CN113592662A - Data information intelligent processing method, device, equipment and medium - Google Patents
Data information intelligent processing method, device, equipment and medium Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment and a medium for intelligently processing data information, wherein the method comprises the following steps: and when the data acquisition time point is reached, acquiring newly added data information from the data source terminal, carrying out standardized processing on the newly added data information to obtain standard data information, extracting corresponding quantitative characteristic information from the standard data information, carrying out abnormal analysis on the quantitative characteristic information according to an abnormal analysis model to obtain an abnormal analysis result, acquiring target data information from the newly added data information, storing the target data information in the temporary database, and distributing the target data information in the temporary database to the settlement terminal for processing according to a distribution rule. The invention belongs to the technical field of data processing, and aims to improve the intelligent degree of data processing, namely the efficiency of processing data information, by performing exception analysis on newly added data information, acquiring target data information according to exception analysis results, storing the target data information in a temporary database for sorting and distributing the target data information to a settlement terminal for processing.
Description
Technical Field
The invention relates to the technical field of data processing, belongs to an application scene of intelligent processing on data information in intelligent medical treatment, and particularly relates to a data information intelligent processing method, a device, equipment and a medium.
Background
With the more and more perfect coverage of medical insurance, more and more people reimburse the hospitalization cost through the medical insurance, and the insurance people need to settle accounts based on the hospitalization information of the insurance people when applying for reimbursement of the hospitalization cost. The existing technical method is that after data to be claimed are copied from a medical insurance system by business personnel 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 one by one or in batches for claim settlement, the data processing mode has the problem of low intelligent degree, the data are illegally utilized due to the fact that the data cannot be supervised in the manual carrying process, the workload of manual arrangement is huge, a large amount of human resources are occupied, and data errors are caused due to the fact that omission easily occurs in manual arrangement. Therefore, the method for processing the hospitalization data of the insured person 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, and aims to solve the problem of low processing efficiency of a method for processing medical data of a paramedic in the prior art.
In a first aspect, an embodiment of the present invention provides an intelligent processing method for data information, including:
if the preset data acquisition time point is reached, acquiring newly added data information stored by each data source terminal;
standardizing the newly added data information according to a preset standardized processing rule to obtain standard data information corresponding to each newly added data information;
extracting corresponding quantitative characteristic information from each standard data information according to a preset extraction rule;
performing anomaly analysis on each quantization characteristic information according to a preset anomaly analysis model to obtain an anomaly analysis result corresponding to each 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 in a preset temporary database;
and generating a settlement list according to preset distribution rules and target data information in the temporary database and distributing the settlement list to the settlement terminal for settlement processing.
In a second aspect, an embodiment of the present invention provides an intelligent processing apparatus for data information, 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 standardization processing unit is used for 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 newly added data information;
the quantization characteristic information acquisition unit is used for extracting corresponding quantization characteristic information from each standard data information according to a preset extraction rule;
an anomaly analysis result obtaining unit, configured to perform 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 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 preset distribution rules and target data information in the temporary database 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 on the memory and executable on the processor, where the processor implements the intelligent data information processing method according to the first aspect when executing the computer program.
In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and the computer program, when executed by a processor, causes the processor to execute the method for intelligently processing data information according to the first aspect.
