A method, device and terminal device for optimizing and allocating local life resources

Abstract

The embodiment of the present invention provides a method, apparatus and terminal device for optimizing and allocating local life resources, which belongs to the field of data analysis technology. The method includes: clustering according to initial consumption information and user driving paths to obtain target clustering results; obtaining relevant label information and corresponding relevant frequency information involved in the first sub-cluster in the target clustering results; determining the initial relevance index of the relevant label information to the initial consumption label according to the relevant frequency information; performing time decay on the initial relevance index according to the consumption time of the relevant label information to obtain the target relevance index; performing label screening from the initial consumption label according to the target relevance index to obtain the initial consumption type; obtaining the relevant driving path of the first sub-cluster from the user driving path, and adjusting the initial consumption type according to the relevant driving path to obtain the target consumption type; optimizing the initial surrounding configuration information according to the target consumption type to obtain the target surrounding configuration information.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to a method, apparatus, and terminal device for optimizing and allocating local life resources. Background Art

[0002] When collecting user consumption information based on target objects (such as gas stations), existing technologies typically rely on a single data source, for example, using only gas station consumption records to analyze user consumption behavior. However, this single-source data often fails to fully cover user consumption scenarios, resulting in incomplete and incomplete data that fails to accurately reflect users' true consumption preferences and behavior patterns. Furthermore, due to a lack of multi-dimensional data support, the constructed consumption profiles or identified consumption types are often overly partial, making it difficult to deeply analyze users' consumption habits and potential needs. This limitation further hinders the precise allocation of living resources, reducing the efficiency and effectiveness of resource allocation and making it difficult to achieve optimal resource utilization. Summary of the Invention

[0003] The main purpose of the embodiments of the present invention is to provide a method, device and terminal equipment for optimizing and allocating local life resources, aiming to solve the problem in related technologies that accurate allocation cannot be performed during the optimization and allocation of life resources, thereby reducing the efficiency and effectiveness of resource allocation and making it difficult to achieve optimal resource utilization.

[0004] In a first aspect, an embodiment of the present invention provides a method for optimizing and allocating local life resources, including:

[0005] Determine a target object and obtain initial surrounding configuration information corresponding to the target object;

[0006] Obtaining the initial consumption information of the target user under the target object and the user travel path corresponding to the target user;

[0007] Performing user clustering on the target user according to the initial consumption information and the user's travel path to obtain a target clustering result;

[0008] Obtain relevant label information involved in each first subclass cluster in the target clustering result, and obtain relevant frequency information corresponding to the relevant label information;

[0009] Determining an initial consumption tag, and determining an initial relevance index corresponding to the initial consumption tag by the relevant tag information based on the relevant frequency information;

[0010] Obtaining the consumption time corresponding to the relevant tag information, and performing time decay on the initial correlation index according to the consumption time to obtain a target correlation index;

[0011] Filter the initial consumption tags according to the target relevance index to obtain the initial consumption type corresponding to the first sub-category cluster;

[0012] Obtaining a related driving path corresponding to the first subclass cluster from the user's driving path, and adjusting information of the initial consumption type according to the related driving path to obtain a target consumption type;

[0013] The initial peripheral configuration information is resource optimized according to the target consumption type to obtain target peripheral configuration information.

[0014] In a second aspect, an embodiment of the present invention provides a local life resource optimization and allocation device, including:

[0015] A data acquisition module is used to determine a target object and obtain initial surrounding configuration information corresponding to the target object;

[0016] An information acquisition module is used to obtain the initial consumption information corresponding to the target user under the target object and the user travel path corresponding to the target user;

[0017] A data clustering module, configured to cluster the target users according to the initial consumption information and the user travel paths, and obtain target clustering results;

[0018] A data determination module, configured to obtain relevant label information involved in each first subclass cluster in the target clustering result, and obtain relevant frequency information corresponding to the relevant label information;

[0019] a data analysis module, configured to determine an initial consumption tag, and determine an initial correlation index corresponding to the initial consumption tag by the correlation tag information based on the correlation frequency information;

[0020] a data adjustment module, configured to obtain the consumption time corresponding to the relevant tag information, and perform time decay on the initial correlation index according to the consumption time to obtain a target correlation index;

[0021] A data screening module, configured to screen the initial consumption tags according to the target relevance index to obtain the initial consumption type corresponding to the first sub-category cluster;

[0022] an information adjustment module, configured to obtain a related driving path corresponding to the first sub-category cluster from the user's driving path, and adjust information of the initial consumption type according to the related driving path to obtain a target consumption type;

[0023] The resource optimization module is used to perform resource optimization on the initial peripheral configuration information according to the target consumption type to obtain target peripheral configuration information.

[0024] In the third aspect, an embodiment of the present invention also provides a terminal device, which includes a processor, a memory, a computer program stored on the memory and executable by the processor, and a data bus for realizing connection and communication between the processor and the memory, wherein when the computer program is executed by the processor, the steps of any local life resource optimization and allocation method provided in the specification of the present invention are implemented.

[0025] An embodiment of the present invention provides a method, apparatus, and terminal device for optimizing and allocating local life resources. The method includes: determining a target object and obtaining initial surrounding configuration information corresponding to the target object; obtaining initial consumption information corresponding to a target user under the target object and a user driving path corresponding to the target user, thereby clustering the target users according to the initial consumption information and the user driving path to obtain target clustering results, and then clustering the users by combining the initial consumption information and the driving path of the users, thereby more comprehensively capturing the user's consumption behavior and travel habits and avoiding the limitations of a single data source. Then obtain the relevant label information involved in each first subclass cluster in the target clustering result, and obtain the relevant frequency information corresponding to the relevant label information, and then determine the initial consumption label, and determine the initial relevance index corresponding to the relevant label information to the initial consumption label based on the relevant frequency information; obtain the consumption time corresponding to the relevant label information, and perform time decay on the initial relevance index based on the consumption time to obtain the target relevance index, and then perform label screening from the initial consumption label based on the target relevance index to obtain the initial consumption type corresponding to the first subclass cluster, and then obtain the initial consumption type corresponding to each first subclass cluster to avoid analysis errors caused by analyzing a single user, and then obtain the relevant driving path corresponding to the first subclass cluster from the user's driving path, and adjust the initial consumption type based on the relevant driving path to obtain the target consumption type; by analyzing the user's relevant driving path, the initial consumption type can be dynamically adjusted to ensure that the identification of the consumption type is more accurate and reflects the user's real needs, and finally optimize the initial surrounding configuration information based on the target consumption type to obtain the target surrounding configuration information. Therefore, by optimizing the surrounding configuration information according to the target consumption type, it is possible to achieve personalized allocation of resources and meet the specific needs of different user groups under the target object. It is also possible to avoid resource waste through precise resource optimization, ensure more reasonable resource allocation, and improve overall resource utilization efficiency. The optimized surrounding configuration information can better match user needs, improve the service quality of the target object, enhance user stickiness and loyalty, and thus significantly improve the accuracy of resource allocation around the target object, while enhancing the market competitiveness of the target object, reducing operating costs, and achieving data-driven scientific decision-making and continuous optimization. This all-round analysis and optimization method based on user behavior and needs provides strong support for the intelligent allocation of life resources. It also solves the problem in related technologies that the optimization and allocation of life resources cannot be accurately allocated, thereby reducing the efficiency and effectiveness of resource allocation and making it difficult to achieve optimal resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0027] Figure 1 A flowchart of a method for optimizing and allocating local living resources provided by an embodiment of the present invention;

[0028] Figure 2 A schematic diagram of the module structure of a local life resource optimization and allocation device provided by an embodiment of the present invention;

[0029] Figure 3 A schematic block diagram of the structure of a terminal device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0031] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0032] It should be understood that the terms used in this specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the specification and appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0033] Embodiments of the present invention provide a method, apparatus, and terminal device for optimizing and allocating local life resources. The method can be applied to a terminal device, which can be an electronic device such as a tablet computer, laptop computer, desktop computer, personal digital assistant, or wearable device. The terminal device can also be a server or a server cluster.

