Path calculation method and device based on object query, equipment and medium

Abstract

The invention relates to the technical field of cloud computing, and discloses a path calculation method and device based on object query, equipment and a medium, the method comprises the following steps: adding the sum of customer type serial numbers between a first time sequence data set and a second time sequence data set obtained by calculation to obtain a fusion data set; performing data filtering according to the fused data set to obtain a change type data set; and querying the change type data set, and calculating according to the change type to obtain a corresponding path feature tag. In the invention, aiming at the problems of insufficient insurance service data processing efficiency and incapability of supporting full-path analysis in the prior art, data filtering can be carried out on a fusion data set obtained by calculation, and a corresponding change type is newly added, so that a change type data set is obtained; and querying the change type data set so as to calculate to obtain a corresponding path feature tag. Therefore, full-path analysis of mass data can be supported, and the data processing efficiency is improved.

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] In the operation of insurance business, it is usually necessary to understand the customer's behavior path in order to improve user experience and conversion rate. Insurance business customers will be divided into categories, and customer categories will change dynamically with the status of the current valid insurance policy. This change is not progressive and may also result in category jumps or basic regressions. Therefore, the operation team will extract useful information from complex user behavior data through user path analysis to help optimize the operation strategy.

[0003] The traditional data processing method is to complete data processing through the path feature analysis process. The specific steps include: first calculate the customer category of each time segment, then create a wide table of customer labels, and update the customer category in sequence so that it is not earlier than the earliest time of the previous category, and finally classify and count according to the feature label. However, this method has obvious defects: first, this method only supports the analysis of preset paths (such as positive conversion processes) and cannot achieve coverage of all scenario paths; second, it requires date comparison operations between different categories, which makes data processing inefficient.

[0004] In summary, when faced with path analysis of large amounts of data, the existing technology has 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 prior art in insufficient data processing efficiency and inability to support full path analysis when facing path analysis of large amounts of data.

[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 achievement 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 to obtain a first time-sequential data set;

[0010] Reverse the customer type sequence number in the first time-sequential data set, and increase the sequence number, and integrate them to obtain a second time-sequential data set;

[0011] Calculate 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 data set is queried, and a corresponding path feature label is 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, 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 time of achievement;

[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, used to sort the serialized data set by time, add corresponding sequence numbers, and integrate to obtain a first time sequence data set;

[0018] A second sorting module is used to reverse the customer type sequence number in the first time sequence data set, and increase the sequence number to obtain a second time sequence data set;

[0019] A data fusion module, used for 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;

[0020] A data change module, used for filtering data according to the fused data set, and adding 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, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned object query-based path calculation method when executing the computer program.

[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 scheme implemented by the above-mentioned object query-based path calculation method, device, equipment and medium, the initial data set can be obtained by obtaining customer data and grouping the customer data; wherein the initial data set includes customer ID, customer type and time of achievement; the initial data set is serialized and assigned, the corresponding customer type serial number is added, and the serialized data set is integrated; the serialized data set is time-sorted, the corresponding sequence number is added, and the first time sequence data set is integrated; the customer type sequence number in the first time sequence data set is reversed, and the sequence number is incremented at the same time, and the second time sequence data set is integrated; 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 is calculated to obtain a fused data set; data is filtered according to 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 according to the change type. In the present invention, in view of 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 accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0026] Figure 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 data set diagram of 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 data set diagram of 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 data set diagram of 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] Fig. 9 This is a data set diagram of a path calculation method based on object query in one embodiment of the present invention. Figure 4 ;

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

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

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

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

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

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

[0041] Fig.16 is a schematic diagram of a structure of a computer device in one embodiment of the present invention;

[0042] Fig.17 It is another structural schematic diagram of a computer device in one embodiment of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are 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 achievement time.

[0046] Combination 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 reaching an agreement.

[0047] Combination 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 number data and classification data from the data source, where customer number data is used as a unique identifier, and classification data represents the classification information of customers in different time segments, such as new users, prospective customers, customers, loyal customers, and qualified customers. Then, associate customer number data with classification data to form an information table containing customer ID, customer type, and its corresponding achievement time, namely, the customer classification vertical table.

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

[0051] On the basis of the customer classification vertical table, the customer classification conversion time is sorted, and the earlier classification records are arranged in front to construct the first intermediate vertical table. The sorted data table reflects the classification change sequence of customers at different time points, which helps to track the transformation process of customer types and provides a clear time series for path calculation. In addition, the sorting method of time difference can effectively capture the successive conversion path of customer classification and help identify the order of classification changes.

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

[0053] After the first intermediate vertical table is sorted by time difference, the last process time of each customer is extracted from the first intermediate vertical table, and the time information is 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 the customer's classification changes, ensuring that each step in the subsequent path calculation has a reference to the previous and next time. The data in the second intermediate vertical table will be used for further screening 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 that meet the target process are retained to generate the third intermediate vertical table. The setting of filtering conditions can be based on the needs of path calculation, such as specific customer classification categories or time ranges. Through this filtering, the third intermediate vertical table only retains process data related to path analysis, removes irrelevant or redundant information, and makes the data structure more concise and clear.

[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, the process time and customer process information are aggregated to integrate the classification changes of customers at different time nodes. Specifically, the aggregation operation includes grouping the process data of each customer, aggregating multiple process records of the same customer in chronological order, and forming customer data containing a 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 order. For example, you can assign values ​​from 1 to 6 in the order from "new users" to "qualified customers" to ensure that customer types are ranked according to conversion priority. After completing the serialization assignment, integrate the initial data set with the newly added customer type serial numbers to form a serialized data set.

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

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

[0062] A unique customer type serial number is assigned to each customer type according to the predetermined level order of the customer type. For example, "new user" to "qualified customer" can be numbered in the order of 1 to 6, so that each customer type corresponds to a unique customer type serial number. Specifically, new user is 1, user is 2, prospective customer is 3, customer is 4, loyal customer is 5, qualified customer is 6, and so on. The corresponding customer types can be increased or decreased according to demand.

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

[0064] After obtaining the customer type sequence number, it is necessary to further extract the conversion level of the customer type, indicating the conversion order between different customer types. Using the difference in conversion levels, the hierarchical distance between customer type changes is calculated. For example, if a customer is directly converted from a "new user" to a "loyal customer", the hierarchical distance can be obtained by calculating the difference in customer type sequence numbers, thereby quantifying the jump or 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-ordering the serialized data sets, adding corresponding sequence numbers, and integrating to obtain a first time-ordered data set.

[0068] The serialized data set is sorted by time to ensure that the time sequence of customer type changes is accurately recorded. Specifically, the serialized data set is sorted in ascending order according to the time of each customer's achievement, so that each customer's type conversion record is arranged in chronological order, thereby forming a time series. After the time sorting is completed, a sequence number is added to each sorted record. The sequence number is numbered one by one according to the chronological order to indicate the specific time sequence of customer type changes. Finally, the time-sorted serialized data set is integrated with the newly added sequence number to form the first time sequence data set.

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

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

[0071] First, the achievement time field of the serialized data set is sorted in descending order to ensure that the latest customer type conversion information is prioritized. When multiple entries have the same achievement time, the system further sorts them in ascending order based on the customer ID, so that the data is arranged in the order of the customer's unique identifier within the same time node, thereby avoiding order conflicts.

[0072] S32: Assign 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, a sequence number is assigned to each data entry to indicate the chronological order of changes in customer types. The sequence number starts from a predetermined initial value (such as 1) to ensure the uniformity of the numbering. The increasing order of the sequence number 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 sorting data table to form the final first time sequence data set. This first time sequence data set can reflect the time sequence of customer type changes and lay the foundation for path calculation and subsequent time interval analysis.

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

[0077] First, the customer type sequence number in the first time sequence data set is negated. The negation of the customer type sequence number is to reverse 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 number and the incremented sequence number are integrated into the first time sequence data set to obtain the second time sequence data set.

[0078] Combination Figure 8 and Fig. 9As shown, in step S40, the customer type sequence number in the first time sequence data set is reversed, and the sequence number is 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 operation on the customer type sequence number to change a positive sequence number into a negative sequence number, or vice versa. This inversion operation introduces a reverse identifier into the customer type hierarchy, which helps to distinguish the 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 according to a predetermined increment value (e.g. +1) to ensure that the sequence number has a new unique identifier in the second time sequence data set. 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 value-added operations, the negated customer type sequence number, the value-added 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 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.

[0086] Combination Fig.10 As shown, the customer type sequence number is taken from the first time sequence data set, the inverted customer type sequence number is taken from the second time sequence data set, and the customer type sequence number and the inverted customer type sequence number are staggered and added. Staggered addition refers to establishing a "front and back staggered" relationship between different time sequence data sets (i.e. constructing hedge data) and calculating the customer type change characteristics at adjacent time points. This staggered sum calculation can capture the degree of change of customer types in adjacent time slices, such as whether there is a type promotion, demotion, or no change. Finally, all the staggered sum results 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, filter the fused data set according to the preset filtering conditions. For example, you can filter out data entries whose customer type sequence sum is 0 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 completing the data filtering, add a change type field to each retained data entry to indicate the specific changes 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"; 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] Combination Fig.11 and Fig.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: filtering 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 filtering out empty data in the fused data set to obtain a filtering result.

[0091] First, filter the customer type sequence and each data entry in the fused data set, and extract the entries that meet the conditions according to the preset conditions (such as the sum of the sequence numbers is not 0). The purpose of this filtering operation is to filter out data entries whose customer types have not changed in adjacent time slices. At the same time, exclude data entries containing null values ​​in the fused data set to ensure the integrity and accuracy of the data and obtain the final filtering results.

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

[0093] The change type is used to identify the specific changes in the customer type, such as "promotion", "downgrade" or "transition". These change types can be determined based on the positive and negative values ​​of the customer type sequence number and the magnitude of the change. For example, a positive value indicates "promotion", a negative value indicates "downgrade", and a larger magnitude of change indicates "transition", thereby clearly classifying customer behavior.

[0094] S63: Integrate the change type with the data items 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 data set to generate the final change type data set. The change type data set contains customer ID, sequence number, customer type, change time, customer type sequence number, customer type sequence and change type, providing a more comprehensive change record.

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

[0097] The change type identifies the category changes of the customer at different time nodes. 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 its path, such as "steady promotion", "frequent transition", "occasional rollback", etc. The generation of these labels can identify and mark the customer's behavior characteristics on the path according to 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. Finally, the calculation result of the path feature label provides a feature description based on the customer's behavior path for each customer, which helps to summarize and classify the customer's overall behavior and provide an important basis for subsequent analysis and decision-making.

[0098] Combination Fig.13 and Fig.14 As shown, in step S70, the change type data set is queried, and a corresponding path feature label is calculated according to the change type, which specifically includes the following steps:

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

[0100] The change type data set is queried to extract the change type information in each customer's data entry. The change type information reflects the type changes of the customer at different time nodes.

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

[0102] After obtaining the change type, the type change of each customer can be sorted in time according to the time of achievement to form a customer state transition sequence. The customer state transition sequence arranges the type change records of each customer in chronological order to show the type transformation trajectory of the customer at each stage in the life cycle.

[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 state transition sequence with the preset positive path model (i.e., the ideal customer type conversion path) to identify the deviation of customer behavior during the life cycle. Through this matching, it can be determined whether the customer's path conforms to the preset positive development trend, or whether there are behaviors such as jumps and regressions that deviate from the positive path.

[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 in the life cycle. For example, if the customer's state transition sequence highly 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. Path feature labels summarize customer behavior patterns and provide a basis for subsequent customer stratification management and marketing.

[0107] like Fig.14 As shown in the figure, taking customer A as an example, the positive process of customer development is: prospective user (new user and prospective user are the same), user, prospective customer, customer, qualified customer, then the positive path is 1-2-3-4-6, but customer A had a policy expiration, resulting in a downgrade, and then became a loyal customer. In the subsequent period, the policy expired for the second time, and the level was further reduced, and became a customer, then the downgrade path is 6-5-4. Later, the customer purchased a new policy and upgraded from a customer to a qualified customer, which is a level transition, and the transition path is 4-6. At this time, the overall behavior path of customer A is 1-2-3-4-6-5-4. Since customer A has been upgraded from a customer to a qualified customer twice, the number of transitions is marked as 2.

[0108] It can be seen that the above solution avoids the drawbacks of the traditional solution that requires data migration and only supports sample analysis, and also solves the problem that the traditional solution cannot effectively filter time slices without changes and cannot support full path analysis. By assigning customer type sequences and constructing hedge data, the types of changes in customer types in adjacent time slices are marked. 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 order of execution of the steps in the above embodiment does not necessarily 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 embodiment of the present invention.