The embodiment of the invention provides a method, a device, equipment and a medium for intelligently processing data information. And when the data acquisition time point is reached, acquiring newly added data information from the data source terminal, carrying out standardized processing on the newly added data information to obtain standard data information, extracting corresponding quantitative characteristic information from the standard data information, carrying out abnormal analysis on the quantitative characteristic information according to an abnormal analysis model to obtain an abnormal analysis result, acquiring target data information from the newly added data information, storing the target data information in the temporary database, and distributing the target data information in the temporary database 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 the target data information 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 efficiency of processing the data information can also be improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of an intelligent data information processing method according to an embodiment of the present invention;
fig. 2 is a schematic view of an application scenario of the intelligent data information processing method according to the embodiment of the present invention;
fig. 3 is a schematic sub-flow diagram of an intelligent data information processing method according to an embodiment of the present invention;
fig. 4 is another schematic sub-flow chart of the intelligent processing method for data information according to the embodiment of the present invention;
fig. 5 is another schematic flow chart of an intelligent processing method for data information according to an embodiment of the present invention;
fig. 6 is another schematic sub-flow chart of the intelligent processing method for data information according to the embodiment of the present invention;
fig. 7 is another schematic sub-flow chart of the intelligent processing method for data information according to the embodiment of the present invention;
fig. 8 is another schematic flow chart of an intelligent processing method for data information according to an embodiment of the present invention;
fig. 9 is a schematic block diagram of an intelligent data information processing device according to an embodiment of the present invention;
FIG. 10 is a schematic block diagram of a computer device provided by an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "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 the specification of the present invention 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 this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1 and fig. 2, fig. 1 is a schematic flow chart of an intelligent data information processing method according to an embodiment of the present invention; fig. 2 is a schematic view of an application scenario of the intelligent data information processing method according to the embodiment of the present invention; the intelligent processing method of the data information is applied to a 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 simultaneously establishes network connection with at least one data source terminal 20 and at least one settlement terminal 30 to realize transmission of the data information, the data source terminal 20 is a terminal device used for inputting and storing newly added data information, for example, a terminal device configured in a medical insurance bureau and used for inputting the newly added data information, and the data source terminal 20 can be a desktop computer, a notebook computer, a tablet computer, a mobile phone or the like; the management server 10 is a server side that can obtain the new data information from the data source terminal 20 and perform the screening and distribution process, such as a server constructed by an enterprise or a government department, and the settlement terminal 30 is a terminal device for performing the payment settlement process 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 management server can acquire the newly added data information stored in the data source terminal at the data acquisition time point, specifically, can judge whether the storage time of each data information in each data source terminal is within the acquisition time period, and acquire the data information of which the storage time is within the acquisition time period according to the judgment result to obtain the newly added data information, wherein the acquisition time period is the interval time between the current data acquisition time period and the previous data acquisition time point. For example, if the interval time is 2 months, the current data acquisition time point is a time point 2 months after the previous data acquisition time point.
For example, if the data source terminal is a terminal device configured in a 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, information of insured persons, medical insurance orders, medical advice bills, medical advice details, collection information, and the like.
And S120, standardizing the newly added data information according to a preset standardization processing rule to obtain standard data information corresponding to each 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 standardization processing through standardization processing rules, and standard data information corresponding to each newly added data information is obtained. The standardization processing rule is a specific rule for carrying out standardization processing on the newly added data information, wherein the standardization 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 non-standard dimension information in the newly added data information.
In an embodiment, as shown in fig. 3, step S120 includes sub-steps S121, S122 and S123.
And S121, judging whether the dimension information corresponding to each dimension in each 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, the medical insurance order can include 'hospitalizing type information', the 'hospitalizing type information' corresponds to one dimension, and data corresponding to the dimension of the 'hospitalizing type information' in a piece of newly added data information is the dimension information corresponding to the dimension in the newly added data information.
The standard template may perform matching judgment on whether the characters included in each dimension information meet the corresponding standard, and specifically, whether the character length and the character type of the characters included in each dimension information meet the corresponding standard may be verified through the standard template.
For example, for the medical type information included in the medical bill, if the matching rule corresponding to the "medical type information" in the standard template includes that the character length is [3,7] and the character type is a chinese character, it can be determined whether the dimension information corresponding to the dimension of the "medical type information" in each piece of newly added data information conforms to the matching rule.
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 uneven dimension information of a certain newly added data information is 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 nonstandard 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, if the dimension information is matched with any standard dimension field, the dimension information is standard dimension information, and subsequent mapping processing is not needed; and if the dimension information is not matched with the standard dimension field, the dimension information is non-standard dimension information, and the non-standard dimension information needs to be mapped.
