CN114999665A - Data processing method and device, electronic equipment and storage medium - Google Patents
Data processing method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a data processing method, a data processing device, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring settlement data associated with the target area and corresponding to each historical visit record data and a corresponding weight value to be updated; determining an array to be adjusted corresponding to each historical visit record data according to at least one array field which is preset, determining a historical balance attribute of each array to be adjusted according to settlement data corresponding to at least one historical visit record number in each array to be adjusted and a corresponding weight value to be updated, and obtaining a target array with at least one array to be adjusted removed based on each historical balance attribute; determining corresponding target balance attributes based on the historical balance attributes of the target arrays; and updating the weight value to be updated of the corresponding target array based on each target balance attribute so as to perform data settlement according to the updated weight to be updated.
Description
Technical Field
The present invention relates to the field of medical informatization technology, and in particular, to a data processing method and apparatus, an electronic device, and a storage medium.
Background
In recent years, according to factors such as information of a doctor, a treatment mode, severity, returning, resource consumption and the like, the doctor is divided into a plurality of groups for management, so that the service efficiency and the cost rationality can be improved. The weight corresponding to each array is one of the most important influence factors in the whole payment process, and the reasonability of the weight directly influences the payment effect.
At present, according to the continuous improvement of the relevant policy, the weights corresponding to the respective arrays need to be continuously adjusted to adapt to the payment variation trend brought by the relevant policy, and the existing weight adjustment method is relatively wide and has bias, and a unified standard or an implementation method is lacked to guide how to adjust the weights.
Disclosure of Invention
The invention provides a data processing method, a data processing device, electronic equipment and a storage medium, which are used for realizing dynamic adjustment of an array weight value and ensuring the reasonability of data settlement.
According to an aspect of the present invention, there is provided a data processing method, the method including:
acquiring settlement data associated with the target area and corresponding to each historical visit record data and a corresponding weight value to be updated;
determining an array to be adjusted corresponding to each historical visit record data according to at least one array field which is preset, determining a historical balance attribute of each array to be adjusted according to settlement data corresponding to at least one historical visit record number in each array to be adjusted and a corresponding weight value to be updated, and obtaining a target array with at least one array to be adjusted removed based on each historical balance attribute;
determining corresponding target balance attributes based on the historical balance attributes of the target arrays;
and updating the weight value to be updated of the corresponding target array based on the balance attribute of each target, so as to perform data settlement according to the updated weight to be updated.
According to another aspect of the present invention, there is provided a data processing apparatus comprising:
the data acquisition module is used for acquiring settlement data which are associated with the target area and correspond to each historical clinic record data and corresponding weight values to be updated;
the historical balance attribute determining module is used for determining an array to be adjusted corresponding to each historical visit record data according to at least one array field which is preset, determining the historical balance attribute of each array to be adjusted according to settlement data corresponding to at least one historical visit record number in each array to be adjusted and a corresponding weight value to be updated, and obtaining a target array with at least one array to be adjusted removed based on each historical balance attribute;
the target balance attribute determining module is used for determining corresponding target balance attributes based on the historical balance attributes of the target arrays;
and the weight value updating module is used for updating the weight values to be updated of the corresponding target arrays based on the balance attributes of the targets so as to settle data according to the updated weights to be updated.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the data processing method according to any of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement a data processing method according to any one of the embodiments of the present invention when the computer instructions are executed.
According to the technical scheme of the embodiment of the invention, settlement data corresponding to each historical visit record data and a corresponding weight value to be updated, which are associated with a target area, are acquired, further, a to-be-adjusted array corresponding to each historical visit record data is determined according to at least one array field which is preset, a historical balance attribute of each to-be-adjusted array is determined according to the settlement data corresponding to at least one historical visit record number in each to-be-adjusted array and the corresponding weight value to be updated, so that a target array with at least one to-be-adjusted array removed is obtained based on each historical balance attribute, a corresponding target balance attribute is determined based on the historical balance attribute of each target array, finally, the to-be-updated weight value of the corresponding target array is updated based on each target balance attribute, so that data settlement is performed according to the updated weight value, the problem that the existing adjusting method is relatively wide and has bias, and a unified standard or an implementation method is lacked to guide the weight adjustment is solved, the dynamic adjustment of the array weight value is realized, and the reasonability of data settlement is ensured.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only 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 flowchart of a data processing method according to an embodiment of the present invention;
fig. 2 is a flowchart of a data processing method according to a second embodiment of the present invention;
fig. 3 is a flowchart of a data processing method according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a data processing apparatus according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device implementing the data processing method according to the embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, 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 only a part of the embodiments of the present invention, and not all of the embodiments. 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 should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It is understood that before the technical solutions disclosed in the embodiments of the present disclosure are used, the type, the use range, the use scene, etc. of the personal information related to the present invention should be informed to the user and authorized by the user in a proper manner according to the relevant laws and regulations.