[0034] The following embodiments of the present invention are described in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features therein may be combined with each other.

[0035] Please refer to Figure 1 , Figure 1 A flowchart of a method for optimizing and allocating local living resources provided by an embodiment of the present invention.

[0036] like Figure 1 As shown, the local life resource optimization and allocation method includes steps S101 to S109.

[0037] Step S101: Determine a target object and obtain initial surrounding configuration information corresponding to the target object.

[0038] Exemplarily, the target object may be a user consumption place such as a gas station, supermarket, or restaurant, and the initial surrounding configuration information configured around the target object is determined based on the location information corresponding to the target object. The initial surrounding configuration information includes but is not limited to information about car washes, convenience stores, supermarkets, restaurants, etc. around the target object. The initial surrounding configuration information is used to characterize the relevant supporting facilities or stores that already exist around the target object.

[0039] Step S102: Obtain the initial consumption information corresponding to the target user under the target object and the user travel path corresponding to the target user.

[0040] Exemplarily, the initial consumption record of the target user is obtained from the management system of the target object. For example, when the target object is a gas station, the initial consumption record includes the refueling time, refueling amount, refueling type, consumption amount, etc., and the gas station's membership system is used to record the target user's consumption behavior and preferences, including membership card number, points record, etc.

[0041] Exemplarily, a target sensor deployed from the target object and initial surrounding configuration information obtains a relevant video corresponding to the target user, thereby identifying a target license plate corresponding to the target user in the relevant video and obtaining a user driving path corresponding to the target user from the relevant video.

[0042] In some embodiments, obtaining the user driving path corresponding to the target user includes: obtaining initial vehicle image information corresponding to a target sensor associated with the target object; preprocessing the initial vehicle image information to obtain corresponding target vehicle image information; windowing the target vehicle image information and then performing Fourier transform to obtain target spectrum information corresponding to the target vehicle image information; performing information detection on the target spectrum information to obtain a blur direction and blur length corresponding to the initial vehicle image information; performing image restoration processing on the target vehicle image information according to the blur direction and the blur length to obtain repaired image information corresponding to the target vehicle image information; performing license plate information recognition on the repaired image information to obtain predicted license plate information corresponding to the initial vehicle image information; obtaining target license plate information corresponding to the target user, and comparing the predicted license plate information with the target license plate information to obtain the user driving path corresponding to the target user.

[0043] For example, suitable target sensors, such as cameras, are selected and installed at key locations around a target object (e.g., a gas station) to ensure they can capture images of vehicles. The sensors capture images of vehicles passing by in real time to obtain initial vehicle image information. Image enhancement techniques (such as histogram equalization and contrast adjustment) are then used to improve image clarity and contrast, and to remove noise, such as Gaussian noise and salt-and-pepper noise, to obtain the target vehicle image information.

[0044] For example, a suitable window function (such as a Hanning window, a Hamming window, etc.) is selected to perform windowing processing on the target vehicle image information to reduce spectrum leakage, and then a two-dimensional Fourier transform is performed on the windowed image to convert the target vehicle image information from the spatial domain to the frequency domain to obtain the target spectrum information.

[0045] For example, the target spectrum is analyzed to identify key features within the spectrum, such as blur direction and blur length. These are then extracted for subsequent image restoration. Based on the blur direction and blur length, a blur kernel (such as a Gaussian blur kernel or a motion blur kernel) is estimated. A deblurring algorithm (such as Wiener filtering or iterative deconvolution) is then used to restore the image, restoring a clear image of the vehicle and obtaining repaired image information.

[0046] For example, a license plate positioning algorithm (such as edge detection, morphological operations, etc.) is used to determine the position of the license plate, and then the license plate area is extracted from the repaired image information to ensure that the extracted license plate area is clear and complete. Then, a license plate recognition algorithm (such as OCR technology, deep learning model, etc.) is used to recognize the characters on the license plate and generate predicted license plate information.

[0047] For example, the target license plate information corresponding to the target user is obtained from the gas station membership system, and then the predicted license plate information is compared with the target license plate information. When the predicted license plate information is consistent with the target license plate information, the corresponding installation position of the target sensor is obtained, and the user driving path corresponding to the target user is simulated according to the installation position.

[0048] Step S103: clustering the target users according to the initial consumption information and the user travel paths to obtain target clustering results.

[0049] For example, user consumption characteristics are extracted from the initial consumption information, such as consumption amount, consumption frequency, consumption time, etc. User travel characteristics are extracted from the user's driving path, such as frequently visited places, driving distance, travel time, etc.

[0050] For example, a suitable clustering algorithm is selected according to the characteristics of the data, such as K-means, hierarchical clustering, DBSCAN, etc., and then the parameters of the clustering algorithm are set, such as the K value (the number of clusters) in K-means, epsilon and min_samples in DBSCAN, etc., and then the user consumption characteristics and user travel characteristics are clustered to obtain the target clustering results.

[0051] In some embodiments, the target user is clustered according to the initial consumption information and the user driving path to obtain a target clustering result, including: determining the user departure position and user arrival position corresponding to the target user according to the user driving path; determining the target intention label corresponding to the target user and the first label probability corresponding to the target intention label according to the user departure position and the user arrival position; performing label identification according to the initial consumption information to obtain the user label information corresponding to the target user and the second label probability corresponding to the user label information; clustering the target user according to the target intention label, the first label probability, the user label information and the second label probability to obtain an initial clustering result; performing data correlation calculation on each second subclass cluster in the initial clustering result to obtain a data correlation value between the cluster center and the cluster data in the second subclass cluster; adjusting the initial clustering result according to the data correlation value to obtain the target clustering result corresponding to the target user.

[0052] For example, the user's travel path is analyzed to identify the location corresponding to each entry into the monitoring range of the target object and determine it as the user's departure location. The location corresponding to each exit from the monitoring range of the target object is determined as the user's arrival location. In other words, the location corresponding to the target user's earliest appearance in the video is determined as the user's departure location, and the location corresponding to the target user's last appearance in the video is determined as the user's arrival location.

[0053] Exemplarily, based on the user departure location and user arrival location corresponding to the target user and the member registration information corresponding to the target user, such as residential location, the intention classification model is used to infer the target user's travel intention, such as commuting, shopping, leisure, etc., so as to obtain the target intention label corresponding to the target user, and calculate the corresponding probability, that is, the first label probability, which is used to represent the confidence of the target intention label.

[0054] For example, the target user's initial consumption information is analyzed to identify consumption patterns and preferences, such as high-frequency consumption and specific types of consumption. Based on the corresponding consumption behaviors in the initial consumption information, the target user is labeled accordingly, such as "high-spending user" or "oil product preference user," and the probability of each label is calculated to obtain the second label probability.

[0055] Exemplarily, features such as the target intention label, the first label probability, the user label information, and the second label probability are fused to form a behavioral feature vector of the target user, and then a clustering algorithm (such as K-means, hierarchical clustering, etc.) is used to cluster the user feature vector to obtain an initial clustering result.

[0056] Exemplarily, the Euclidean distance or cosine similarity is used to calculate the correlation between the cluster center in each second subclass cluster and the cluster data to obtain the data correlation value between the cluster center in the second subclass cluster and the cluster data, and then the initial clustering result is adjusted according to the data correlation, such as merging similar clusters, splitting uneven clusters, etc., and then the final clustering result, i.e., the target clustering result, is determined to ensure the accuracy and rationality of the clustering.

