CN113592662B - Intelligent processing method, device, equipment and medium for data information - Google Patents
Intelligent processing method, device, equipment and medium for data information Download PDFInfo
- Publication number
- CN113592662B CN113592662B CN202110874670.6A CN202110874670A CN113592662B CN 113592662 B CN113592662 B CN 113592662B CN 202110874670 A CN202110874670 A CN 202110874670A CN 113592662 B CN113592662 B CN 113592662B
- Authority
- CN
- China
- Prior art keywords
- data information
- information
- data
- processing
- newly
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/08—Insurance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/903—Querying
- G06F16/90335—Query processing
- G06F16/90344—Query processing by using string matching techniques
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Databases & Information Systems (AREA)
- General Physics & Mathematics (AREA)
- Development Economics (AREA)
- Technology Law (AREA)
- General Business, Economics & Management (AREA)
- Strategic Management (AREA)
- Marketing (AREA)
- Computational Linguistics (AREA)
- Economics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method, a device, equipment and a medium for intelligently processing data information, wherein the method comprises the following steps: when the data acquisition time point is reached, new data information is acquired from the data source terminal, standard data information is obtained by carrying out standardization processing on the new data information, corresponding quantized feature information is extracted from the standard data information, an abnormal analysis result is obtained by carrying out abnormal analysis on the quantized feature information according to an abnormal analysis model, target data information is acquired from the new data information and is stored in a temporary database, and target data information in the temporary database is distributed to a settlement terminal for processing according to a distribution rule. The invention belongs to the technical field of data processing, and particularly relates to a method for processing data, which comprises the steps of carrying out exception analysis on newly added data information, obtaining target data information according to an exception analysis result, storing the target data information into a temporary database for arrangement and distributing the target data information to a settlement terminal for processing, so that the intelligent degree of processing the data is greatly improved, and the processing efficiency of the data information is also improved.
Description
Technical Field
The invention relates to the technical field of data processing, belongs to an application scene for performing intelligent processing on data information in intelligent medical treatment, and particularly relates to a method, a device, equipment and a medium for performing intelligent processing on the data information.
Background
Along with the more and more perfect coverage of medical insurance, more and more people reimburse the expense of seeking medical attention through the medical insurance, and settlement is needed based on the information of seeking medical attention of the paramedic when the paramedic applies for reimbursement of the expense of seeking medical attention. The existing technical methods are that business personnel copy the data to be clawed from a medical insurance system through storage media such as a hard disk, the data are carried to an office place for manual arrangement and then uploaded to a medical reimbursement system for claiming, the data processing mode has the problem of low intelligent degree, the data cannot be monitored and illegally utilized in the manual carrying process, the workload of arrangement by adopting a manual mode is huge, a large amount of manpower resources are occupied, and errors are easily caused by the data due to omission of manual arrangement. Therefore, the method for processing the medical data of the ginseng and the insurer in the prior art has the problem of low processing efficiency.
Disclosure of Invention
The embodiment of the invention provides a method, a device, equipment and a medium for intelligently processing data information, which aim to solve the problem of low processing efficiency in the method for processing the medical data of a participant in the prior art.
In a first aspect, an embodiment of the present invention provides a method for intelligently processing data information, including:
if the preset data acquisition time point is reached, acquiring newly-added data information stored by each data source terminal;
carrying out standardization processing on the newly-added data information according to a preset standardization processing rule to obtain standard data information corresponding to each piece of newly-added data information;
extracting corresponding quantized feature information from each piece of standard data information according to a preset extraction rule;
performing anomaly analysis on each piece of quantized feature information according to a preset anomaly analysis model to obtain an anomaly analysis result corresponding to each piece of newly-added data information;
acquiring target data information from the newly-added data information according to the abnormal analysis result and storing the target data information into a preset temporary database;
and generating a settlement list according to the target data information in the temporary database according to a preset allocation rule, and allocating the settlement list to the settlement terminal for settlement processing.
In a second aspect, an embodiment of the present invention provides an intelligent data information processing apparatus, including:
the newly added data information acquisition unit is used for acquiring newly added data information stored by each data source terminal if a preset data acquisition time point is reached;
the standardized processing unit is used for carrying out standardized processing on the newly-added data information according to a preset standardized processing rule to obtain standard data information corresponding to each piece of newly-added data information;
the quantized feature information acquisition unit is used for extracting corresponding quantized feature information from each piece of standard data information according to a preset extraction rule;
the abnormal analysis result acquisition unit is used for carrying out abnormal analysis on each piece of quantized characteristic information according to a preset abnormal analysis model to obtain an abnormal analysis result corresponding to each piece of newly-added data information;
the target data information storage unit is used for acquiring target data information from the newly-added data information according to the abnormal analysis result and storing the target data information into a preset temporary database;
and the distribution settlement unit is used for generating a settlement list according to the target data information in the temporary database according to a preset distribution rule and distributing the settlement list to the settlement terminal for settlement processing.
In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the data information intelligent processing method described in the first aspect when executing the computer program.
In a fourth aspect, an embodiment of the present invention further provides a computer readable storage medium, where the computer readable storage medium stores a computer program, where the computer program when executed by a processor causes the processor to perform the data information intelligent processing method described in the first aspect.
