiment of this disclosure, the formula (5) for calculating the diagonal elements in the affinity matrix is:
[0109] s(k, k) = θ age ·θ vol ·θ price ·θ scale (5)
[0110] In formula (5), s(k, k) is the element in the kth row and kth column of the affinity matrix, representing the probability of constructing a resource supply module in grid k.
[0111] The formula (6) for calculating off-diagonal elements is:
[0112] s(i, k) = -|ik| 2 (6)
[0113] In formula (6), |ik| represents the distance between grid i and grid k.
[0114] The formula (7) for calculating the attraction matrix is:
[0115]
[0116] In formula (7), r(i, k) is the attraction of grid k as the cluster center of grid i, s(i, k) is the degree of belonging of grid i and grid k as cluster centers of each other, a(i, k′) is the degree of suitability of point k being chosen by point i as the cluster center, and s(i, k′) is the degree of belonging of grid i and grid k′ as cluster centers of each other.
[0117] In the attraction matrix, the formula (8) for calculating the diagonal elements is:
[0118]
[0119] In formula (8), r(i′, k) is the attraction of grid k as the cluster center of grid i', and a(k, k) is the diagonal element of the membership matrix.
[0120] In the attraction matrix, the formula (9) for calculating the off-diagonal elements is:
[0121]
[0122] In formula (9), r(k, k) is the attraction of grid k as the cluster center of grid k, r(i′, k) is the attraction of grid k as the cluster center of grid i′, and a(i, k) is the element in the i-th row and k-th column of the membership matrix.
[0123] Using formulas (5) to (9) above, the central grid image unit can be obtained. It can be preliminarily determined that the central grid image unit can attract relevant personnel from other grid image units in the same cluster to obtain resources, such as conducting business transactions like cash withdrawal. Therefore, the central grid image unit can initially narrow down the geographical range suitable for deploying resource supply modules.
[0124] According to an embodiment of this disclosure, in operation S240, processing the resource demand marker objects corresponding to the central grid image unit according to the clustering algorithm includes: if the resource demand marker objects contained in the central grid image unit meet preset conditions, determining the central grid image unit as a candidate central grid image unit; determining the resource demand marker objects contained in the candidate central grid image unit as candidate resource demand marker objects; and processing the candidate resource demand marker objects according to the clustering algorithm.
[0125] According to embodiments of this disclosure, the preset conditions can be a preset threshold for the number of marked objects or a preset distance threshold, which are not limited here.
[0126] Central grid image units that meet preset conditions are identified as candidate central grid image units. Resource demand markers contained within these candidate central grid image units are identified as candidate resource demand markers. The target resource demand markers are obtained by processing these candidate resource demand markers using a clustering algorithm.
[0127] According to embodiments of this disclosure, the preset conditions include the number of resource demand marker objects being greater than a preset marker object number threshold; or the number of resource demand marker objects being greater than a preset marker object number threshold, and the distance between multiple resource demand marker objects being less than or equal to a preset distance threshold.
[0128] Figure 4 The illustration shows a schematic diagram of a preset geographic image being gridded according to an embodiment of the present disclosure to obtain grid image units.
[0129] Figure 5 A schematic diagram of a central grid image unit according to an embodiment of the present disclosure is shown.
[0130] Figure 6A A schematic diagram of a target resource requirement object according to an embodiment of the present disclosure is shown.
[0131] Combination Figure 4 and Figure 5 As shown, a gridded image is generated based on a preset geographic image, resulting in multiple grid image units. The circular icon objects in each grid image unit represent resource demand marker objects contained within that unit. Based on the grid weights of each grid image unit, clustering is performed to obtain central grid image unit 501, central grid image unit 502, central grid image unit 503, and central grid image unit 504.
[0132] When the preset threshold for the number of marked objects is 1, if the number of resource requirement marked objects contained in the central grid image unit 501, central grid image unit 502, and central grid image unit 503 is greater than the preset threshold for the number of marked objects, then the central grid image unit 501, central grid image unit 502, and central grid image unit 503 are determined as candidate central grid image units.
[0133] According to embodiments of this disclosure, processing candidate resource demand marker objects using a clustering algorithm includes determining the candidate marker object weights of each candidate resource demand marker object based on the candidate marker object attribute information, wherein the candidate marker object attribute information represents the deployment condition attribute information corresponding to the candidate resource demand marker object; processing the candidate resource demand marker objects using a neighbor propagation clustering algorithm; and determining the candidate marker object weights corresponding to each candidate resource demand marker object.
[0134] According to embodiments of this disclosure, candidate resource demand marker objects and their respective candidate marker weights can be processed based on any type of clustering algorithm. For example, the AffinityPropagation clustering algorithm can be used to process candidate resource demand marker objects and their respective candidate marker weights. However, this is not limited to this; other types of clustering algorithms can also be used for clustering processing. The embodiments of this disclosure do not limit the specific type of clustering algorithm used for clustering processing.