Example one
Fig. 1 is a flowchart of a data processing method, which is applicable to a case where a visit settlement problem of a target area is analyzed according to an embodiment of the present invention, and the method may be executed by a data processing apparatus, which may be implemented in hardware and/or software, and the data processing apparatus may be configured in a terminal and/or a server. As shown in fig. 1, the method includes:
and S110, acquiring settlement data corresponding to each historical clinic record data and corresponding weight values to be updated, wherein the settlement data are associated with the target area.
In this embodiment, the target area may be any one of areas determined according to data processing requirements. Illustratively, the target area may be a province, a city, a hospital, or the like. The historical encounter record data may be understood as detailed encounter data stored by the encounter user during the encounter with the target area. The historical visit record data may be the visit record data associated with the target area for a preset length of time prior to the current time. For example, the preset time period may be 1 year, 2 years, 3 years, or the like, and this embodiment is not particularly limited thereto. The settlement data can be understood as a result of settlement of the treatment costs determined from the treatment data recorded in the corresponding historical treatment log data, i.e. all costs spent by the treatment user from the beginning to the end of the treatment. The weight value to be updated can be understood as a weight value required when calculating the settlement data corresponding to the historical visit record data. In practical application, according to the content recorded in the history visit record data, each history visit record data may be divided into a plurality of arrays, each array has a corresponding weight value, and after the weight values are applied for a period of time, the update is required periodically or occasionally, and the weight values may be used as the weight values to be updated.
It should be noted that, before the technical solution of the embodiment of the present invention is used, the user is informed of the acquired information and the usage by law, and is authorized by the user.
Specifically, when performing data analysis on the diagnosis settlement condition of the target area, it is necessary to first obtain settlement data corresponding to each historical diagnosis record data in the target area and a weight value to be updated used in settlement, so that the diagnosis settlement of the target area can be more adapted to the current variation trend by analyzing the obtained historical settlement data and the corresponding weight value.
S120, determining an array to be adjusted corresponding to each historical visiting record data according to at least one array field which is preset, determining a historical balance attribute of each array to be adjusted according to settlement data corresponding to at least one historical visiting record number in each array to be adjusted and a corresponding weight value to be updated, and obtaining a target array with at least one array to be adjusted removed based on each historical balance attribute.
In this embodiment, the array field may be understood as a key field for distinguishing the group to which the visit record data belongs in the field recorded by the visit record data. For example, the array fields may include a treatment user information related field, a treatment mode related field, a treatment resource consumption related field, and the like. The array to be adjusted can be understood as an important tool for measuring the efficiency of the treatment service quality and the treatment settlement. For example, the array to be adjusted may be a DRG. In practical applications, the array to be adjusted may be all arrays associated with each historical visit record data, or may be an array in which the number of the visit record data in the array exceeds a preset sample size threshold. For example, the predetermined sample size threshold may be 30 samples within 3 years and 5 samples within 1 year.
The historical balance attribute may be understood as a ratio between a difference between the settlement standard and the total settlement data corresponding to each array and the settlement standard in a certain period. For example, the historical balance attribute may be expressed as a percentage. The settlement criterion may be understood as a criterion of a partial settlement fee to be borne by the target area for the total settlement fee corresponding to each array. The difference between the settlement criteria and the total settlement data may be historical balance data for the current array. The target array may be understood as an array that satisfies the balance attribute adjustment condition. It should be noted that the number of target arrays may be 1 or more, which is not specifically limited in this embodiment.
Optionally, obtaining a target array from which at least one array to be adjusted is removed based on each historical balance attribute, including: determining balance attribute distribution curves corresponding to the target areas according to the historical balance attributes; and determining a target array from the arrays to be adjusted based on the balance attribute distribution curve.
In this embodiment, the balance attribute distribution curve may be understood as a curve reflecting the historical balance attribute distribution rule of each array to be adjusted.
Specifically, after the historical balance attributes of each array to be adjusted are obtained, statistical analysis can be performed on each historical balance attribute through probability statistical analysis software, a corresponding balance attribute distribution curve is obtained and used for reflecting the visit balance distribution rule of the target area, and further, the balance attribute distribution curve is subjected to graphic analysis, and the target array is determined from each array to be adjusted according to the analysis result.
Optionally, determining a target array from each array to be adjusted based on the balance attribute distribution curve includes: determining data distribution corresponding to the historical balance attribute of each array to be adjusted according to the balance attribute distribution curve; and determining a corresponding preset array rejection standard based on the data distribution, and rejecting at least one array to be adjusted based on the preset array rejection standard to obtain a target array.
In the present embodiment, the data distribution includes a normal distribution or a skewed distribution. The preset array rejection standard can be understood as a preset method for determining the array rejection standard and rejecting the arrays which do not meet the standard. For example, when the data distribution is a skewed distribution, the default array rejection standard may be a middle section method; when the data distribution is normal distribution, the preset array rejection standard can be a standard deviation-by-multiple method.