The embodiment of the invention provides a method, a device, equipment and a medium for intelligently processing data information. When the data acquisition time point is reached, new data information is acquired from the data source terminal, standard data information is obtained by carrying out standardization processing on the new data information, corresponding quantized feature information is extracted from the standard data information, an abnormal analysis result is obtained by carrying out abnormal analysis on the quantized feature information according to an abnormal analysis model, target data information is acquired from the new data information and is stored in a temporary database, and target data information in the temporary database is distributed to a settlement terminal for processing according to a distribution rule. According to the method, the newly added data information is subjected to anomaly analysis, the target data information is obtained according to the anomaly analysis result, and is stored in the temporary database to be sorted and distributed to the settlement terminal for processing, so that the intelligent degree of processing the data is greatly improved, and the processing efficiency of the data information is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of an intelligent processing method for data information according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an application scenario of a data information intelligent processing method according to an embodiment of the present invention;
FIG. 3 is a schematic sub-flowchart of a method for intelligent processing of data information according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of another sub-flow of the data information intelligent processing method according to the embodiment of the present invention;
FIG. 5 is another flow chart of the data information intelligent processing method according to the embodiment of the invention;
FIG. 6 is a schematic diagram of another sub-flow of the data information intelligent processing method according to the embodiment of the present invention;
FIG. 7 is a schematic diagram of another sub-flow of the data information intelligent processing method according to the embodiment of the present invention;
FIG. 8 is another flow chart of the data information intelligent processing method according to the embodiment of the invention;
FIG. 9 is a schematic block diagram of an intelligent data information processing apparatus according to an embodiment of the present invention;
fig. 10 is a schematic block diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be understood that the terms "comprises" and "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
Referring to fig. 1 and fig. 2, fig. 1 is a flow chart of an intelligent processing method for data information according to an embodiment of the invention; fig. 2 is a schematic diagram of an application scenario of a data information intelligent processing method according to an embodiment of the present invention; the intelligent processing method of the data information is applied to the management server 10, the intelligent processing method of the data information is executed through application software installed in the management server 10, the management server 10 establishes network connection with at least one data source terminal 20 and at least one settlement terminal 30 at the same time to realize the transmission of the data information, the data source terminal 20 is a terminal device for inputting and storing newly-added data information, such as a terminal device configured in a medical insurance bureau for inputting the newly-added data information, and the data source terminal 20 can be a desktop computer, a notebook computer, a tablet computer or a mobile phone and the like; the management server 10 is a server that can acquire new data information from the data source terminal 20 and perform screening and distribution processes, such as a server constructed by an enterprise or a government department, and the settlement terminal 30 is a terminal device for performing payment settlement processing on the data information, such as a processing cluster constructed inside the enterprise or the government department. As shown in fig. 1, the method includes steps S110 to S160. As shown in fig. 1, the method includes steps S110 to S160.
And S110, if the preset data acquisition time point is reached, acquiring newly-added data information stored by each data source terminal.
And if the preset data acquisition time point is reached, acquiring newly-added data information stored by each data source terminal. The user of the data source terminal can upload the data information to the data source terminal, and the management server can collect the newly-added data information stored in the data source terminal at a data collection time point, specifically, can judge whether the storage time of each data information in each data source terminal is located in a collection time period or not, so as to obtain the newly-added data information by acquiring the data information with the storage time located in the collection time period according to the judging result, wherein the collection time period is the interval time between the current data collection time period and the last data collection time point. For example, if the interval time is 2 months, the current data acquisition time point is a time point corresponding to 2 months after the previous data acquisition time point.
For example, if the data source terminal is a terminal device configured in the medical insurance bureau, the acquired newly-added data information is medical insurance reimbursement data, and the medical insurance reimbursement data includes data source address information, participant information, medical insurance orders, medical advice, and collection information.
And S120, carrying out standardization processing on the newly-added data information according to a preset standardization processing rule to obtain standard data information corresponding to each piece of newly-added data information.
In order to improve the efficiency of processing the newly-added data information, after the newly-added data information is obtained, the newly-added data information can be subjected to standardized processing through a standardized processing rule to obtain standard data information corresponding to each piece of newly-added data information. The standardized processing rule is a specific rule for carrying out standardized processing on the newly-added data information, wherein the standardized processing rule comprises a standard template, a standard dimension set and a field mapping rule, the standard template is a template for carrying out matching judgment on each dimension information in the newly-added data information, the standard dimension set is a set obtained by combining a plurality of standard dimension fields, and the field mapping rule is a specific rule for carrying out mapping processing on the non-standard dimension information in the newly-added data information.
In one embodiment, as shown in FIG. 3, step S120 includes substeps S121, S122, and S123.
S121, judging whether dimension information corresponding to each dimension in each piece of newly-added data information is matched with the standard template.
Each piece of newly-added data information contains dimension information corresponding to a plurality of dimensions, for example, medical insurance orders can include "medical treatment type information", the "medical treatment type information" corresponds to one dimension, and data corresponding to the "medical treatment type information" in one piece of newly-added data information is dimension information corresponding to the dimension in the newly-added data information.
The standard template can be used for carrying out matching judgment on whether the characters contained in each dimension information accord with the corresponding standards, and specifically, the standard template can be used for verifying whether the character length and the character type of the characters contained in each dimension information accord with the corresponding standards.
For example, for the medical treatment type information contained in the medical treatment bill, the matching rule corresponding to the medical treatment type information in the standard template includes that the character length is [3,7], and the character type is a Chinese character, so that whether the dimension information corresponding to the dimension of the medical treatment type information in each piece of newly added data information accords with the matching rule can be judged.
If the dimension information contained in one piece of newly-added data information is matched with the matching rule contained in the standard template, carrying out next processing on the newly-added data information; if the dimension information of a certain piece of newly added data information is not uniform and matched with the matching rule contained in the standard template, the newly added data information can be stored in the abnormal list.
And S122, if the dimension information contained in the newly-added data information is matched with the standard template, verifying whether the newly-added data information contains non-standard dimension information according to the standard dimension set.
The standard dimension set is a set formed by standard dimension fields corresponding to each dimension, the standard dimension fields are not repeated, whether each dimension information of each newly added data information is matched with any standard dimension field in the standard dimension set can be judged, and if the dimension information is matched with any standard dimension field, the dimension information is standard dimension information without carrying out subsequent mapping processing; if the dimension information is not matched with the standard dimension field, the dimension information is non-standard dimension information, and mapping processing is needed to be carried out on the non-standard dimension information.
