CN115204741A - Mechanism digital transformation processing method and device and related equipment - Google Patents

Mechanism digital transformation processing method and device and related equipment Download PDF

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CN115204741A
CN115204741A CN202210959326.1A CN202210959326A CN115204741A CN 115204741 A CN115204741 A CN 115204741A CN 202210959326 A CN202210959326 A CN 202210959326A CN 115204741 A CN115204741 A CN 115204741A
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黄柳金
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Ping An Technology Shenzhen Co Ltd
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Abstract

The application belongs to the technical field of data processing, and provides a mechanism digital transformation processing method, a device, computer equipment and a computer readable storage medium, aiming at solving the problem that the accuracy of a target result is low when an enterprise carries out digital transformation, the business of a preset mechanism is quantized into preset quantization indexes, all the preset mechanisms are clustered in a clustering mode according to the preset quantization indexes to obtain a mechanism group, a test point mechanism is determined from the mechanism group, the test point mechanism is digitally transformed, the test point index of the test point mechanism is obtained based on the digital transformation of the test point mechanism, the non-test point index of the non-test point mechanism is obtained, the test point mechanism is compared with the non-test point mechanism according to the test point index and the non-test point index, the objectivity of the test point mechanism to the target and the non-test point mechanism can be improved, and the accuracy of the target result is further improved.

Description

Mechanism digital transformation processing method and device and related equipment
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a mechanism digital transformation processing method and apparatus, a computer device, and a computer readable storage medium.
Background
The digital transformation is a great revolution that enterprises build comprehensive and high-quality underlying data structures, and the business transformation modes of the enterprises are promoted and the development of the businesses of the enterprises is guided by means of data analysis, data modeling, digital management and the like. In the current society, with the rapid development of information technology, the external competition of enterprises is increasingly fierce, and the enterprises are forced to carry out digital transformation. Important values for digital transformation include: 1) The service development is quantified into a measurable index, which is convenient to understand; 2) The number is objective, real and reliable, and rational decision making by a management layer is facilitated; 3) The digitization can improve the enterprise operation efficiency, reduce cost and improve efficiency.
Because digitalization brings a change to the operation mode, enterprises need to balance the steady growth of the existing business while carrying out digital transformation, and ensure that the digital transformation and the business development are not delayed. Especially when the enterprise scale is large and the number of involved branches is large, the digital transformation generally selects a test point mechanism, and the digital transformation is comprehensively popularized on the basis that the digitization of the test point mechanism is successful. Therefore, when the digital effect of the pilot mechanism is evaluated, the service effect of the pilot mechanism and the service effect of the non-pilot mechanism need to be compared, that is, the service effect of the pilot mechanism and the service effect of the non-pilot mechanism are compared, and if the service effect of the pilot mechanism is better than the service effect of the non-pilot mechanism, the digital transformation of the pilot mechanism is proved to be effective. In the traditional benchmarking mode, when a benchmarking mechanism is selected, a business data analyst selects the benchmarking mechanism and relevant contents thereof to compare based on experience to obtain a benchmarking result, and the benchmarking result is greatly influenced by subjective factors of the data analyst, so that the objectivity and accuracy of the benchmarking result are low, misleading of the benchmarking result is easily generated, and an enterprise decision layer makes wrong judgment to cause enterprise loss.
Disclosure of Invention
The application provides a mechanism digital transformation processing method, a mechanism digital transformation processing device, computer equipment and a computer readable storage medium, which can solve the technical problem that in the traditional technology, the accuracy of a calibration result is low when an enterprise carries out digital transformation.
In a first aspect, the present application provides a mechanism digital transformation processing method, including: acquiring preset quantization indexes of a plurality of preset mechanisms, and clustering all the preset mechanisms according to the preset quantization indexes to obtain a plurality of mechanism groups, wherein the preset quantization indexes describe indexes obtained by quantizing services of the preset mechanisms; determining a test point mechanism from the mechanism group, performing digital transformation on the test point mechanism, and obtaining a test point index of the test point mechanism based on the digital transformation of the test point mechanism, wherein the test point mechanism describes a mechanism for performing digital transformation on a test point, and the test point index describes an index for quantifying the service of the test point mechanism; determining a non-test point mechanism from other mechanisms except the test point mechanism, and acquiring a non-test point index of the non-test point mechanism, wherein the non-test point mechanism is a mechanism which is not subjected to digital transformation, the non-test point index describes an index for quantifying the service of the non-test point mechanism, and the non-test point index and the test point index are benchmarking indexes; and comparing the test point mechanism with the non-test point mechanism according to the test point index and the non-test point index to obtain a benchmarking result.