And S123, if the newly added data information contains nonstandard dimension information, mapping the nonstandard 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 the nonstandard dimension information, the newly added data information can be directly used as the standard data information. If the newly added data information includes the nonstandard dimension information, the nonstandard dimension information may be mapped according to a field mapping rule and a standard dimension set, specifically, the matching degree between the nonstandard dimension information and each standard dimension field may be obtained according to the field mapping rule, and one standard dimension field with the highest matching degree is obtained to map the nonstandard dimension information, that is, the nonstandard dimension information is mapped and replaced with the corresponding standard dimension field, and the standard data information corresponding to the newly added data information may be obtained after the mapping process. The method comprises the steps of firstly obtaining a coincidence character ratio, a character length difference value and a coincidence character pinyin ratio between non-standard dimension information and each standard dimension field, wherein the coincidence character ratio is the ratio of the number of coincident characters to the number of characters in the non-standard dimension information, the character length difference is the difference value between the character length of the non-standard dimension information and the character length of the standard dimension field, the coincidence character pinyin ratio is the ratio of the number of characters coincident with pinyin to the number of characters in the standard dimension information, and the coincidence character ratio, the character length difference value and the coincidence character pinyin ratio are mapped and calculated according to field mapping rules to obtain corresponding matching degrees.
And S130, extracting corresponding quantitative feature information from each standard data information according to a preset extraction rule.
In order to obtain the characteristics of each standard data message, corresponding quantization characteristic information can be extracted from each standard data message through an extraction rule, the quantization characteristic information can be used for performing quantization representation on the characteristics of the standard data message, and the extraction rule is a specific rule for extracting the quantization 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 extraction rules so as to extract digitized quantization characteristic information from the dimension information represented by the numbers, the letters or the Chinese characters, and the obtained quantization characteristic information contains quantization characteristic values corresponding to each dimension. Specifically, if the dimension information is a number, the number can be converted into a corresponding decimal as a quantization characteristic value of the dimension information. If the dimension information is not a number, the dimension information can be converted into a quantization characteristic value represented by a number through a conversion relation corresponding to the dimension information in the extraction rule. For example, the conversion relation corresponding to the dimension of "type of medical attendance information" in the extraction rule is "general outpatient service: 1 "," general hospitalization: 2 "," special clinic: 3 "," out-patient for major diseases: 4".
And S140, performing anomaly analysis on each quantization characteristic information according to a preset anomaly analysis model to obtain an anomaly analysis result corresponding to each newly added data information.
And carrying out anomaly analysis on each quantization characteristic information according to a preset anomaly analysis model to obtain an anomaly analysis result corresponding to each newly added data information. In order to ensure that all the dimension information of the newly added data information does not conflict with each other, the anomaly analysis model is used for carrying out anomaly analysis on the characteristic quantitative information of the newly added data information so as to analyze whether all the dimension information of each newly added data information has an abnormal conflict or not, and the abnormal conflict is that one dimension information does not correspond to other dimension information or a plurality of dimension information.
In an embodiment, as shown in fig. 4, step S140 includes sub-steps S141 and S142.
And S141, respectively inputting each quantization characteristic information into the anomaly analysis model to obtain check output information corresponding to each quantization characteristic information.
Specifically, the anomaly analysis model is an intelligent verification model constructed based on a neural network, and may be composed of an input layer, a plurality of intermediate layers, and an output layer, where the input layer and the intermediate layers, the intermediate layers and other intermediate layers adjacent to each other in front and behind, and the intermediate layers and the output layer are all associated by association formulas, for example, a certain association formula may 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 quantization characteristic information, each quantization characteristic value in the quantization characteristic information corresponds to one input node, each quantization characteristic 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 anomaly analysis model, wherein the verification output information is an output node value of the output node, the anomaly analysis model comprises two output nodes, the verification output information corresponding to each quantization characteristic information comprises two output node values, the output node value is a probability value corresponding to the quantization characteristic 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 represented by a decimal, and the value range is [0, 1]. And respectively inputting each quantization characteristic information into the abnormal analysis model to obtain corresponding verification output information, wherein the verification output information comprises probability values corresponding to two output nodes of the abnormal analysis model.