[0057] Specifically, by combining user travel paths and consumption information, we can build more accurate user profiles and gain a deeper understanding of user behavior and preferences. Based on the target clustering results, we can optimize resource allocation around target objects (such as gas stations) and improve resource utilization efficiency.

[0058] In some embodiments, clustering the target user according to the target intent label, the first label probability, the user label information and the second label probability to obtain an initial clustering result includes: obtaining a first user and a second user from the target user, and obtaining a first intent label corresponding to the first user and a third label probability corresponding to the first intent label from the target intent label and the first label probability, and obtaining a second intent label corresponding to the second user and a fourth label probability corresponding to the second intent label; obtaining a first label information corresponding to the first user and a fifth label probability corresponding to the first label information from the user label information and the second label probability, and obtaining a first label information corresponding to the second user and a fifth label probability corresponding to the first label information The method further comprises: obtaining a sixth label probability corresponding to the second label information and the second label information; performing intersection processing on the first intention label and the second intention label to obtain first intersection information corresponding to the first user and the second user; performing intersection processing on the first label information and the second label information to obtain second intersection information corresponding to the first user and the second user; determining the user similarity between the first user and the second user by fusing the third label probability, the fourth label probability, the fifth label probability, and the sixth label probability based on the first intersection information and the second intersection information; clustering the target users according to the user similarity to obtain the initial clustering result; wherein, the user similarity is obtained according to the following formula:

[0059]

[0060] in, represents the user similarity between the first user and the second user, Indicates the number of tags corresponding to the first intent tag corresponding to the first user, represents the third label probability corresponding to the t-th first intention label of the first user; Indicates the number of tags corresponding to the first tag information corresponding to the first user, represents the fifth label probability corresponding to the mth first label information corresponding to the first user; Indicates the number of tags corresponding to the second intent tag corresponding to the second user, The fourth label probability corresponding to the kth second intention label of the second user; Indicates the number of tags corresponding to the second tag information corresponding to the second user; represents the sixth label probability corresponding to the r-th second label information of the second user; represents the number of intersections corresponding to the first intersection information corresponding to the first user and the second user, represents the label probability corresponding to the first user under the d-th first intersection information; represents the label probability corresponding to the second user under the d-th first intersection information; represents the number of intersections corresponding to the second intersection information corresponding to the first user and the second user, represents the label probability corresponding to the first user under the sth second intersection information; represents the label probability corresponding to the second user under the sth second intersection information.

[0061] For example, two users are selected from the target user group, namely the first user and the second user. The first intent label and its third label probability corresponding to the first user are extracted from the target intent label and the first label probability, and the second intent label and its fourth label probability corresponding to the second user are extracted. Furthermore, the first label information and its fifth label probability corresponding to the first user are extracted from the user label information and the second label probability, and the second label information and its sixth label probability corresponding to the second user are extracted.

[0062] For example, the first intention tag of the first user and the second intention tag of the second user are compared to find their common tags and determine them as the first intersection information. At the same time, the first tag information of the first user and the second tag information of the second user are compared to find their common tags and determine them as the second intersection information.

[0063] Exemplarily, the user similarity between the first user and the second user is determined using the following formula based on the first intersection information and the second intersection information, by fusing the third label probability, the fourth label probability, the fifth label probability, and the sixth label probability:

[0064]

[0065] in, represents the user similarity between the first user and the second user, Indicates the number of tags corresponding to the first intent tag corresponding to the first user, represents the third label probability corresponding to the t-th first intention label of the first user; Indicates the number of tags corresponding to the first tag information corresponding to the first user, represents the fifth label probability corresponding to the mth first label information corresponding to the first user; Indicates the number of tags corresponding to the second intent tag corresponding to the second user, represents the fourth label probability corresponding to the k-th second intention label of the second user; Indicates the number of tags corresponding to the second tag information corresponding to the second user; represents the sixth label probability corresponding to the r-th second label information of the second user; Indicates the number of intersections corresponding to the first intersection information of the first user and the second user, represents the label probability corresponding to the first user under the d-th first intersection information; represents the label probability corresponding to the second user under the d-th first intersection information; represents the number of intersections corresponding to the second intersection information of the first user and the second user, represents the label probability corresponding to the first user under the s-th second intersection information; Represents the label probability corresponding to the second user under the s-th second intersection information.

[0066] For example, the similarity between any two users is calculated for all target users according to the above formula to form a similarity matrix. Then, a clustering algorithm (such as hierarchical clustering, spectral clustering, etc.) is used to cluster the users according to the similarity matrix to obtain an initial clustering result. Each cluster represents a user group with a high similarity.

[0067] In some embodiments, the data correlation calculation is performed on each second sub-cluster in the initial clustering result to obtain the data correlation value between the cluster center and the cluster data in the second sub-cluster, including: performing center point calculation on the second sub-cluster to obtain the cluster center corresponding to the second sub-cluster; obtaining the third intention label corresponding to the cluster center and the first probability information corresponding to the third intention label from the target intention label; obtaining the third label information corresponding to the cluster center and the second probability information corresponding to the third label information from the user label information; obtaining the fourth intention label corresponding to the cluster data in the second sub-cluster and the third probability information corresponding to the fourth intention label from the target intention label; obtaining the fourth label information corresponding to the cluster data in the second sub-cluster and the fourth probability information corresponding to the fourth label information from the user label information; determining the distance information between the cluster center and the cluster data based on the third intention label, the first probability information, the third label information and the second probability information and the fourth intention label, the third probability information, the fourth label information and the fourth probability information; and determining the data correlation value between the cluster center and the cluster data in the second sub-cluster based on the distance information.

[0068] For example, an average value calculation (such as the centroid calculation in K-means) or other center point calculation methods are used to calculate the center points of all data points in the second sub-cluster to obtain the cluster center. The intent label of the cluster center, i.e., the third intent label, is then extracted from the target intent label. The first probability information corresponding to the third intent label is also extracted to indicate the confidence level of the intent label. The label information of the cluster center, i.e., the third label information, is extracted from the user label information. The second probability information corresponding to the third label information is also extracted to indicate the confidence level of the label.

[0069] Exemplarily, the intent label for each cluster data in the second sub-cluster, i.e., the fourth intent label, is extracted from the target intent label. Third probability information corresponding to the fourth intent label is extracted to indicate the confidence level of the intent label. Label information for each cluster data in the second sub-cluster, i.e., the fourth label information, is extracted from the user label information. Fourth probability information corresponding to the fourth label information is also extracted to indicate the confidence level of the label.

[0070] For example, the third and fourth intent labels, as well as the third and fourth label information, are fused to form a comprehensive feature vector. The first, second, third, and fourth probability information are then fused (e.g., weighted summation) to obtain a comprehensive probability value. Based on the fused feature vector and probability values, the distance between the cluster center and each cluster data point is calculated using Euclidean distance and cosine similarity. Finally, based on this distance information, the data correlation value between the cluster center and the cluster data point is calculated. For example, smaller distances correspond to larger correlation values, while larger distances correspond to smaller correlation values.

[0071] Step S104: Obtain relevant label information involved in each first subclass cluster in the target clustering result, and obtain relevant frequency information corresponding to the relevant label information.

[0072] For example, relevant features of relevant users are extracted from each first sub-cluster, including consumption behavior, intent tags, and location information. Consumption records of each relevant user in the first sub-cluster at the target location (e.g., a gas station) are then analyzed, including consumption frequency, amount, and time of consumption. The user's intent tags are also analyzed to understand the user's travel purpose, such as commuting, traveling, or shopping. Patterns and trends in user consumption behavior are then identified based on the consumption records and travel intentions of the relevant users in the first sub-cluster. Based on these consumption patterns and trends, the consumption types of the relevant users in the first sub-cluster are then determined, such as daily consumption, travel consumption, or business consumption.