In a second aspect, the present application further provides a device for processing mechanism digital transformation, including: the system comprises a clustering unit, a service processing unit and a service processing unit, wherein the clustering unit is used for acquiring preset quantization indexes of a plurality of preset mechanisms and clustering all the preset mechanisms according to the preset quantization indexes to obtain a plurality of mechanism groups, and the preset quantization indexes describe indexes obtained by quantizing services of the preset mechanisms; the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for determining a test point mechanism from the mechanism group, performing digital transformation on the test point mechanism and obtaining a test point index of the test point mechanism based on the digital transformation of the test point mechanism, the test point mechanism describes a mechanism used as a test point for performing digital transformation, and the test point index describes an index for quantifying the service of the test point mechanism; a second obtaining unit, configured to determine a non-test-point mechanism from other mechanisms outside the test-point mechanism, and obtain a non-test-point index of the non-test-point mechanism, where the non-test-point mechanism is a mechanism that is not subjected to digital transformation, the non-test-point index describes an index that quantifies a service of the non-test-point mechanism, and the non-test-point index and the test-point index are benchmarking indexes; and the comparison unit is used for comparing the test point mechanism with the non-test point mechanism according to the test point index and the non-test point index to obtain a benchmarking result.
In a third aspect, the present application further provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the processing method for digital transformation of the mechanism when executing the computer program.
In a fourth aspect, the present application further provides a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to execute the steps of the processing method of the mechanism digital transformation.
The application provides a processing method, a device, computer equipment and a computer readable storage medium for mechanism digital transformation, wherein the processing method comprises the steps of quantifying services of preset mechanisms into preset quantitative indexes, clustering all the preset mechanisms in a clustering mode according to the preset quantitative indexes to obtain mechanism groups, determining the mechanism groups based on scientific statistics and analysis of the preset quantitative indexes, determining test point mechanisms from the mechanism groups, carrying out digital transformation on the test point mechanisms, obtaining the test point indexes of the test point mechanisms based on the digital transformation of the test point mechanisms, obtaining non-test point indexes of the non-test point mechanisms, comparing the test point mechanisms with the non-test point mechanisms according to the test point indexes and the non-test point indexes to obtain a benchmarking result, obtaining the preset quantitative indexes of the preset mechanisms due to the fact that the services of the preset mechanisms are quantified on the indexes, obtaining the benchmarking result, and selecting the test point mechanisms to obtain the analysis based on the preset quantitative indexes, improving the subjective results determined by data analysis personnel and further improving the accuracy of the benchmarking result of the non-target mechanisms, and further avoiding the problem that the traditional benchmarking mechanism makes mistakes in the objective decision-making of the benchmarking result of the benchmarking mechanism.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, 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 application, 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 a processing method for digital transformation of a facility according to an embodiment of the present application;
FIG. 2 is a schematic view of a first sub-flow of a mechanism digital transformation processing method according to an embodiment of the present application;
FIG. 3 is a second sub-flowchart of a processing method for digital transformation of a facility according to an embodiment of the present disclosure;
FIG. 4 is a third sub-flowchart of a mechanism digital transformation processing method according to an embodiment of the present disclosure;
FIG. 5 is a fourth sub-flowchart of a mechanism digital transformation processing method according to an embodiment of the present disclosure;
FIG. 6 is a schematic block diagram of a processing device for digital transformation of an organization according to an embodiment of the present application;
fig. 7 is a schematic block diagram of a computer device provided in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
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.
The embodiment of the application provides a mechanism digital transformation processing method, which can be applied to computer equipment such as smart phones, tablet computers, notebook computers and desktop computers and can be adopted when the digital transformation effect of an enterprise is evaluated.
In the prior art, when a benchmarking mode is adopted to evaluate the effect of digital transformation, because the traditional benchmarking mode depends on the subjective experience of a service data analyst to select a test point mechanism of digital transformation and obtain a benchmarking result according to the comparison between the service effect of the test point mechanism and the service effect of a non-test point mechanism, the benchmarking result is not objective, so that misleading of the benchmarking result is easily generated, and the problem that an enterprise decision layer makes wrong judgment and enterprise loss is caused is solved. Quantifying the service of a preset mechanism into different preset quantification indexes, clustering all the preset mechanisms based on the preset quantification indexes to obtain a plurality of mechanism groups, determining a test point mechanism from the mechanism groups, operating the test point mechanism in a digital transformation mode, determining a non-test point mechanism from other mechanisms outside the test point mechanism, operating the non-test point mechanism in a non-digital transformation mode, taking the test point mechanism and the non-test point mechanism as a label alignment mechanism, acquiring the label alignment indexes of the test point mechanism and the non-test point mechanism after the services of the test point mechanism and the non-test point mechanism respectively operate for a period of time, and comparing the label alignment indexes to obtain a label alignment result. The business of the preset mechanism is quantized to obtain the preset quantized index of the preset mechanism, all the preset mechanisms are grouped based on clustering according to the preset quantized index to obtain a mechanism group, the mechanism group consists of the preset mechanisms with similar preset quantized indexes, the mechanism group determines the test point mechanism, and the objectivity and accuracy of the standard result can be improved due to the test point mechanism which is subjectively determined by non-data analysts, so that the problem that the error judgment is made on an enterprise decision layer due to misleading of the standard result easily caused in the traditional standard mode is solved.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a schematic flow chart of a processing method for digital transformation of a mechanism according to an embodiment of the present application. As shown in fig. 1, the method comprises the following steps S11-S13:
s11, acquiring preset quantization indexes of a plurality of preset mechanisms, clustering all the preset mechanisms according to the preset quantization indexes to obtain a plurality of mechanism groups, wherein the preset quantization indexes describe indexes obtained by quantizing services of the preset mechanisms.