And S142, acquiring a corresponding abnormal analysis result according to the check output information of each quantization characteristic information.
And according to the check output information of the quantization characteristic information, determining an output node with a larger probability value as a corresponding abnormal analysis result. That is, if the probability value of the first output node is larger, an abnormal analysis result with an abnormal conflict is obtained, and if the probability value of the second output node is larger, an abnormal analysis result without an abnormal conflict is obtained.
In one embodiment, as shown in fig. 5, step S140 further includes steps S1401, S1402, S1403, and S1404.
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, according to the dimension number contained in the standard data information, randomly extracting a plurality of dimension information corresponding to one dimension from the plurality of standard data information corresponding to the dimension number; s14012, combining the plurality of dimensional information to obtain a data check sample.
For example, if the standard data information includes 40 dimensions, then 40 pieces of standard data information are randomly extracted, and dimension information corresponding to one dimension is respectively obtained from the 40 pieces of standard data information, then 40 pieces of dimension information are respectively obtained from the 40 pieces of standard data information, and the dimensions corresponding to the 40 pieces of dimension information are not repeated, so that a data check sample is obtained by randomly extracting a combination of a plurality of pieces of dimension information corresponding to each dimension, and information of the data check sample comes from the randomly extracted plurality of pieces of standard data information. The method is repeatedly executed, a plurality of data check samples can be randomly extracted, a check sample set can be obtained by combining the obtained data check samples, and the number of the data check samples contained in the check sample set can be smaller than the total number of the standard data information
S1402, 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.
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 conflicts or not. Due to the fact that the calculation amount of performing the abnormal verification on the data verification samples through the abnormal data verification rule is large, the abnormal verification can be performed on the verification sample set with small data amount, the initial abnormal analysis model is trained through the sample verification information of each data verification sample, the standard data information is subjected to abnormal analysis based on the trained abnormal analysis model, and therefore the efficiency of performing the abnormal analysis on the identification data information can be greatly improved.
And S1403, extracting corresponding sample characteristic information from each data check sample in the check sample set according to the extraction rule.
The corresponding sample characteristic information can be extracted from each data verification 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 quantitative characteristic information from the standard data information. And then, performing iterative training on the initial anomaly analysis model according to the model training rule, the sample characteristic information and the sample verification information to obtain a trained anomaly analysis model. The model updating rule is a specific rule for training and updating parameter values in the initial anomaly analysis model, and the model updating rule comprises a loss value calculation formula and a gradient calculation formula.
And S1404, performing iterative training on the initial anomaly analysis model according to the pre-stored model training rules, the sample characteristic information and the sample verification information to obtain a trained anomaly analysis model.
Specifically, the characteristic information of one sample obtained sequentially may be input to the initial anomaly analysis model to obtain corresponding prediction output information, and the prediction output information includes probability values corresponding to two output nodes of the anomaly analysis model respectively. And then, calculating the prediction output information and the sample verification information corresponding to the sample characteristic information according to a loss value calculation formula in the model updating rule to obtain a loss value, calculating an update value of each parameter value in the abnormal analysis model according to the loss value and the gradient calculation formula, namely updating the abnormal analysis model once, and updating the parameter values of all the parameters in the abnormal analysis model once according to one piece of sample characteristic information and the corresponding sample verification information, namely completing one-time training and updating of the abnormal analysis model. And then obtaining next sample characteristic information and repeating the training process until all the sample characteristic information is used for iteratively updating the abnormal analysis model, wherein the training method can be called a gradient descent training method.
And S150, acquiring target data information from the newly added data information according to the abnormal analysis result and storing the target data information in 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 in 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 configured in the management server in advance, wherein the temporary database 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.
