A data storage method, system and data center platform

Abstract

The application relates to the technical field of electric digital data processing, in particular to a data storage method, a system and a data center platform. The method comprises the following steps: determining a target selection bias of a target user for parking at a target parking space; the selection bias represents the possibility of the user selecting any parking space for parking; grouping the parking spaces based on parking time points, determining the time point consistency degree and the parking space distance between the parking spaces in a target group; determining a target parking range where the target parking space is located according to the target selection bias, the time point consistency degree and the parking space distance; determining a storage priority coefficient by using the target selection bias of the target parking space and the average selection bias of the target parking range, and determining a storage priority according to the storage priority coefficient. Through the data storage method, the adaptive ability of the user parking data storage is effectively improved, and the storage effect is enhanced.

Description

Technical Field

[0001] This invention relates to the field of data storage technology, specifically to a data storage method, system, and data center platform. Background Technology

[0002] Parking space edge data storage refers to the process of analyzing the parking characteristics of fragmented user parking data, which was originally stored in discrete forms, through the similar parking behaviors of multiple users. This allows for the classification and storage of the data, significantly improving system response speed and efficiency. Retrieving pre-stored data avoids the computational burden of real-time image processing, ensuring smooth operation, especially during peak hours. However, this consumes storage space and increases maintenance costs. Therefore, edge data should be selectively retained for longer or shorter periods based on its importance to improve the effective utilization of storage space.

[0003] In real-world scenarios, information is often stored in a fixed order, such as according to the time of entry or according to a preset alphabetical order. In this way, due to the varying importance of parking space information, important data is easily stored irrationally, making it difficult to retrieve. This results in poor adaptability of user parking data storage and poor storage performance. Summary of the Invention

[0004] To address the technical problems of weak adaptability and poor storage performance of user parking data, the present invention aims to provide a data storage method, system, and data center platform. The specific technical solution adopted is as follows:

[0005] This invention provides a data storage method, the method comprising:

[0006] Determine the target user's preference for parking in the target parking space; preference characterizes the likelihood of a user choosing to park in any parking space.

[0007] Parking spaces are grouped based on parking time points to determine the consistency of time points and the distance between parking spaces in the target group;

[0008] Based on the target selection emphasis, the consistency of time points, and the distance between parking spaces, determine the target parking area where the target parking space is located;

[0009] When the target parking space is within the initial target parking range, the storage priority coefficient of the target parking space for the target user information is calculated by using the target selection weight of the target parking space, the target selection weight change information, and the average selection weight of the target parking range.

[0010] When the target parking space is within the latest target parking range, obtain the target user's behavior change information from the initial target parking range to the latest target parking range; use the target selection weight of the target parking space, the average selection weight of the target parking range, and the behavior change information to calculate the storage priority coefficient of the target parking space for the target user information.

[0011] The storage priority of the target parking space for the target user information is determined based on the storage priority coefficient. The larger the storage priority coefficient, the higher the storage priority.

[0012] Furthermore, the determination of the target user's parking space based on the target selection bias includes:

[0013] Determine the target number of times the target user will park in the target parking space within a preset time period before the current moment, as well as the total number of times the user will park in all parking spaces and the maximum number of times the user will park.

[0014] By using the target number of parking sessions, the total number of parking sessions, and the maximum number of parking sessions, the target user's preference for parking in the target parking space can be calculated.

[0015] Furthermore, determining the time point consistency among parking spaces in the target group includes:

[0016] Determine the target number of parking sessions for the target parking spaces and the reference number of parking sessions for the reference parking spaces within the target group;

[0017] By using the target number of parking sessions for the target parking space and the reference number of parking sessions for the reference parking space in the target group, the consistency of time points between the target parking space and the reference parking space can be calculated.

[0018] In this context, each parking space in the target group is designated as either a target parking space or a reference parking space. When any parking space is designated as a target parking space, any other parking space is designated as a reference parking space.

[0019] Furthermore, determining the target parking area based on target selection bias, time point consistency, and parking space distance includes:

[0020] Determine the degree of consistency of time points in each target group, and obtain the maximum degree of consistency of time points in all target groups;

[0021] Based on the target selection bias, the degree of consistency of the maximum time point, and the distance between parking spaces, the degree of conformity between the target parking space and the reference parking space being in the same parking range is calculated;

[0022] If the compliance degree is greater than or equal to the preset compliance threshold, the target parking space and the reference parking space are determined to be in the same parking range to obtain the target parking range where the target parking space is located.