Specifically, the preset mechanism performs digital transformation, which is to perform digital transformation on the service of the preset mechanism. The method comprises the steps of carrying out digital transformation on a preset mechanism, quantizing the service of the preset mechanism by adopting a preset index, and obtaining a preset quantization index of the preset mechanism, wherein the preset quantization index describes an index obtained by quantizing the service of the preset mechanism, the preset quantization index is adopted to describe the service content of the preset mechanism, and the preset quantization index can be adopted to evaluate the digital transformation effect of the preset mechanism. For example, in the insurance industry, an insurance organization is digitally transformed so as to process insurance business in an online digital mode, or a new digital mode is adopted to process insurance business, wherein the exhibition business of an insurance salesman is digitally transformed, the exhibition business process of the insurance salesman can be divided into a behavior module, a quality module and a performance module, the behavior module describes exhibition business behavior of the insurance salesman, the quality module describes exhibition business quality of the insurance salesman, and the performance module describes exhibition business performance of the insurance salesman, wherein the behavior module, the quality module and the performance module can further comprise a plurality of further refined quantitative indexes, so that the exhibition business process of the insurance salesman is digitally described.
Further, the preset quantitative index is a preset behavior index, wherein the preset behavior index describes an index corresponding to a behavior of the preset mechanism for processing the service.
Specifically, a preset behavior index describes an index corresponding to a behavior of the preset mechanism for processing the service, the behavior index of the service is a pilot index of the service, a result of the service can be determined to a certain extent, and the behavior index has the characteristics of high controllability and high quantifiability. For example, in the insurance industry, for the exhibition industry of insurance businessmen, the preset insurance behavior indexes can include augmented behavior indexes, cultivation behavior indexes, capacity behavior indexes, performance optimization behavior indexes, management behavior indexes and the like, wherein the augmented behavior indexes include refined indexes such as 70-minute human resources and augmented activity, the cultivation behavior indexes include refined indexes such as part class training popularization rate and training function group operation scores, the capacity behavior indexes include refined indexes such as average interview amount of people and average performance activity amount, the performance optimization behavior indexes include refined indexes such as 200-minute activity amount and performance function group operation scores, the management behavior indexes include refined indexes such as participation rate and daily function group operation scores, and the digital transformation of the exhibition behavior of the insurance businessmen can be better described through the preset insurance behavior indexes.
Based on the above description, when performing mechanism digital transformation, obtaining preset quantization indexes of a plurality of preset mechanisms, where the preset quantization indexes may be preset behavior indexes, each preset mechanism may correspond to a plurality of preset quantization indexes, each preset quantization index may include a plurality of different preset refined quantization indexes, and clustering all the preset mechanisms according to the preset quantization indexes, for example, a K-means clustering method may be adopted, and first determining a central point mechanism of each class, where the central point mechanism is used as a preset mechanism corresponding to a central point of each class, and all mechanisms except the central point mechanism in all the preset mechanisms are non-central point mechanisms, and then calculating a distance between each non-central point mechanism and the central point mechanism, and minimizing a sum of all distances related to the mechanism groups, so as to classify all the preset mechanisms to obtain a plurality of mechanism groups, where each mechanism group is a mechanism type and is a preset mechanism cluster, and each mechanism group is composed of the preset mechanisms whose preset quantization indexes are similar to each other.
S12, determining a test point mechanism from the mechanism group, performing digital transformation on the test point mechanism, and obtaining a test point index of the test point mechanism based on the digital transformation of the test point mechanism, wherein the test point mechanism describes a mechanism for performing digital transformation on the test point, and the test point index describes an index for quantifying the service of the test point mechanism.