[0135] According to embodiments of this disclosure, clustering of candidate resource demand markers and their corresponding weights yields one or more clusters. The target resource demand marker can be the cluster center of a cluster composed of multiple candidate resource demand markers. Correspondingly, the geographical region corresponding to the target resource demand marker can more accurately represent the central location of resource demand across different geographical areas (i.e., within clusters) in a preset geographical image. Thus, the target resource supply object is determined based on the target resource demand marker. Therefore, the target resource demand marker can further narrow down the geographical range suitable for deploying resource supply modules, enabling resource supply modules set based on the target resource supply object to accurately meet resource demand across a larger geographical area. This improves the planning efficiency and accuracy of precisely planning the deployment location of resource supply modules, while at least partially reducing the number of resource supply modules such as ATMs and lowering deployment costs.
[0136] For example, such as Figure 5 As shown, after processing the candidate resource demand labeled objects of candidate center grid image units 501, 502, and 503 using the nearest neighbor propagation clustering algorithm, the following is obtained: Figure 6A The circular icon objects of candidate center grid image units 501, 502 and 503 can be used as target resource requirement marker objects.
[0137] Figure 6B A schematic diagram of a target resource provisioning object according to an embodiment of the present disclosure is shown.
[0138] Figure 6C A schematic diagram of a target resource provisioning object according to another embodiment of the present disclosure is shown.
[0139] According to embodiments of this disclosure, in operation S250, determining the target resource supply object suitable for characterizing the resource supply module based on the target resource demand marker object includes:
[0140] Based on the target resource demand marker objects, a first resource supply object is generated in a preset geographic image; if the number of resource demand marker objects contained in the central grid image unit is less than or equal to a preset marker object number threshold, a second resource supply object is generated in the central grid image unit; and a target resource supply object is determined based on the first resource supply object and the second resource supply object.
[0141] The circular icon objects representing the target resource demand marker objects of candidate center grid image units 501, 502, and 503 are used to generate a first resource supply object in the preset geographic image. Therefore, the following is obtained: Figure 6B The triangle-shaped icon represents the first resource supply object.
[0142] In another embodiment of this disclosure, when the preset threshold for the number of marked objects is 1, Figure 5 If the number of resource demand marker objects contained in the central grid image unit 504 is less than or equal to a preset marker object number threshold, a second resource supply object can be generated based on the resource demand marker objects in the central grid image unit 504. Therefore, the following is obtained: Figure 6C The triangle-shaped icon object in the diagram represents the second resource supply object.
[0143] Determining the target resource supply object based on the first and second resource supply objects allows the resource supply modules set up according to the target resource supply objects to accurately meet the resource demand within a large geographical area, improve the planning efficiency of the deployment location of the precise resource supply modules, improve the deployment accuracy, and at the same time, at least partially reduce the number of resource supply modules such as ATMs deployed, thereby reducing deployment costs.
[0144] Based on the above information processing method, this disclosure also provides an information processing apparatus. The following will be combined with... Figure 7 The device is described in detail.
[0145] Figure 7 A schematic block diagram of an information processing apparatus according to an embodiment of the present disclosure is shown.
[0146] like Figure 7 As shown, the information processing device 700 of this embodiment includes a first obtaining module 710, a first determining module 720, a second obtaining module 730, a third obtaining module 740, and a second determining module 750.
[0147] The first acquisition module 710 is used to perform gridding processing on a preset geographic image to obtain grid image units, wherein the preset geographic image contains resource demand marker objects. In one embodiment, the first acquisition module 710 can be used to perform the operation S210 described above, which will not be repeated here.
[0148] The first determining module 720 is used to determine the grid weight of the grid image unit based on the deployment condition attribute information corresponding to the grid image unit. The deployment condition attribute information represents the constraints of the deployment resource supply module. In one embodiment, the first determining module 720 can be used to perform the operation S220 described above, which will not be repeated here.
[0149] The second obtaining module 730 is used to cluster the grid image units according to their respective grid weights to obtain the central grid image unit. In one embodiment, the second obtaining module 730 can be used to perform the operation S230 described above, which will not be repeated here;
[0150] The third obtaining module 740 is used to process the resource demand label objects corresponding to the central grid image units according to the clustering algorithm to obtain the target resource demand label objects. In one embodiment, the third obtaining module 740 can be used to perform the operation S240 described above, which will not be repeated here;
[0151] The second determining module 750 is used to determine the target resource supply object suitable for the characterizing resource supply module based on the target resource demand marking object. In one embodiment, the second determining module 750 can be used to perform the operation S250 described above, which will not be repeated here.