And S123, if the newly-added data information contains non-standard dimension information, mapping the non-standard dimension information according to the field mapping rule and the standard dimension set to obtain standard data information corresponding to the newly-added data information.
If the newly added data information does not contain non-standard dimension information, the newly added data information can be directly used as standard data information. If the newly added data information contains non-standard dimension information, mapping processing can be performed on the non-standard dimension information according to a field mapping rule and a standard dimension set, specifically, the matching degree between the non-standard dimension information and each standard dimension field can be obtained according to the field mapping rule, one standard dimension field with the highest matching degree is obtained to perform mapping processing on the non-standard dimension information, that is, mapping is performed on the non-standard dimension information to replace the corresponding standard dimension field, and standard data information corresponding to the newly added data information can be obtained after mapping processing. The overlapping character ratio, the character length difference and the overlapping character pinyin ratio between the nonstandard dimension information and each standard dimension field can be obtained firstly, the overlapping character ratio is the ratio of the number of overlapped characters to the number of characters in the nonstandard dimension information, the character length difference is the difference between the character length of the nonstandard dimension information and the character length of the standard dimension field, the overlapping character pinyin ratio is the ratio of the number of characters overlapped with pinyin to the number of characters in the nonstandard dimension information, and the corresponding matching degree is obtained by carrying out mapping calculation on the overlapping character ratio, the character length difference and the overlapping character pinyin ratio according to field mapping rules.
S130, extracting corresponding quantization characteristic information from each piece of standard data information according to a preset extraction rule.
In order to obtain the characteristics of each standard data information, the corresponding quantized characteristic information can be extracted from each standard data information through an extraction rule, and the quantized characteristic information can be used for carrying out quantized representation on the characteristics of the standard data information, wherein the extraction rule is a specific rule for extracting the quantized characteristic information. The dimension information contained in each standard data information can be numbers, letters or Chinese characters, and the dimension information contained in each standard data information can be respectively extracted through an extraction rule so as to extract and obtain the quantized characteristic information with numerical value from the dimension information represented by the numbers, letters or Chinese characters, and the obtained quantized characteristic information contains the quantized characteristic value corresponding to each dimension. Specifically, if the dimension information is a number, the number may be converted into a corresponding decimal as a quantization characteristic value of the dimension information. If the dimension information is non-digital, the dimension information can be converted into a quantization characteristic value represented by a number by extracting a conversion relation corresponding to the dimension information in the rule. For example, the conversion relationship corresponding to the dimension "doctor type information" in the extraction rule is "general outpatient service: 1"," general hospitalization: 2"," special disease clinic: 3"," major disease clinic: 4".
S140, carrying out anomaly analysis on each piece of quantized feature information according to a preset anomaly analysis model to obtain an anomaly analysis result corresponding to each piece of newly-added data information.
And carrying out anomaly analysis on each piece of quantized feature information according to a preset anomaly analysis model to obtain an anomaly analysis result corresponding to each piece of newly-added data information. In order to ensure that conflict can not be generated among the dimension information of the newly added data information, the feature quantization information of the newly added data information can be subjected to exception analysis through an exception analysis model so as to analyze whether exception conflict exists among the dimension information of each piece of newly added data information, wherein the exception conflict is that one dimension information does not correspond to one or more other dimension information.
In one embodiment, as shown in FIG. 4, step S140 includes sub-steps S141 and S142.
S141, inputting each piece of quantized feature information into the anomaly analysis model to obtain verification output information corresponding to each piece of quantized feature information.
Specifically, the anomaly analysis model is an intelligent verification model constructed based on a neural network, and the anomaly analysis model can be composed of an input layer, a plurality of middle layers and an output layer, wherein the input layer and the middle layers, the middle layers and other middle layers adjacent to each other in front and back, and the middle layers and the output layer are all associated through association formulas, for example, a certain association formula can be expressed as y=p×x+q, and p and q are parameter values in the association formula. The number of input nodes contained in the input layer is equal to the number of dimensions contained in the quantized feature information, each quantized feature value in the quantized feature information corresponds to one input node, each quantized feature information is sequentially input into an anomaly analysis model for intelligent verification, and verification output information can be obtained from an output layer of the quantized feature information, wherein the verification output information is an output node value of the output node, the anomaly analysis model contains two output nodes, the verification output information corresponding to each quantized feature information contains two output node values, the output node value is a probability value corresponding to the quantized feature information and the corresponding output node, the first output node value is a probability value with abnormal conflict, the second output node value is a probability value without abnormal conflict, the probability value can be expressed by a decimal, and the value range is [0,1]. And respectively inputting each piece of quantized characteristic information into an anomaly analysis model to obtain corresponding verification output information, wherein the verification output information comprises probability values respectively corresponding to two output nodes of the anomaly analysis model.
S142, obtaining corresponding abnormal analysis results according to the verification output information of each piece of quantitative characteristic information.
And determining the output node with a larger probability value as a corresponding abnormal analysis result according to the verification output information of the quantization characteristic information. That is, if the probability value of the first output node is larger, an abnormal analysis result with abnormal conflict is obtained, and if the probability value of the second output node is larger, an abnormal analysis result without abnormal conflict is obtained.
In an embodiment, as shown in fig. 5, step S140 further includes steps S1401, S1402, S1403, and S1404 before step S140.
S1401, randomly extracting a plurality of data check samples from the standard data information to obtain a check sample set.
In one embodiment, as shown in fig. 6, step S1401 includes sub-steps S14011 and S14012.
S14011, randomly extracting a plurality of dimension information corresponding to a dimension respectively in a plurality of pieces of standard data information corresponding to the dimension number according to the dimension number contained in the standard data information; s14012, combining the plurality of dimension information to obtain a data check sample.