Specifically, because the preset quantization indexes corresponding to the preset mechanisms included in each mechanism group are similar indexes, that is, the preset mechanisms included in each mechanism group are the same or similar mechanisms, the mechanism groups determine the test point mechanisms, and according to the actual test point arrangement, a plurality of mechanisms can be determined as the test point mechanisms from each mechanism group, or a plurality of mechanisms can be determined as the test point mechanisms from some mechanism groups. For example, in the insurance industry, the selection of the trial-and-error organization may refer to the following conditions: 1) The method is in accordance with the hierarchical sampling standard of the insurance mechanism, and each index of the insurance mechanism can represent the hierarchy of the mechanism group in which the insurance mechanism is positioned; 2) The insurance mechanism has digital transformation data conditions, personnel conditions and material conditions, and has high data index saturation and high accuracy; 3) The digital solution of the digital tool and the business processing has the attitude of hugging changes, so that the initial selection trial mechanism of the insurance industry is selected according to the conditions, and the initial selection trial mechanism is confirmed to be used as the trial mechanism for digital transformation.
And then, carrying out digital transformation on the test point mechanism, namely, running the test point mechanism in a digital transformation mode, wherein the digital transformation mode is mainly used for converting the service of the test point mechanism from an offline processing mode to an online digital processing mode, or updating the service of the test point mechanism from an original online old processing mode to an online new processing mode. It should be noted that, the business processing mode herein is converted from offline to online, not only to migrate the offline business processing flow to online, but also to reconstruct the business processing process by using the online digital function, for example, in the insurance business, based on the digital transformation, the real-time statistics can be performed on the exhibition business information of the insurance businessman, and the statistical data is fed back to the insurance businessman, so that the insurance businessman can perform the adjustment of the exhibition business according to the fed-back information, and part of the digital functions can be opened to the insurance businessman of the trial-point institution, and the same digital functions are not opened to the insurance businessman of the non-trial-point institution.
Further, the digitally transforming the test point mechanism includes:
and displaying a preset digital service processing function to the pilot point mechanism.
Specifically, for a test point mechanism, a preset digital service processing function is displayed to the test point mechanism, that is, the preset digital service processing function is opened to the test point mechanism, wherein the preset digital service processing function is a way of digitally processing a preset service, for a non-test point mechanism, the preset digital service processing function is not opened to the non-test point mechanism, and the non-test point mechanism still processes the service in an original service processing way, so that service processing results corresponding to the test point mechanism and the non-test point mechanism are aligned, and the effect of digital transformation of the test point mechanism can be evaluated. The method comprises the steps of performing digital transformation on the test point mechanism, wherein the original offline processing of the test point mechanism, service tracking, service data statistics, service data display and the like can be transformed into online processing from the original offline processing, or the original online old processing mode is transformed into an online new processing mode, and a test point index of the test point mechanism is obtained based on the digital transformation of the test point mechanism, wherein the test point mechanism describes a mechanism for performing digital transformation as a test point, and the test point index describes an index for quantifying the service of the test point mechanism. For example, in the insurance field, for the exhibition of insurance business staffs of a trial insurance organization, statistics and acquisition of trial indexes such as business processing, business tracking and business data statistics of insurance businesses such as the activity of added staffs, the popularization rate of training in department class, the per-capita interview amount, the activity amount and the participation rate are carried out, and the trial indexes are displayed to the insurance staffs, so that the insurance staffs adjust the own exhibition process according to the trial indexes, namely the content of digital transformation of the trial insurance organization, particularly for activities such as training and meeting, the trial insurance organization is transferred from offline to online, and the activity efficiency, the activity effect and the statistical efficiency of the trial indexes of related activities can be improved.
S13, determining a non-test-point mechanism from other mechanisms except the test-point mechanism, and obtaining a non-test-point index of the non-test-point mechanism, wherein the non-test-point mechanism is a mechanism which is not subjected to digital transformation, the non-test-point index describes an index for quantifying the service of the non-test-point mechanism, and the non-test-point index and the test-point index are benchmarking indexes.
S14, comparing the test point mechanism with the non-test point mechanism according to the test point index and the non-test point index to obtain a benchmarking result.
Specifically, a non-pilot-test mechanism is determined from other mechanisms except the pilot-test mechanism, the non-pilot-test mechanism is a mechanism which does not perform digital transformation, and particularly when the non-pilot-test mechanism is determined from other mechanisms included in a mechanism group to which the pilot-test mechanism belongs, if the pilot-test mechanism and the non-pilot-test mechanism belong to the same level, the objectivity and comparability of the non-pilot-test mechanism can be further improved, the objectivity and accuracy of evaluating the digital transformation performed by the pilot-test mechanism can be further improved, and the accuracy of determining the transformation effect of the digital transformation can be further improved. Meanwhile, the same indexes among different mechanisms are comparable, so that the non-test point index and the test point index are the same index, namely the non-test point index and the test point index are benchmarking indexes, the non-test point index describes an index for quantifying the service of the non-test point mechanism, the test point mechanism and the non-test point mechanism can be compared, and the digital transformation effect of the test point mechanism is further judged according to the comparison result.
The method comprises the steps of obtaining a non-test point index of the non-test point mechanism, wherein the non-test point index can be data input by a person of the non-test point mechanism, and the non-test point index can also be an index generated based on a system function corresponding to digital transformation of the test point mechanism, for example, when the test point mechanism is transformed from an original on-line old processing mode to an on-line new processing mode, the non-test point index can be an index generated by the non-test point mechanism based on the original on-line old processing mode, and comparing the test point mechanism with the non-test point mechanism according to the test point index and the non-test point index, namely directly comparing the test point index with the non-test point index, or after calculating the test point index and the non-test point index, comparing a calculation result, namely indirectly comparing the test point index with the non-test point index, so as to obtain a benchmarking result between the test point mechanism and the non-test point mechanism. For example, in the insurance field, for the interview amount and the interview rate of an insurance salesman, the interview amount of a trial organization is compared with the interview amount of a non-trial organization, the interview rate of the trial organization is compared with the interview rate of the non-trial organization, and a user can know whether the interview amount and the interview rate of the trial organization are superior to the interview amount and the interview rate of the non-trial organization from the comparison result, if the interview amount and the interview rate of the trial organization are superior to the interview rate and the interview rate of the non-trial organization, it is indicated that the digital transformation of the trial organization promotes the exhibition of the insurance salesman, and if the interview amount and the interview rate of the trial organization are not superior to the interview rate and the interview rate of the non-trial organization, it is indicated that the digital transformation of the trial organization does not promote the better exhibition of the insurance salesman, the digital transformation of the trial organization may have problems, and further optimization of the digital transformation is required.
Further, the target result can be displayed in a preset graphic mode. For example, the bid-targeting result can be displayed in an intuitive and vivid display manner such as a diagram and a table according to different bid-targeting results, so that the efficiency and effect of displaying the bid-targeting result to the user can be improved.
According to the embodiment of the application, all the preset mechanisms are clustered in a clustering mode through quantizing the services of the preset mechanisms into preset quantization indexes, a mechanism group is obtained, the mechanism group is determined based on scientific statistics and analysis of the preset quantization indexes, the test point mechanisms are determined from the mechanism group, the test point mechanisms are digitally transformed, the test point indexes of the test point mechanisms are obtained based on the digital transformation of the test point mechanisms, the non-test point indexes of the non-test point mechanisms are obtained, the test point mechanisms are compared with the non-test point mechanisms according to the test point indexes, the objective decision results are obtained based on the analysis of the quantitative preset indexes, the test point mechanisms are not determined by data analysis personnel, the objective decision layer of the test point mechanisms and the non-test point mechanisms can be improved, the objective decision of the test point results obtained by the non-test point mechanisms can be easily made by the traditional method, and the objective decision layer of the traditional test point result can be avoided.
In an embodiment, please refer to fig. 2, fig. 2 is a first sub-flow diagram of a processing method for digital transformation of a mechanism according to an embodiment of the present application, and as shown in fig. 2, in this embodiment, the obtaining a test point index of the test point mechanism based on the digital transformation of the test point mechanism includes:
s21, determining an alignment time based on a preset time sequence method;
and S22, acquiring a test point index corresponding to the test point mechanism within the benchmarking time according to the benchmarking time.
Specifically, the Time series, in english Time series, is a method for arranging observed values of the test point indicators in a system into a numerical sequence according to a Time sequence (with the same Time interval), displaying a variation process of the test point indicators in a certain period, and searching and analyzing change characteristics, development trends and laws of objects from the variation process, wherein the Time series method includes the following steps: 1) Autoregression, in English, autoregression model, abbreviated as AR model; moving Average, english is Moving Average, and is called MA model for short; 3) Autoregressive Moving Average, in English, auto-Regression and Moving Average, abbreviated as ARMA model.
And determining a calibration time based on a preset time sequence method, and acquiring a test point index corresponding to the test point mechanism within the calibration time according to the calibration time. According to the preset time sequence method, the time sequence characteristics can be fully utilized to enable the obtained test point index to be more stable, and special influences of factors of aperiodic fluctuation on the test point index are avoided, such as requirements or guidance of national policy and regulations, special time factors of the industry, consumption trends of the industry such as festivals and holidays and the like, for example, in the insurance industry, 1 month door opening red is the main selling season of an insurance company within one year, in addition, 618, twenty-one, national celebration and other special dates also have special influences on the test point index, and the objectivity of the benchmarking result obtained by benchmarking the test point mechanism and the non-test point mechanism can be further improved subsequently, so that the accuracy of the benchmarking result is improved, and the benchmarking result can more accurately represent the digital transformation effect of the test point mechanism.
It should be noted that, based on a preset time sequence method, a benchmarking time is determined, and a test point index corresponding to the test point mechanism within the benchmarking time is obtained according to the benchmarking time, and since the non-test point index and the test point index are benchmarking indexes, in this embodiment, the non-test point index is an index of the non-test point mechanism within the benchmarking time, and the non-test point index and the test point index are made to have comparability, so that the benchmarking result can more accurately reflect the effect of digital transformation of the test point mechanism.
In an embodiment, please refer to fig. 3, fig. 3 is a second sub-flowchart of a processing method for digital transformation of an organization provided in the embodiment of the present application, as shown in fig. 3, in which the determining a trial organization from the organization group includes:
s31, acquiring a preset mechanism level identifier corresponding to a preset mechanism contained in the mechanism group, wherein the preset mechanism level identifier describes the level of the preset mechanism;
and S32, determining the current test point mechanism for digital transformation from the mechanism group according to the sequence of the preset mechanism level identifications from low to high.
Specifically, for the preset mechanism, a corresponding preset mechanism level identifier is preset, the preset mechanism level identifier is used for describing the level of the preset mechanism, the preset mechanism level identifier can be determined according to the size of an area range corresponding to the preset mechanism, the preset mechanism level identifier can also be determined according to the size of various indexes such as the scale and the output value corresponding to the preset mechanism, and the preset mechanism level identifier can be content description such as a numerical value or a letter which embodies the size, the height or the sequence. For example, the level of the insurance institution may be divided into an insurance business department, an insurance business area, an insurance business second-level organization, an insurance business first-level organization, and the like from small to large according to factors such as the scale of the insurance institution. Therefore, different preset mechanisms correspond to different preset mechanism level identifications, and the preset mechanism level identifications describe the level heights of the preset mechanisms.
And when the mechanism group determines the test point mechanism, acquiring a preset mechanism level identification corresponding to a preset mechanism contained in the mechanism group, and determining the current test point mechanism for digital transformation according to the sequence of the preset mechanism level identification from low to high, so that the test point mechanism is subjected to digital transformation step by step in the sequence of from low to high and from small to large, thereby realizing stable promotion of test point and improving the stability and safety of digital transformation.
Further, referring to fig. 4, fig. 4 is a schematic view of a third sub-flow of a processing method for mechanism digital transformation according to an embodiment of the present application, as shown in fig. 4, in this embodiment, the determining a current trial mechanism for digital transformation from the mechanism group includes:
s41, randomly extracting a plurality of initial test point mechanisms from the preset mechanisms at the same level according to the level identification of the preset mechanisms;
and S42, confirming the initial test point mechanism, and taking the confirmed initial test point mechanism as a test point mechanism.
Specifically, according to the preset mechanism level identifier, in the preset mechanism at the same level, a plurality of initial test point mechanisms are randomly extracted, and then the initial test point mechanisms are confirmed, so that the initial test point mechanisms can be confirmed by the relevant personnel of the initial test point mechanisms, that is, the relevant personnel of the initial test point mechanisms agree to serve as the test point mechanisms for digital transformation, and the confirmation of the test point mechanisms can also be carried out by the relevant personnel configured in advance, and the confirmed initial test point mechanisms serve as the test point mechanisms. For example, in the insurance industry, a plurality of insurance departments may be randomly selected as initial trial-run institutions from all the preset institutions belonging to the insurance departments.
Because the presetting mechanisms at the same level are more comparable, a plurality of initial trial point mechanisms are randomly extracted from the presetting mechanisms at the same level, so that the objectivity and the accuracy of the digital transformation of the presetting mechanisms can be improved, and the accuracy of the transformation effect of the digital transformation can be further improved.
In an embodiment, please refer to fig. 5, fig. 5 is a schematic view of a fourth sub-process of a processing method for mechanism digital transformation according to an embodiment of the present application, as shown in fig. 5, in this embodiment, the clustering all the preset mechanisms according to the preset quantization index to obtain a plurality of mechanism groups includes:
s51, acquiring the number of clusters input by a user;
s52, clustering all the preset mechanisms according to the preset quantization indexes and the clustering quantity to obtain mechanism groups with the number corresponding to the clustering quantity.
Specifically, a clustering parameter is set, the clustering parameter describes the number of groups for clustering all the preset mechanisms, and a user inputs the clustering number through the clustering parameter, so that the user can determine the number of groups for classifying all the preset mechanisms according to requirements. And after the user inputs the clustering quantity through an input device, acquiring the clustering quantity, and clustering all the preset mechanisms according to the preset quantization index and the clustering quantity to obtain mechanism groups with the number corresponding to the clustering quantity. By setting the clustering parameters, a user can input the clustering quantity through the clustering parameters, and then the clustering quantity for classifying all the preset mechanisms is determined, so that the flexibility and convenience for classifying all the preset mechanisms by the user according to actual requirements can be improved. Furthermore, the clustering number is not more than 10, the preset mechanisms are clustered according to the clustering number specified by the user, all the preset mechanisms are grouped into cluster categories of not more than 10, such as 3, 5, 8 and the like, the calculation amount of clustering performed by all the preset mechanisms is not too large, and the clustering efficiency of the preset mechanisms is not too slow.
It should be noted that, in the digital transformation processing method of the mechanism described in each of the above embodiments, the technical features included in different embodiments may be recombined as required to obtain a combined implementation, but all of them are within the protection scope of the present application.
Referring to fig. 6, fig. 6 is a schematic block diagram of a processing device for digital transformation of a mechanism according to an embodiment of the present application. Corresponding to the mechanism digital transformation processing method, the embodiment of the application also provides a mechanism digital transformation processing device. As shown in fig. 6, the processing apparatus for mechanical digital transformation includes a unit for executing the processing method for mechanical digital transformation, and the processing apparatus for mechanical digital transformation may be configured in a computer device. Specifically, referring to fig. 6, the processing device 60 for mechanism digital transformation includes a clustering unit 61, a first obtaining unit 62, a second obtaining unit 63, and a comparing unit 64.
The clustering unit 61 is configured to obtain preset quantization indexes of a plurality of preset mechanisms, and cluster all the preset mechanisms according to the preset quantization indexes to obtain a plurality of mechanism groups, where the preset quantization indexes describe indexes obtained by quantizing services of the preset mechanisms;
a first obtaining unit 62, configured to determine a test point mechanism from the mechanism group, perform digital transformation on the test point mechanism, and obtain a test point index of the test point mechanism based on the digital transformation of the test point mechanism, where the test point mechanism describes a mechanism that performs digital transformation as a test point, and the test point index describes an index that quantifies a service of the test point mechanism;
a second obtaining unit 63, configured to determine a non-test-point mechanism from other mechanisms outside the test-point mechanism, and obtain a non-test-point index of the non-test-point mechanism, where the non-test-point mechanism is a mechanism that is not subjected to digital transformation, the non-test-point index describes an index that quantifies a service of the non-test-point mechanism, and the non-test-point index and the test-point index are benchmarking indexes;
and the comparison unit 64 is configured to compare the test point mechanism with the non-test point mechanism according to the test point index and the non-test point index, so as to obtain a benchmarking result.
In one embodiment, the first obtaining unit 62 includes:
the first determining subunit is used for determining the time alignment time based on a preset time sequence method;
and the first acquisition subunit is used for acquiring the test point index corresponding to the test point mechanism within the benchmarking time according to the benchmarking time.
In one embodiment, the first obtaining unit 62 includes:
the second acquiring subunit is configured to acquire a preset mechanism level identifier corresponding to a preset mechanism included in the mechanism group, where the preset mechanism level identifier describes a level of the preset mechanism;
and the second determining subunit is used for determining the current test point mechanism for digital transformation from the mechanism group according to the sequence of the preset mechanism level identifications from low to high.
In an embodiment, the second determining subunit includes:
the extraction subunit is used for randomly extracting a plurality of initial trial-run mechanisms from the preset mechanisms at the same level according to the preset mechanism level identification;
and the confirmation subunit is used for confirming the initial trial mechanism and taking the confirmed initial trial mechanism as a trial mechanism.
In an embodiment, the clustering unit 61 includes:
a third obtaining subunit, configured to obtain the number of clusters input by the user;
and the clustering subunit is used for clustering all the preset mechanisms according to the preset quantization index and the clustering quantity to obtain mechanism groups with the number corresponding to the clustering quantity.
In an embodiment, the first obtaining unit 62 is configured to display a preset digital service processing function to the point of test mechanism.
In an embodiment, the preset quantitative index is a preset behavior index, where the preset behavior index describes an index corresponding to a behavior of the preset mechanism for processing the service.
It should be noted that, as can be clearly understood by those skilled in the art, the specific implementation process of the processing apparatus and each unit for digital transformation of the mechanism may refer to the corresponding description in the foregoing method embodiment, and for convenience and conciseness of description, no further description is provided herein.