Specifically, the curve shape of the balance attribute distribution curve is analyzed to determine the data distribution condition corresponding to each historical balance attribute, the corresponding preset array rejection standard is determined according to the data distribution condition, and the corresponding array to be adjusted is rejected according to the array rejection standard, so that the target array with the singular value removed is obtained.
In specific implementation, after obtaining each historical visit record data, firstly, array division needs to be performed on each historical visit record data, an array to be adjusted corresponding to each historical visit record data can be determined according to each array field set in advance, further, according to settlement data corresponding to each historical visit record data in each array to be adjusted and a corresponding weight value to be updated, total settlement data and a corresponding settlement standard of each array to be adjusted are determined, and according to each total settlement data and a corresponding settlement standard, a historical balance attribute of each array to be adjusted is determined, so that balance conditions of each array to be adjusted can be analyzed according to each historical balance attribute, and arrays to be adjusted which do not meet balance attribute adjustment conditions are eliminated, so that a target array is obtained.
S130, determining corresponding target balance attributes based on the historical balance attributes of the target arrays.
Generally, after the historical balance attribute of each target array is obtained, the historical balance attribute which does not meet the requirement is adjusted according to the current actual balance requirement, so that an updated balance attribute is obtained, and the updated balance attribute can be used as the target balance attribute.
In practical application, the probability distribution condition of the historical balance attribute corresponding to each target array can be determined by analyzing the historical balance attribute of each target array, and further, each historical balance attribute can be adjusted according to the probability distribution condition and the current actual balance requirement, so that the corresponding target balance attribute can be obtained.
Optionally, determining a corresponding target balance attribute based on the historical balance attribute of each target array includes: determining balance attribute reference ranges corresponding to the target array according to the historical balance attributes; and adjusting the historical balance attribute of each target array based on the balance attribute reference range to obtain the target balance attribute.
In this embodiment, the balance attribute reference range may be understood as a range of areas where a relatively great majority of historical balance attributes are located. It should be noted that the balance attribute reference range may reflect a probability distribution of the historical balance attributes of each target array.
In practical application, after the historical balance attributes of each target array are obtained, the historical balance attributes can be analyzed through data statistical software to determine the probability distribution condition of each historical balance attribute, and further, the corresponding balance attribute reference range is determined according to the probability distribution condition.
Optionally, determining a balance attribute reference range corresponding to the target array according to each historical balance attribute, including: determining balance attribute distribution curves corresponding to the target array according to the historical balance attributes, and determining data distribution corresponding to the historical balance attributes based on the balance attribute distribution curves; and determining a corresponding preset range determination standard based on the data distribution so as to determine a balance attribute reference range corresponding to the target array based on the preset range determination standard.
In this embodiment, the balance attribute distribution curve may be understood as a curve reflecting the distribution rule of each historical balance attribute. The data distribution may include a normal distribution and a biased distribution, wherein the biased distribution may further include a positive biased distribution and a negative biased distribution. The positive off-state distribution curve is a curve with a longer right side and a shorter left side; the negative bias distribution curve is a curve with a longer left side and a shorter right side. The preset range determination criterion may be understood as a preset method criterion for dividing the balance attribute reference range. For example, when the data distribution is a normal distribution, the preset range determination criterion may be a standard deviation method, that is, according to the policy related to the target region, a confidence level of two sides or one side is set, the corresponding confidence level corresponds to a multiple of the standard deviation (for example, when the confidence level is 80%, the corresponding multiple of the standard deviation is 1.28 times), an expectation and the standard deviation corresponding thereto are determined by a normal distribution function, a product of the standard deviation and the multiple thereof is determined, a sum of the expectation and the product is used as an upper limit of the balance attribute reference range, and a difference value between the expectation and the product is used as a lower limit of the balance attribute reference range. When the data distribution is a skewed distribution, the predetermined range determination criterion may be a percentile method, which is a conventional statistical method and will not be described in detail in this embodiment.
Specifically, through statistical analysis of each historical balance attribute, a corresponding balance attribute distribution curve can be determined, corresponding data distribution is determined according to the curve shape displayed by the distribution curve, and a preset balance attribute range determination standard is determined according to the data distribution, so that a balance attribute reference range can be obtained according to the corresponding range determination standard, and the historical balance attributes of each target array can be adjusted according to the reference range.
In practical application, when the historical balance attributes are adjusted according to the balance attribute reference range, the historical balance attributes which are larger than the upper limit of the reference range and smaller than the lower limit of the reference range can be respectively adjusted to obtain the target balance attributes.
Optionally, adjusting the historical balance attribute of each target array based on the balance attribute reference range includes: determining at least one first array to be processed of which the historical balance attribute is larger than the maximum balance attribute in the balance attribute reference range in the target array, and adjusting the historical balance attribute corresponding to each first array to be processed to the maximum balance attribute; and determining at least one second array to be processed with the historical balance attribute smaller than the minimum balance attribute in the balance attribute reference range in the target array, and adjusting the historical balance attribute corresponding to each second array to be processed to the minimum balance attribute.
In the present embodiment, the maximum balance attribute may be understood as an upper limit of the balance attribute reference range. Accordingly, the minimum balance attribute may be understood as a lower limit of the balance attribute reference range. The first to-be-processed array may be understood as each array having a historical balance attribute greater than the upper limit of the balance attribute reference range. The second pending array may be understood as each array for which the historical balance attribute is less than the lower limit of the balance attribute reference range.
Specifically, the balance attributes of the histories are adjusted according to the balance attribute reference range, the balance attribute larger than the upper limit of the reference range in the balance attributes of the histories can be adjusted to the balance attribute corresponding to the upper limit, and the balance attribute smaller than the lower limit of the reference range in the balance attributes of the histories can be adjusted to the balance attribute corresponding to the lower limit. Illustratively, the balance attribute reference range is [ -20%, 20% ], when the historical balance attribute of a certain array is greater than 20%, the historical balance attribute of the array is adjusted to 20%; correspondingly, when the historical balance attribute of an array is less than-20%, the historical balance attribute of the array is adjusted to-20%.
It should be noted that, after determining the balance attribute reference range, the adjustment may be performed by the total balance attribute of the profit array or the loss array. The corresponding array with the positive historical balance attribute in each target array is a profit array, and the corresponding array with the negative historical balance attribute in each target array is a loss array. Illustratively, the balance attribute reference range is [ -20%, 20% ], the total balance attribute of the profit array is 15%, the total balance attribute of the loss array is-15%, and when the historical balance attribute of a certain array is greater than 20%, the historical balance attribute of the array is adjusted to 15%; correspondingly, when the historical balance attribute of a certain array is less than-20%, the historical balance attribute of the array is adjusted to-15%. The advantages of such an arrangement are: the profit and loss conditions of the target arrays can be relatively balanced.
And S140, updating the weight values to be updated of the corresponding target arrays based on the balance attributes of the targets, and performing data settlement according to the updated weight values to be updated.
In practical application, after the balance attributes of each target are obtained, the weight values to be updated of the corresponding array can be adjusted and updated according to the corresponding relation between the balance attributes and the weight values, so that the treatment record data in the corresponding target array are settled according to the updated weight values.
Optionally, updating the weight value to be updated of the corresponding target array based on each target balance attribute includes: acquiring payment attributes corresponding to the target area, and determining data to be applied according to the target balance attributes and the payment attributes; determining average settlement data corresponding to each target array, and determining a ratio between each average settlement data and corresponding data to be applied so as to obtain a target weight value corresponding to the target array; and updating the weight value to be updated of the corresponding target array based on each target weight value.
In this embodiment, the payment attribute may be understood as a payment rate established by the target area for the visit settlement problem. For example, the payment attribute may be represented by a rate. The data to be applied can be the difference value between 100% and the target balance attribute, and the difference value is multiplied by the payment attribute to obtain the data. The average settlement data may be understood as a ratio between total settlement data corresponding to all the historical visit record data contained in the current target array and the number of the historical visit records contained in the current target array. Illustratively, the average settlement data may be represented by a use-average cost.
Specifically, payment attributes corresponding to the target area are determined, the difference value between each target balance attribute and 100% is determined, the difference value is multiplied by the payment attributes to obtain data to be applied, corresponding average settlement data is determined according to the total settlement data and the corresponding number of the treatment records of each target array, further, the ratio between each average settlement data and the corresponding data to be applied is determined, a target weight value corresponding to the corresponding target array can be obtained, the target weight value is replaced by the previous weight value to be updated to update the weight value to be updated, and then the treatment record data belonging to the corresponding target array is subjected to data settlement according to the updated weight value.
It should be noted that, according to the similarity of the data of the medical records in each array to be adjusted, each array to be adjusted may also be divided into a plurality of core arrays. The weight values of each array to be adjusted in each core array are sequentially arranged according to a preset weight setting standard, after the weight values to be updated of each target array are updated, the weight value arrangement conditions of all the arrays to be adjusted in a target area can be obtained, and because the weight values of some arrays are updated when the weight values are updated, but the weight values of some arrays are not updated, the situation that the weight values of some arrays to be adjusted in the same core array do not accord with the preset weight setting standard can possibly occur.
According to the technical scheme of the embodiment of the invention, settlement data corresponding to each historical visit record data and a corresponding weight value to be updated, which are associated with a target area, are acquired, further, a to-be-adjusted array corresponding to each historical visit record data is determined according to at least one array field which is preset, a historical balance attribute of each to-be-adjusted array is determined according to the settlement data corresponding to at least one historical visit record number in each to-be-adjusted array and the corresponding weight value to be updated, so that a target array with at least one to-be-adjusted array removed is obtained based on each historical balance attribute, a corresponding target balance attribute is determined based on the historical balance attribute of each target array, finally, the to-be-updated weight value of the corresponding target array is updated based on each target balance attribute, so that data settlement is performed according to the updated weight value, the problem that the existing adjusting method is relatively wide and has bias, and a unified standard or an implementation method is lacked to guide the weight adjustment is solved, the dynamic adjustment of the array weight value is realized, and the reasonability of data settlement is ensured.