And S151, acquiring the newly added data information without abnormal conflict as target data information according to the abnormal analysis result.
Specifically, the newly added data information including the abnormal analysis result may be determined, and if the abnormal analysis result of the newly added data information indicates that there is an abnormal conflict, the newly added data information is stored in the abnormal list, and if the abnormal analysis result of the newly added data information indicates that there is no abnormal conflict, the newly added data information is determined as the target data information.
S152, acquiring corresponding basic data from each target data message and storing the basic data into 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, corresponding basic data can be obtained from each piece of target data information, and only partial information of each piece of target data information is stored in the basic data table. For example, for medical insurance reimbursement data, the basic data includes data source address information, basic information of a insured person, settlement date of a basic section of medical insurance, and reimbursement processing state, wherein the data source address information is a network address of a data source terminal, the basic information of the insured person is basic information capable of determining the identity of the insured person, and includes the type, number, name, sex and date of birth of the insured person, the settlement date of the basic section of medical insurance is date information for performing periodic settlement of the insured person, and the reimbursement processing state is processing state of a piece of newly added data information corresponding to the insured person.
S153, judging whether the same main body corresponds to a plurality of pieces of basic data or not 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, sequencing and sorting the plurality of pieces of basic data corresponding to the same main body.
Whether the same main body corresponds to multiple pieces of basic data or not can be judged based on the main body information of the basic data, and if the same main body corresponds to multiple pieces of basic data, the multiple pieces of basic data corresponding to the same main body in the basic data table can be sorted.
For example, for the medical insurance reimbursement data, the subject information may be the basic information of the insured person in the basic data, if the basic information of the insured person of the two pieces of basic data is the same, it is determined that the two pieces of basic data belong to the same subject, that is, the attention corresponds to a plurality of pieces of basic data, and if the basic information of the insured person of the basic data is not the same as the basic information of the insured person of any other piece of basic data, it is determined that the subject of the basic data corresponds to only one piece of 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 sorted and sorted according to the size of the settlement date of the basic section of the medical insurance in the plurality of pieces of basic data, the basic data with the smaller basic settlement date of the medical insurance is sorted in the front, and the basic data with the larger basic settlement date of the medical insurance is sorted in the back. If one main body only corresponds to one piece of basic data, the basic data of the main body does not need to be sorted.
And S155, sequentially storing the target data information respectively corresponding to each basic data into the ground data table of the temporary database according to the sequence of the basic data in the basic data table.
Each piece of basic data in the basic data table corresponds to one piece of target data information, and then 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 ground data table, and then the complete information of each piece of target data information is stored in the ground data table.
And S160, generating a settlement list according to preset distribution rules and target data information in the temporary database, and distributing 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 perform settlement processing on the target data information through the settlement terminal. The target data information can be settled finally only when needing to be distributed to the settlement terminal, and then the target data information can be distributed to the corresponding settlement terminal for settlement processing through a distribution rule, wherein the distribution rule comprises a ex-warehouse condition and a distribution condition.
In one embodiment, as shown in fig. 8, step S160 includes sub-steps S161, S162, S163, and S164.
S161, allocating a settlement terminal to each main body according to the settlement terminal monitoring information and the allocation conditions, and generating corresponding processing identification information according to the allocated settlement terminals.
The settlement terminal monitors each settlement terminal in real time through the management server so as to acquire corresponding settlement terminal monitoring information when the state of the settlement terminal changes constantly in the settlement processing process, specifically, the settlement terminal monitoring information includes the use state information and the resource occupation information of each settlement terminal, the use state information is information for recording whether the settlement terminal is available, and the resource occupation information is information for recording the occupation situation of each hardware resource in the settlement terminal. The corresponding settlement terminal can be allocated to each main body according to the allocation condition and the settlement terminal monitoring information, specifically, all the settlement terminals with available use state information can be obtained as alternative settlement terminals, one alternative settlement terminal with the least occupation of the current resource occupation information is obtained according to the allocation condition as one settlement terminal allocated to the current main body, and the terminal identification corresponding to the settlement terminal allocated to each main body is obtained as the corresponding processing identification information.