[0073] Exemplarily, the number and proportion of users of consumption types corresponding to relevant users in each first sub-category cluster are counted, and then based on the statistical results, the initial consumption type of each first sub-category cluster is determined, and the consumption type with a high proportion is selected as the representative.

[0074] Exemplarily, relevant tag information involved in the user data in the first sub-category cluster is obtained from the target intent tag and user tag information, and then the frequency of each relevant tag appearing in the first sub-category cluster, ie, relevant frequency information, is counted.

[0075] Step S105: Determine an initial consumption tag, and determine an initial relevance index corresponding to the initial consumption tag of the relevant tag information according to the relevant frequency information.

[0076] For example, based on business needs and user characteristics, possible initial consumption tags (such as daily consumption, travel consumption, business consumption, etc.) are determined. For each initial consumption tag, the support degree of the relevant tag information for the initial consumption tag is calculated using the relevant frequency information combined with the weight information of the relevant tag information in the initial consumption tag to obtain the initial relevance index.

[0077] Step S106: Obtain the consumption time corresponding to the relevant tag information, and perform time decay on the initial correlation index according to the consumption time to obtain a target correlation index.

[0078] For example, the consumption time corresponding to each relevant tag is extracted from the user data, and then the initial relevance index is time-decayed based on the consumption time. For example, the target relevance index is equal to the initial relevance index multiplied by exp(-λ *Δt), where λ is the decay coefficient and Δt is the difference between the consumption time and the earliest consumption time.

[0079] Step S107: Filter the initial consumption tags according to the target relevance index to obtain the initial consumption type corresponding to the first sub-category cluster.

[0080] Exemplarily, the target relevance indexes of all initial consumption tags are sorted, tags with higher relevance indexes are screened out, and thresholds are set according to business needs. Initial consumption tags with target relevance indexes higher than the threshold are retained, and tags with target relevance indexes lower than the threshold are removed. Then, based on the screened initial consumption tags, the initial consumption type of the first subclass cluster is determined.

[0081] In some embodiments, performing time decay on the initial correlation index according to the consumption time to obtain a target correlation index includes: obtaining an earliest behavior time and a latest behavior time from the consumption time; determining a time decay parameter corresponding to the consumption time according to the earliest behavior time and the latest behavior time; performing time decay on the initial correlation index according to the time decay parameter to obtain the target correlation index; wherein the target correlation index is obtained according to the following formula:

[0082]

[0083] in, Indicates the target relevance index corresponding to the i-th initial consumption tag of the relevant tag information; represents the consumption time corresponding to the j-th related tag information, Indicates the earliest behavior time, Indicates the latest behavior time, 、 represents the adjustment parameters determined by the initial consumption tag; Indicates the number of tags corresponding to the relevant tag information, represents the tag weight corresponding to the jth related tag information under the i-th initial consumption tag, represents the relevant frequency information corresponding to the j-th relevant tag information.

[0084] Exemplarily, a target relevance index is determined based on a mechanism of time decay and related frequency information to optimize the identification of the initial consumption type, taking into account the impact of time factors on user behavior, and adjusting the relevance index through the time decay parameter so that the most recent consumption behavior has a greater impact on the initial consumption type, while the earlier behavior has a smaller impact.

[0085] For example, relevant tag information and its corresponding consumption time are extracted from the user data in the first sub-cluster. The earliest and latest behavior times in the consumption time are determined. These time points are used to calculate a time decay parameter, which measures the degree to which each consumption time has decayed relative to the latest behavior time.

[0086] For example, the time decay parameter is used to adjust the initial relevance index to obtain the target relevance index. The target relevance index combines the relevant frequency information and the time decay factor to more accurately reflect the current consumption tendency of users in the first sub-cluster. The initial consumption tags are sorted and filtered according to the target relevance index, and the initial relevance index is time decayed according to the time decay parameter to obtain the target relevance index.

[0087] Exemplarily, the target correlation index is obtained by performing time decay on the initial correlation index according to the time decay parameter using the following formula:

[0088]

[0089] in, Represents the target relevance index of the relevant tag information corresponding to the i-th initial consumption tag; Indicates the consumption time corresponding to the jth related tag information, represents the earliest behavior time, Indicates the latest behavior time, 、 Indicates the adjustment parameters determined by the initial consumption tag; Indicates the number of tags corresponding to the relevant tag information, Indicates the label weight corresponding to the jth related label information under the i-th initial consumption label, Represents the relevant frequency information corresponding to the j-th relevant label information.

[0090] For example, all relevant tag information and the consumption time corresponding to these tags are extracted from the user data of the first sub-cluster. The consumption time is the time point when the user performs the behavior related to the tag. Among all the consumption times, the earliest behavior time and the latest behavior time are found, and then the consumption time is calculated based on the consumption time. To determine the time decay parameter, so as to weight each relevant label information and related frequency information It is fused with the time decay parameter to obtain the target correlation index.

[0091] For example, the above formula can consider the degree of influence of different relevant users in the first sub-cluster at different consumption times on the target relevance index calculation, thereby making the introduction of the time decay parameter significantly impact the definition of consumption type, thereby more accurately determining the initial consumption type of the first sub-cluster. This makes the identification of consumption type more closely aligned with the current status and preferences of the first sub-cluster, helping to provide more precise personalized services and resource optimization.

[0092] Step S108: Obtain a related driving path corresponding to the first subclass cluster from the user's driving path, and adjust information of the initial consumption type according to the related driving path to obtain a target consumption type.

[0093] Exemplarily, relevant driving paths related to the first subclass cluster are extracted from the user driving data, and then the relevant driving paths in the same first subclass cluster are integrated to form a typical driving path of the first subclass cluster (such as the most common path, high-frequency path, etc.).

[0094] For example, key features are extracted from typical driving routes, such as route length, area type (e.g., commercial, residential, industrial), route congestion, and distance between the route and the target destination. This is then used to analyze the correlation between these route features and the initial consumption type. For example, a route passing through a commercial area may be associated with "shopping," a route passing through a gas station may be associated with "refueling," and a route length may be associated with "long-distance travel." Weights are then assigned to each relevant route based on the importance of these features (e.g., route frequency, route length, and the degree of match between the route and the target destination). Based on these route features and weights, an adjustment factor for the initial consumption type is calculated.

[0095] For example, if a route passes through multiple gas stations and the user travels frequently, the weight of the "gas consumption" type may be increased; if a route passes through a commercial area and the user stays for a long time, the weight of the "shopping consumption" type may be increased.

[0096] For example, the initial consumption types are weighted according to the adjustment factor. The weight of each initial consumption type is multiplied by the adjustment factor to update the weight of the initial consumption type. The adjusted initial consumption type weights are sorted to select the consumption type with the highest weight, and then the consumption type with the highest weight is selected as the target consumption type.

[0097] In some embodiments, adjusting the information of the initial consumption type according to the relevant driving path to obtain the target consumption type includes: performing path prediction according to the relevant driving path to obtain the driving destination corresponding to the relevant users in the first subclass cluster; determining the user consumption intention corresponding to the relevant users in the first subclass cluster according to the driving destination; and adjusting the information of the initial consumption type according to the user consumption intention to obtain the target consumption type.

[0098] For example, historical driving route data is used to build a prediction model, such as a machine learning-based classification model, a sequence model (e.g., LSTM, GRU), or a deep learning model. Features are extracted from the relevant driving routes, including starting points, waypoints, route length, travel time, frequently visited locations, and route frequency. These extracted features are input into the prediction model to predict the user's next likely destination.