[0023] Further, determining the storage priority of the target parking space for the target user information based on the storage priority coefficient includes:

[0024] The storage priorities are sorted in descending order according to the storage priority coefficient, and the sorting result is used as the storage priority.

[0025] Furthermore, information on the shifting preferences for target parking spaces is obtained, including:

[0026] Obtain the target selection bias difference of the target parking space between adjacent analysis time points, and determine the change type corresponding to the target selection bias difference;

[0027] Determine the number of decreases for target selection bias differences with a decreasing change type and the total number of changes for all target selection bias differences;

[0028] The ratio between the number of decreases and the total number of changes is used as the target selection bias information for the target parking space.

[0029] Furthermore, obtaining information on the change in the target user's parking behavior from the initial target parking range to the latest target parking range includes:

[0030] Obtain the latest differences in the number of parking spaces within the target parking area between adjacent analysis time points, as well as the differences in target selection bias between adjacent analysis time points;

[0031] Determine the number of increments for the increasing difference in the number of parking spaces, the total number of increments for all differences in the number of parking spaces, and the parking space quantity change information between the two.

[0032] Determine the number of increments for target selection bias differences with an increasing variation type, the total number of variations for all target selection bias differences, and the target selection bias variation information between the two.

[0033] Determine the reference parking ranges that were previously used during the same time period as the latest target parking range, and determine the average selection bias for parking spaces in all reference parking ranges.

[0034] By using the information on the changing number of parking spaces, the information on the changing bias of target selection, and the average value of selection bias, the behavioral change information of the target user from the initial target parking range to the latest target parking range is calculated.

[0035] The present invention also provides a data storage system, the system comprising:

[0036] The bias analysis module is used to determine the target user's target selection bias when parking in the target parking space; the selection bias represents the probability that the user will choose to park in any parking space.

[0037] The range positioning module is used to group parking spaces based on parking time points, determine the consistency of time points and the distance between parking spaces in the target group; and determine the target parking range where the target parking space is located based on the target selection bias, the consistency of time points, and the distance between parking spaces.

[0038] The first storage adjustment module is used to calculate the storage priority coefficient of the target parking space for the target user information by using the target selection weight of the target parking space, the target selection weight change information, and the average selection weight of the target parking range when the target parking space is within the initial target parking range.

[0039] The second storage adjustment module is used to acquire information on the change in the target user's parking behavior from the initial target parking range to the latest target parking range when the target parking space is in the latest target parking range; and to calculate the storage priority coefficient of the target parking space for the target user information using the target selection bias of the target parking space, the average selection bias of the target parking range, and the behavior change information.

[0040] The priority analysis module is used to determine the storage priority of the target parking space for the target user information based on the storage priority coefficient. The larger the storage priority coefficient, the higher the storage priority.

[0041] The present invention also provides a data center platform, the platform including a processor, a memory, and a data storage program stored in the memory that can be executed by the processor, wherein when the data storage program is executed by the processor, it implements the steps of the data storage method as described in any of the preceding claims.

[0042] The present invention has the following beneficial effects:

[0043] Compared to traditional methods that store information in a fixed order, such as storing data according to the time of entry or according to a preset alphabetical order, this approach can lead to problems such as unreasonable storage of important parking space information due to varying degrees of importance. This makes it difficult to access the data, resulting in poor adaptability of user parking data storage and poor storage performance.

[0044] This invention obtains user preference for each parking space based on user usage behavior over a certain period. Parking spaces are divided into zones based on distance and the degree of preference. Changes in user parking behavior are identified based on changes in parking zones and preferences. The storage priority of user information for different parking spaces is adjusted according to changes in parking location and behavior, achieving dynamic edge storage of user parking data, improving the adaptive capability of user parking data storage, and enhancing storage efficiency. Attached Figure Description

[0045] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 This is a flowchart illustrating the steps of a data storage method according to an embodiment of the present invention.