And S162, adding the processing identification information to the 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 the same as that of the main body only because the processing identification information is associated with the main body.
And S163, sequentially judging that the target data information of the same main body in the landing data table meets the ex-warehouse condition.
Whether the target data information of the same main body in the floor data table meets the ex-warehouse condition can be sequentially judged, and the ex-warehouse condition is the condition for judging whether the target data information can be ex-warehouse. Specifically, the ex-warehouse condition may be a specific judgment condition such as whether a plurality of pieces of target data information of the subject are duplicated, whether there is a conflict between the pieces of target data information, whether the total amount is smaller than the amount threshold, or not.
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 sending 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 meets 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 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 automatically settle the settlement list. If the target data information of the same main body does not accord with the ex-warehouse condition, the target data information of the main body is indicated to be incapable of carrying out automatic settlement processing, and the target data information of the main body can be sent to a manual processing terminal to carry out manual checking settlement processing.
The technical method can be applied to scenes such as intelligent medical treatment and the like which comprise intelligent processing of data information, and accordingly construction of the intelligent medical treatment is promoted.
In the intelligent processing method for data information provided by the embodiment of the invention, newly added data information is obtained from a data source terminal when the data acquisition time point is reached, the newly added data information is subjected to standardization processing to obtain standard data information, corresponding quantitative characteristic information is extracted from the standard data information to obtain corresponding quantitative characteristic information, the quantitative characteristic information is subjected to abnormity analysis according to an abnormity analysis model to obtain an abnormity analysis result, target data information is obtained from the newly added data information and stored in a temporary database, and the 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 the target data information 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 efficiency of processing the data information can also be improved.
An embodiment of the present invention further provides an intelligent data information processing device, where the intelligent data information processing device is configured to execute any embodiment of the foregoing intelligent data information processing method, and specifically, please refer to fig. 9, and fig. 9 is a schematic block diagram of the intelligent data information processing device according to the embodiment of the present invention.
As shown in fig. 9, the data information intelligent processing apparatus 100 includes a new 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 newly added data information according to a preset normalization processing rule, so as to obtain standard data information corresponding to each newly added data information.
In one embodiment, the normalization processing unit 120 includes sub-units: the dimension information judging unit is used for judging whether the dimension information corresponding to each dimension in each newly added data information is matched with the standard template; the newly added data information verification unit is used for verifying whether the newly added data information contains nonstandard dimension information or not 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 mapping the nonstandard dimension information according to the field mapping rule and the standard dimension set if the newly added data information contains the nonstandard dimension information to obtain standard data information corresponding to the newly added data information.
The quantization characteristic information obtaining unit 130 is configured to extract corresponding quantization characteristic information from each of the standard data information according to a preset extraction rule.
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, to obtain an anomaly analysis result corresponding to each piece of newly added data information.
In one embodiment, the anomaly analysis result obtaining unit 140 includes sub-units: the verification output information acquisition unit is used for respectively inputting each quantization characteristic information into the anomaly analysis model so as to acquire verification output information corresponding to each quantization characteristic information; and the result acquisition unit is used for acquiring a corresponding abnormal analysis result according to the check output information of each quantization characteristic information.
In an embodiment, the data information intelligent processing device 100 further includes a sub-unit: a check sample set obtaining unit, configured to randomly extract a plurality of data check samples from the standard data information to obtain a check sample set; the sample verification information acquisition unit is used for 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; the sample characteristic information extraction unit is used for extracting corresponding sample characteristic information from each data verification sample in the verification sample set according to the extraction rule; and the anomaly analysis model training unit is used for carrying out iterative training on the initial anomaly analysis model according to the pre-stored model training rules, the sample characteristic information and the sample verification information to obtain the trained anomaly analysis model.
In an embodiment, the check sample set obtaining unit includes a sub-unit: a dimension information extraction unit, configured to randomly extract, according to a number of dimensions included in the standard data information, a plurality of pieces of dimension information, which respectively correspond to a dimension, from among a plurality of pieces of standard data information corresponding to the number of dimensions; and the dimension information combination 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 sub-units: a target data information obtaining unit, configured to obtain, according to the anomaly analysis result, the newly added data information without an anomaly conflict as target data information; a basic data storage unit, configured to obtain corresponding basic data from each piece of target data information and store the corresponding basic data in a basic data table of the temporary database; a main body information judging unit 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 sorting and sorting unit is used for sorting 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 respectively corresponding to each basic data into the ground 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 preset distribution rules and distributing the settlement list to the settlement terminal for settlement processing according to the target data information in the temporary database.
In one embodiment, the distribution settlement unit 160 includes sub-units: a processing identification information generating unit for allocating a settlement terminal to each of the subjects according to the settlement terminal monitoring information and the allocation condition, and generating corresponding processing identification information according to the allocated settlement terminal; a processing identification information adding unit, configured to add the processing identification information to a storage location of the basic data corresponding to each of the subjects in the basic 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 landing 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 and sending 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.
When the intelligent processing method of the data information is applied to the intelligent processing device of the data information provided by the embodiment of the invention, newly added data information is obtained from a data source terminal when the data acquisition time point is reached, the newly added data information is subjected to standardization processing to obtain standard data information, corresponding quantitative characteristic information is extracted from the standard data information, the quantitative characteristic information is subjected to abnormal analysis according to an abnormal analysis model to obtain an abnormal analysis result, target data information is obtained from the newly added data information and stored in a temporary database, and the 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 the target data information 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 efficiency of processing the data information can also be improved.
The data information intelligent processing device can be realized in the form of a computer program, and the computer program can 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 executing the data information intelligent processing method to perform intelligent processing on the data information.
Referring to fig. 10, the computer device 500 includes a processor 502, memory, and a network interface 505 connected by a system bus 501, where 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 execute a data information intelligent processing method, wherein the storage medium 503 may be a volatile storage medium or a non-volatile storage medium.
The processor 502 is used to provide computing and control capabilities that support the operation of the overall computer device 500.
The internal memory 504 provides an environment for the operation of the computer program 5032 in the storage medium 503, and when the computer program 5032 is executed by the processor 502, the processor 502 can execute the intelligent data information processing method.
The network interface 505 is used for network communication, such as providing transmission of data information. Those skilled in the art will appreciate that the configuration shown in fig. 10 is a block diagram of only a portion of the configuration associated with aspects of the present invention and is not intended to limit the computing device 500 to which aspects of the present invention may be applied, and that a particular computing device 500 may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
The processor 502 is configured to run the computer program 5032 stored in the memory to implement the corresponding functions in the above-mentioned intelligent data information processing method.
Those skilled in the art will appreciate that the embodiment of a computer device illustrated in fig. 10 does not constitute a limitation on the specific construction of the computer device, and that in other embodiments a computer device may include more or fewer components than those illustrated, or some components may be combined, or a different arrangement of components. For example, in some embodiments, the computer device may only include a memory and a processor, and in such embodiments, the structures and functions of the memory and the processor are consistent with those of the embodiment shown in fig. 10, and are not described herein again.
It should be understood that, in the embodiment of the present invention, the Processor 502 may be a Central Processing Unit (CPU), and the Processor 502 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Wherein a 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 non-volatile computer readable storage medium. The computer readable storage medium stores a computer program, wherein the computer program realizes the above intelligent data information processing method when executed by a processor.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses, devices and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly 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 implementation. 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 embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only a logical division, and there may be other divisions when the actual implementation is performed, or units having the same function may be grouped into one unit, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electric, mechanical or other form of connection.
The units described as separate parts may or may not be physically separate, and parts displayed 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 can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention essentially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a computer-readable storage medium, which includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned computer-readable storage media comprise: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An intelligent processing method for data information is characterized in that the method is applied to a management server, the management server simultaneously establishes network connection with at least one data source terminal and at least one settlement terminal to realize the transmission of the data information, and the method comprises the following steps:
if the preset data acquisition time point is reached, acquiring newly added data information stored by each data source terminal;
standardizing the newly added data information according to a preset standardized processing rule to obtain standard data information corresponding to each newly added data information;
extracting corresponding quantitative characteristic information from each standard data information according to a preset extraction rule;
performing anomaly analysis on each quantization characteristic information according to a preset anomaly analysis model to obtain an anomaly analysis result corresponding to each 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 in a preset temporary database;
and generating a settlement list according to preset distribution rules and target data information in the temporary database and distributing the settlement list to the settlement terminal for settlement processing.
2. The intelligent processing method of data information according to claim 1, wherein the standardization processing rule includes a standard template, a standard dimension set, and a field mapping rule, and the standardization processing of the newly added data information according to a preset standardization processing rule to obtain standard data information corresponding to each newly added data information includes:
judging whether the dimension information corresponding to each dimension in each newly added data information is matched with the standard template;
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 nonstandard dimension information according to the standard dimension set;
and if the newly added data information contains nonstandard dimension information, mapping the nonstandard 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 intelligent data information processing method according to claim 1, wherein the performing an anomaly analysis on each of the quantized feature information according to a preset anomaly analysis model to obtain an anomaly analysis result corresponding to each of the newly added data information includes:
inputting each quantization characteristic information into the anomaly analysis model respectively to obtain check output information corresponding to each quantization characteristic information;
and acquiring a corresponding abnormal analysis result according to the check output information of each quantitative characteristic information.
4. The intelligent data information processing method according to claim 1, wherein before 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, the method further comprises:
randomly extracting a plurality of data check samples from the standard data information to obtain a check sample set;
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;
extracting corresponding sample characteristic information from each data verification sample in the verification sample set according to the extraction rule;
and 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.
5. The intelligent processing method for data information according to claim 4, wherein the randomly extracting a plurality of data verification samples from the standard data information to obtain a verification sample set comprises:
according to the dimension number contained in the standard data information, randomly extracting a plurality of dimension information respectively corresponding to one dimension from a plurality of standard data information corresponding to the dimension number;
and combining the plurality of dimensional information to obtain a data verification sample.
6. The intelligent processing method of data information according to claim 1, wherein the temporary database includes a basic data table and a floor data table, and the acquiring target data information from the newly added data information according to the anomaly analysis result and storing the target data information in a preset temporary database includes:
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 or not according to the main body information of the basic data;
if the same main body corresponds to a plurality of pieces of basic data, sequencing and sorting the plurality of pieces of basic data corresponding to the same main body;
and sequentially storing the target data information respectively corresponding to each basic data into a floor data table of a temporary database according to the sequence of the basic data in the basic data table.
7. The intelligent processing method of data information according to claim 1, wherein the distribution rule includes a delivery condition and a distribution condition, and the generating of the settlement list according to the preset distribution rule based on the target data information in the temporary database and the distribution to the settlement terminal for settlement processing includes:
allocating settlement terminals to each main body according to the settlement terminal monitoring information and the allocation conditions, and generating corresponding processing identification information according to the allocated 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 that the 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.
8. An intelligent data information processing device, 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 standardization processing unit is used for 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 newly added data information;
the quantization characteristic information acquisition unit is used for extracting corresponding quantization characteristic information from each standard data information according to a preset extraction rule;
an anomaly analysis result obtaining unit, configured to perform 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 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 preset distribution rules and target data information in the temporary database and distributing the settlement list to the settlement terminal for settlement processing.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the intelligent processing method of data information according to any one of claims 1 to 7 when executing the computer program.
10. 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 intelligent processing method of data information according to any one of claims 1 to 7.
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