[0099] For example, a mapping relationship is established between travel destinations and consumption types, such as shopping malls for shopping, and gas stations for refueling. Based on the predicted destination, the corresponding consumption intention is mapped. Historical consumption data at the destination is also considered, such as the user's consumption frequency and amount at that location. The weight of consumption intention is then calculated based on the consumption intensity at the destination and the user's historical consumption behavior.

[0100] For example, the initial consumption type is compared with the user's consumption intention to identify any differences or similarities between the two. An adjustment factor for the initial consumption type is calculated based on the weight of the user's consumption intention. The initial consumption type and the adjustment factor are combined using methods such as weighted averaging and maximum weight selection to determine the target consumption type.

[0101] Step S109: Optimize resources on the initial peripheral configuration information according to the target consumption type to obtain target peripheral configuration information.

[0102] For example, the specific requirements of the target consumption type for the surrounding configuration, such as the type, quantity, and layout of facilities, are determined. Based on the importance and urgency of the user's needs, the surrounding configuration requirements are prioritized. The requirements of the target consumption type are then compared with the actual situation of the initial surrounding configuration information to identify any gaps or deficiencies. Based on the needs analysis, decisions are made to add or remove certain facilities, optimize the location and layout of facilities to better align with user mobility and behavioral habits, or upgrade or modify the initial surrounding configuration information to better meet user needs, thereby obtaining the target surrounding configuration information.

[0103] For example, if the target consumption type is "shopping consumption," the initial surrounding configuration information includes a small number of parking lots, a few convenience stores, and some dining options. This type of user may require more parking spaces, more shopping areas, rest areas, and children's play areas. Gap analysis reveals that the initial surrounding configuration information has insufficient parking lots to meet peak demand, a limited variety of shopping areas, and a lack of rest areas and children's play areas, failing to meet the needs of family users. The optimization strategy then suggests increasing the number of parking lots, expanding the parking area, introducing more shopping brands, increasing shopping options, providing rest areas and children's play areas, and enhancing the shopping experience. This allows for further resource optimization and the target surrounding configuration information.

[0104] See also Figure 2 , Figure 2A local life resource optimization and allocation device 200 is provided in an embodiment of the present application. The local life resource optimization and allocation device 200 includes a data acquisition module 201, an information acquisition module 202, a data clustering module 203, a data determination module 204, a data analysis module 205, a data adjustment module 206, a data screening module 207, an information adjustment module 208, and a resource optimization module 209, wherein the data acquisition module 201 is used to determine the target object and obtain the initial surrounding configuration information corresponding to the target object; the information acquisition module 202 is used to obtain the initial consumption information corresponding to the target user under the target object and the user driving path corresponding to the target user; the data clustering module 203 is used to cluster the target user according to the initial consumption information and the user driving path to obtain a target clustering result; the data determination module 204 is used to obtain the relevant label information involved in each first subclass cluster in the target clustering result, and Obtain relevant frequency information corresponding to the relevant tag information; a data analysis module 205 is used to determine the initial consumption tag, and determine the initial relevant index corresponding to the relevant tag information to the initial consumption tag based on the relevant frequency information; a data adjustment module 206 is used to obtain the consumption time corresponding to the relevant tag information, and perform time decay on the initial relevant index based on the consumption time to obtain the target relevant index; a data screening module 207 is used to perform tag screening from the initial consumption tag based on the target relevant index to obtain the initial consumption type corresponding to the first subclass cluster; an information adjustment module 208 is used to obtain the relevant driving path corresponding to the first subclass cluster from the user driving path, and perform information adjustment on the initial consumption type based on the relevant driving path to obtain the target consumption type; a resource optimization module 209 is used to perform resource optimization on the initial surrounding configuration information based on the target consumption type to obtain the target surrounding configuration information.

[0105] In some implementations, the information acquisition module 202 performs the following steps when obtaining the user travel path corresponding to the target user:

[0106] Obtaining initial vehicle image information corresponding to a target sensor associated with the target object;

[0107] Preprocessing the initial vehicle image information to obtain corresponding target vehicle image information;

[0108] Performing a windowing process on the target vehicle image information and then performing a Fourier transform to obtain target spectrum information corresponding to the target vehicle image information;

[0109] Performing information detection on the target spectrum information to obtain a blur direction and a blur length corresponding to the initial vehicle image information;

[0110] Performing image restoration processing on the target vehicle image information according to the blur direction and the blur length to obtain repaired image information corresponding to the target vehicle image information;

[0111] Performing license plate information recognition on the repaired image information to obtain predicted license plate information corresponding to the initial vehicle image information;

[0112] The target license plate information corresponding to the target user is obtained, and the predicted license plate information is compared with the target license plate information to obtain the user driving path corresponding to the target user.

[0113] In some embodiments, the data clustering module 203 performs the following steps during the process of clustering the target users according to the initial consumption information and the user travel paths to obtain target clustering results:

[0114] Determine the user's departure location and the user's arrival location corresponding to the target user according to the user's travel path;

[0115] Determine the target intention label corresponding to the target user and the first label probability corresponding to the target intention label according to the user's departure location and the user's arrival location;

[0116] Perform tag recognition based on the initial consumption information to obtain user tag information corresponding to the target user and a second tag probability corresponding to the user tag information;

[0117] Performing user clustering on the target user according to the target intention label, the first label probability, the user label information, and the second label probability to obtain an initial clustering result;

[0118] Calculating data correlation for each second sub-cluster in the initial clustering result to obtain a data correlation value between the cluster center and the cluster data in the second sub-cluster;

[0119] The initial clustering result is adjusted according to the data correlation value to obtain the target clustering result corresponding to the target user.

[0120] In some embodiments, the data clustering module 203 performs the following steps during the process of clustering the target user according to the target intent label, the first label probability, the user label information, and the second label probability to obtain the initial clustering result:

[0121] Obtain a first user and a second user from the target user, and obtain a first intent label corresponding to the first user and a third label probability corresponding to the first intent label from the target intent label and the first label probability, and obtain a second intent label corresponding to the second user and a fourth label probability corresponding to the second intent label;

[0122] Obtaining, from the user label information and the second label probability, first label information corresponding to the first user and a fifth label probability corresponding to the first label information, and obtaining second label information corresponding to the second user and a sixth label probability corresponding to the second label information;

[0123] Performing intersection processing on the first intent label and the second intent label to obtain first intersection information corresponding to the first user and the second user;

[0124] Performing intersection processing on the first label information and the second label information to obtain second intersection information corresponding to the first user and the second user;

[0125] Determine the user similarity between the first user and the second user by fusing the third label probability, the fourth label probability, the fifth label probability, and the sixth label probability according to the first intersection information and the second intersection information;

[0126] Clustering the target users according to the user similarities to obtain the initial clustering results;

[0127] The user similarity is obtained according to the following formula:

[0128]

[0129] in, represents the user similarity between the first user and the second user, Indicates the number of tags corresponding to the first intent tag corresponding to the first user, represents the third label probability corresponding to the t-th first intention label of the first user; Indicates the number of tags corresponding to the first tag information corresponding to the first user, represents the fifth label probability corresponding to the mth first label information corresponding to the first user; Indicates the number of tags corresponding to the second intent tag corresponding to the second user, The fourth label probability corresponding to the kth second intention label of the second user; Indicates the number of tags corresponding to the second tag information corresponding to the second user; represents the sixth label probability corresponding to the r-th second label information of the second user; represents the number of intersections corresponding to the first intersection information corresponding to the first user and the second user, represents the label probability corresponding to the first user under the d-th first intersection information; represents the label probability corresponding to the second user under the d-th first intersection information; represents the number of intersections corresponding to the second intersection information corresponding to the first user and the second user, represents the label probability corresponding to the first user under the sth second intersection information; represents the label probability corresponding to the second user under the sth second intersection information.