For example, if the standard data information includes 40 dimensions, 40 standard data information is randomly extracted, and dimension information corresponding to one dimension is respectively obtained from the 40 standard data information, 40 dimension information can be obtained from the 40 standard data information, and the dimension corresponding to the 40 dimension information is not repeated, so that a data check sample is obtained by randomly extracting a plurality of dimension information combinations corresponding to each dimension, and information of the data check sample is from the plurality of standard data information which are randomly extracted. The method can be repeatedly executed to randomly extract a plurality of data check samples, the obtained plurality of data check samples are combined to obtain a check sample set, and the number of the data check samples contained in the check sample set can be smaller than the total number of standard data information
S1402, respectively performing abnormal verification on each data verification sample in the verification sample set according to a preset abnormal data verification rule to obtain sample verification information of each data verification sample.
And respectively carrying out abnormal verification on each data verification sample in the verification sample set according to a preset abnormal data verification rule to obtain sample verification information of whether the data verification samples have abnormal conflict. Because the calculated amount of carrying out the anomaly verification on the data verification samples through the anomaly data verification rule is large, the anomaly verification can be carried out on a verification sample set with small data amount, the initial anomaly analysis model is trained through the sample verification information of each data verification sample, and the anomaly analysis is carried out on the standard data information based on the trained anomaly analysis model, so that the anomaly analysis efficiency on the identification data information can be greatly improved.
S1403, extracting corresponding sample characteristic information from each data check sample of the check sample set according to the extraction rule.
The corresponding sample characteristic information can be extracted from each data check sample according to the extraction rule, and the specific technical method for extracting the sample characteristic information is the same as the technical method for extracting the quantized characteristic information from the standard data information. And then, carrying out iterative training on the initial abnormal analysis model according to the model training rules, the sample characteristic information and the sample verification information to obtain a trained abnormal analysis model. The model updating rule is a specific rule for training and updating the parameter values in the initial anomaly analysis model, and comprises a loss value calculation formula and a gradient calculation formula.
S1404, performing iterative training on the initial anomaly analysis model according to a pre-stored model training rule, the sample characteristic information and the sample verification information to obtain a trained anomaly analysis model.
Specifically, a piece of sample characteristic information obtained in sequence can be input into an initial anomaly analysis model to obtain corresponding prediction output information, and the prediction output information comprises probability values corresponding to two output nodes of the anomaly analysis model respectively. And then, calculating the predicted output information and sample verification information corresponding to the sample characteristic information according to a loss value calculation formula in a model updating rule to obtain a loss value, calculating an updating value of each parameter value in the abnormal analysis model according to the loss value and a gradient calculation formula, and updating the abnormal analysis model once, wherein the parameter values of all parameters in the abnormal analysis model can be updated once according to one piece of sample characteristic information and the corresponding sample verification information, namely, the one-time training updating of the abnormal analysis model is completed. And then obtaining the next piece of sample characteristic information and repeating the training process until all pieces of sample characteristic information are used for carrying out iterative updating on the abnormal analysis model, wherein the training method can be called as a gradient descent training method.
S150, acquiring target data information from the newly-added data information according to the abnormal analysis result and storing the target data information into a preset temporary database.
And acquiring target data information from the newly-added data information according to the abnormal analysis result and storing the target data information into a preset temporary database. The corresponding target data information can be acquired from the newly-added data and stored according to the abnormal analysis result of the newly-added data information, and the acquired target data information can be stored in a temporary database which is pre-configured in the management server and is used for temporarily storing the target data information. The temporary database comprises a basic data table and a floor data table.
In one embodiment, as shown in FIG. 7, step S150 includes sub-steps S151, S152, S153, S154, and S155.
S151, acquiring the newly added data information without abnormal conflict as target data information according to the abnormal analysis result.
Specifically, the new data information including the abnormal analysis result may be determined, if the abnormal analysis result of the new data information is that there is an abnormal conflict, the new data information is stored in the abnormal list, and if the abnormal analysis result of the new data information is that there is no abnormal conflict, the new data information is determined as the target data information.
S152, acquiring corresponding basic data from each piece of target data information, and storing the basic data in a basic data table of the temporary database.
Corresponding basic data can be obtained from the target data information and stored in a basic data table of the temporary database, then a corresponding piece of basic data can be obtained from each piece of target data information, and only part of information of each piece of target data information is stored in the basic data table. For example, for the medical insurance reimbursement data, the basic data comprises data source address information, the basic information of the insurer, settlement date of the basic segment of the medical insurance, and reimbursement processing state, wherein the data source address information is the network address of the data source terminal, the basic information of the insurer is the basic information capable of determining the identity of the insurer, the basic information comprises the type of the insurer certificate, the certificate number, the name, the sex and the birth date, the settlement date of the basic segment of the medical insurance is the date information of the insurer for carrying out periodic settlement, and the reimbursement processing state is the processing state of a piece of newly added data information corresponding to the insurer.
S153, judging whether the same main body corresponds to a plurality of pieces of basic data according to the main body information of the basic data; and S154, if the same main body corresponds to a plurality of pieces of basic data, sorting and sorting the plurality of pieces of basic data corresponding to the same main body.
Whether the same main body corresponds to a plurality of pieces of basic data can be judged based on main body information of the basic data, and if the same main body corresponds to the plurality of pieces of basic data, the plurality of pieces of basic data corresponding to the same main body in the basic data table can be ordered and tidied.
For example, for the medical insurance reimbursement data, the main body information may be the basic information of the participants in the basic data, if the basic information of the participants in the two basic data is the same, it is determined that the two basic data belong to the same main body, that is, the attention corresponds to a plurality of basic data, and if the basic information of the participants in the basic data is not the same as the basic information of the participants in any other basic data, it is determined that the main body of the basic data corresponds to only one basic data. If the same main body corresponds to a plurality of pieces of basic data, the plurality of pieces of basic data can be ordered and arranged according to the size of the basic settlement date of the medical insurance in the plurality of pieces of basic data, the basic data with smaller basic settlement date of the medical insurance is ordered before, and the basic settlement date of the medical insurance is ordered after. If a main body corresponds to only one piece of basic data, the basic data of the main body does not need to be ordered and tidied.