[0110] In one embodiment, a path calculation device based on object query is provided, and the path calculation device based on object query corresponds one-to-one to the path calculation method based on object query in the above embodiment. Fig.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 achievement 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 set by time, add corresponding sequence numbers, and integrate to obtain a first time sequence data set.

[0114] The second sorting module 104 is used to reverse the customer type sequence number in the first time sequence data set, and increase the sequence number to obtain a second time sequence data set.

[0115] The data fusion module 105 is used to calculate 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.

[0116] The data change module 106 is used to filter data according to 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 path calculation device based on object query is further specifically used for:

[0119] Get customer number data and classification data respectively, 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] Acquire the last process time according to the first intermediate vertical table to construct a second intermediate vertical table;

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

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

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

[0125] Performing serialization and value 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 using the conversion level to calculate a corresponding level distance;

[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] The serialized data sets are sorted in descending order according to the achievement time, and when the achievement time is the same, they are sorted 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 used to:

[0137] The sum of the customer type serial number of each data item in the fused data set is respectively screened to extract data items that meet preset conditions, and at the same time, empty data in the fused data set is filtered to obtain a screening result;

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

[0139] The change type is integrated with data items 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] Query the change type data set to obtain the change type;

[0142] Building a customer status 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 label 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 path calculation device based on object query, please refer to the definition of the path calculation method based on object query above, which will not be repeated here. Each module in the above-mentioned path calculation device based on object query can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to 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: Fig.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: Fig.17 As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, 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 the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external server through 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 refer to the relevant descriptions on 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 can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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. As an illustration and not limitation, RAM is available in many 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 can clearly understand that for the convenience and simplicity 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 the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope 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 achievement 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 to obtain a first time-sequential data set; Reverse the customer type sequence number in the first time-sequential data set, and increase the sequence number, and integrate them to obtain a second time-sequential data set; Calculate 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; The change type data set is queried, and a corresponding path feature label is calculated according to the change type.

2. The object query-based path calculation method according to claim 1, characterized in that: Before obtaining the customer data and grouping the customer data to obtain the initial data set, the method includes: Get customer number data and classification data respectively, and build a customer classification vertical table; Sorting the customer classification vertical table by conversion time difference to construct a first intermediate vertical table; Acquire the last process time according to the first intermediate vertical table to construct a second intermediate vertical table; Filtering the target process data in the second intermediate vertical table to construct a third intermediate vertical table; The time and process in the third intermediate vertical table are aggregated to obtain the customer data.

3. The object query-based path calculation method according to claim 1, characterized in that: The initial data set is serialized and assigned a value, a corresponding customer type serial number is added, and a serialized data set is obtained by integration, including: Performing serialization and value assignment processing on the customer type to obtain at least one customer type serial number; Obtaining a conversion level of the customer type, and using the conversion level to calculate a corresponding level distance; 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 set, adding corresponding sequence numbers, and integrating to obtain a first temporally ordered data set includes: The serialized data sets are sorted in descending order according to the achievement time, and when the achievement time is the same, they are sorted 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 number in the first time sequence data set and increasing the sequence number to obtain a second time sequence 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: The sum of the customer type serial number of each data item in the fused data set is respectively screened to extract data items that meet preset conditions, and at the same time, empty data in the fused data set is filtered to obtain a screening result; Adding the change type to each data entry that meets the preset condition based on the screening result; The change type is integrated with data items corresponding to the fused data set to obtain the change type data set.

7. The object query-based path calculation method according to claim 1, characterized in that: The querying of the change type data set and calculating the corresponding path feature label according to the change type includes: Query the change type data set to obtain the change type; Building a customer status transition sequence based on the change type and the time of achievement; 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; The path feature label 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.

8. A path calculation device based on object query, characterized in that: include: A data acquisition module, 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 time of achievement; 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, used to sort the serialized data set by time, add corresponding sequence numbers, and integrate to obtain a first time sequence data set; A second sorting module is used to reverse the customer type sequence number in the first time sequence data set, and increase the sequence number to obtain a second time sequence data set; A data fusion module, used for 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; A data change module, used for filtering data according to the fused data set, and adding corresponding change types to obtain a change type data set; 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.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: 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 7 are implemented.

10. 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 7 are implemented.

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