Example two
Fig. 2 is a flowchart of a data processing method according to a second embodiment of the present invention, and a method for determining payment attributes is added on the basis of the second embodiment. As shown in fig. 2, the method includes:
and S210, acquiring settlement data associated with the target area and corresponding to each historical clinic record data and corresponding weight values to be updated.
S220, summing the settlement data corresponding to the historical visit record data to obtain total settlement data, and summing the weight values to be updated corresponding to the historical visit record data to obtain the total weight values.
In this embodiment, the total settlement data may be understood as the sum of the settlement data corresponding to all the historical visit record data. The total weight value may be understood as a sum of the weight values to be updated corresponding to all the historical visit record data.
Specifically, after the settlement data corresponding to each historical visit record data and the corresponding weight value to be updated are obtained, summing processing is performed on each settlement data to obtain total settlement data corresponding to all the visit record data associated with the target area, and meanwhile, summing processing is performed on the weight value to be updated corresponding to each historical visit record data to obtain a total weight value corresponding to all the historical visit record data associated with the target area, so that a subsequent data analysis process can be performed based on the total settlement data and the total weight value.
S230, determining the ratio between the total settlement data and the total weight value to obtain the corresponding payment attribute of the target area, and determining the historical balance attribute based on the payment attribute.
In this embodiment, the payment attribute may be understood as a payment rate established by the target area for the visit settlement problem. For example, the payment attribute may be represented by a rate.
Specifically, after total settlement data corresponding to all historical visit record data associated with the target area and corresponding total weight values are obtained, quotient calculation processing is carried out on the total settlement data and the total weight values, so that payment attributes of the target area for the visit settlement problem can be obtained, and therefore the historical balance attributes corresponding to the arrays to be adjusted are determined according to the payment attributes.
S240, determining an array to be adjusted corresponding to each historical visiting record data according to at least one array field which is preset, determining a historical balance attribute of each array to be adjusted according to settlement data corresponding to at least one historical visiting record number in each array to be adjusted and a corresponding weight value to be updated, and obtaining a target array with at least one array to be adjusted removed based on each historical balance attribute.
In practical application, the historical balance attribute of each array to be adjusted can be determined according to the settlement data corresponding to each historical record data, the corresponding weight value to be updated, the payment attribute corresponding to the target area and the quantity of the historical visiting records contained in each array to be adjusted.
Optionally, determining the historical balance attribute of each array to be adjusted according to the settlement data corresponding to at least one historical visit record data in each array to be adjusted and the corresponding weight value to be updated, including: for each array to be adjusted, multiplying the weight value to be updated and the payment attribute corresponding to the array to be adjusted at present to obtain a settlement standard corresponding to the array to be adjusted at present; determining the number of historical clinic records corresponding to each array to be adjusted, determining array settlement data corresponding to each array to be adjusted according to settlement data corresponding to each historical clinic record data in each array to be adjusted, and determining average settlement data corresponding to each array to be adjusted based on the number of historical clinic records and the array settlement data; and performing difference processing on the settlement standard corresponding to each array to be adjusted and the corresponding average settlement data to obtain data to be processed, and determining the ratio between each data to be processed and the corresponding settlement standard to obtain the historical balance attribute of each array to be adjusted.
In this embodiment, the settlement standard may be understood as a standard value according to which each historical visiting medical record data in each array to be adjusted is subjected to data settlement.
It should be noted that, the calculation methods of the historical balance attributes of the arrays to be adjusted are the same, and therefore, the determination process of the historical balance attribute of any one of the arrays to be adjusted can be taken as an example for description.
Specifically, for each array to be adjusted, multiplying the weight value to be updated and the payment attribute corresponding to the array to be adjusted currently to obtain the settlement standard corresponding to the array to be adjusted currently, further, summing up the settlement data corresponding to all the historical attendance record data in the array to be adjusted currently to obtain the total settlement data of the array to be adjusted, determining the number of the historical attendance records contained in the array to be adjusted currently, and determining the ratio of the total settlement data to the number of the historical attendance records, so as to obtain the average settlement data corresponding to the current array to be adjusted, and then the average settlement data and the corresponding settlement standard are processed by difference value to obtain the data to be processed, and determining the ratio of the data to be processed to the corresponding settlement standard, thereby finally obtaining the historical balance attribute of the array to be adjusted.
And S250, determining corresponding target balance attributes based on the historical balance attributes of the target arrays.
And S260, updating the weight values to be updated of the corresponding target arrays based on the balance attributes of the targets, and performing data settlement according to the updated weights to be updated.
According to the technical scheme of the embodiment of the invention, settlement data corresponding to each historical visit record data and a corresponding weight value to be updated, which are associated with a target area, are acquired, further, a to-be-adjusted array corresponding to each historical visit record data is determined according to at least one array field which is preset, a historical balance attribute of each to-be-adjusted array is determined according to the settlement data corresponding to at least one historical visit record number in each to-be-adjusted array and the corresponding weight value to be updated, so that a target array with at least one to-be-adjusted array removed is obtained based on each historical balance attribute, a corresponding target balance attribute is determined based on the historical balance attribute of each target array, finally, the to-be-updated weight value of the corresponding target array is updated based on each target balance attribute, so that data settlement is performed according to the updated weight value, the problem that the existing adjusting method is relatively wide and has bias, and a unified standard or an implementation method is lacked to guide the weight adjustment is solved, the dynamic adjustment of the array weight value is realized, and the reasonability of data settlement is ensured.
EXAMPLE III
Fig. 3 is a flowchart of a data processing method according to a third embodiment of the present invention. This embodiment is a preferred example of the above-described embodiments. As shown in fig. 3, the method includes:
1. determining the historical balance attribute and the corresponding weight value to be updated of each array;
2. determining whether the sample size of each array is larger than a preset sample size threshold value N, if so, executing a step 3; if not, not adjusting;
3. removing at least one singular value array from each array to obtain a target array;
4. selecting a corresponding method from a normal distribution method and a percentile method according to the historical balance attribute of each target array, and determining a balance attribute reference range;
5. selecting a corresponding method from a large number interval method and a consistent interval method according to the historical balance attribute of each target array, and determining the adjustment range of the historical balance attribute;
6. determining a corresponding target weight value according to the adjusted target balance attribute;
7. identifying a weight reverse hanging group and carrying out weight fine adjustment;
8. and substituting the adjusted weight into the data to carry out simulated settlement, determining whether the settled balance attribute is in the balance attribute reference range, if not, continuing to execute the step 6, and if so, obtaining the final weight.
According to the technical scheme of the embodiment of the invention, settlement data corresponding to each historical visit record data and a corresponding weight value to be updated, which are associated with a target area, are acquired, further, a to-be-adjusted array corresponding to each historical visit record data is determined according to at least one array field which is preset, a historical balance attribute of each to-be-adjusted array is determined according to the settlement data corresponding to at least one historical visit record number in each to-be-adjusted array and the corresponding weight value to be updated, so that a target array with at least one to-be-adjusted array removed is obtained based on each historical balance attribute, a corresponding target balance attribute is determined based on the historical balance attribute of each target array, finally, the to-be-updated weight value of the corresponding target array is updated based on each target balance attribute, so that data settlement is performed according to the updated weight value, the problem that the existing adjusting method is wide and partial, and lacks of a unified standard or an implementation method to guide how to adjust the weight is solved, dynamic adjustment of the array weight value is realized, and the reasonability of data settlement is ensured.
Example four
Fig. 4 is a schematic structural diagram of a data processing apparatus according to a fourth embodiment of the present invention. As shown in fig. 4, the apparatus includes: a data acquisition module 310, a historical balance attribute determination module 320, a target balance attribute determination module 330, and a weight value update module 340.
A data obtaining module 310, configured to obtain settlement data corresponding to each historical visit record data and corresponding weight values to be updated, where the settlement data is associated with a target area;
the historical balance attribute determining module 320 is configured to determine, according to at least one preset array field, an array to be adjusted corresponding to each historical visit record data, and determine, according to the settlement data corresponding to at least one historical visit record number in each array to be adjusted and a corresponding weight value to be updated, a historical balance attribute of each array to be adjusted, so as to obtain a target array from which at least one array to be adjusted is removed, based on each historical balance attribute;
a target balance attribute determining module 330, configured to determine a corresponding target balance attribute based on the historical balance attributes of the target arrays;
and the weight value updating module 340 is configured to update the weight values to be updated of the corresponding target arrays based on the target balance attributes, so as to perform data settlement according to the updated weights to be updated.
According to the technical scheme of the embodiment of the invention, settlement data corresponding to each historical visit record data and a corresponding weight value to be updated, which are associated with a target area, are acquired, further, a to-be-adjusted array corresponding to each historical visit record data is determined according to at least one array field which is preset, a historical balance attribute of each to-be-adjusted array is determined according to the settlement data corresponding to at least one historical visit record number in each to-be-adjusted array and the corresponding weight value to be updated, so that a target array with at least one to-be-adjusted array removed is obtained based on each historical balance attribute, a corresponding target balance attribute is determined based on the historical balance attribute of each target array, finally, the to-be-updated weight value of the corresponding target array is updated based on each target balance attribute, so that data settlement is performed according to the updated weight value, the problem that the existing adjusting method is wide and partial, and lacks of a unified standard or an implementation method to guide how to adjust the weight is solved, dynamic adjustment of the array weight value is realized, and the reasonability of data settlement is ensured.
Optionally, the apparatus further comprises: the system comprises a summation processing module and a payment attribute determining module.
The summation processing module is used for summing the settlement data corresponding to the historical visit record data to obtain total settlement data and summing the weight values to be updated corresponding to the historical visit record data to obtain the total weight values before determining the historical balance attribute of each array to be adjusted;
and the payment attribute determining module is used for determining the ratio between the total settlement data and the total weight value to obtain a payment attribute corresponding to the target area so as to determine a historical balance attribute based on the payment attribute.
Optionally, the historical balance attribute determination module 320 includes a settlement criterion determination unit, an average settlement data determination unit, and a historical balance attribute determination unit.
The settlement standard determining unit is used for determining a weight value to be updated of the current array to be adjusted according to each array to be adjusted, and multiplying the weight value to be updated and the payment attribute to obtain a settlement standard corresponding to the current array to be adjusted;
the average settlement data determining unit is used for determining the number of the historical visit records corresponding to each array to be adjusted, determining the array settlement data of the corresponding array to be adjusted according to the settlement data corresponding to the historical visit record data in each array to be adjusted, and determining the average settlement data corresponding to each array to be adjusted based on the number of the historical visit records and the array settlement data;
and the historical balance attribute determining unit is used for performing difference processing on the settlement standards corresponding to the arrays to be adjusted and the corresponding average settlement data to obtain data to be processed, and determining the ratio of the data to be processed and the corresponding settlement standards to obtain the historical balance attribute of each array to be adjusted.
Optionally, the historical balance attribute determination module 320 includes a distribution curve determination unit and a target array determination unit.
The distribution curve determining unit is used for determining balance attribute distribution curves corresponding to the target areas according to the historical balance attributes;
and the target array determining unit is used for determining a target array from the arrays to be adjusted based on the balance attribute distribution curve.
Optionally, the target array determining unit is further configured to determine, according to the balance attribute distribution curve, data distribution corresponding to the historical balance attribute of each array to be adjusted, where the data distribution includes normal distribution or skewed distribution; and determining a corresponding preset array rejection standard based on the data distribution, and rejecting at least one array to be adjusted based on the preset array rejection standard to obtain a target array.
Optionally, the target balance attribute determining module 330 includes a balance attribute reference range determining unit and a target balance attribute determining unit.
The balance attribute reference range determining unit is used for determining a balance attribute reference range corresponding to the target array according to each historical balance attribute;
and the target balance attribute determining unit is used for adjusting the historical balance attribute of each target array based on the balance attribute reference range to obtain the target balance attribute.
Optionally, the balance attribute reference range determining unit is further configured to determine a balance attribute distribution curve corresponding to the target array according to each historical balance attribute, so as to determine data distribution corresponding to each historical balance attribute based on the balance attribute distribution curve;
and determining a corresponding preset range determination standard based on the data distribution so as to determine a balance attribute reference range corresponding to the target array based on the preset range determination standard.
Optionally, the target balance attribute determining unit is further configured to determine, in the target array, at least one first array to be processed whose historical balance attribute is greater than the maximum balance attribute in the balance attribute reference range, and adjust the historical balance attribute corresponding to each first array to be processed to the maximum balance attribute, so as to obtain the target balance attribute; and determining at least one second array to be processed, of which the historical balance attribute is smaller than the minimum balance attribute in the balance attribute reference range, in the target array, and adjusting the historical balance attribute corresponding to each second array to be processed to the minimum balance attribute to obtain the target balance attribute.
Optionally, the weighted value updating module 340 is further configured to obtain a payment attribute corresponding to the target area, and determine data to be applied according to each target balance attribute and the payment attribute; determining average settlement data corresponding to each target array, and determining a ratio between each average settlement data and corresponding data to be applied so as to obtain a target weight value corresponding to each target array; and updating the weight value to be updated of the corresponding target array based on each target weight value.
The data processing device provided by the embodiment of the invention can execute the data processing method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
EXAMPLE five
FIG. 5 illustrates a schematic diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM)12, a Random Access Memory (RAM)13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM)12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to the bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as a data processing method.
In some embodiments, the data processing method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the data processing method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the data processing method by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (12)
1. A data processing method, comprising:
acquiring settlement data associated with the target area and corresponding to each historical visit record data and a corresponding weight value to be updated;
determining an array to be adjusted corresponding to each historical visit record data according to at least one array field which is preset, determining a historical balance attribute of each array to be adjusted according to settlement data corresponding to at least one historical visit record number in each array to be adjusted and a corresponding weight value to be updated, and obtaining a target array with at least one array to be adjusted removed based on each historical balance attribute;
determining corresponding target balance attributes based on the historical balance attributes of the target arrays;
and updating the weight values to be updated of the corresponding target arrays based on the target balance attributes so as to perform data settlement according to the updated weights to be updated.
2. The method of claim 1, further comprising, before determining the historical balance attribute of each array to be adjusted according to the settlement data and the corresponding weight value to be updated corresponding to at least one historical visit record number in each array to be adjusted, the following steps:
summing settlement data corresponding to each historical visit record data to obtain total settlement data, and summing weighted values to be updated corresponding to each historical visit record data to obtain total weighted values;
and determining the ratio between the total settlement data and the total weight value to obtain a payment attribute corresponding to the target area, so as to determine a historical balance attribute based on the payment attribute.
3. The method according to claim 2, wherein the determining the historical balance attribute of each array to be adjusted according to the settlement data corresponding to at least one historical visit record data in each array to be adjusted and the corresponding weight value to be updated comprises:
aiming at each array to be adjusted, determining a weight value to be updated of the current array to be adjusted, and multiplying the weight value to be updated and the payment attribute to obtain a settlement standard corresponding to the current array to be adjusted;
determining the historical visiting record number corresponding to each array to be adjusted, determining array settlement data corresponding to the array to be adjusted according to settlement data corresponding to the historical visiting record data in each array to be adjusted, and determining average settlement data corresponding to each array to be adjusted based on the historical visiting record number and the array settlement data;
and performing difference processing on the settlement standard corresponding to each array to be adjusted and the corresponding average settlement data to obtain data to be processed, and determining the ratio between each data to be processed and the corresponding settlement standard to obtain the historical balance attribute of each array to be adjusted.
4. The method of claim 1, wherein obtaining a target array from which at least one array to be adjusted is culled based on historical balance attributes comprises:
determining a balance attribute distribution curve corresponding to the target area according to each historical balance attribute;
and determining the target array from each array to be adjusted based on the balance attribute distribution curve.
5. The method of claim 4, wherein determining the target array from each of the arrays to be adjusted based on the balance attribute distribution curve comprises:
determining data distribution corresponding to the historical balance attribute of each array to be adjusted according to the balance attribute distribution curve, wherein the data distribution comprises normal distribution or skewed distribution;
and determining a corresponding preset array rejection standard based on the data distribution, and rejecting at least one array to be adjusted based on the preset array rejection standard to obtain the target array.
6. The method of claim 1, wherein determining a corresponding target balance attribute based on historical balance attributes of each of the target arrays comprises:
determining a balance attribute reference range corresponding to the target array according to each historical balance attribute;
and adjusting the historical balance attribute of each target array based on the balance attribute reference range to obtain the target balance attribute.
7. The method according to claim 6, wherein the balance attribute reference range corresponding to the target array is determined according to each historical balance attribute; the method comprises the following steps:
determining a balance attribute distribution curve corresponding to the target array according to each historical balance attribute so as to determine data distribution corresponding to each historical balance attribute based on the balance attribute distribution curve;
and determining a corresponding preset range determination standard based on the data distribution, so as to determine a balance attribute reference range corresponding to the target array based on the preset range determination standard.
8. The method of claim 6, wherein adjusting the historical balance attribute of each of the target arrays based on the balance attribute reference range to obtain the target balance attribute comprises:
determining at least one first array to be processed, of which the historical balance attribute is larger than the maximum balance attribute in the balance attribute reference range, in the target array, and adjusting the historical balance attribute corresponding to each first array to be processed to the maximum balance attribute to obtain the target balance attribute; and the number of the first and second groups,
and determining at least one second array to be processed, of which the historical balance attribute is smaller than the minimum balance attribute in the balance attribute reference range, in the target array, and adjusting the historical balance attribute corresponding to each second array to be processed to the minimum balance attribute to obtain the target balance attribute.
9. The method according to claim 1, wherein updating the weight value to be updated of the corresponding target array based on each of the target balance attributes comprises:
acquiring payment attributes corresponding to the target area, and determining data to be applied according to the target balance attributes and the payment attributes;
determining average settlement data corresponding to each target array, and determining a ratio between each average settlement data and corresponding data to be applied to obtain a target weight value corresponding to each target array;
and updating the weight value to be updated of the corresponding target array based on each target weight value.
10. A data processing apparatus, characterized by comprising:
the data acquisition module is used for acquiring settlement data which are associated with the target area and correspond to each historical clinic record data and corresponding weight values to be updated;
the historical balance attribute determining module is used for determining an array to be adjusted corresponding to each historical visit record data according to at least one array field which is preset, determining the historical balance attribute of each array to be adjusted according to settlement data corresponding to at least one historical visit record number in each array to be adjusted and a corresponding weight value to be updated, and obtaining a target array with at least one array to be adjusted removed based on each historical balance attribute;
the target balance attribute determining module is used for determining corresponding target balance attributes based on the historical balance attributes of the target arrays;
and the weight value updating module is used for updating the weight values to be updated of the corresponding target arrays based on the target balance attributes so as to perform data settlement according to the updated weights to be updated.
11. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the data processing method of any one of claims 1-9.
12. A computer-readable storage medium, having stored thereon computer instructions for causing a processor, when executing the computer instructions, to implement the data processing method of any one of claims 1-9.
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| CN116306937B (en) * | 2023-03-22 | 2023-11-10 | 中航信移动科技有限公司 | Rule extraction method, medium and device based on time sequence offline data |
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