S155, sequentially storing target data information corresponding to each basic data in a floor data table of a temporary database according to the sequence of the basic data in the basic data table.
And each piece of basic data in the basic data table corresponds to one piece of target data information, the target data information corresponding to each piece of basic data can be respectively obtained according to the sequence of the basic data in the basic data table and sequentially stored in the floor data table, and the floor data table stores the complete information of each piece of target data information.
S160, generating a settlement list according to the target data information in the temporary database according to a preset allocation rule, and allocating the settlement list to the settlement terminal for settlement processing.
And distributing the target data information in the temporary database to the settlement terminal according to a preset distribution rule so as to carry out settlement processing on the target data information through the settlement terminal. And if the target data information needs to be distributed to the settlement terminal to finish the final settlement processing, distributing the target data information to the corresponding settlement terminal for settlement processing through a distribution rule, wherein the distribution rule comprises a warehouse-out condition and a distribution condition.
In one embodiment, as shown in FIG. 8, step S160 includes sub-steps S161, S162, S163, and S164.
S161, distributing settlement terminals to each main body according to the settlement terminal monitoring information and the distribution conditions, and generating corresponding processing identification information according to the distributed settlement terminals.
The settlement terminals change in state in the settlement processing process, so that each settlement terminal can be monitored in real time through the management server to obtain corresponding settlement terminal monitoring information, and specifically, the settlement terminal monitoring information comprises use state information and resource occupation information of each settlement terminal, wherein the use state information is information for recording whether the settlement terminal is available or not, and the resource occupation information is information for recording the occupation condition of each hardware resource in the settlement terminal. The corresponding settlement terminals can be allocated to each main body according to allocation conditions and settlement terminal monitoring information, specifically, all settlement terminals with available use state information can be obtained to serve as alternative settlement terminals, one alternative settlement terminal with the least occupied current resource occupation information can be obtained according to allocation conditions to serve as one settlement terminal allocated to the current main body, and terminal identifiers corresponding to the settlement terminals allocated to each main body can be obtained to serve as corresponding processing identification information.
And S162, adding the processing identification information to a storage position of the basic data corresponding to each main body in the basic data table.
And adding the processing identification information allocated to each main body to the storage position of the basic data corresponding to the corresponding main body in the basic data table, wherein the processing identification information of the basic data corresponding to the same main body is identical as the processing identification information is only associated with the main body.
S163, sequentially judging that the target data information of the same main body in the floor data table meets the ex-warehouse condition.
Whether the target data information of the same main body in the floor data table accords with the ex-warehouse condition or not can be judged in sequence, and the ex-warehouse condition is a condition for judging whether the target data information can be ex-warehouse or not. Specifically, the ex-warehouse condition may be a specific judgment condition such as whether the multi-item target data information of the main body is repeated, whether there is a conflict between the target data information, whether the total amount is smaller than the amount threshold value, and the like.
And S164, if the target data information of the same main body accords with the ex-warehouse condition, generating a settlement list according to the target data information, and transmitting the settlement list to a settlement terminal corresponding to the processing identification information in the target data information.
If the target data information of the same main body accords with the ex-warehouse condition, a settlement list is generated according to one or more pieces of target data information corresponding to the main body, the settlement list comprises specific settlement information corresponding to the pieces of target data information of the same main body, and the generated settlement list can be sent to a settlement terminal corresponding to the processing identification information in the target data information of the main body, so that the settlement terminal can perform automatic settlement processing on the settlement list. If the target data information of the same main body does not meet the ex-warehouse condition, the target data information of the main body is indicated to be incapable of performing automatic settlement processing, and the target data information of the main body can be sent to a manual processing terminal to perform manual audit settlement processing.
The technical method can be applied to intelligent medical treatment and other scenes including intelligent processing of data information, so that construction of intelligent medical treatment is promoted.
In the data information intelligent processing method provided by the embodiment of the invention, when the data acquisition time point is reached, newly-increased data information is acquired from the data source terminal, standard data information is obtained by carrying out standardization processing on the newly-increased data information, corresponding quantized feature information is extracted from the newly-increased data information, an abnormal analysis result is obtained by carrying out abnormal analysis on the quantized feature information according to an abnormal analysis model, target data information is acquired from the newly-increased data information and is stored in the temporary database, and the target data information in the temporary database is distributed to the settlement terminal for processing according to a distribution rule. According to the method, the newly added data information is subjected to anomaly analysis, the target data information is obtained according to the anomaly analysis result, and is stored in the temporary database to be sorted and distributed to the settlement terminal for processing, so that the intelligent degree of processing the data is greatly improved, and the processing efficiency of the data information is improved.
The embodiment of the invention also provides a data information intelligent processing device, which is used for executing any embodiment of the data information intelligent processing method, specifically referring to fig. 9, and fig. 9 is a schematic block diagram of the data information intelligent processing device provided by the embodiment of the invention.
As shown in fig. 9, the data information intellectualized processing apparatus 100 includes a newly added data information acquisition unit 110, a normalization processing unit 120, a quantization characteristic information acquisition unit 130, an abnormality analysis result acquisition unit 140, a target data information storage unit 150, and an allocation settlement unit 160.
And the newly added data information acquiring unit 110 is configured to acquire newly added data information stored in each data source terminal if a preset data acquisition time point is reached.
The normalization processing unit 120 is configured to perform normalization processing on the new data information according to a preset normalization processing rule, so as to obtain standard data information corresponding to each piece of the new data information.