[0152] According to embodiments of this disclosure, the second obtaining module includes a determining first submodule and a first obtaining submodule. The first determining submodule is used to determine an affinity matrix based on the respective grid weights of the grid image units. The first obtaining submodule is used to process the affinity matrix based on a neighbor propagation clustering algorithm to obtain the central grid image unit.
[0153] According to embodiments of this disclosure, the third obtaining module includes a second determining submodule, a third determining submodule, and a processing submodule. The second determining submodule is used to determine a central grid image unit as a candidate central grid image unit if the resource demand marker objects contained in the central grid image unit meet preset conditions. The third determining submodule is used to determine the resource demand marker objects contained in the candidate central grid image unit as candidate resource demand marker objects. The processing submodule is used to process the candidate resource demand marker objects according to a clustering algorithm.
[0154] According to embodiments of this disclosure, the preset conditions include: the number of resource demand marker objects is greater than a preset marker object number threshold; or the number of resource demand marker objects is greater than a preset marker object number threshold, and the distance between multiple resource demand marker objects is less than or equal to a preset distance threshold.
[0155] According to embodiments of this disclosure, the second determining module includes a first generating submodule, a second generating submodule, and a fourth determining submodule. The first generating submodule is used to generate a first resource supply object in a preset geographic image based on the target resource demand marker object. The second generating submodule is used to generate a second resource supply object in the central grid image unit if the number of resource demand marker objects contained in the central grid image unit is less than or equal to a preset marker object number threshold. The fourth determining submodule is used to determine a target resource supply object based on the first and second resource supply objects.
[0156] According to embodiments of this disclosure, the processing submodule includes a determining subunit and a processing subunit. The determining subunit is used to determine the candidate labeling weights of each candidate resource demand labeling object based on the candidate labeling object attribute information, wherein the candidate labeling object attribute information represents the deployment condition attribute information corresponding to the candidate resource demand labeling object. The processing subunit is used to process the candidate resource demand labeling objects and their respective candidate labeling weights according to a neighbor propagation clustering algorithm.
[0157] According to embodiments of this disclosure, the first obtaining module includes a second obtaining submodule and a third obtaining submodule. The second obtaining submodule is used to perform gridding processing on a preset geographic image based on a first grid cell to obtain initial grid image cells. The third obtaining submodule is used to perform gridding processing on the initial grid image cells based on second grid cells when the deployment condition attribute information corresponding to the initial grid image cells is greater than a preset resource requirement attribute threshold, to obtain grid image cells. The area of the second grid cell is smaller than the area of the first grid cell.
[0158] According to embodiments of this disclosure, the deployment condition attribute information includes at least one of the following: user traffic attribute information, user age structure attribute information, fund consumption attribute information, and the number of resource demand marker objects.
[0159] According to embodiments of this disclosure, the resource supply module includes at least one of the following: ATMs and business transaction outlets.
[0160] According to embodiments of this disclosure, any plurality of modules among the first obtaining module 710, the first determining module 720, the second obtaining module 730, the third obtaining module 740, and the second determining module 750 may be combined into one module, or any one of these modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in one module. According to embodiments of this disclosure, at least one of the first obtaining module 710, the first determining module 720, the second obtaining module 730, the third obtaining module 740, and the second determining module 750 may be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging the circuitry, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in a suitable combination of any of these. Alternatively, at least one of the first obtaining module 710, the first determining module 720, the second obtaining module 730, the third obtaining module 740, and the second determining module 750 may be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.
[0161] Figure 8 A block diagram schematically illustrates an electronic device suitable for implementing an information processing method according to an embodiment of the present disclosure.
[0162] like Figure 8 As shown, an electronic device 800 according to an embodiment of this disclosure includes a processor 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage portion 808 into a random access memory (RAM) 803. The processor 801 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 801 may also include onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this disclosure.
[0163] RAM 803 stores various programs and data required for the operation of electronic device 800. Processor 801, ROM 802, and RAM 803 are interconnected via bus 804. Processor 801 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 802 and / or RAM 803. It should be noted that the programs may also be stored in one or more memories other than ROM 802 and RAM 803. Processor 801 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.
[0164] According to embodiments of this disclosure, the electronic device 800 may further include an input / output (I / O) interface 805, which is also connected to a bus 804. The electronic device 800 may also include one or more of the following components connected to the input / output (I / O) interface 805: an input section 806 including a keyboard, mouse, etc.; an output section 807 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN card, modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the input / output (I / O) interface 805 as needed. A removable medium 811, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 810 as needed so that computer programs read from it can be installed into the storage section 808 as needed.
[0165] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.
[0166] According to embodiments of this disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 802 and / or RAM 803 and / or one or more memories other than ROM 802 and RAM 803 described above.
[0167] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to cause the computer system to implement the item recommendation method provided in the embodiments of this disclosure.
[0168] When the computer program is executed by the processor 801, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.
[0169] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 809, and / or installed from a removable medium 811. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.
[0170] In such an embodiment, the computer program can be downloaded and installed from a network via communication section 809, and / or installed from removable medium 811. When the computer program is executed by processor 801, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.
[0171] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages include, but are not limited to, languages such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).
[0172] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
[0173] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.
[0174] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of this disclosure is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.
Claims
1. An information processing method, comprising: A preset geographic image is processed into a grid to obtain grid image units, wherein the preset geographic image contains resource requirement marker objects; The grid weight of the grid image unit is determined based on the deployment condition attribute information corresponding to the grid image unit, and the deployment condition attribute information represents the constraints of the deployment resource supply module. Based on the grid weights of each grid image unit, the grid image units are clustered to obtain the central grid image unit; The target resource demand marker object is obtained by processing the resource demand marker object corresponding to the central grid image unit using a clustering algorithm; and Based on the target resource demand marker objects, a first resource supply object is generated in the preset geographic image; if the number of resource demand marker objects contained in the central grid image unit is less than or equal to a preset marker object number threshold, a second resource supply object is generated in the central grid image unit; and the target resource supply object is determined based on the first resource supply object and the second resource supply object.
2. The method according to claim 1, wherein, The step of clustering the grid image units according to their respective grid weights includes: The affinity matrix is determined based on the grid weights of each grid image unit. The affinity matrix is processed using the nearest neighbor propagation clustering algorithm to obtain the central grid image unit.
3. The method according to claim 1, wherein, The process of processing the resource requirement marker object corresponding to the central grid image unit according to the clustering algorithm includes: If the resource requirement marker objects contained in the central grid image unit meet the preset conditions, the central grid image unit is determined as a candidate central grid image unit. The resource requirement marker objects contained in the candidate center grid image unit are identified as candidate resource requirement marker objects; The candidate resource requirement marker objects are processed according to the clustering algorithm.
4. The method according to claim 3, wherein, The preset conditions include: The number of resource demand marker objects is greater than the preset marker object number threshold; or The number of resource demand marker objects is greater than the preset marker object number threshold, and the distance between multiple resource demand marker objects is less than or equal to the preset distance threshold.
5. The method according to claim 3, wherein, The step of processing the candidate resource demand marker objects according to the clustering algorithm includes: Based on the candidate marker object attribute information, the candidate marker object weight of each candidate resource demand marker object is determined, wherein the candidate marker object attribute information represents the deployment condition attribute information corresponding to the candidate resource demand marker object; The candidate resource demand marker objects are processed according to the nearest neighbor propagation clustering algorithm, and the weights of the candidate marker objects corresponding to each candidate resource demand marker object are determined.
6. The method according to claim 1, wherein, The step of performing gridding processing on the preset geographic image to obtain grid image units includes: The preset geographic image is gridded according to the first grid unit to obtain the initial grid image unit; If the deployment condition attribute information corresponding to the initial grid image unit is greater than the preset resource requirement attribute threshold, the initial grid image unit is meshed according to the second grid unit to obtain the grid image unit. The area of the second grid cell is smaller than the area of the first grid cell.
7. The method according to any one of claims 1 to 6, wherein, The deployment condition attribute information includes at least one of the following: User traffic attribute information, user age structure attribute information, financial consumption attribute information, and the number of resource demand marker objects.
8. The method according to any one of claims 1 to 6, wherein, The resource supply module includes at least one of the following: ATMs and business transaction outlets.
9. An information processing apparatus, comprising: The first acquisition module is used to perform gridding processing on a preset geographic image to obtain grid image units, wherein the preset geographic image contains resource demand marker objects; The first determining module is used to determine the grid weight of the grid image unit based on the deployment condition attribute information corresponding to the grid image unit, wherein the deployment condition attribute information represents the constraint conditions of the deployment resource supply module. The second obtaining module is used to perform clustering processing on the grid image units according to their respective grid weights to obtain the central grid image unit; The third obtaining module is used to process the resource demand marker objects corresponding to the central grid image unit according to the clustering algorithm to obtain the target resource demand marker object; and The second determining module generates a first resource supply object in the preset geographic image based on the target resource demand marker object; generates a second resource supply object in the central grid image unit if the number of the resource demand marker objects contained in the central grid image unit is less than or equal to a preset marker object number threshold; and determines the target resource supply object based on the first resource supply object and the second resource supply object.
10. An electronic device, comprising: One or more processors; Storage device for storing one or more programs. Wherein, when the one or more programs are executed by the one or more processors, the one or more processors perform the method according to any one of claims 1 to 8.
11. A computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the method according to any one of claims 1 to 8.
12. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 8.