[0130] In some embodiments, the data clustering module 203 performs the following steps during the process of calculating the data correlation for each second sub-cluster in the initial clustering result and obtaining the data correlation value between the cluster center and the cluster data in the second sub-cluster:

[0131] Performing center point calculation on the second subclass cluster to obtain the cluster center corresponding to the second subclass cluster;

[0132] Obtaining, from the target intent label, a third intent label corresponding to the cluster center and first probability information corresponding to the third intent label;

[0133] Obtaining third label information corresponding to the cluster center and second probability information corresponding to the third label information from the user label information;

[0134] Obtaining, from the target intent label, a fourth intent label corresponding to the cluster data in the second sub-cluster and third probability information corresponding to the fourth intent label;

[0135] Obtaining, from the user label information, fourth label information corresponding to the cluster data in the second sub-cluster and fourth probability information corresponding to the fourth label information;

[0136] Determine the distance information between the cluster center and the cluster data according to the third intent label, the first probability information, the third label information and the second probability information and the fourth intent label, the third probability information, the fourth label information and the fourth probability information;

[0137] The data correlation value between the cluster center and the cluster data in the second sub-cluster is determined according to the distance information.

[0138] In some embodiments, during the process of performing time decay on the initial correlation index according to the consumption time to obtain the target correlation index, the data adjustment module 206 executes:

[0139] Obtaining the earliest behavior time and the latest behavior time from the consumption time;

[0140] Determine a time decay parameter corresponding to the consumption time according to the earliest behavior time and the latest behavior time;

[0141] Performing time decay on the initial correlation index according to the time decay parameter to obtain the target correlation index;

[0142] The target correlation index is obtained according to the following formula:

[0143]

[0144] in, Indicates the target relevance index corresponding to the i-th initial consumption tag of the relevant tag information; represents the consumption time corresponding to the j-th related tag information, Indicates the earliest behavior time, Indicates the latest behavior time, 、 represents the adjustment parameters determined by the initial consumption tag; Indicates the number of tags corresponding to the relevant tag information, represents the tag weight corresponding to the jth related tag information under the i-th initial consumption tag, represents the relevant frequency information corresponding to the j-th relevant tag information.

[0145] In some embodiments, during the process of adjusting the information of the initial consumption type according to the relevant driving route to obtain the target consumption type, the information adjustment module 208 performs:

[0146] Performing route prediction based on the relevant travel routes to obtain travel destinations corresponding to relevant users in the first sub-cluster;

[0147] Determining user consumption intentions corresponding to the relevant users in the first sub-cluster according to the travel destination;

[0148] The target consumption type is obtained by adjusting information of the initial consumption type according to the user's consumption intention.

[0149] In some implementations, the local life resource optimization and allocation apparatus 200 may be applied to a terminal device.

[0150] It should be noted that technical personnel in the relevant field can clearly understand that for the convenience and conciseness of description, the specific working process of the local life resource optimization and allocation device 200 described above can refer to the corresponding process in the aforementioned local life resource optimization and allocation method embodiment, and will not be repeated here.

[0151] See also Figure 3 , Figure 3 A schematic block diagram of the structure of a terminal device provided in an embodiment of the present invention.

[0152] like Figure 3 As shown, the terminal device 300 includes a processor 301 and a memory 302 , and the processor 301 and the memory 302 are connected via a bus 303 , such as an I2C (Inter-integrated Circuit) bus.

[0153] Specifically, processor 301 is used to provide computing and control capabilities to support the operation of the entire terminal device. Processor 301 can be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0154] Specifically, the memory 302 may be a Flash chip, a read-only memory (ROM) disk, an optical disk, a USB flash drive, or a mobile hard disk.

[0155] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the embodiment of the present invention, and does not constitute a limitation on the terminal device to which the embodiment of the present invention is applied. The specific server may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0156] The processor is configured to run a computer program stored in a memory, and implement any one of the local life resource optimization and allocation methods provided in an embodiment of the present invention when executing the computer program.

[0157] In one embodiment, the processor is configured to run a computer program stored in the memory, and implement the following steps when executing the computer program:

[0158] Determine a target object and obtain initial surrounding configuration information corresponding to the target object;

[0159] Obtaining the initial consumption information of the target user under the target object and the user travel path corresponding to the target user;

[0160] Performing user clustering on the target user according to the initial consumption information and the user's travel path to obtain a target clustering result;

[0161] Obtain relevant label information involved in each first subclass cluster in the target clustering result, and obtain relevant frequency information corresponding to the relevant label information;

[0162] Determining an initial consumption tag, and determining an initial relevance index corresponding to the initial consumption tag by the relevant tag information based on the relevant frequency information;

[0163] Obtaining the consumption time corresponding to the relevant tag information, and performing time decay on the initial correlation index according to the consumption time to obtain a target correlation index;

[0164] Filter the initial consumption tags according to the target relevance index to obtain the initial consumption type corresponding to the first sub-category cluster;

[0165] Obtaining a related driving path corresponding to the first subclass cluster from the user's driving path, and adjusting information of the initial consumption type according to the related driving path to obtain a target consumption type;

[0166] The initial peripheral configuration information is resource optimized according to the target consumption type to obtain target peripheral configuration information.

[0167] In some implementations, the processor 301, in the process of obtaining the user driving path corresponding to the target user, executes:

[0168] Obtaining initial vehicle image information corresponding to a target sensor associated with the target object;

[0169] Preprocessing the initial vehicle image information to obtain corresponding target vehicle image information;

[0170] Performing a windowing process on the target vehicle image information and then performing a Fourier transform to obtain target spectrum information corresponding to the target vehicle image information;

[0171] Performing information detection on the target spectrum information to obtain a blur direction and a blur length corresponding to the initial vehicle image information;

[0172] Performing image restoration processing on the target vehicle image information according to the blur direction and the blur length to obtain repaired image information corresponding to the target vehicle image information;

[0173] Performing license plate information recognition on the repaired image information to obtain predicted license plate information corresponding to the initial vehicle image information;

[0174] The target license plate information corresponding to the target user is obtained, and the predicted license plate information is compared with the target license plate information to obtain the user driving path corresponding to the target user.

[0175] In some implementations, during the process of clustering the target user according to the initial consumption information and the user travel path to obtain a target clustering result, the processor 301 executes:

[0176] Determine the user's departure location and the user's arrival location corresponding to the target user according to the user's travel path;

[0177] Determine the target intention label corresponding to the target user and the first label probability corresponding to the target intention label according to the user's departure location and the user's arrival location;

[0178] Perform tag recognition based on the initial consumption information to obtain user tag information corresponding to the target user and a second tag probability corresponding to the user tag information;

[0179] Performing user clustering on the target user according to the target intention label, the first label probability, the user label information, and the second label probability to obtain an initial clustering result;

[0180] Calculating data correlation for each second sub-cluster in the initial clustering result to obtain a data correlation value between the cluster center and the cluster data in the second sub-cluster;

[0181] The initial clustering result is adjusted according to the data correlation value to obtain the target clustering result corresponding to the target user.