In one embodiment, the standardized processing unit 120 includes a subunit: the dimension information judging unit is used for judging whether dimension information corresponding to each dimension in each piece of newly-added data information is matched with the standard template or not; the newly-added data information verification unit is used for verifying whether the newly-added data information contains non-standard dimension information according to the standard dimension set if the dimension information contained in the newly-added data information is matched with the standard template; and the mapping processing unit is used for carrying out mapping processing on the nonstandard dimension information according to the field mapping rule and the standard dimension set if the newly added data information contains nonstandard dimension information, so as to obtain standard data information corresponding to the newly added data information.
And a quantized feature information obtaining unit 130, configured to extract corresponding quantized feature information from each piece of standard data information according to a preset extraction rule.
And an anomaly analysis result obtaining unit 140, configured to perform anomaly analysis on each piece of quantized feature information according to a preset anomaly analysis model, so as to obtain an anomaly analysis result corresponding to each piece of newly-added data information.
In an embodiment, the anomaly analysis result acquisition unit 140 includes a subunit: the verification output information acquisition unit is used for inputting each piece of quantized feature information into the anomaly analysis model respectively to acquire verification output information corresponding to each piece of quantized feature information; and the result acquisition unit is used for acquiring a corresponding abnormal analysis result according to the verification output information of each piece of quantization characteristic information.
In one embodiment, the data information intelligent processing apparatus 100 further includes a subunit: the verification sample set acquisition unit is used for randomly extracting a plurality of data verification samples from the standard data information to obtain a verification sample set; the sample verification information acquisition unit is used for respectively carrying out abnormal verification on each data verification sample in the verification sample set according to a preset abnormal data verification rule to obtain sample verification information of each data verification sample; the sample characteristic information extraction unit is used for extracting corresponding sample characteristic information from each data check sample of the check sample set according to the extraction rule; the abnormal analysis model training unit is used for carrying out iterative training on the initial abnormal analysis model according to a pre-stored model training rule, the sample characteristic information and the sample verification information to obtain a trained abnormal analysis model.
In an embodiment, the check sample set acquisition unit comprises a subunit: the dimension information extraction unit is used for randomly extracting a plurality of dimension information corresponding to a dimension respectively in a plurality of pieces of standard data information corresponding to the dimension number according to the dimension number contained in the standard data information; and the dimension information combining unit is used for combining the plurality of dimension information to obtain a data check sample.
And a target data information storage unit 150, configured to obtain target data information from the newly added data information according to the anomaly analysis result, and store the target data information in a preset temporary database.
In one embodiment, the target data information storage unit 150 includes a subunit: a target data information acquisition unit for acquiring the newly added data information without abnormal conflict as target data information according to the abnormal analysis result; a basic data storage unit, configured to acquire corresponding basic data from each piece of target data information, and store the basic data in a basic data table of the temporary database; the main body information judging unit is used for judging whether the same main body corresponds to a plurality of pieces of basic data according to the main body information of the basic data; the ordering and sorting unit is used for ordering and sorting the plurality of pieces of basic data corresponding to the same main body if the same main body corresponds to the plurality of pieces of basic data; and the information storage unit is used for sequentially storing the target data information corresponding to each basic data respectively into a floor data table of the temporary database according to the sequence of the basic data in the basic data table.
And the distribution settlement unit 160 is used for generating a settlement list according to the target data information in the temporary database according to a preset distribution rule and distributing the settlement list to the settlement terminal for settlement processing.
In one embodiment, the distribution settlement unit 160 includes a subunit: the processing identification information generating unit is used for distributing the settlement terminals to each main body according to the settlement terminal monitoring information and the distribution conditions and generating corresponding processing identification information according to the distributed settlement terminals; a processing identification information adding unit configured to add the processing identification information to a storage location of the base data corresponding to each of the main bodies in the base data table; the ex-warehouse condition judging unit is used for sequentially judging whether the target data information of the same main body in the floor data table accords with the ex-warehouse condition; and the settlement list distribution unit is used for generating a settlement list according to the target data information if the target data information of the same main body accords with the ex-warehouse condition, and sending the settlement list to a settlement terminal corresponding to the processing identification information in the target data information.
The data information intelligent processing device provided by the embodiment of the invention applies the data information intelligent processing method, acquires newly-added data information from a data source terminal when the data acquisition time point is reached, performs standardization processing on the newly-added data information to obtain standard data information, extracts corresponding quantized feature information from the standard data information, performs abnormal analysis on the quantized feature information according to an abnormal analysis model to obtain an abnormal analysis result, acquires target data information from the newly-added data information, stores the target data information in a temporary database, and distributes the target data information in the temporary database to a settlement terminal according to a distribution rule for processing. According to the method, the newly added data information is subjected to anomaly analysis, the target data information is obtained according to the anomaly analysis result, and is stored in the temporary database to be sorted and distributed to the settlement terminal for processing, so that the intelligent degree of processing the data is greatly improved, and the processing efficiency of the data information is improved.
The above-described data information intelligent processing apparatus may be implemented in the form of a computer program that can be run on a computer device as shown in fig. 10.
Referring to fig. 10, fig. 10 is a schematic block diagram of a computer device according to an embodiment of the present invention. The computer device may be a management server for performing the data information intelligent processing method to perform intelligent processing of the data information.
With reference to fig. 10, the computer device 500 includes a processor 502, a memory, and a network interface 505, which are connected by a system bus 501, wherein the memory may include a storage medium 503 and an internal memory 504.
The storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032, when executed, may cause the processor 502 to perform a data information intelligent processing method, wherein the storage medium 503 may be a volatile storage medium or a nonvolatile storage medium.
The processor 502 is used to provide computing and control capabilities to support the operation of the overall computer device 500.
The internal memory 504 provides an environment for the execution of a computer program 5032 in the storage medium 503, which computer program 5032, when executed by the processor 502, enables the processor 502 to perform a data information intelligent processing method.