[0047] Figure 2 This is a detailed flowchart of step S1 in a data storage method provided in an embodiment of the present invention;

[0048] Figure 3 This is a detailed flowchart of step S2 in a data storage method provided in an embodiment of the present invention;

[0049] Figure 4 This is a detailed flowchart of step S3 in a data storage method provided in an embodiment of the present invention;

[0050] Figure 5 A detailed flowchart of step S4 in a data storage method provided in an embodiment of the present invention;

[0051] Figure 6 A detailed flowchart of step S4 in a data storage method provided in another embodiment of the present invention;

[0052] Figure 7 This is a schematic diagram of the hardware operating environment of the data center platform involved in the embodiments of the present invention;

[0053] Figure 8 This is a schematic diagram illustrating the increasing number of parking spaces involved in a data storage method provided in one embodiment of the present invention. Detailed Implementation

[0054] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following detailed description, in conjunction with the accompanying drawings and preferred embodiments, provides a detailed explanation of the specific implementation, structure, features, and effects of a data storage method proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0056] It should be noted that, for ease of calculation, all indicator data involved in the calculation in this embodiment of the invention have undergone data preprocessing to eliminate the influence of dimensions. The specific methods for eliminating the influence of dimensions are well known to those skilled in the art and are not limited here.

[0057] It should be noted that, in order to ensure that the calculation results are meaningful, when performing fractional operations, if the denominator is 0, a parameter adjustment factor greater than 0 needs to be added to the denominator to prevent the denominator from being 0. The value of the parameter adjustment factor shall be set by the implementer according to the actual situation, and this application does not impose any special restrictions.

[0058] The specific scheme of a data storage method provided by the present invention will be described in detail below with reference to the accompanying drawings.

[0059] Example 1:

[0060] For a data storage method provided by this invention, please refer to [link / reference]. Figure 1 The diagram illustrates a flowchart of a data storage method according to an embodiment of the present invention.

[0061] The method includes:

[0062] Step S1: Determine the target user's preference for parking in the target parking space; preference represents the likelihood of the user choosing to park in any parking space.

[0063] In this embodiment, real-time parking information of users can be obtained through sensors in the equipment deployed in the parking space. The obtained parking data is first sent to the edge server node for caching, and then distributed to the base station through the network distribution and scheduling server.

[0064] The data is then analyzed by a processor or compressed and stored in a database. In this embodiment, by retrieving user parking information from the database, a set of parking information for different parking instances is obtained, mainly including the parking time, parking duration, and parking space for each instance.

[0065] Changes in users' commuting hours cause variations in the number of parked cars in a parking lot, resulting in changes in the distance of parking spaces from the parking lot entrance. Simultaneously, changes in users' commuting routes may lead to changes in the entrance they choose when entering the parking lot, further altering their parking location. For frequently chosen parking spaces, user data receives higher priority in the edge processor for those spaces. This allows for immediate access to the user's basic information when they park in the same space again shortly afterward, reducing the time spent downloading data from the base station.

[0066] The user's choice of any single parking space can be evaluated by the number of times the user parks within a certain period of time and the frequency of the user's parking within that period of time.

[0067] Specifically, please refer to Figure 2 Step S1 includes:

[0068] Step S11: Determine the target number of times the target user will park in the target parking space within the preset time period before the current moment, as well as the total number of times the user will park in all parking spaces and the maximum number of times the user will park.

[0069] Step S12: Using the target number of parkings, the total number of parkings, and the maximum number of parkings, calculate the target user's preference for parking in the target parking space.

[0070] First, it should be noted that, for ease of description, the target user here refers to any user, and similarly, the target parking space refers to any parking space, and so on.

[0071] Get the number of times the target user parks in the target parking space i within a time period T (i.e., a preset time period, which can be set according to actual conditions, such as one week) from the current time. The total number of times a user parks in all parking spaces within a time period T. and the maximum number of stops To obtain.

[0072] Total number of stops within user time period T Fewer, but the number of times parking occurs in parking space i. The larger the value, the greater the user's preference for parking space i, and the higher the priority of parking space i for the user's data storage should be.

[0073] This reveals the user's preference for parking space i over a time period T prior to the current moment. .

[0074] In the formula, This indicates a bias in target selection; Indicates the target number of stops; Indicates the maximum number of stops; This indicates the total number of stops.

[0075] The above methods are used to obtain users' preferences for different parking spaces within a time period T.

[0076] Step S2: Group parking spaces based on parking time points, and determine the consistency of time points and distance between parking spaces in the target group;

[0077] In public parking lots, users generally don't park in the same spot every time. However, due to lifestyle or commuting reasons, they often park in the same parking lot. But different entry times and chosen entrances can lead to different real-time distributions of remaining parking spaces, resulting in variations in the user's actual parking location. Parking spaces within frequently used areas are more likely to be selected by the user. Therefore, to more accurately analyze parking space selection biases, the range of user parking behavior should be segmented.

[0078] Specifically, please refer to Figure 3 Step S2 includes:

[0079] Step S21: Determine the target number of parking sessions for the target parking spaces and the reference number of parking sessions for the reference parking spaces in the target group; wherein, each parking space in the target group is a target parking space or a reference parking space, and when any parking space is a target parking space, any other parking space is a reference parking space.

[0080] Step S22: Using the target parking times of the target parking space and the reference parking times of the reference parking space in the target group, calculate the consistency of time points between the target parking space and the reference parking space.

[0081] To further improve the accuracy of the analysis, more parking information data needs to be obtained. Here, we can obtain the user's preference for each parking space within a time period of 5T (5 times the preset time period, or other time periods longer than the above preset time period). Users' preferences when choosing a particular parking space When the distance is large, the closer the parking space is, the more likely it is to be selected. Therefore, you can refer to the distribution of parking spaces in the parking space planning map and use the Euclidean distance formula to calculate the distance between different parking spaces i and p. The process is as follows: Here, parking space i still represents the target parking space, and p represents the reference parking space. It should be noted that both target and reference parking spaces refer to any parking space; this distinction is made for ease of description. When analyzing a particular parking space, for example, if a user selects the parking space they prefer and that has the highest selection frequency as the target parking space, any other parking space can be used as a reference for comparative analysis.

[0082] Get the parking time points of a user in each parking space i for each day (j represents) and each parking time (m represents) within a duration T. Randomly select a parking time point. Add the parking spaces corresponding to parking times less than one hour away from this time point, along with the number of times the car parked in that space, to a group for this parking time point. For the remaining parking time points, randomly select more parking time points and repeat the above operation until all parking time points have corresponding groups.

[0083] Count the number of times parking occurs in target group b containing parking spaces i and p at a single parking time point. , When group b contains , The more, and the greater the numerical difference The smaller the time, the more consistent the selection time points of parking spaces i and p. Therefore, the degree of consistency between the time points of parking spaces i and p for target group b can be obtained as follows:

[0084] .

[0085] In the formula, The degree of consistency between the target parking space i and the reference parking space p at a given time point; Indicates the target number of stops; This represents the reference number of stops, where 1 is added to prevent the denominator from being 0.

[0086] Step S3: Determine the target parking area based on the target selection emphasis, the consistency of time points, and the distance between parking spaces;

[0087] Specifically, please refer to Figure 4 Step S3 includes:

[0088] Step S31: Determine the degree of consistency of time points in each target group, and obtain the maximum degree of consistency of time points in all target groups;

[0089] Step S32: Based on the target selection bias, the degree of consistency of the maximum time point, and the distance between parking spaces, calculate the degree of conformity between the target parking space and the reference parking space as being in the same parking range;

[0090] Step S33: If the compliance degree is greater than or equal to the preset compliance threshold, determine that the target parking space and the reference parking space are in the same parking range to obtain the target parking range where the target parking space is located.

[0091] Based on the above implementation process, the degree of consistency between parking spaces i and p in different groups is calculated, and the degree of consistency is compared to obtain the maximum degree of consistency. This serves as a reference for determining whether parking spaces i and p belong to the same parking area.

[0092] When the maximum parking time points of parking spaces i and p are consistent The larger the value, the more the selection biases towards the difference. The smaller the distance between parking spaces The smaller the space, the greater the likelihood that parking spaces i and p will be randomly selected by users under the same circumstances, and the greater the possibility that they can be substituted for each other. Therefore, they should be divided into the same parking area.

[0093] Therefore, we can determine the degree of conformity in classifying parking spaces i and p as belonging to the same parking area:

[0094]

[0095] in, This indicates the degree to which the target parking space i and the reference parking space p are within the same parking area; Indicates the degree of consistency at the maximum time point; Indicates the distance between parking spaces; , These represent the target selection bias for the target parking space and the reference selection bias for the reference parking space, respectively.

[0096] Therefore, the sigmoid function can be used to... Perform a normalization operation to obtain the normalized result. Its range is (0,1). When When (a preset threshold is met, which can be adjusted according to the actual situation), it is determined that parking spaces i and p can be assigned to the same parking area.

[0097] Based on the above process, it can be determined whether any two parking spaces are in the same parking area, and thus the target parking area of ​​any parking space (target parking space) can be determined. This embodiment mainly targets parking spaces where users have parked, because only such parking spaces have user parking information.

[0098] Step S4: Based on the relevant parking information within the target parking area, determine the storage priority coefficient of the target parking space for the target user information, and determine the storage priority of the target parking space for the target user information based on the storage priority coefficient.

[0099] Every T time interval (the specific value can be customized), the parking area of ​​different parking spaces is analyzed once. There are two scenarios: one is that the target parking area is the original parking area, or the initial target parking area; the other is that the target parking area is the latest target parking area, mainly due to the change in the entrance to the parking lot. Simply put, there is a scenario where there is no new parking area, and a scenario where there is a new parking area.

[0100] Specifically, in the second scenario mentioned above, users might change their routes due to road construction or other reasons along their commute, leading to changes in the parking lot entrance and consequently, the parking location. To ensure the isolation and priority analysis of user data for each parking space, changes in user parking behavior should be identified promptly. After a change in parking behavior, the probability of a parking space within a given parking area being selected increases, inevitably decreasing the probability of other parking areas being selected. Therefore, changes in user parking behavior can be determined by comparing the increase in the number of parking spaces included in newly added areas and the increase in selection emphasis with the decrease in selection emphasis of existing parking areas during the same parking time period.

[0101] Specifically, in one embodiment, please refer to Figure 5 If the target parking space is within the initial target parking range, step S4 includes:

[0102] Step S41: Calculate the storage priority coefficient of the target parking space for the target user information using the target selection bias and target selection bias change information of the target parking space and the average selection bias of the target parking range.

[0103] Step S41, obtaining target selection bias information for the target parking space, includes:

[0104] Obtain the target selection bias difference of the target parking space between adjacent analysis time points, and determine the change type corresponding to the target selection bias difference;

[0105] Determine the number of decreases for target selection bias differences with a decreasing change type and the total number of changes for all target selection bias differences;

[0106] The ratio between the number of decreases and the total number of changes is used as the target selection bias information for the target parking space.

[0107] For cases where the target parking range remains unchanged, when obtaining the current analysis, the initial target parking range g is analyzed over the previous V consecutive analysis time points (a preset number of analysis time points, which can be set according to actual conditions, such as 20). The analysis mainly considers the decreasing trend of target selection bias.

[0108] Calculate the difference in target selection bias between target parking spaces in the initial target parking range g and adjacent analysis time points v-1 and v. The target selection at time point v-1 is biased. Subtracting the target selection bias at time point v The results show that the data is obtained from statistical analysis of adjacent analysis time points within a consecutive V analysis time points. The number of elements (decreasing order) .

[0109] Will The selection of target parking spaces emphasizes evolving information, meaning it prioritizes decreasing performance. In the formula, This indicates that the selection of the target parking space emphasizes progressive information. This indicates the decreasing number of times the target selection biases towards differences. This represents the total number of changes in the bias of all target selections.

[0110] Based on the above implementation process, the storage priority of the target parking space can be obtained, which refers to the storage priority coefficient of the target parking space for the target user information:

[0111]

[0112] In the formula, This indicates the storage priority coefficient of the target parking space for the target user's information; This indicates the average selection bias of the original (initial) target parking area, that is, the average selection bias of all parking spaces in the target parking area. This indicates that the selection of the target parking space emphasizes incremental information.

[0113] Step S42: Sort the data according to the storage priority coefficient from largest to smallest, and use the sorting result as the storage priority.

[0114] The higher the storage priority coefficient, the more important the corresponding data is, that is, the higher the storage priority. Storage priority can be used for specific storage classification. For example, high priority data can be stored in storage media with stronger security and faster read speed. Alternatively, it can be used only as a storage read order to facilitate subsequent read calls.

[0115] Specifically, in another embodiment, please refer to Figure 6 If the target parking space is within the latest target parking range, step S4 includes:

[0116] Step S401: Obtain information on the change in the target user's parking behavior from the initial target parking range to the latest target parking range;

[0117] Step S401 specifically includes:

[0118] Obtain the latest differences in the number of parking spaces within the target parking area between adjacent analysis time points, as well as the differences in target selection bias between adjacent analysis time points;

[0119] Determine the number of increments for the increasing difference in the number of parking spaces, the total number of increments for all differences in the number of parking spaces, and the parking space quantity change information between the two.

[0120] Determine the number of increments for target selection bias differences with an increasing variation type, the total number of variations for all target selection bias differences, and the target selection bias variation information between the two.

[0121] Determine the reference parking ranges that were previously used during the same time period as the latest target parking range, and determine the average selection bias for parking spaces in all reference parking ranges.

[0122] By using the information on the changing number of parking spaces, the information on the changing bias of target selection, and the average value of selection bias, the behavioral change information of the target user from the initial target parking range to the latest target parking range is calculated.

[0123] Similarly, this embodiment can be described with reference to the above embodiments:

[0124] The parking range of different parking spaces is analyzed every T time interval. The analysis captures the changes in the number of parking spaces in the newly emerging parking range h (the latest target parking range) within the previous V consecutive analysis time points, as well as the increase in the selection bias, at subsequent analysis time points.

[0125] Calculate the difference in the number of parking spaces included in the same latest parking range h between adjacent analysis time points v-1 and v (the parking range h obtained at time point v-1 and the parking range obtained at time point v are considered to be the same parking range when they contain the most identical parking spaces). The number of parking spaces at time point v-1 Subtract the number of parking spaces at time point v The results show that the data is obtained from statistical analysis of adjacent analysis time points within a consecutive V analysis time points. Number of ,Will This represents the changing information on the number of parking spaces within the latest parking range h, specifically the increasing number of parking spaces. In the formula, This indicates the number of increments in the difference in the number of parking spaces, where the change type is increasing. This represents the total number of changes in the number of parking spaces.

[0126] To better understand the above process, please refer to... Figure 8 You can focus on parking range 3 in the figure. From the analysis time point v-1 to the analysis time point v, the number of parking spaces included in it increases.

[0127] Similarly, based on the magnitude of the selection bias obtained at different analysis time points within the same latest parking range h, the mean of the target selection bias variation information corresponding to different target parking spaces i is obtained. .

[0128] Calculate the degree of consistency between parking spaces i in each initial target parking area g at the time of the latest analysis, when the parking area was divided. When the midpoint of each parking time point in the corresponding parking time point group is used as the reference parking time for the latest analysis, parking range g is used as the reference parking time. The difference between the reference parking time of different parking ranges g and the latest target parking range h is calculated. When the difference is less than 1 hour, parking range g is used as the reference parking range for parking in the same time period as the latest parking range h.

[0129] The latest information on the number of parking spaces within the parking range h over V consecutive analysis time points. The target selection biased towards the mean of changing information The larger the target parking range h, the more biased the selection of multiple parking spaces i among multiple reference parking ranges g during the same period. mean The smaller the distance, the more significant the change in user behavior towards parking within the parking range h. Therefore, we can obtain the following information regarding the change in user behavior towards parking within the parking range h (behavior change information):

[0130] .

[0131] In the formula, Indicates information about behavioral change. This indicates the changing number of parking spaces. This indicates that the target selection biases towards the mean of progressively changing information; This indicates that the selection of parking spaces within all reference parking ranges leans towards the average.

[0132] Step S402: Calculate the storage priority coefficient of the target parking space for the target user information using the target selection bias of the target parking space, the average selection bias of the target parking range, and the behavior change information.

[0133] Step S403: Sort the data according to the storage priority coefficient from largest to smallest, and use the sorting result as the storage priority.

[0134] Parking spaces within the same parking area can all be selected by a user. Therefore, even if a particular parking space is less frequently selected over a period of time, a user is still quite likely to park in that space. Thus, the importance of data is not limited to the selection of a single parking space, but also to the parking area in which it is located and changes in user behavior.

[0135] The greater the mean of the selection bias of the reference parking range g corresponding to parking space i at the latest analysis time point, the greater the change in user behavior corresponding to target parking space i. The larger the value, the greater the likelihood that parking space i will be selected by the user, and the higher the storage priority should be configured.

[0136] This allows us to determine the importance of the target parking space information, which refers to the storage priority of the target parking space for the target user information (based on the latest target parking range):

[0137]

[0138] In the formula, This indicates the storage priority coefficient of the target parking space for the target user's information; This indicates the average selection bias within the target parking area, meaning the average selection bias across all parking spaces within that area. This specifically refers to the average selection bias of the initial target parking range, while the latest target parking range refers to the reference parking range. This indicates information about a change in behavior.

[0139] Similarly, the max-min normalization method is used to... Perform a normalization operation to obtain Its range is (-1, 1). When the normalized storage priority coefficient... The larger the value, the higher the storage priority of the target parking space i for user data.

[0140] The above operations are used to analyze the user parking information input into the central processing unit, obtain the storage priority of user information for different parking spaces, and transmit the obtained analysis data to the database for corresponding storage.

[0141] This invention obtains user preference for each parking space based on user usage behavior over a certain period. Parking spaces are divided into zones based on distance and the degree of preference. Changes in parking zones and preferences are used to identify changes in user parking behavior. The storage priority of user information for different parking spaces is adjusted according to changes in parking location and behavior, enabling dynamic edge storage of user parking data. This avoids wasting storage space by storing excessive amounts of invalid or outdated edge data, effectively reducing storage space operation and maintenance costs.

[0142] Example 2:

[0143] This invention also proposes a data center platform. The data center platform can be a data storage and computing device such as a computer or a server, or a combination of multiple devices.

[0144] like Figure 7 As shown, Figure 7 This is a schematic diagram of the hardware operating environment of the data center platform involved in the embodiments of the present invention.

[0145] like Figure 7 As shown, the data center platform may include: a processor 1001, such as a CPU; a network interface 1004; a user interface 1003; a memory 1005; and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display or an input unit such as a control panel; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001. The memory 1005, as a computer storage medium, may include a data storage program.

[0146] Those skilled in the art will understand that Figure 7 The hardware structure shown does not constitute a limitation on the device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0147] Continue to refer to Figure 7 , Figure 7 The memory 1005, which is a computer-readable storage medium, may include an operating system, a user interface module, a network communication module, and a data storage program.

[0148] exist Figure 7 In this embodiment, the network communication module is mainly used to connect to the server and can communicate with the server for data; while the processor 1001 can call the data storage program stored in the memory 1005 and execute the steps in the above embodiments.

[0149] Based on the hardware structure of the aforementioned data center platform, various embodiments of the data storage method of the present invention are implemented.

[0150] Furthermore, the present invention also provides a data storage system, the data storage system comprising:

[0151] The bias analysis module is used to determine the target user's target selection bias when parking in the target parking space; the selection bias represents the probability that the user will choose to park in any parking space.

[0152] The range positioning module is used to group parking spaces based on parking time points, determine the consistency of time points and the distance between parking spaces in the target group; and determine the target parking range where the target parking space is located based on the target selection bias, the consistency of time points, and the distance between parking spaces.

[0153] The first storage adjustment module is used to calculate the storage priority coefficient of the target parking space for the target user information by using the target selection weight of the target parking space, the target selection weight change information, and the average selection weight of the target parking range when the target parking space is within the initial target parking range.

[0154] The second storage adjustment module is used to acquire information on the change in the target user's parking behavior from the initial target parking range to the latest target parking range when the target parking space is in the latest target parking range; and to calculate the storage priority coefficient of the target parking space for the target user information using the target selection bias of the target parking space, the average selection bias of the target parking range, and the behavior change information.

[0155] The priority analysis module is used to determine the storage priority of the target parking space for the target user information based on the storage priority coefficient. The larger the storage priority coefficient, the higher the storage priority.

[0156] The specific implementation of the data storage system of the present invention is basically the same as the embodiments of the data storage method described above, and will not be repeated here.

[0157] Furthermore, the present invention also provides a computer-readable storage medium. The computer-readable storage medium of the present invention stores a data storage program, wherein when the data storage program is executed by a processor, it implements the steps of the data storage method as described above.

[0158] The method implemented when the data storage program is executed can be referred to in various embodiments of the data storage method of the present invention, and will not be repeated here.

[0159] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0160] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0161] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0162] The above description is only a preferred embodiment of the present invention and does not limit the scope of protection of the present invention. All equivalent structural / method transformations made under the inventive concept of the present invention using the contents of the present invention specification and drawings, or direct / indirect applications in other related technical fields, are included within the scope of protection of the present invention.