[0182] In some implementations, during the process of clustering the target user according to the target intent label, the first label probability, the user label information, and the second label probability to obtain an initial clustering result, the processor 301 executes:

[0183] Obtain a first user and a second user from the target user, and obtain a first intent label corresponding to the first user and a third label probability corresponding to the first intent label from the target intent label and the first label probability, and obtain a second intent label corresponding to the second user and a fourth label probability corresponding to the second intent label;

[0184] Obtaining, from the user label information and the second label probability, first label information corresponding to the first user and a fifth label probability corresponding to the first label information, and obtaining second label information corresponding to the second user and a sixth label probability corresponding to the second label information;

[0185] Performing intersection processing on the first intent label and the second intent label to obtain first intersection information corresponding to the first user and the second user;

[0186] Performing intersection processing on the first label information and the second label information to obtain second intersection information corresponding to the first user and the second user;

[0187] Determine the user similarity between the first user and the second user by fusing the third label probability, the fourth label probability, the fifth label probability, and the sixth label probability according to the first intersection information and the second intersection information;

[0188] Clustering the target users according to the user similarities to obtain the initial clustering results;

[0189] The user similarity is obtained according to the following formula:

[0190]

[0191] in, represents the user similarity between the first user and the second user, Indicates the number of tags corresponding to the first intent tag corresponding to the first user, represents the third label probability corresponding to the t-th first intention label of the first user; Indicates the number of tags corresponding to the first tag information corresponding to the first user, represents the fifth label probability corresponding to the mth first label information corresponding to the first user; Indicates the number of tags corresponding to the second intent tag corresponding to the second user, The fourth label probability corresponding to the kth second intention label of the second user; Indicates the number of tags corresponding to the second tag information corresponding to the second user; represents the sixth label probability corresponding to the r-th second label information of the second user; represents the number of intersections corresponding to the first intersection information corresponding to the first user and the second user, represents the label probability corresponding to the first user under the d-th first intersection information; represents the label probability corresponding to the second user under the d-th first intersection information; represents the number of intersections corresponding to the second intersection information corresponding to the first user and the second user, represents the label probability corresponding to the first user under the sth second intersection information; represents the label probability corresponding to the second user under the sth second intersection information.

[0192] In some embodiments, during the process of calculating the data correlation for each second sub-cluster in the initial clustering result and obtaining the data correlation value between the cluster center and the cluster data in the second sub-cluster, the processor 301 executes:

[0193] Performing center point calculation on the second subclass cluster to obtain the cluster center corresponding to the second subclass cluster;

[0194] Obtaining, from the target intent label, a third intent label corresponding to the cluster center and first probability information corresponding to the third intent label;

[0195] Obtaining third label information corresponding to the cluster center and second probability information corresponding to the third label information from the user label information;

[0196] Obtaining, from the target intent label, a fourth intent label corresponding to the cluster data in the second sub-cluster and third probability information corresponding to the fourth intent label;

[0197] Obtaining, from the user label information, fourth label information corresponding to the cluster data in the second sub-cluster and fourth probability information corresponding to the fourth label information;

[0198] Determine the distance information between the cluster center and the cluster data according to the third intent label, the first probability information, the third label information and the second probability information and the fourth intent label, the third probability information, the fourth label information and the fourth probability information;

[0199] The data correlation value between the cluster center and the cluster data in the second sub-cluster is determined according to the distance information.

[0200] In some implementations, during the process of performing time decay on the initial correlation index according to the consumption time to obtain the target correlation index, the processor 301 executes:

[0201] Obtaining the earliest behavior time and the latest behavior time from the consumption time;

[0202] Determine a time decay parameter corresponding to the consumption time according to the earliest behavior time and the latest behavior time;

[0203] Performing time decay on the initial correlation index according to the time decay parameter to obtain the target correlation index;

[0204] The target correlation index is obtained according to the following formula:

[0205]

[0206] in, Indicates the target relevance index corresponding to the relevant tag information for the i-th initial consumption tag; represents the consumption time corresponding to the j-th related tag information, Indicates the earliest behavior time, Indicates the latest behavior time, 、 represents the adjustment parameters determined by the initial consumption tag; Indicates the number of tags corresponding to the relevant tag information, represents the tag weight corresponding to the jth related tag information under the i-th initial consumption tag, represents the relevant frequency information corresponding to the j-th relevant tag information.

[0207] In some embodiments, during the process of adjusting the information of the initial consumption type according to the relevant driving path to obtain the target consumption type, the processor 301 executes:

[0208] Performing route prediction based on the relevant travel routes to obtain travel destinations corresponding to relevant users in the first sub-cluster;

[0209] Determining user consumption intentions corresponding to the relevant users in the first sub-cluster according to the travel destination;

[0210] The target consumption type is obtained by adjusting information of the initial consumption type according to the user's consumption intention.

[0211] It should be noted that technical personnel in the relevant field can clearly understand that for the convenience and conciseness of description, the specific working process of the terminal device described above can refer to the corresponding process in the aforementioned local life resource optimization and allocation method embodiment, and will not be repeated here.

[0212] An embodiment of the present invention also provides a storage medium for computer-readable storage, wherein the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any local life resource optimization and allocation method provided in the description of the embodiment of the present invention.

[0213] The storage medium may be an internal storage unit of the terminal device described in the aforementioned embodiment, such as a hard disk or memory of the terminal device. The storage medium may also be an external storage device of the terminal device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash memory card, etc. equipped on the terminal device.

[0214] Those skilled in the art will appreciate that all or some of the steps, systems, and functional modules / units in the methods, systems, and devices disclosed above may be implemented as software, firmware, hardware, or any combination thereof. In hardware embodiments, the division between functional modules / units described above does not necessarily correspond to the division between physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on computer-readable media, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is well known to those skilled in the art, the term computer storage media encompasses both volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, as is well known to those skilled in the art, communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0215] It should be understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations. It should be noted that, in this article, the terms "include", "comprise" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system that includes a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements that are inherent to such process, method, article or system. In the absence of further limitations, an element defined by the sentence "including a..." does not exclude the presence of other identical elements in the process, method, article or system that includes the element.

[0216] The serial numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be based on the scope of protection of the claims.

Claims

1. A method for optimizing and allocating local life resources, characterized in that: The method comprises: Determine a target object and obtain initial surrounding configuration information corresponding to the target object; Obtaining the initial consumption information of the target user under the target object and the user travel path corresponding to the target user; Performing user clustering on the target user according to the initial consumption information and the user's travel path to obtain a target clustering result; Obtain relevant label information involved in each first subclass cluster in the target clustering result, and obtain relevant frequency information corresponding to the relevant label information; Determining an initial consumption tag, and determining an initial relevance index corresponding to the initial consumption tag by the relevant tag information based on the relevant frequency information; Obtaining the consumption time corresponding to the relevant tag information, and performing time decay on the initial correlation index according to the consumption time to obtain a target correlation index; Filter the initial consumption tags according to the target relevance index to obtain the initial consumption type corresponding to the first sub-category cluster; Obtaining a related driving path corresponding to the first subclass cluster from the user's driving path, and adjusting information of the initial consumption type according to the related driving path to obtain a target consumption type; Performing resource optimization on the initial peripheral configuration information according to the target consumption type to obtain target peripheral configuration information; The step of clustering the target users according to the initial consumption information and the user travel paths to obtain target clustering results includes: Determine the user's departure location and the user's arrival location corresponding to the target user according to the user's travel path; Determine the target intention label corresponding to the target user and the first label probability corresponding to the target intention label according to the user's departure location and the user's arrival location; Perform tag recognition based on the initial consumption information to obtain user tag information corresponding to the target user and a second tag probability corresponding to the user tag information; Performing user clustering on the target user according to the target intention label, the first label probability, the user label information, and the second label probability to obtain an initial clustering result; Calculating data correlation for each second sub-cluster in the initial clustering result to obtain a data correlation value between the cluster center and the cluster data in the second sub-cluster; The initial clustering result is adjusted according to the data correlation value to obtain the target clustering result corresponding to the target user.

2. The method according to claim 1, characterized in that Obtaining the user travel path corresponding to the target user includes: Obtaining initial vehicle image information corresponding to a target sensor associated with the target object; Preprocessing the initial vehicle image information to obtain corresponding target vehicle image information; Performing a windowing process on the target vehicle image information and then performing a Fourier transform to obtain target spectrum information corresponding to the target vehicle image information; Performing information detection on the target spectrum information to obtain a blur direction and a blur length corresponding to the initial vehicle image information; Performing image restoration processing on the target vehicle image information according to the blur direction and the blur length to obtain repaired image information corresponding to the target vehicle image information; Performing license plate information recognition on the repaired image information to obtain predicted license plate information corresponding to the initial vehicle image information; The target license plate information corresponding to the target user is obtained, and the predicted license plate information is compared with the target license plate information to obtain the user driving path corresponding to the target user.

3. The method according to claim 1, characterized in that The performing user clustering on the target user according to the target intention label, the first label probability, the user label information, and the second label probability to obtain an initial clustering result includes: Obtain a first user and a second user from the target user, and obtain a first intent label corresponding to the first user and a third label probability corresponding to the first intent label from the target intent label and the first label probability, and obtain a second intent label corresponding to the second user and a fourth label probability corresponding to the second intent label; Obtaining, from the user label information and the second label probability, first label information corresponding to the first user and a fifth label probability corresponding to the first label information, and obtaining second label information corresponding to the second user and a sixth label probability corresponding to the second label information; Performing intersection processing on the first intent label and the second intent label to obtain first intersection information corresponding to the first user and the second user; Performing intersection processing on the first label information and the second label information to obtain second intersection information corresponding to the first user and the second user; Determine the user similarity between the first user and the second user by fusing the third label probability, the fourth label probability, the fifth label probability, and the sixth label probability according to the first intersection information and the second intersection information; Clustering the target users according to the user similarities to obtain the initial clustering results; The user similarity is obtained according to the following formula: ； in, represents the user similarity between the first user and the second user, Indicates the number of tags corresponding to the first intent tag corresponding to the first user, represents the third label probability corresponding to the t-th first intention label of the first user; Indicates the number of tags corresponding to the first tag information corresponding to the first user, represents the fifth label probability corresponding to the mth first label information corresponding to the first user; Indicates the number of tags corresponding to the second intent tag corresponding to the second user, The fourth label probability corresponding to the kth second intention label of the second user; Indicates the number of tags corresponding to the second tag information corresponding to the second user; represents the sixth label probability corresponding to the r-th second label information of the second user; represents the number of intersections corresponding to the first intersection information corresponding to the first user and the second user, represents the label probability corresponding to the first user under the d-th first intersection information; represents the label probability corresponding to the second user under the d-th first intersection information; represents the number of intersections corresponding to the second intersection information corresponding to the first user and the second user, represents the label probability corresponding to the first user under the sth second intersection information; represents the label probability corresponding to the second user under the sth second intersection information.

4. The method according to claim 1, wherein The step of performing data correlation calculation on each second sub-cluster in the initial clustering result to obtain a data correlation value between the cluster center and the cluster data in the second sub-cluster includes: Performing center point calculation on the second subclass cluster to obtain the cluster center corresponding to the second subclass cluster; Obtaining, from the target intent label, a third intent label corresponding to the cluster center and first probability information corresponding to the third intent label; Obtaining third label information corresponding to the cluster center and second probability information corresponding to the third label information from the user label information; Obtaining, from the target intent label, a fourth intent label corresponding to the cluster data in the second sub-cluster and third probability information corresponding to the fourth intent label; Obtaining, from the user label information, fourth label information corresponding to the cluster data in the second sub-cluster and fourth probability information corresponding to the fourth label information; Determine the distance information between the cluster center and the cluster data according to the third intent label, the first probability information, the third label information and the second probability information and the fourth intent label, the third probability information, the fourth label information and the fourth probability information; The data correlation value between the cluster center and the cluster data in the second sub-cluster is determined according to the distance information.

5. The method according to claim 1, wherein The step of performing time decay on the initial correlation index according to the consumption time to obtain a target correlation index includes: Obtaining the earliest behavior time and the latest behavior time from the consumption time; Determine a time decay parameter corresponding to the consumption time according to the earliest behavior time and the latest behavior time; Performing time decay on the initial correlation index according to the time decay parameter to obtain the target correlation index; The target correlation index is obtained according to the following formula: ； in, Indicates the target relevance index corresponding to the relevant tag information for the i-th initial consumption tag; represents the consumption time corresponding to the j-th related tag information, Indicates the earliest behavior time, Indicates the latest behavior time, 、 represents the adjustment parameters determined by the initial consumption tag; Indicates the number of tags corresponding to the relevant tag information, represents the tag weight corresponding to the jth related tag information under the i-th initial consumption tag, represents the relevant frequency information corresponding to the j-th relevant tag information.

6. The method according to claim 1, characterized in that The step of adjusting information of the initial consumption type according to the relevant driving path to obtain a target consumption type includes: Performing route prediction based on the relevant travel routes to obtain travel destinations corresponding to relevant users in the first sub-cluster; Determining user consumption intentions corresponding to the relevant users in the first sub-cluster according to the travel destination; The target consumption type is obtained by adjusting information of the initial consumption type according to the user's consumption intention.

7. A local life resource optimization and allocation device, characterized in that: include: A data acquisition module is used to determine a target object and obtain initial surrounding configuration information corresponding to the target object; An information acquisition module is used to obtain the initial consumption information corresponding to the target user under the target object and the user travel path corresponding to the target user; A data clustering module is used to perform user clustering on the target user according to the initial consumption information and the user driving path to obtain a target clustering result, wherein the user clustering on the target user according to the initial consumption information and the user driving path to obtain a target clustering result includes: determining the user departure position and user arrival position corresponding to the target user according to the user driving path; determining the target intention label corresponding to the target user and the first label probability corresponding to the target intention label according to the user departure position and the user arrival position; performing label identification according to the initial consumption information to obtain the user label information corresponding to the target user and the second label probability corresponding to the user label information; performing user clustering on the target user according to the target intention label, the first label probability, the user label information and the second label probability to obtain an initial clustering result; performing data correlation calculation on each second subclass cluster in the initial clustering result to obtain a data correlation value between the cluster center and the cluster data in the second subclass cluster; adjusting the initial clustering result according to the data correlation value to obtain the target clustering result corresponding to the target user; A data determination module, configured to obtain relevant label information involved in each first subclass cluster in the target clustering result, and obtain relevant frequency information corresponding to the relevant label information; a data analysis module, configured to determine an initial consumption tag, and determine an initial correlation index corresponding to the initial consumption tag by the correlation tag information based on the correlation frequency information; a data adjustment module, configured to obtain the consumption time corresponding to the relevant tag information, and perform time decay on the initial correlation index according to the consumption time to obtain a target correlation index; A data screening module, configured to screen the initial consumption tags according to the target relevance index to obtain the initial consumption type corresponding to the first sub-category cluster; an information adjustment module, configured to obtain a related driving path corresponding to the first sub-category cluster from the user's driving path, and adjust information of the initial consumption type according to the related driving path to obtain a target consumption type; The resource optimization module is used to perform resource optimization on the initial peripheral configuration information according to the target consumption type to obtain target peripheral configuration information.

8. A terminal device, characterized in that: The terminal device includes a processor and a memory; The memory is used to store computer programs; The processor is used to execute the computer program and implement the local life resource optimization and allocation method according to any one of claims 1 to 6 when executing the computer program.

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