The network interface 505 is used for network communication, such as providing for transmission of data information, etc. It will be appreciated by those skilled in the art that the structure shown in FIG. 10 is merely a block diagram of some of the structures associated with the present inventive arrangements and does not constitute a limitation of the computer device 500 to which the present inventive arrangements may be applied, and that a particular computer device 500 may include more or fewer components than shown, or may combine certain components, or may have a different arrangement of components.
The processor 502 is configured to execute a computer program 5032 stored in a memory, so as to implement the corresponding functions in the data information intelligent processing method.
Those skilled in the art will appreciate that the embodiment of the computer device shown in fig. 10 is not limiting of the specific construction of the computer device, and in other embodiments, the computer device may include more or less components than those shown, or certain components may be combined, or a different arrangement of components. For example, in some embodiments, the computer device may include only a memory and a processor, and in such embodiments, the structure and function of the memory and the processor are consistent with the embodiment shown in fig. 10, and will not be described again.
It should be appreciated that in embodiments of the present invention, the processor 502 may be a central processing unit (Central Processing Unit, CPU), the processor 502 may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf Programmable gate arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In another embodiment of the invention, a computer-readable storage medium is provided. The computer readable storage medium may be a volatile or nonvolatile computer readable storage medium. The computer readable storage medium stores a computer program, wherein the computer program realizes the data information intelligent processing method when being executed by a processor.
It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the apparatus, device and unit described above may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein. Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the units is merely a logical function division, there may be another division manner in actual implementation, or units having the same function may be integrated into one unit, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices, or elements, or may be an electrical, mechanical, or other form of connection.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment of the present invention.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention is essentially or part of what contributes to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a computer-readable storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned computer-readable storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
While the invention has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (9)
1. The method is applied to a management server, and the management server establishes network connection with at least one data source terminal and at least one settlement terminal at the same time to realize the transmission of data information, and comprises the following steps:
if the preset data acquisition time point is reached, acquiring newly-added data information stored by each data source terminal;
carrying out standardization processing on the newly-added data information according to a preset standardization processing rule to obtain standard data information corresponding to each piece of newly-added data information;
extracting corresponding quantized feature information from each piece of standard data information according to a preset extraction rule;
performing anomaly analysis on each piece of quantized feature information according to a preset anomaly analysis model to obtain an anomaly analysis result corresponding to each piece of newly-added data information;
Acquiring target data information from the newly-added data information according to the abnormal analysis result and storing the target data information into a preset temporary database;
generating a settlement list according to the target data information in the temporary database according to a preset allocation rule, and allocating the settlement list to the settlement terminal for settlement processing;
the temporary database comprises a basic data table and a floor data table, and the step of acquiring target data information from the newly-added data information according to the abnormal analysis result and storing the target data information into a preset temporary database comprises the following steps:
acquiring the newly added data information without abnormal conflict as target data information according to the abnormal analysis result;
acquiring corresponding basic data from each piece of target data information and storing the basic data into a basic data table of the temporary database;
judging whether the same main body corresponds to a plurality of pieces of basic data according to the main body information of the basic data;
if the same main body corresponds to a plurality of pieces of basic data, sorting and sorting the plurality of pieces of basic data corresponding to the same main body;
and sequentially storing the target data information corresponding to each basic data in the floor data table of the temporary database according to the sequence of the basic data in the basic data table.
2. The method for intelligent processing of data information according to claim 1, wherein the standardized processing rule includes a standard template, a standard dimension set, and a field mapping rule, and the standardized processing is performed on the newly added data information according to a preset standardized processing rule to obtain standard data information corresponding to each piece of newly added data information, including:
judging whether dimension information corresponding to each dimension in each piece of newly-added data information is matched with the standard template or not;
if the dimension information contained in the newly-added data information is matched with the standard template, verifying whether the newly-added data information contains non-standard dimension information according to the standard dimension set;
and if the newly added data information contains non-standard dimension information, mapping the non-standard dimension information according to the field mapping rule and the standard dimension set to obtain standard data information corresponding to the newly added data information.
3. The method for intelligent processing of data information according to claim 1, wherein the performing, according to a preset anomaly analysis model, anomaly analysis on each piece of quantized feature information to obtain an anomaly analysis result corresponding to each piece of newly added data information includes:
Inputting each piece of quantized feature information into the anomaly analysis model to obtain verification output information corresponding to each piece of quantized feature information;
and acquiring a corresponding abnormal analysis result according to the verification output information of each piece of quantization characteristic information.
4. The method for intelligent processing of data information according to claim 1, wherein before performing an anomaly analysis on each piece of quantized feature information according to a preset anomaly analysis model to obtain an anomaly analysis result corresponding to each piece of newly added data information, the method further comprises:
randomly extracting a plurality of data check samples from the standard data information to obtain a check sample set;
respectively carrying out abnormal verification on each data verification sample in the verification sample set according to a preset abnormal data verification rule to obtain sample verification information of each data verification sample;
extracting corresponding sample characteristic information from each data check sample of the check sample set according to the extraction rule;
and carrying out iterative training on the initial anomaly analysis model according to a pre-stored model training rule, the sample characteristic information and the sample verification information to obtain a trained anomaly analysis model.
5. The method for intelligent processing of data information according to claim 4, wherein said randomly extracting a plurality of data check samples from said standard data information to obtain a check sample set comprises:
randomly extracting a plurality of dimension information corresponding to one dimension respectively from a plurality of pieces of standard data information corresponding to the dimension number according to the dimension number contained in the standard data information;
and combining the plurality of dimensional information to obtain a data check sample.
