Residential portrait determination method and device, equipment and storage medium

Abstract

The invention discloses a resident portrait determination method and device, equipment and a storage medium, and relates to the technical field of smart communities, and the method comprises the steps: obtaining the continuous behavior data of residents in a preset region in a preset time period, and determining the continuous behavior characteristics of the residents according to the continuous behavior data; dividing the continuous behavior characteristics and the static behavior characteristics of the residents according to the service scenes and the behavior categories to obtain the continuous behavior characteristics and the static behavior characteristics corresponding to the behavior categories in the service scenes; and determining a resident portrait according to the continuous behavior characteristics and the static behavior characteristics corresponding to each behavior category in each service scene. According to the technical scheme, the continuous behavior characteristics and the static behavior characteristics are combined, precise construction of the resident portrait of the residents in the preset area within the preset time period is achieved, the constructed resident portrait can reflect living habits and behavior habits of the community residents, and precise description of the community residents is achieved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of smart community technology, and in particular, to a method, device, equipment and storage medium for determining a resident portrait. Background Art

[0002] Smart community is a new model of community management and service proposed with the development of society. It makes full use of the achievements of the new generation of digital technology and integrates various applications and devices to provide community residents with stronger perception, more convenient communication, and the ability to quickly organize various resources to complete corresponding service functions. When smart communities provide services to community residents, they need to conduct demand analysis, which often requires reference to resident portraits. Therefore, the determination of resident portraits for community residents has become a technical problem that needs to be solved urgently.

[0003] In the prior art, various information reported by community residents is usually collected through online surveys, and resident portraits are determined based on the information provided by the residents.

[0004] However, the information provided by community residents cannot fully reflect the living conditions and behavioral habits of community residents, and the accuracy of the determined resident portraits is not high. Summary of the invention

[0005] The present invention provides a method, device, equipment and storage medium for determining a resident portrait, so as to achieve accurate portrait determination of community residents.

[0006] In a first aspect, an embodiment of the present invention provides a method for determining a resident portrait, comprising:

[0007] Acquire continuous behavior data of residents in a preset area within a preset time period, and determine continuous behavior characteristics of the residents according to the continuous behavior data;

[0008] Dividing the continuous behavior characteristics and static behavior characteristics of the residents according to service scenarios and behavior categories, and obtaining the continuous behavior characteristics and static behavior characteristics corresponding to each behavior category under each service scenario;

[0009] A resident portrait is determined based on the continuous behavior characteristics and the static behavior characteristics corresponding to each behavior category in each service scenario.

[0010] The technical solution of the embodiment of the present invention provides a method for determining a resident portrait, including: obtaining continuous behavior data of residents in a preset area within a preset time period, and determining the continuous behavior characteristics of the residents based on the continuous behavior data; dividing the continuous behavior characteristics and static behavior characteristics of the residents according to service scenarios and behavior categories, and obtaining the continuous behavior characteristics and static behavior characteristics corresponding to each behavior category under each service scenario; determining the resident portrait based on the continuous behavior characteristics and static behavior characteristics corresponding to each behavior category under each service scenario. The above technical scheme can firstly perform statistical processing on the continuous behavior data of residents in a preset area within a preset time period obtained from the server to determine the continuous behavior characteristics of the residents. Secondly, the continuous behavior characteristics and static behavior characteristics of the residents can be divided according to the service scenarios and behavior categories, and the continuous behavior characteristics and static behavior characteristics corresponding to each behavior category under each service scenario can be determined to achieve accurate division of the behavior characteristics of the residents. Furthermore, the continuous behavior characteristics and static behavior characteristics of multiple behavior categories under multiple service scenarios can be integrated to obtain the resident portraits of the residents in the preset area within the preset time period. By combining the continuous behavior characteristics and the static behavior characteristics, the resident portraits of the residents in the preset area within the preset time period can be accurately constructed. Moreover, the constructed resident portraits can reflect the living habits and behavioral habits of the community residents, and achieve accurate description of the community residents.

[0011] Further, determining the continuous behavior characteristics of the resident according to the continuous behavior data includes:

[0012] Determine the number and frequency of each action of the resident in each time interval of the preset time period according to the continuous behavior data;

[0013] The number and frequency of each action of the resident in each time interval of the preset time period are determined as the continuous behavior feature.

[0014] Furthermore, the continuous behavior characteristics and static behavior characteristics of the residents are divided according to service scenarios and behavior categories, including:

[0015] Dividing the continuous behavior characteristics and the static behavior characteristics according to the service scenarios to obtain the continuous behavior characteristics and the static behavior characteristics corresponding to each of the service scenarios;

[0016] The continuous behavior features and the static behavior features corresponding to each of the service scenarios are divided according to the behavior categories to obtain the continuous behavior features and the static behavior features corresponding to each of the behavior categories in each of the service scenarios.

[0017] Further, determining a resident portrait according to the continuous behavior characteristics and the static behavior characteristics corresponding to each behavior category in each service scenario includes:

[0018] For each behavior category in each service scenario, a classification feature is obtained by clustering and weighted averaging the continuous behavior feature and the static behavior feature;

[0019] The resident portrait is constructed according to the classification features corresponding to each behavior category in each service scenario.

[0020] Furthermore, by performing clustering weighted averaging on the continuous behavior features and the static behavior features, classification features are obtained, including:

[0021] Clustering the continuous behavior features and the static behavior features to obtain a preset number of clustering results;

[0022] Determine the feature mean and weight of each clustering result, and determine the classification feature according to the weight and feature mean of each clustering result.

[0023] Furthermore, the resident portrait is constructed according to the classification features corresponding to each behavior category in each service scenario, including:

[0024] Determine the target feature corresponding to the service demand from the classification features corresponding to each of the behavior categories in each of the service scenarios;

[0025] The target features corresponding to each of the service needs are integrated to obtain the resident portrait.

[0026] Furthermore, determining the target feature corresponding to the service demand in the classification features corresponding to each of the behavior categories in each of the service scenarios includes:

[0027] The classification features corresponding to each behavior category in each service scenario are input into a pre-trained classification integration model so that the classification integration model determines the target features corresponding to each service requirement, wherein the classification integration model is used to classify the classification features according to service requirements.

[0028] In a second aspect, an embodiment of the present invention further provides a resident portrait determination device, comprising:

[0029] An acquisition module, used to acquire continuous behavior data of residents in a preset area within a preset time period, and determine the continuous behavior characteristics of the residents according to the continuous behavior data;

[0030] A classification module, used to classify the continuous behavior characteristics and static behavior characteristics of the residents according to service scenarios and behavior categories, and obtain the continuous behavior characteristics and static behavior characteristics corresponding to each behavior category under each service scenario;

[0031] A determination module is used to determine a resident portrait based on the continuous behavior characteristics and the static behavior characteristics corresponding to each behavior category in each service scenario.

[0032] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0033] at least one processor; and a memory communicatively coupled to the at least one processor;

[0034] Wherein, the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the resident portrait determination method as described in any one of the first aspects.

[0035] In a fourth aspect, an embodiment of the present invention further provides a storage medium comprising computer executable instructions, wherein the computer executable instructions, when executed by a computer processor, are used to execute a resident portrait determination method as described in any one of the first aspects.

[0036] In a fifth aspect, the present application provides a computer program product, which includes computer instructions. When the computer instructions are executed on a computer, the computer executes the resident portrait determination method provided in the first aspect.

[0037] It should be noted that the above-mentioned computer instructions may be stored in whole or in part on a computer-readable storage medium. The computer-readable storage medium may be packaged together with the processor of the resident portrait determination device, or may be packaged separately from the processor of the resident portrait determination device, and this application does not limit this.

[0038] The description of the second, third, fourth and fifth aspects in this application can refer to the detailed description of the first aspect; and the beneficial effects of the description of the second, third, fourth and fifth aspects can refer to the beneficial effect analysis of the first aspect, which will not be repeated here.

[0039] In this application, the name of the above-mentioned resident portrait determination device does not limit the device or functional module itself. In actual implementation, these devices or functional modules may appear with other names. As long as the functions of each device or functional module are similar to those of this application, they fall within the scope of the claims of this application and their equivalent technologies.

[0040] These and other aspects of the present application will become more apparent from the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 A flowchart of a method for determining a resident portrait provided by an embodiment of the present invention;

[0043] Figure 2 A flowchart of another method for determining a resident portrait provided by an embodiment of the present invention;

[0044] Figure 3 A schematic diagram of the structure of a resident portrait determination device provided by an embodiment of the present invention;

[0045] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0047] The term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0048] The terms "first" and "second" and the like in the specification and drawings of this application are used to distinguish different objects, or to distinguish different processing of the same object, rather than to describe a specific order of objects.

[0049] In addition, the terms "including" and "having" and any variations thereof mentioned in the description of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0050] It should be mentioned before discussing exemplary embodiments in more detail that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe various operations (or steps) as sequential processes, many operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of various operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. The process can correspond to methods, functions, procedures, subroutines, subprograms, etc. In addition, the embodiments in the present invention and the features in the embodiments can be combined with each other without conflict.

[0051] It should be noted that, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0052] In the description of the present application, unless otherwise specified, “plurality” means two or more.

[0053] Figure 1 Flow chart of a method for determining a resident portrait provided by an embodiment of the present invention. This embodiment is applicable to situations where an accurate resident portrait needs to be determined. The method can be executed by a resident portrait determination device, such as Figure 1 As shown, the specific steps include:

[0054] Step 110: Acquire continuous behavior data of residents in a preset area within a preset time period, and determine continuous behavior characteristics of the residents based on the continuous behavior data.

[0055] The preset area may be understood as an area where resident portraits need to be determined, and the preset time period may be the time for collecting data for resident portraits.

[0056] Continuous behavior data may include daily life behavior data, travel behavior data, social behavior data, consumption behavior data, and health behavior data, etc. Continuous behavior characteristics may include the frequency and number of various behaviors.

[0057] Specifically, the server stores the data acquired by the service equipment and image acquisition equipment in the area in chronological order. Therefore, the data acquired by the service equipment and image acquisition equipment within a preset time period can be determined in the server according to the time identifier. These data are continuous behavior data, which may include residents' daily life behavior data, travel behavior data, social behavior data, consumption behavior data, and health behavior data, etc.

[0058] Furthermore, the acquired continuous behavior data can be statistically processed to determine the continuous behavior characteristics of the residents, and the continuous behavior characteristics can include the number and frequency of the behavior. For example, when the continuous behavior data is fitness data in the health behavior data, the fitness behavior includes the residents entering the fitness place without fitness, passing by the fitness place, or entering the fitness place for fitness, and the fitness data includes the image of the fitness area and the use data of fitness equipment, etc. Therefore, the number and frequency of each fitness behavior can be determined by statistically processing the fitness data, that is, the number and frequency of the residents entering the fitness place without fitness, passing by the fitness place, and entering the fitness place for fitness can be determined. The number here can be understood as person-times.

[0059] In the embodiment of the present invention, the continuous behavior characteristics of the residents are determined by statistically processing the continuous behavior data of the residents in the preset area within the preset time period obtained from the server.

[0060] Step 120: Divide the continuous behavior characteristics and static behavior characteristics of the residents according to service scenarios and behavior categories, and obtain the continuous behavior characteristics and static behavior characteristics corresponding to each behavior category under each service scenario.

[0061] Among them, static behavioral characteristics can be understood as behavioral characteristics determined based on the activity data provided by residents. The activity data provided by residents may include fitness information, social security information, pension information, medical information, subsistence allowances information, disabled people information, information on elderly people living alone, information on families with difficulties in life and information on unemployed people, etc. Static behavioral characteristics may include the number and frequency corresponding to each fitness behavior and the number and frequency corresponding to each social security processing behavior, etc.

[0062] Service scenarios may include fitness services, social security services, elderly care services, medical services, services for low-income households, services for the disabled, services for elderly people living alone, services for families in difficulties, and services for the unemployed.

[0063] Specifically, first, the continuous behavior characteristics and static behavior characteristics can be divided according to the service scenarios to obtain the continuous behavior characteristics and static behavior characteristics corresponding to each service scenario. Secondly, the continuous behavior characteristics and static behavior characteristics corresponding to each service scenario can be divided according to the behavior categories within each service scenario to obtain the continuous behavior characteristics and static behavior characteristics corresponding to each behavior category under each service scenario.

[0064] In the embodiment of the present invention, the continuous behavior characteristics and static behavior characteristics of residents are divided by service scenarios and behavior categories, and the continuous behavior characteristics and static behavior characteristics corresponding to each behavior category under each service scenario are determined to achieve accurate division of residents' characteristics.

[0065] Step 130: Determine a resident portrait based on the continuous behavior characteristics and the static behavior characteristics corresponding to each behavior category in each service scenario.

[0066] Specifically, a weighted average is taken of the continuous behavior characteristics and static behavior characteristics corresponding to each behavior category in each service scenario, the target number and target frequency of each behavior category in each service scenario are determined, and the target number and target frequency of each behavior category in each service scenario are integrated to obtain a resident portrait of the residents in a preset area within a preset time period.

[0067] In an embodiment of the present invention, by integrating the continuous behavior features and static behavior features of multiple behavior categories in multiple service scenarios, a resident portrait of residents in a preset area within a preset time period is obtained, thereby achieving accurate construction of a resident portrait of residents in a preset area within a preset time period.

[0068] The method for determining a resident portrait provided in an embodiment of the present invention includes: obtaining continuous behavior data of residents in a preset area within a preset time period, and determining continuous behavior characteristics of the residents based on the continuous behavior data; dividing the continuous behavior characteristics and static behavior characteristics of the residents according to service scenarios and behavior categories, and obtaining the continuous behavior characteristics and static behavior characteristics corresponding to each behavior category under each service scenario; determining the resident portrait based on the continuous behavior characteristics and static behavior characteristics corresponding to each behavior category under each service scenario. The above technical scheme can firstly perform statistical processing on the continuous behavior data of residents in a preset area within a preset time period obtained from the server to determine the continuous behavior characteristics of the residents. Secondly, the continuous behavior characteristics and static behavior characteristics of the residents can be divided according to the service scenarios and behavior categories, and the continuous behavior characteristics and static behavior characteristics corresponding to each behavior category under each service scenario can be determined to achieve accurate division of the behavior characteristics of the residents. Furthermore, the continuous behavior characteristics and static behavior characteristics of multiple behavior categories under multiple service scenarios can be integrated to obtain the resident portraits of the residents in the preset area within the preset time period. By combining the continuous behavior characteristics and the static behavior characteristics, the resident portraits of the residents in the preset area within the preset time period can be accurately constructed. Moreover, the constructed resident portraits can reflect the living habits and behavioral habits of the community residents, and achieve accurate description of the community residents.

[0069] Figure 2 This is a flow chart of another method for determining a resident portrait provided by an embodiment of the present invention. This embodiment is specific based on the above embodiment. Figure 2 As shown, in this embodiment, the method may further include:

[0070] Step 210: Obtain continuous behavior data of residents in a preset area within a preset time period.

[0071] The preset area may be a community, and the preset time period may be one month before the current time.

[0072] Specifically, the data acquired by the community service device and the image acquisition device within one month before the current moment can be determined in the server according to the time identifier.

[0073] Step 220: Determine the continuous behavior characteristics of the resident based on the continuous behavior data.

[0074] In one implementation, step 220 may specifically include:

[0075] The number and frequency of each action of the resident in each time interval of the preset time period are determined according to the continuous behavior data; the number and frequency of each action of the resident in each time interval of the preset time period are determined as the continuous behavior feature.

[0076] Among them, the time interval is formed after dividing a natural day according to actual needs, and the time period corresponding to each time interval can be set according to actual needs. For example, a natural day is divided into morning, noon, afternoon, and night, with morning being 07:00-11:00, noon being 11:00-13:00, afternoon being 13:00-17:00, and night being 18:00-06:00.

[0077] Specifically, first, the continuous behavior data can be divided according to time intervals to determine the continuous behavior data corresponding to each time interval. Secondly, the behaviors corresponding to the continuous behavior data corresponding to each time interval can be counted to determine the number and frequency of each behavior in each time interval. Then, the number and frequency of each behavior in each time interval can be determined as continuous behavior characteristics.

[0078] In an embodiment of the present invention, the number and frequency of each behavior in each time interval are determined based on the continuous behavior data corresponding to each time interval in a preset time period, and the number and frequency of each behavior in each time interval are determined as continuous behavior features, thereby achieving more accurate continuous behavior features.

[0079] Step 230: Divide the continuous behavior characteristics and the static behavior characteristics according to the service scenarios to obtain the continuous behavior characteristics and the static behavior characteristics corresponding to each service scenario.

[0080] Step 240: Divide the continuous behavior features and the static behavior features corresponding to each of the service scenarios according to the behavior categories to obtain the continuous behavior features and the static behavior features corresponding to each of the behavior categories in each of the service scenarios.

[0081] Specifically, first, the continuous behavior characteristics and static behavior characteristics can be divided according to the service scenarios to obtain the continuous behavior characteristics and static behavior characteristics corresponding to each service scenario. Secondly, the continuous behavior characteristics and static behavior characteristics corresponding to each service scenario can be divided according to the behavior categories within each service scenario to obtain the continuous behavior characteristics and static behavior characteristics corresponding to each behavior category under each service scenario.

[0082] Step 250: For each behavior category in each service scenario, obtain classification features by clustering and weighted averaging the continuous behavior features and the static behavior features.

[0083] In one implementation, step 250 may specifically include:

[0084] The continuous behavior features and the static behavior features are clustered to obtain a preset number of clustering results; a feature mean and a weight of each clustering result are determined, and the classification feature is determined according to the weight and feature mean of each clustering result.

[0085] Specifically, first, the continuous behavior features and static behavior features corresponding to the current behavior category in the current service scenario can be clustered to obtain a preset number of clustering results, that is, the behavior features of the preset category. Secondly, the feature mean and weight of each type of behavior feature can be determined. When determining the feature mean of each type of behavior feature, the mean of all continuous behavior features and static behavior features corresponding to each type of behavior feature can be calculated to obtain the feature mean of each type of behavior feature; when determining the weight of each type of behavior feature, the total number of features of each type of behavior feature is first determined based on the continuous behavior features and static behavior features corresponding to each type of behavior feature, and then the weight of each type of behavior feature is determined based on the total number of continuous behavior features and static behavior features corresponding to the current behavior category in the current service scenario and the total number of features of each type of behavior feature. Then, the weights and feature means of each type of behavior feature can be weighted averaged to obtain the classification features of the current behavior category in the current service scenario.

[0086] In the embodiment of the present invention, by clustering and weighted averaging the continuous behavior features and static behavior features corresponding to each behavior category in each service scenario, the classification features of each behavior category in each service scenario are determined, thereby achieving an accurate description of each behavior category in each service scenario.

[0087] Step 260: construct the resident portrait according to the classification features corresponding to each behavior category in each service scenario.

[0088] In one implementation, step 260 may specifically include:

[0089] Determine the target features corresponding to the service needs in the classification features corresponding to each of the behavior categories in each of the service scenarios; fuse the target features corresponding to each of the service needs to obtain the resident portrait.

[0090] Furthermore, determining the target feature corresponding to the service demand in the classification features corresponding to each of the behavior categories in each of the service scenarios includes:

[0091] The classification features corresponding to each behavior category in each service scenario are input into a pre-trained classification integration model so that the classification integration model determines the target features corresponding to each service requirement, wherein the classification integration model is used to classify the classification features according to service requirements.

[0092] Among them, service needs include at least health needs, dietary needs, housing needs, transportation needs, education needs, shopping needs, leisure needs and housekeeping needs.

[0093] Specifically, the classification integration model can be used to classify classification features according to service requirements, which is obtained by training the classification model with the historical classification features corresponding to each behavior category in each service scenario and the actual service requirements. Therefore, the classification features corresponding to each behavior category in each service scenario are input into the classification integration model, and the classification integration model can classify the classification features corresponding to each behavior category in each service scenario according to the service requirements, and determine the target features corresponding to each service requirement.

[0094] After determining the target features corresponding to each service demand, the target features corresponding to each service demand can be integrated to obtain the demand features corresponding to each service demand. Furthermore, the demand features corresponding to all service demands can be determined as resident portraits.

[0095] In an embodiment of the present invention, the target features corresponding to each service demand are integrated to obtain the demand features corresponding to each service demand, and the demand features corresponding to all service demands are determined as resident portraits, thereby achieving accurate construction of resident portraits of residents in a preset area within a preset time period.

[0096] The method for determining a resident portrait provided in an embodiment of the present invention includes: obtaining continuous behavior data of residents in a preset area within a preset time period; determining the continuous behavior characteristics of the residents based on the continuous behavior data; dividing the continuous behavior characteristics and the static behavior characteristics according to the service scenarios to obtain the continuous behavior characteristics and the static behavior characteristics corresponding to each of the service scenarios; dividing the continuous behavior characteristics and the static behavior characteristics corresponding to each of the service scenarios according to the behavior categories to obtain the continuous behavior characteristics and the static behavior characteristics corresponding to each of the behavior categories under each of the service scenarios; for each of the behavior categories under each of the service scenarios, obtaining classification characteristics by clustering and weighted averaging the continuous behavior characteristics and the static behavior characteristics; and constructing the resident portrait based on the classification characteristics corresponding to each of the behavior categories under each of the service scenarios. The above technical scheme, after obtaining the continuous behavior data of residents in a preset area within a preset time period from the server, can determine the number and frequency of each behavior in each time interval according to the continuous behavior data corresponding to each time interval in the preset time period, and determine the number and frequency of each behavior in each time interval as continuous behavior characteristics, so as to determine more accurate continuous behavior characteristics, divide the continuous behavior characteristics and static behavior characteristics of residents by service scenarios and behavior categories, determine the continuous behavior characteristics and static behavior characteristics corresponding to each behavior category in each service scenario, and achieve accurate division of resident behavior characteristics, and then cluster weighted average the continuous behavior characteristics and static behavior characteristics corresponding to each behavior category in each service scenario, determine the classification characteristics of each behavior category in each service scenario, and achieve accurate description of each behavior category in each service scenario, after determining the target characteristics corresponding to each service demand in the classification characteristics of each behavior category in each service scenario, integrate the target characteristics corresponding to each service demand to obtain the demand characteristics corresponding to each service demand, and determine the demand characteristics corresponding to all service demands as resident portraits, so as to achieve accurate construction of resident portraits of residents in the preset area within the preset time period. The constructed resident portrait can reflect the living habits and behavioral habits of community residents, achieve an accurate description of community residents, and provide an accurate description for the design and construction of various service functions of the smart community.

[0097] Figure 3 This is a schematic diagram of the structure of a resident portrait determination device provided by an embodiment of the present invention, which can be applied to situations where accurate resident portraits need to be determined. The device can be implemented by software and / or hardware and is generally integrated in an electronic device, such as a computer device.

[0098] like Figure 3 As shown, the device comprises:

[0099] An acquisition module 310 is used to acquire continuous behavior data of residents in a preset area within a preset time period, and determine continuous behavior characteristics of the residents according to the continuous behavior data;

[0100] A division module 320 is used to divide the continuous behavior characteristics and static behavior characteristics of the residents according to service scenarios and behavior categories, and obtain the continuous behavior characteristics and static behavior characteristics corresponding to each behavior category under each service scenario;

[0101] The determination module 330 is used to determine the resident portrait according to the continuous behavior characteristics and the static behavior characteristics corresponding to each behavior category in each service scenario.

[0102] The resident portrait determination device provided in this embodiment obtains continuous behavior data of residents in a preset area within a preset time period, and determines the continuous behavior characteristics of the residents based on the continuous behavior data; divides the continuous behavior characteristics and static behavior characteristics of the residents according to service scenarios and behavior categories, and obtains the continuous behavior characteristics and static behavior characteristics corresponding to each behavior category in each service scenario; determines the resident portrait based on the continuous behavior characteristics and static behavior characteristics corresponding to each behavior category in each service scenario. The above technical scheme can firstly perform statistical processing on the continuous behavior data of residents in a preset area within a preset time period obtained from the server to determine the continuous behavior characteristics of the residents. Secondly, the continuous behavior characteristics and static behavior characteristics of the residents can be divided according to the service scenarios and behavior categories, and the continuous behavior characteristics and static behavior characteristics corresponding to each behavior category under each service scenario can be determined to achieve accurate division of the behavior characteristics of the residents. Furthermore, the continuous behavior characteristics and static behavior characteristics of multiple behavior categories under multiple service scenarios can be integrated to obtain the resident portraits of the residents in the preset area within the preset time period. By combining the continuous behavior characteristics and the static behavior characteristics, the resident portraits of the residents in the preset area within the preset time period can be accurately constructed. Moreover, the constructed resident portraits can reflect the living habits and behavioral habits of the community residents, and achieve accurate description of the community residents.

[0103] Based on the above embodiment, the acquisition module 310 is specifically used for:

[0104] Acquire continuous behavior data of residents in a preset area within a preset time period; determine the number and frequency of each action of the residents in each time interval of the preset time period based on the continuous behavior data; determine the number and frequency of each action of the residents in each time interval of the preset time period as the continuous behavior feature.

[0105] Based on the above embodiment, the division module 320 is specifically used for:

[0106] The continuous behavior characteristics and the static behavior characteristics are divided according to the service scenarios to obtain the continuous behavior characteristics and the static behavior characteristics corresponding to each service scenario; the continuous behavior characteristics and the static behavior characteristics corresponding to each service scenario are divided according to the behavior categories to obtain the continuous behavior characteristics and the static behavior characteristics corresponding to each behavior category under each service scenario.

[0107] Based on the above embodiment, the determination module 330 is specifically used for:

[0108] For each behavior category in each service scenario, classification features are obtained by clustering and weighted averaging the continuous behavior features and the static behavior features; and the resident portrait is constructed according to the classification features corresponding to each behavior category in each service scenario.

[0109] In one implementation, the classification features are obtained by clustering and weighted averaging the continuous behavior features and the static behavior features, including:

[0110] The continuous behavior features and the static behavior features are clustered to obtain a preset number of clustering results; a feature mean and a weight of each clustering result are determined, and the classification feature is determined according to the weight and feature mean of each clustering result.

[0111] In one implementation, constructing the resident portrait according to the classification features corresponding to each behavior category in each service scenario includes:

[0112] Determine the target features corresponding to the service needs in the classification features corresponding to each of the behavior categories in each of the service scenarios; fuse the target features corresponding to each of the service needs to obtain the resident portrait.

[0113] Furthermore, determining the target feature corresponding to the service demand in the classification features corresponding to each of the behavior categories in each of the service scenarios includes:

[0114] The classification features corresponding to each behavior category in each service scenario are input into a pre-trained classification integration model so that the classification integration model determines the target features corresponding to each service requirement, wherein the classification integration model is used to classify the classification features according to service requirements.

[0115] The resident portrait determination device provided in the embodiment of the present invention can execute the resident portrait determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the resident portrait determination method.

[0116] It is worth noting that in the embodiment of the above-mentioned resident portrait determination device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0117] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 4 A block diagram of an exemplary electronic device 4 suitable for implementing embodiments of the present invention is shown. Figure 4 The electronic device 4 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0118] like Figure 4 As shown, the electronic device 4 is in the form of a general purpose computing electronic device. The components of the electronic device 4 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 connecting different system components (including the system memory 28 and the processing unit 16).

[0119] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0120] The electronic device 4 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 4, including volatile and non-volatile media, removable and non-removable media.

[0121] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 4 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 4 not shown, usually called a "hard drive"). Although Figure 4Not shown in the figure, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The system memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0122] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. Program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0123] The electronic device 4 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), one or more devices that enable a user to interact with the electronic device 4, and / or any device that enables the electronic device 4 to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the electronic device 4 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. Figure 4 As shown, the network adapter 20 communicates with other modules of the electronic device 4 via the bus 18. It should be understood that although Figure 4 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 4, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0124] The processing unit 16 executes various functional applications and page displays by running the program stored in the system memory 28, for example, implementing the resident portrait determination method provided in the embodiment of the present invention, which includes:

[0125] Acquire continuous behavior data of residents in a preset area within a preset time period, and determine continuous behavior characteristics of the residents according to the continuous behavior data;

[0126] Dividing the continuous behavior characteristics and static behavior characteristics of the residents according to service scenarios and behavior categories, and obtaining the continuous behavior characteristics and static behavior characteristics corresponding to each behavior category under each service scenario;

[0127] A resident portrait is determined based on the continuous behavior characteristics and the static behavior characteristics corresponding to each behavior category in each service scenario.

[0128] Of course, those skilled in the art will understand that the processor can also implement the technical solution of the resident portrait determination method provided in any embodiment of the present invention.

[0129] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, for example, a method for determining a resident portrait provided by an embodiment of the present invention is implemented. The method includes:

[0130] Acquire continuous behavior data of residents in a preset area within a preset time period, and determine continuous behavior characteristics of the residents according to the continuous behavior data;

[0131] Dividing the continuous behavior characteristics and static behavior characteristics of the residents according to service scenarios and behavior categories, and obtaining the continuous behavior characteristics and static behavior characteristics corresponding to each behavior category under each service scenario;

[0132] A resident portrait is determined based on the continuous behavior characteristics and the static behavior characteristics corresponding to each behavior category in each service scenario.

[0133] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0134] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0135] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0136] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0137] It should be understood by those skilled in the art that the modules or steps of the present invention described above can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, optionally, they can be implemented by a program code executable by a computer device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0138] In addition, the acquisition, storage, use, and processing of data in the technical solution of the present invention are in compliance with the relevant provisions of national laws and regulations.

[0139] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for determining a resident portrait, characterized in that: include: Acquire continuous behavior data of residents in a preset area within a preset time period, and determine continuous behavior characteristics of the residents according to the continuous behavior data; Dividing the continuous behavior characteristics and static behavior characteristics of the residents according to service scenarios and behavior categories, and obtaining the continuous behavior characteristics and static behavior characteristics corresponding to each behavior category under each service scenario; A resident portrait is determined based on the continuous behavior characteristics and the static behavior characteristics corresponding to each behavior category in each service scenario.

2. The method for determining a resident portrait according to claim 1, characterized in that: Determining the continuous behavior characteristics of the resident according to the continuous behavior data includes: Determine the number and frequency of each action of the resident in each time interval of the preset time period according to the continuous behavior data; The number and frequency of each action of the resident in each time interval of the preset time period are determined as the continuous behavior feature.

3. The method for determining a resident portrait according to claim 1, characterized in that: The continuous behavior characteristics and static behavior characteristics of the residents are divided according to service scenarios and behavior categories, including: Dividing the continuous behavior characteristics and the static behavior characteristics according to the service scenarios to obtain the continuous behavior characteristics and the static behavior characteristics corresponding to each of the service scenarios; The continuous behavior features and the static behavior features corresponding to each of the service scenarios are divided according to the behavior categories to obtain the continuous behavior features and the static behavior features corresponding to each of the behavior categories in each of the service scenarios.

4. The method for determining a resident portrait according to claim 1, characterized in that: Determining a resident profile according to the continuous behavior characteristics and the static behavior characteristics corresponding to each behavior category in each service scenario includes: For each behavior category in each service scenario, a classification feature is obtained by clustering and weighted averaging the continuous behavior feature and the static behavior feature; The resident portrait is constructed according to the classification features corresponding to each behavior category in each service scenario.

5. The method for determining a resident portrait according to claim 4, characterized in that: The classification features are obtained by clustering and weighted averaging the continuous behavior features and the static behavior features, including: Clustering the continuous behavior features and the static behavior features to obtain a preset number of clustering results; Determine the feature mean and weight of each clustering result, and determine the classification feature according to the weight and feature mean of each clustering result.

6. The method for determining a resident portrait according to claim 4, characterized in that: The resident portrait is constructed according to the classification features corresponding to each behavior category in each service scenario, including: Determine the target feature corresponding to the service demand from the classification features corresponding to each of the behavior categories in each of the service scenarios; The target features corresponding to each of the service needs are integrated to obtain the resident portrait.

7. The method for determining a resident portrait according to claim 6, characterized in that: Determining target features corresponding to service requirements from classification features corresponding to each behavior category in each service scenario includes: The classification features corresponding to each behavior category in each service scenario are input into a pre-trained classification integration model so that the classification integration model determines the target features corresponding to each service requirement, wherein the classification integration model is used to classify the classification features according to service requirements.

8. A resident portrait determination device, characterized in that: include: An acquisition module, used to acquire continuous behavior data of residents in a preset area within a preset time period, and determine the continuous behavior characteristics of the residents according to the continuous behavior data; A classification module, used to classify the continuous behavior characteristics and static behavior characteristics of the residents according to service scenarios and behavior categories, and obtain the continuous behavior characteristics and static behavior characteristics corresponding to each behavior category under each service scenario; A determination module is used to determine a resident portrait based on the continuous behavior characteristics and the static behavior characteristics corresponding to each behavior category in each service scenario.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; Wherein, the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the resident portrait determination method as described in any one of claims 1-7.

10. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions, when executed by a computer processor, are used to execute the resident portrait determination method as described in any one of claims 1-7.

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