Claims

1. A data storage method, characterized by, The data storage method comprises: determining a target selection bias of a target user parking at a target parking space; the target selection bias represents a possibility of the user selecting the target parking space for parking; grouping the parking spaces based on parking time points, and determining a time point consistency degree and a parking space distance between the parking spaces in a target group; determining a target parking range in which the target parking space is located according to the target selection bias, the time point consistency degree and the parking space distance; when the target parking space is in an initial target parking range, calculating a storage priority coefficient of the target parking space for the target user information by using the target selection bias of the target parking space, target selection bias evolution information and an average selection bias of the target parking range; when the target parking space is in a latest target parking range, obtaining behavior change information of the target user from the initial target parking range to the latest target parking range; and calculating the storage priority coefficient of the target parking space for the target user information by using the target selection bias of the target parking space, the average selection bias of the target parking range and the behavior change information; determining a storage priority of the target parking space for the target user information according to the storage priority coefficient; the greater the storage priority coefficient is, the higher the storage priority is; a method for determining the time point consistency degree, comprising: determining a target parking frequency of the target parking space and a reference parking frequency of a reference parking space in the target group; calculating the time point consistency degree between the target parking space and the reference parking space by using the target parking frequency and the reference parking frequency; wherein each parking space in the target group is regarded as the target parking space or the reference parking space, and any parking space is regarded as the target parking space and any other parking space is regarded as the reference parking space; a method for obtaining the target selection bias evolution information, comprising: obtaining a target selection bias difference of the target parking space between adjacent analysis time points, and determining an evolution type corresponding to the target selection bias difference; determining a decreasing number of the target selection bias difference with a decreasing evolution type and a total evolution number of all the target selection bias differences; regarding a ratio between the decreasing number and the total evolution number as the target selection bias evolution information of the target parking space; a method for obtaining the behavior change information, comprising: obtaining a parking space number difference of the latest target parking range between adjacent analysis time points and a target selection bias difference of the target parking space between adjacent analysis time points; determining an increasing number of the parking space number difference with an increasing evolution type and a total evolution number of all the parking space number differences, and obtaining parking space number evolution information between the increasing number and the total evolution number; determining an increasing number of the target selection bias difference with an increasing evolution type and a total evolution number of all the target selection bias differences, and obtaining target selection bias evolution information between the increasing number and the total evolution number; determining a reference parking range that has parked in the same period as the latest target parking range, and determining an average selection bias of the parking spaces in all the reference parking ranges. ​ The behavior change information of the target user from the initial target parking range to the latest target parking range is calculated by using the parking space quantity gradient information, the target selection bias gradient information and the average target selection bias.

2. The data storage method of claim 1, wherein, The target selection bias of the target user parking at the target parking space is determined, including: The target parking times of the target user parking at the target parking space and the total parking times and the maximum parking times of parking at all parking spaces within a preset time period before the current time point are determined. The target selection bias of the target user parking at the target parking space is calculated by using the target parking times, the total parking times and the maximum parking times.

3. The data storage method of claim 1, wherein, The target parking range where the target parking space is located is determined according to the target selection bias, the time point consistency degree and the parking space distance, including: The maximum time point consistency degree in all target groups is obtained by determining the time point consistency degree in each target group. The coincidence degree of the target parking space and the reference parking space being in the same parking range is calculated according to the target selection bias, the maximum time point consistency degree and the parking space distance. If the coincidence degree is greater than or equal to a preset coincidence threshold, it is determined that the target parking space and the reference parking space are in the same parking range to obtain the target parking range where the target parking space is located.

4. The data storage method of claim 1, wherein, The storage priority of the target parking space to the target user information is determined according to the storage priority coefficient, including: The storage priority is sorted in descending order of the storage priority coefficient, and the sorting result is used as the storage priority.

5. A data storage system, characterized by The system is used to implement the data storage method of any one of claims 1-4; the system includes: The bias analysis module is used to determine the target selection bias of the target user parking at the target parking space; the selection bias represents the possibility of the user selecting any parking space for parking; The range positioning module is used to group the parking spaces based on the parking time points, determine the time point consistency degree and the parking space distance between the parking spaces in the target group, and determine the target parking range where the target parking space is located according to the target selection bias, the time point consistency degree and the parking space distance; The first storage adjustment module is used to calculate the storage priority coefficient of the target parking space to the target user information by using the target selection bias of the target parking space, the target selection bias gradient information and the average selection bias of the target parking range when the target parking space is in the initial target parking range. The second storage adjustment module is used to obtain the behavior change information of the target user from the initial target parking range to the latest target parking range when the target parking space is in the latest target parking range, and calculate the storage priority coefficient of the target parking space to the target user information by using the target selection bias of the target parking space, the average selection bias of the target parking range and the behavior change information. The priority analysis module is used to determine the storage priority of the target parking space to the target user information according to the storage priority coefficient; the greater the storage priority coefficient, the higher the storage priority.

6. A data center platform, characterized by, The platform comprises a processor, a memory, and a data storage program stored on the memory and executable by the processor, wherein the data storage program, when executed by the processor, implements the steps of the data storage method according to any one of claims 1 to 4.

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