6. The method for intelligent processing of data information according to claim 1, wherein the allocation rule includes a delivery condition and an allocation condition, the generating a settlement list according to the target data information in the temporary database according to a preset allocation rule and allocating the settlement list to the settlement terminal for settlement processing, includes:
distributing settlement terminals to each main body according to the settlement terminal monitoring information and the distribution conditions, and generating corresponding processing identification information according to the distributed settlement terminals;
adding the processing identification information to a storage position of basic data corresponding to each main body in the basic data table;
Sequentially judging whether target data information of the same main body in the floor data table meets the ex-warehouse condition;
and if the target data information of the same main body accords with the ex-warehouse condition, generating a settlement list according to the target data information, and sending the settlement list to a settlement terminal corresponding to the processing identification information in the target data information.
7. A data information intellectualized processing apparatus for performing the data information intellectualized processing method according to any one of claims 1 to 6, the apparatus comprising:
the newly added data information acquisition unit is used for acquiring newly added data information stored by each data source terminal if a preset data acquisition time point is reached;
the standardized processing unit is used for carrying out standardized processing on the newly-added data information according to a preset standardized processing rule to obtain standard data information corresponding to each piece of newly-added data information;
the quantized feature information acquisition unit is used for extracting corresponding quantized feature information from each piece of standard data information according to a preset extraction rule;
the abnormal analysis result acquisition unit is used for carrying out abnormal analysis on each piece of quantized characteristic information according to a preset abnormal analysis model to obtain an abnormal analysis result corresponding to each piece of newly-added data information;
The target data information storage unit is used for acquiring target data information from the newly-added data information according to the abnormal analysis result and storing the target data information into a preset temporary database;
and the distribution settlement unit is used for generating a settlement list according to the target data information in the temporary database according to a preset distribution rule and distributing the settlement list to the settlement terminal for settlement processing.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the data information intelligent processing method according to any one of claims 1 to 6 when executing the computer program.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, implements the data information intelligent processing method according to any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110874670.6A CN113592662B (en) | 2021-07-30 | 2021-07-30 | Intelligent processing method, device, equipment and medium for data information |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110874670.6A CN113592662B (en) | 2021-07-30 | 2021-07-30 | Intelligent processing method, device, equipment and medium for data information |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113592662A CN113592662A (en) | 2021-11-02 |
| CN113592662B true CN113592662B (en) | 2023-07-28 |
Family
ID=78252924
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110874670.6A Active CN113592662B (en) | 2021-07-30 | 2021-07-30 | Intelligent processing method, device, equipment and medium for data information |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113592662B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116383753B (en) * | 2023-05-26 | 2023-08-18 | 深圳市博昌智控科技有限公司 | Abnormal behavior prompting method, device, equipment and medium based on Internet of things |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020119403A1 (en) * | 2018-12-13 | 2020-06-18 | 平安医疗健康管理股份有限公司 | Hospitalization data abnormity detection method, apparatus and device, and readable storage medium |
| CN111626865A (en) * | 2020-05-22 | 2020-09-04 | 泰康保险集团股份有限公司 | Data processing method and device, electronic equipment and storage medium |
| CN112348079A (en) * | 2020-11-05 | 2021-02-09 | 平安科技（深圳）有限公司 | Data dimension reduction processing method and device, computer equipment and storage medium |
-
2021
- 2021-07-30 CN CN202110874670.6A patent/CN113592662B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020119403A1 (en) * | 2018-12-13 | 2020-06-18 | 平安医疗健康管理股份有限公司 | Hospitalization data abnormity detection method, apparatus and device, and readable storage medium |
| CN111626865A (en) * | 2020-05-22 | 2020-09-04 | 泰康保险集团股份有限公司 | Data processing method and device, electronic equipment and storage medium |
| CN112348079A (en) * | 2020-11-05 | 2021-02-09 | 平安科技（深圳）有限公司 | Data dimension reduction processing method and device, computer equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113592662A (en) | 2021-11-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10289869B2 (en) | Personal information anonymization method, recording medium, and information processing apparatus | |
| CN107784063B (en) | Algorithm generation method and terminal equipment | |
| WO2017214181A1 (en) | System and method for dynamic healthcare insurance claims decision support | |
| CN108197795B (en) | Malicious group account identification method, device, terminal and storage medium | |
| CN112541745A (en) | User behavior data analysis method and device, electronic equipment and readable storage medium | |
| CN110956269A (en) | Data model generation method, device, equipment and computer storage medium | |
| CN111986039A (en) | Claims wind control identification method and device, computer equipment and readable storage medium | |
| CN112529429A (en) | Customer information checking method and device, computer equipment and storage medium | |
| CN113592662B (en) | Intelligent processing method, device, equipment and medium for data information | |
| CN111062819A (en) | Simple claims settlement method, device, medium and electronic equipment | |
| US11075004B2 (en) | Discrimination result providing apparatus, operating method of discrimination result providing apparatus, discrimination result providing program, and discrimination result providing system | |
| CN112751785B (en) | Method and device for sending pending request, computer equipment and storage medium | |
| CN108961071B (en) | Method for automatically predicting combined service income and terminal equipment | |
| CN110781193A (en) | Medical data processing method, device, equipment and storage medium | |
| CN116204428A (en) | Test case generation method and device | |
| CN114064872A (en) | Intelligent storage method, device, equipment and medium for dialogue data information | |
| CN113688206A (en) | Text recognition-based trend analysis method, device, equipment and medium | |
| CN114860608A (en) | Scene construction based system automation testing method, device, equipment and medium | |
| CN109040990B (en) | Information acquisition method and device, computer equipment and storage medium | |
| CN113869996A (en) | Stepped credit analysis measuring and calculating method, device, equipment and medium | |
| CN113436746A (en) | Medicine taking recommendation method, device, equipment and storage medium based on sorting algorithm | |
| CN113992668A (en) | Multi-concurrency-based information real-time transmission method, device, equipment and medium | |
| CN113392138B (en) | Statistical analysis method, device, server and storage medium for private data | |
| CN112199603B (en) | Information pushing method and device based on countermeasure network and computer equipment | |
| CN113989011B (en) | Market data processing method and device, computer equipment and readable storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |