Object query-based path calculation method, device, equipment and medium

Abstract

The present invention relates to the field of cloud computing technology, and discloses a path calculation method, apparatus, device, and medium based on object query. The method comprises obtaining a fused data set by calculating the sum of the customer type serial numbers between a first time-sequential data set and a second time-sequential data set; filtering the fused data set to obtain a change type data set; querying the change type data set, and calculating a corresponding path feature label based on the change type. In the present invention, in order to address the problem of insufficient efficiency in processing insurance business data and inability to support full path analysis in the prior art, the calculated fused data set can be filtered and the corresponding change type can be added to obtain a change type data set, and then the change type data set can be queried to calculate the corresponding path feature label. In this way, full path analysis of large amounts of data can be supported, thereby improving data processing efficiency.

Description

Technical Field

[0001] The present invention relates to the field of cloud computing technology, and in particular to a path calculation method, device, equipment and medium based on object query. Background Art

[0002] Insurance operations often require understanding customer behavior paths to improve user experience and conversion rates. Insurance customers are categorized, and these categories change dynamically based on the status of their current policies. This change isn't a gradual, positive progression; it can lead to category jumps or even regressions. Therefore, operations teams use user path analysis to extract useful insights from complex user behavior data to help optimize operational strategies.

[0003] Traditional data processing methods complete data processing through a path feature analysis process. The specific steps include: first calculating the customer category for each time segment, then creating a wide table of customer tags, sequentially updating the customer category to the earliest time of achievement no earlier than the preceding category, and finally performing classification and statistics based on the feature tags. However, this method has significant flaws: first, it only supports analysis of preset paths (such as the forward conversion process) and cannot cover all scenarios; second, it requires date comparisons between different categories, making data processing inefficient.

[0004] In summary, when faced with path analysis of large amounts of data, existing technologies have problems such as insufficient data processing efficiency and inability to support full path analysis. An improved method is urgently needed to make up for these shortcomings. Summary of the Invention

[0005] The present invention provides a path calculation method, device, equipment and medium based on object query to solve the technical problems of the existing technology in the face of path analysis of large amounts of data, such as insufficient data processing efficiency and inability to support full path analysis.

[0006] In a first aspect, a path calculation method based on object query is provided, comprising:

[0007] Acquire customer data and group the customer data to obtain an initial data set; wherein the initial data set includes customer ID, customer type, and acquisition time;

[0008] Perform serialization and value assignment processing on the initial data set, add corresponding customer type serial numbers, and integrate to obtain a serialized data set;

[0009] Time-sorting the serialized data sets, adding corresponding sequence numbers, and integrating them to obtain a first time-sequential data set;

[0010] Reversing the customer type sequence numbers in the first time-sequential data set, incrementing the sequence numbers, and integrating them to obtain a second time-sequential data set;

[0011] Calculating the sum of the customer type sequence numbers corresponding to the misalignment between the first time sequence data set and the second time sequence data set to obtain a fused data set;

[0012] Filtering data according to the fused data set and adding corresponding change types to obtain a change type data set;

[0013] The change type dataset is queried, and corresponding path feature labels are calculated according to the change type.

[0014] In a second aspect, a path calculation device based on object query is provided, comprising:

[0015] A data acquisition module, configured to acquire customer data and group the customer data to obtain an initial data set; wherein the initial data set includes customer ID, customer type, and time of completion;

[0016] A sequence assignment module is used to perform serialization assignment processing on the initial data set, add corresponding customer type serial numbers, and integrate to obtain a serialized data set;

[0017] A first sorting module is used to sort the serialized data set by time, add corresponding sequence numbers, and integrate them to obtain a first time-sequential data set;

[0018] A second sorting module is configured to reverse the customer type serial numbers in the first time-sequential data set and increment the serial numbers to obtain a second time-sequential data set;

[0019] a data fusion module, configured to calculate the sum of the customer type serial numbers corresponding to the misalignment between the first time sequence data set and the second time sequence data set to obtain a fused data set;

[0020] A data change module is used to filter data based on the fused data set and add corresponding change types to obtain a change type data set;

[0021] The path calculation module is used to query the change type data set and calculate the corresponding path feature label according to the change type.

[0022] In a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned object query-based path calculation method are implemented.

[0023] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned object query-based path calculation method are implemented.

[0024] In the solution implemented by the above-mentioned object query-based path calculation method, device, equipment and medium, customer data can be obtained by obtaining and grouping the customer data to obtain an initial data set; wherein the initial data set includes customer ID, customer type and time of achievement; the initial data set is serialized and assigned, and the corresponding customer type sequence number is added to obtain a serialized data set; the serialized data set is time-sorted, and the corresponding sequence number is added to obtain a first time-sequential data set; the customer type sequence number in the first time-sequential data set is reversed and the sequence number is incremented to obtain a second time-sequential data set; the sum of the customer type sequence numbers corresponding to the misalignment between the first time-sequential data set and the second time-sequential data set is calculated to obtain a fused data set; data is filtered based on the fused data set, and the corresponding change type is added to obtain a change type data set; the change type data set is queried, and the corresponding path feature label is calculated based on the change type. In the present invention, in order to address the problem that the insurance business data processing efficiency of the existing technology is insufficient and cannot support full path analysis, the calculated fused data set can be filtered and the corresponding change type is added to obtain a change type data set, and then the change type data set is queried to calculate the corresponding path feature label. In this way, full-path analysis of large amounts of data can be supported, improving data processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0026] Figure 1 1 is a flow chart of a path calculation method based on object query in one embodiment of the present invention;

[0027] Figure 2 This is a diagram of a data set for a path calculation method based on object query in one embodiment of the present invention. Figure 1 ;

[0028] Figure 3 yes Figure 1 A schematic flow chart of a specific implementation of step S10;

[0029] Figure 4 yes Figure 1 A schematic flow chart of a specific implementation of step S20;

[0030] Figure 5 This is a diagram of a data set for a path calculation method based on object query in one embodiment of the present invention. Figure 2 ;

[0031] Figure 6 yes Figure 1 A schematic flow chart of a specific implementation of step S30;

[0032] Figure 7 This is a diagram of a data set for a path calculation method based on object query in one embodiment of the present invention. Figure 3 ;

[0033] Figure 8 yes Figure 1 A schematic flow chart of a specific implementation of step S40;

[0034] Figure 9 This is a diagram of a data set for a path calculation method based on object query in one embodiment of the present invention. Figure 4 ;

[0035] Figure 10 This is a diagram of a data set for a path calculation method based on object query in one embodiment of the present invention. Figure 5 ;

[0036] Figure 11 yes Figure 1 A schematic flow chart of a specific implementation of step S60;

[0037] Figure 12 This is a diagram of a data set for a path calculation method based on object query in one embodiment of the present invention. Figure 6 ;

[0038] Figure 13 yes Figure 1 A schematic flow chart of a specific implementation of step S70;

[0039] Figure 14 This is a diagram of a data set for a path calculation method based on object query in one embodiment of the present invention. Figure 7 ;

[0040] Figure 15 is a structural diagram of a path calculation device based on object query in one embodiment of the present invention;

[0041] Figure 16 is a structural diagram of a computer device in one embodiment of the present invention;

[0042] Figure 17 FIG. 2 is another structural diagram of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0043] 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.

[0044] See also Figure 1 As shown, Figure 1 A flow chart of a path calculation method based on object query provided by an embodiment of the present invention includes the following steps:

[0045] S10: Acquire customer data and group the customer data to obtain an initial data set; wherein the initial data set includes customer ID, customer type and acquisition time.

[0046] Combine Figure 2 As shown, customer data is obtained and grouped to form an initial data set, which includes column information such as customer ID, customer type, and time of completion.

[0047] Combine Figure 3 As shown, before step S10, obtaining customer data specifically includes the following steps:

[0048] S11: Obtain customer number data and classification data respectively, and construct a customer classification vertical table.

[0049] First, extract customer ID data and category data from the data source. The customer ID data serves as a unique identifier, while the category data represents customer classification information within different time periods, such as new users, prospective customers, customers, loyal customers, and qualified customers. Next, link the customer ID data with the category data to form a table containing customer ID, customer type, and corresponding customer acquisition time, known as the customer category vertical table.

[0050] S12: Sort the customer classification vertical table by conversion time difference to construct a first intermediate vertical table.

[0051] Based on the customer classification vertical table, the first intermediate vertical table is constructed by sorting the customer classification conversion time, placing earlier classification records first. This sorted data table reflects the sequence of customer classification changes at different points in time, helping to track the transition process of customer types and providing a clear time series for path calculation. In addition, sorting by time difference effectively captures the successive conversion paths of customer categories and helps identify the order of category changes.

[0052] S13: Obtain the last process time according to the first intermediate vertical table to construct a second intermediate vertical table.

[0053] After sorting the first intermediate vertical table by time difference, the last process time for each customer is extracted from the first intermediate vertical table and associated with the customer's current classification record to form the second intermediate vertical table. By recording the customer's previous process time, continuous time node information is created for each customer's classification change, ensuring that each step in subsequent path calculation has a reference to the previous and next time. The data in the second intermediate vertical table is used for further filtering and aggregation to track the dynamic changes of customers in the classification path.

[0054] S14: Filter the target process data in the second intermediate vertical table to construct a third intermediate vertical table.

[0055] By setting filtering conditions in the second intermediate vertical table, only data records matching the target process are retained, generating a third intermediate vertical table. These filtering conditions can be based on path calculation requirements, such as specific customer classification categories or time ranges. This filtering allows the third intermediate vertical table to retain only process data relevant to path analysis, removing irrelevant or redundant information and creating a more concise and clear data structure.

[0056] S15: Aggregate the time and process in the third intermediate vertical table to obtain the customer data.

[0057] Based on the filtered third intermediate vertical table, process time and customer process information are aggregated to integrate customer classification changes at different time points. Specifically, the aggregation operation involves grouping each customer's process data and aggregating multiple process records for the same customer in chronological order to form customer data containing the complete path.

[0058] S20: performing serialization and value assignment processing on the initial data set, adding corresponding customer type serial numbers, and integrating to obtain a serialized data set.

[0059] Assign serial numbers to customer types in a predetermined hierarchical order. For example, you could assign values ​​from 1 to 6, from "new user" to "qualified customer," to ensure customer types are prioritized for conversion. After serialization and assignment, combine the initial dataset with the newly added customer type serial numbers to form a serialized dataset.

[0060] Combine Figure 4 and Figure 5 As shown, in step S20, the initial data set is serialized and assigned, and the corresponding customer type serial number is added to obtain a serialized data set through integration, which specifically includes the following steps:

[0061] S21: Perform serialization and value assignment processing on the customer type to obtain at least one customer type serial number.

[0062] Each customer type is assigned a unique customer type sequence number based on a predetermined hierarchy of customer types. For example, "New User" to "Qualified Customer" can be numbered from 1 to 6, so that each customer type has a unique customer type sequence number. Specifically, a new user is numbered 1, a user is numbered 2, a prospective customer is numbered 3, a customer is numbered 4, a loyal customer is numbered 5, a qualified customer is numbered 6, and so on. Corresponding customer types can be added or removed as needed.

[0063] S22: Acquire the conversion level of the customer type, and calculate the corresponding level distance using the conversion level.

[0064] After obtaining the customer type sequence number, we need to further extract the customer type conversion hierarchy, indicating the conversion order between different customer types. Using the differences in conversion hierarchies, we can calculate the hierarchical distance between customer type changes. For example, if a customer converts directly from a "new user" to a "loyal customer," this hierarchical distance can be calculated by calculating the difference in customer type sequence numbers, thereby quantifying the magnitude of the jump or degree of progression between customer types.

[0065] S23: Integrate the customer type serial number and the hierarchical distance to obtain the serialized data set.

[0066] After completing the serialization assignment and hierarchical distance calculation, each customer type serial number and the corresponding hierarchical distance are integrated into the same data set to form a serialized data set, laying the data foundation for subsequent path analysis and customer behavior feature identification.

[0067] S30: Time-sorting the serialized data sets, adding corresponding sequence numbers, and integrating them to obtain a first time-sequential data set.

[0068] The serialized dataset is sorted by time to ensure that the chronological order of changes in customer type is accurately recorded. Specifically, the serialized dataset is sorted in ascending order based on each customer's acquisition time, so that each customer's type conversion record is arranged in chronological order, forming a time series. After the time sorting is completed, a sequence number is added to each sorted record. This sequence number is numbered one by one according to chronological order, indicating the specific chronological order of customer type changes. Finally, the time-sorted serialized dataset is combined with the newly added sequence number to form the first time-sequenced dataset.

[0069] Combine Figure 6 and Figure 7As shown, in step S30, the serialized data sets are time-sorted, corresponding sequence numbers are added, and the first time-sequential data set is obtained by integration, which specifically includes the following steps:

[0070] S31: sorting the serialized data sets in descending order according to the achievement time, and when the achievement time is the same, sorting them in ascending order according to the customer ID to obtain a sorted data table.

[0071] The serialized dataset is first sorted in descending order by the time of achievement field to ensure that the most recent customer type conversion information is prioritized. If multiple entries have the same time of achievement, the system further sorts them in ascending order by customer ID, so that the data is arranged in the order of the customer's unique identifier within the same time node, thus avoiding order conflicts.

[0072] S32: Allocate consecutive corresponding sequence numbers to each data entry in the sorted data table; wherein the sequence numbers are incremented starting from a predetermined initial value.

[0073] In the sorted data table, each data entry is assigned a sequence number to indicate the chronological order of changes in customer types. Sequence numbers start at a predetermined initial value (e.g., 1) to ensure consistent numbering. The ascending order of the sequence numbers directly corresponds to the position of the data entry in the sorted data table, providing a time series identifier for subsequent analysis.

[0074] S33: Merge the sequence number with the sorting data table to obtain the first time sequence data set.

[0075] The generated sequence number field is integrated with the sorted data table to form the final first-time sequence data set. This first-time sequence data set can reflect the chronological order of changes in customer types, laying the foundation for path calculation and subsequent time interval analysis.

[0076] S40: Reverse the customer type sequence numbers in the first time-sequential data set, increment the sequence numbers, and integrate them to obtain a second time-sequential data set.

[0077] First, the customer type sequence numbers in the first time-sequential dataset are negated. Negating the customer type sequence numbers reverses the original hierarchical order. After the negation operation is completed, the sequence number of each data entry is incremented. Finally, the negated customer type sequence numbers and the incremented sequence numbers are combined into the first time-sequential dataset to obtain the second time-sequential dataset.

[0078] Combine Figure 8 and Figure 9As shown, in step S40, the customer type sequence numbers in the first time sequence data set are inverted, and the sequence numbers are incremented, and the second time sequence data set is obtained by integration, which specifically includes the following steps:

[0079] S41: Perform a negation operation on the customer type serial number of each data entry in the first time-sequential data set to obtain a corresponding negated customer type serial number; wherein the negation operation is to negate the mathematical sign of the customer type serial number.

[0080] Perform a mathematical sign inversion on the customer type sequence number, converting a positive sequence number to a negative one, or vice versa. This inversion operation introduces a reverse identifier into the customer type hierarchy, helping to distinguish forward and reverse path information in subsequent analysis.

[0081] S42: Increasing the sequence number of each data entry in the first time-sequential data set by a predetermined increment value to obtain an incremented sequence number.

[0082] The sequence number of each data entry is incremented by a predetermined value (e.g., +1) to ensure that the sequence number has a new unique identifier in the second time-sequential dataset. This increment operation not only preserves the logical structure of the time sequence but also provides a continuous time node reference for subsequent analysis.

[0083] S43: Integrate the inverted customer type sequence number, value-added sequence number, customer ID, achievement time and customer type to obtain the second time sequence data set.

[0084] After completing the negation and increment operations, the negated customer type sequence number, the increment sequence number, and the original customer ID, achievement time, and customer type fields are integrated to form a second time sequence data set.

[0085] S50: Calculate the sum of the customer type serial numbers corresponding to the misalignment between the first time sequence data set and the second time sequence data set to obtain a fused data set.

[0086] Combine Figure 10 As shown, the customer type serial number is taken from the first time sequence data set, and the inverted customer type serial number is taken from the second time sequence data set. The customer type serial number and the inverted customer type serial number are then staggered and added together. Staggered addition refers to establishing a "front-to-back staggered" relationship between different time sequence data sets (i.e., constructing hedge data) and calculating the characteristics of customer type changes at adjacent time points. This staggered sum calculation can capture the degree of change in customer type in adjacent time slices, such as whether there is a type promotion, demotion, or no change. Finally, the results of all staggered sums are integrated to form a fused data set.

[0087] S60: Filter data according to the fused data set, and add corresponding change types to obtain a change type data set.

[0088] First, the fused dataset is filtered according to the preset filtering conditions. For example, data entries whose customer type sequence sum is 0 can be filtered out to exclude records where the customer type has not changed in adjacent time slices, thereby retaining only entries where the customer type has actually changed. After the data filtering is completed, a change type field is added to each retained data entry to indicate the specific change in the customer type. Change types can include categories such as "promotion", "demotion", and "transition", which are determined based on the increase or decrease in the customer type sequence number or the misalignment and value of adjacent time series. For example, when the sum of the customer type sequence numbers is positive, it can be marked as "promotion"; when the sum is negative, it can be marked as "demotion"; and when the sum changes significantly, it is marked as "transition". Finally, a change type dataset is formed through data filtering and the addition of change types.

[0089] Combine Figure 11 and Figure 12 As shown, in step S60, data filtering is performed according to the fused data set, and corresponding change types are added to obtain a change type data set, which specifically includes the following steps:

[0090] S61: Filter the sum of the customer type serial number of each data item in the fused data set respectively to extract data items that meet preset conditions, and filter out empty data in the fused data set to obtain a filtering result.

[0091] First, we filter the customer type sequence sum of each data entry in the fused dataset, extracting entries that meet the criteria based on pre-defined conditions (e.g., the sum of the sequence numbers is not zero). This filtering operation aims to eliminate data entries whose customer type remains unchanged between adjacent time slices. Furthermore, we exclude data entries containing null values ​​in the fused dataset to ensure data integrity and accuracy, ultimately obtaining the final filtering results.

[0092] S62: Adding the change type for each data entry that meets the preset conditions based on the screening result.

[0093] Change types are used to identify specific changes in customer types, such as "promotion," "downgrade," or "transition." These change types are determined by the positive or negative value of the customer type sequence number and the magnitude of the change. For example, a positive value indicates a "promotion," a negative value indicates a "downgrade," and a larger change indicates a "transition," thus clearly categorizing customer behavior.

[0094] S63: Integrate the change type and the data entries corresponding to the fused data set to obtain the change type data set.

[0095] The newly added change type field is integrated with the corresponding data entry in the fused dataset to generate the final change type dataset. This change type dataset contains customer ID, sequence number, customer type, change time, customer type sequence number, customer type sequence, and change type, providing a more comprehensive record of changes.

[0096] S70: Query the change type data set, and calculate a corresponding path feature label according to the change type.

[0097] The change type identifies the category changes of customers at different time points. According to the queried change type, the corresponding path feature label is generated through the preset calculation logic. The path feature label is used to describe the customer's behavior pattern in his path, such as "steady promotion", "frequent jump", "occasional rollback", etc. The generation of these labels can identify and mark the customer's behavioral characteristics on the path according to the different combinations and frequencies of change types. For example, if the change type dataset records multiple data entries of the "promotion" type, a path feature label of "continuous promotion" can be generated for the customer; if there are multiple "downgrades", a label of "repeated rollback" is generated. Ultimately, the calculation result of the path feature label provides each customer with a feature description based on their behavior path, which helps to summarize and classify the customer's overall behavior and provide an important basis for subsequent analysis and decision-making.

[0098] Combine Figure 13 and Figure 14 As shown, in step S70, the change type dataset is queried, and the corresponding path feature label is calculated according to the change type, which specifically includes the following steps:

[0099] S71: Query the change type dataset to obtain the change type.

[0100] Query the change type dataset to extract the change type information from each customer's data entry. This change type information reflects the customer's type changes at different time points.

[0101] S72: Construct a customer status transition sequence based on the change type and the achievement time.

[0102] After obtaining the change type, we can sort each customer's type changes by time and then combine them with the time of achievement to form a customer state transition sequence. This customer state transition sequence arranges each customer's type change records in chronological order, showing the customer's type transformation trajectory at each stage of their lifecycle.

[0103] S73: Match the client state transition sequence with a preset forward path model to identify the degree of deviation between the client's actual path and the forward path.

[0104] Compare the customer's state transition sequence with the pre-set forward path model (i.e., the ideal customer type conversion path) to identify behavioral deviations during the customer's lifecycle. This matching determines whether the customer's path aligns with the pre-set positive development trend or whether there are any deviations from the forward path, such as jumps or regressions.

[0105] S74: Calculate and generate the path feature label for identifying the specific status of the customer in the life cycle using the deviation degree and the customer status transition sequence.

[0106] Based on the customer's degree of deviation and the customer's state transition sequence, corresponding path feature labels can be generated to identify the customer's behavioral characteristics throughout the lifecycle. For example, if the customer's state transition sequence closely matches the forward path model, a "stable development" label can be generated; if there are multiple deviations, labels such as "frequent jumps" or "repeated regressions" can be generated. By summarizing customer behavior patterns, path feature labels provide a basis for subsequent customer tiering management and marketing.

[0107] like Figure 14 As shown in the figure, taking customer A as an example, the forward progression of customer development is: prospective user (new users and prospective users are the same), user, prospective customer, customer, and qualified customer. The forward path is 1-2-3-4-6. However, customer A experienced a policy expiration, resulting in a downgrade, and then became a loyal customer. Later, a second policy expiration occurred, further lowering the level to customer. The downgrade path is 6-5-4. Later, the customer purchased a new policy, upgrading from customer to qualified customer, a level transition with a transition path of 4-6. At this point, customer A's overall behavioral path is 1-2-3-4-6-5-4. Since customer A has upgraded from customer to qualified customer twice, the number of transitions is marked as 2.

[0108] It can be seen that the above solution avoids the drawbacks of traditional solutions that require data migration and only support sample analysis. It also solves the problem that traditional solutions cannot effectively filter unchanged time slices and cannot support full-path analysis. By assigning customer type sequences, hedge data is constructed to mark the types of customer type changes in adjacent time slices. This solution supports both preset path analysis and full-path analysis through HQL (i.e., object query); while calculating the conversion path, the conversion time interval characteristics can be calculated at the same time, effectively improving the efficiency of data analysis and processing.

[0109] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0110] In one embodiment, a path calculation device based on object query is provided, which corresponds to the path calculation method based on object query in the above embodiment. Figure 15 As shown, the object query-based path calculation device includes: a data acquisition module 101, a sequence assignment module 102, a first sorting module 103, a second sorting module 104, a data fusion module 105, a data change module 106, and a path calculation module 107. The functional modules are described in detail as follows:

[0111] The data acquisition module 101 is used to acquire customer data and group the customer data to obtain an initial data set; wherein the initial data set includes customer ID, customer type and completion time.

[0112] The sequence assignment module 102 is used to perform serialization assignment processing on the initial data set, add corresponding customer type serial numbers, and integrate to obtain a serialized data set.

[0113] The first sorting module 103 is used to sort the serialized data sets by time, add corresponding sequence numbers, and integrate them to obtain a first time-sequential data set.

[0114] The second sorting module 104 is configured to invert the customer type sequence numbers in the first time sequence data set and increment the sequence numbers to obtain a second time sequence data set.

[0115] The data fusion module 105 is configured to calculate the sum of the customer type serial numbers corresponding to the misalignment between the first time sequence data set and the second time sequence data set to obtain a fused data set.

[0116] The data change module 106 is configured to filter data based on the fused data set and add corresponding change types to obtain a change type data set.

[0117] The path calculation module 107 is used to query the change type data set and calculate the corresponding path feature label according to the change type.

[0118] In one embodiment, the object query-based path calculation device is further configured to:

[0119] Obtain customer number data and classification data separately, and build a customer classification vertical table;

[0120] sorting the customer classification vertical table by conversion time difference to construct a first intermediate vertical table;

[0121] Obtaining the last process time according to the first intermediate vertical table to construct a second intermediate vertical table;

[0122] filtering target process data in the second intermediate vertical table to construct a third intermediate vertical table;

[0123] Aggregate the time and process in the third intermediate vertical table to obtain the customer data.

[0124] In one embodiment, the sequence assignment module 102 is specifically configured to:

[0125] Performing serialization and assignment processing on the customer type to obtain at least one customer type serial number;

[0126] Obtaining a conversion level of the customer type, and calculating a corresponding level distance using the conversion level;

[0127] The customer type serial number and the hierarchical distance are integrated to obtain the serialized data set.

[0128] In one embodiment, the first sorting module 103 is specifically configured to:

[0129] Sort the serialized data sets in descending order according to the achievement time, and when the achievement time is the same, sort them in ascending order according to the customer ID to obtain a sorted data table;

[0130] Assigning consecutive corresponding sequence numbers to each data entry in the sorted data table; wherein the sequence numbers are incremented starting from a predetermined initial value;

[0131] The sequence number is combined with the sorting data table to obtain the first time sequence data set.

[0132] In one embodiment, the second sorting module 104 is specifically configured to:

[0133] Performing a negation operation on the customer type sequence number of each data entry in the first time-sequential data set to obtain a corresponding negated customer type sequence number; wherein the negation operation is to negate the mathematical sign of the customer type sequence number;

[0134] Increasing the sequence number of each data entry in the first time-sequential data set by a predetermined increment value to obtain an incremented sequence number;

[0135] The inverted customer type sequence number, value-added sequence number, customer ID, achievement time and customer type are integrated to obtain the second time sequence data set.

[0136] In one embodiment, the data modification module 106 is specifically configured to:

[0137] Filtering the sum of the customer type serial numbers of each data entry in the fused data set respectively to extract data entries that meet preset conditions, and filtering out empty data in the fused data set to obtain a screening result;

[0138] Adding the change type to each data entry that meets the preset conditions based on the screening result;

[0139] The change type is integrated with data entries corresponding to the fused data set to obtain the change type data set.

[0140] In one embodiment, the path calculation module 107 is specifically configured to:

[0141] Querying the change type dataset to obtain the change type;

[0142] Constructing a customer state transition sequence based on the change type and the time of achievement;

[0143] Matching the customer state transition sequence with a preset forward path model to identify the degree of deviation between the customer's actual path and the forward path;

[0144] The path feature tag for identifying the specific state of the customer in the life cycle is calculated and generated using the deviation degree and the customer state transition sequence.

[0145] For the specific definition of the object query-based path computation device, please refer to the definition of the object query-based path computation method above and will not be repeated here. Each module in the above-mentioned object query-based path computation device can be implemented in whole or in part through software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each of the above modules.

[0146] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 16As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile and / or volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external client via a network connection. When the computer program is executed by the processor, it implements the functions or steps on the server side of a path calculation method based on object query.

[0147] In one embodiment, a computer device is provided. The computer device may be a client, and its internal structure diagram may be as follows: Figure 17 As shown. The computer device includes a processor, memory, network interface, display screen, and input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server via a network connection. When the computer program is executed by the processor, it implements the functions or steps on the client side of a path calculation method based on object query.

[0148] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed, the steps provided in the above embodiment can be implemented.

[0149] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can be found in the relevant descriptions of the server side and the client side in the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0150] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0151] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0152] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A path calculation method based on object query, characterized in that: include: Acquire customer data and group the customer data to obtain an initial data set; wherein the initial data set includes customer ID, customer type, and acquisition time; Perform serialization and value assignment processing on the initial data set, add corresponding customer type serial numbers, and integrate to obtain a serialized data set; Time-sorting the serialized data sets, adding corresponding sequence numbers, and integrating them to obtain a first time-sequential data set; Reversing the customer type sequence numbers in the first time-sequential data set, incrementing the sequence numbers, and integrating them to obtain a second time-sequential data set; Calculating the sum of the customer type sequence numbers corresponding to the misalignment between the first time sequence data set and the second time sequence data set to obtain a fused data set; Filtering data according to the fused data set and adding corresponding change types to obtain a change type data set; Querying the change type dataset and calculating corresponding path feature labels according to the change type; The querying of the change type dataset and calculating a corresponding path feature label based on the change type includes: querying the change type dataset to obtain the change type; constructing a customer state transition sequence based on the change type and the achievement time; matching the customer state transition sequence with a preset forward path model to identify the degree of deviation between the customer's actual path and the forward path; and calculating and generating the path feature label for identifying the specific state of the customer in the life cycle using the degree of deviation and the customer state transition sequence.

2. The object query-based path calculation method according to claim 1, characterized in that: Before acquiring the customer data and grouping the customer data to obtain the initial data set, the method includes: Obtain customer number data and classification data separately, and build a customer classification vertical table; sorting the customer classification vertical table by conversion time difference to construct a first intermediate vertical table; Obtaining the last process time according to the first intermediate vertical table to construct a second intermediate vertical table; filtering target process data in the second intermediate vertical table to construct a third intermediate vertical table; Aggregate the time and process in the third intermediate vertical table to obtain the customer data.

3. The object query-based path calculation method according to claim 1, characterized in that: The serialization and assignment processing is performed on the initial data set, and the corresponding customer type serial number is added to obtain the serialized data set through integration, including: Performing serialization and assignment processing on the customer type to obtain at least one customer type serial number; Obtaining a conversion level of the customer type, and calculating a corresponding level distance using the conversion level; The customer type serial number and the hierarchical distance are integrated to obtain the serialized data set.

4. The object query-based path calculation method according to claim 1, characterized in that: The step of temporally sorting the serialized data sets, adding corresponding sequence numbers, and integrating them to obtain a first temporally ordered data set includes: Sort the serialized data sets in descending order according to the achievement time, and when the achievement time is the same, sort them in ascending order according to the customer ID to obtain a sorted data table; Assigning consecutive corresponding sequence numbers to each data entry in the sorted data table; wherein the sequence numbers are incremented starting from a predetermined initial value; The sequence number is combined with the sorting data table to obtain the first time sequence data set.

5. The object query-based path calculation method according to claim 1, characterized in that: The step of reversing the customer type sequence numbers in the first time-sequential data set and incrementing the sequence numbers to obtain a second time-sequential data set includes: Performing a negation operation on the customer type sequence number of each data entry in the first time-sequential data set to obtain a corresponding negated customer type sequence number; wherein the negation operation is to negate the mathematical sign of the customer type sequence number; Increasing the sequence number of each data entry in the first time-sequential data set by a predetermined increment value to obtain an incremented sequence number; The inverted customer type sequence number, value-added sequence number, customer ID, achievement time and customer type are integrated to obtain the second time sequence data set.

6. The object query-based path calculation method according to claim 1, characterized in that: The data is filtered according to the fused data set, and corresponding change types are added to obtain a change type data set, including: Filtering the sum of the customer type serial numbers of each data entry in the fused data set respectively to extract data entries that meet preset conditions, and filtering out empty data in the fused data set to obtain a screening result; Adding the change type to each data entry that meets the preset conditions based on the screening result; The change type is integrated with data entries corresponding to the fused data set to obtain the change type data set.

7. A path calculation device based on object query, characterized in that: include: A data acquisition module, configured to acquire customer data and group the customer data to obtain an initial data set; wherein the initial data set includes customer ID, customer type, and time of completion; A sequence assignment module is used to perform serialization assignment processing on the initial data set, add corresponding customer type serial numbers, and integrate to obtain a serialized data set; A first sorting module is used to sort the serialized data set by time, add corresponding sequence numbers, and integrate them to obtain a first time-sequential data set; A second sorting module is configured to reverse the customer type serial numbers in the first time-sequential data set and increment the serial numbers to obtain a second time-sequential data set; a data fusion module, configured to calculate the sum of the customer type serial numbers corresponding to the misalignment between the first time sequence data set and the second time sequence data set to obtain a fused data set; A data change module is used to filter data based on the fused data set and add corresponding change types to obtain a change type data set; A path calculation module, configured to query the change type dataset and calculate corresponding path feature labels according to the change type; The path calculation module is specifically used to query the change type dataset to obtain the change type; construct a customer state transition sequence based on the change type and the achievement time; match the customer state transition sequence with a preset forward path model to identify the degree of deviation between the customer's actual path and the forward path; and use the deviation degree and the customer state transition sequence to calculate and generate the path feature label for identifying the specific state of the customer in the life cycle.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the object query-based path calculation method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the object query-based path calculation method according to any one of claims 1 to 6 are implemented.

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