Meanwhile, the division and connection manner of each unit in the processing device for mechanism digital transformation are only used for illustration, in other embodiments, the processing device for mechanism digital transformation may be divided into different units as required, or each unit in the processing device for mechanism digital transformation may adopt different connection order and manner to complete all or part of the functions of the processing device for mechanism digital transformation.
The processing means of the above described mechanism digital transformation may be implemented in the form of a computer program that can be run on a computer device as shown in fig. 7.
Referring to fig. 7, fig. 7 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device 500 may be a computer device such as a desktop computer or a server, or may be a component or part of another device.
Referring to fig. 7, the computer device 500 includes a processor 502, a memory, which may include a non-volatile storage medium 503 and an internal memory 504, which may also be a volatile storage medium, and a network interface 505 connected by a system bus 501.
The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032, when executed, causes the processor 502 to perform a method for digital transformation of a mechanism as described above.
The processor 502 is used to provide computing and control capabilities to support the operation of the overall computer device 500.
The internal memory 504 provides an environment for the computer program 5032 in the non-volatile storage medium 503 to run, and when the computer program 5032 is executed by the processor 502, the processor 502 can be enabled to perform a processing method of the above-mentioned mechanism digital transformation.
The network interface 505 is used for network communication with other devices. Those skilled in the art will appreciate that the configuration shown in fig. 7 is a block diagram of only a portion of the configuration associated with the present application and does not constitute a limitation of the computer device 500 to which the present application may be applied, and that a particular computer device 500 may include more or less components than those shown, or may combine certain components, or have 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 the embodiment shown in fig. 7, which are not described herein again.
Wherein the processor 502 is configured to run the computer program 5032 stored in the memory to perform the steps of: acquiring preset quantization indexes of a plurality of preset mechanisms, and clustering all the preset mechanisms according to the preset quantization indexes to obtain a plurality of mechanism groups, wherein the preset quantization indexes describe indexes obtained by quantizing services of the preset mechanisms; determining a test point mechanism from the mechanism group, performing digital transformation on the test point mechanism, and obtaining a test point index of the test point mechanism based on the digital transformation of the test point mechanism, wherein the test point mechanism describes a mechanism for performing digital transformation on a test point, and the test point index describes an index for quantifying the service of the test point mechanism; determining a non-test point mechanism from other mechanisms except the test point mechanism, and acquiring a non-test point index of the non-test point mechanism, wherein the non-test point mechanism is a mechanism which is not subjected to digital transformation, the non-test point index describes an index for quantifying the service of the non-test point mechanism, and the non-test point index and the test point index are benchmarking indexes; and comparing the test point mechanism with the non-test point mechanism according to the test point index and the non-test point index to obtain a benchmarking result.
In an embodiment, when the processor 502 implements the digital transformation based on the test point mechanism to obtain the test point index of the test point mechanism, the following steps are specifically implemented:
determining a time alignment time based on a preset time sequence method;
and acquiring a test point index corresponding to the test point mechanism within the benchmarking time according to the benchmarking time.
In an embodiment, when the processor 502 determines the trial-point institution from the institution group, the following steps are specifically implemented:
acquiring a preset mechanism level identifier corresponding to a preset mechanism contained in the mechanism group, wherein the preset mechanism level identifier describes the level of the preset mechanism;
and determining the current test point mechanism for digital transformation from the mechanism group according to the sequence of the preset mechanism level identifications from low to high.
In an embodiment, when the processor 502 implements the determining of the current trial organization for digital transformation from the organization group, the following steps are specifically implemented:
according to the preset mechanism level identification, randomly extracting a plurality of initial trial-run mechanisms in the preset mechanism at the same level;
and confirming the initial trial mechanism, and taking the confirmed initial trial mechanism as a trial mechanism.
In an embodiment, when the processor 502 implements the clustering of all the preset mechanisms according to the preset quantization index to obtain a plurality of mechanism groups, the following steps are specifically implemented:
acquiring the clustering number input by a user;
and clustering all the preset mechanisms according to the preset quantization indexes and the clustering quantity to obtain mechanism groups with the number corresponding to the clustering quantity.
In an embodiment, when the processor 502 implements the digital transformation of the test point mechanism, the following steps are specifically implemented:
and displaying a preset digital service processing function to the pilot mechanism.
In an embodiment, when the processor 502 obtains the preset quantitative indexes of the plurality of preset mechanisms, the preset quantitative indexes are preset behavior indexes, where the preset behavior indexes describe indexes corresponding to behaviors of the preset mechanisms for processing the service.
It should be understood that, in the embodiment of the present Application, 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, and the like. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
It will be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiments may be implemented by a computer program, and the computer program may be stored in a computer readable storage medium. The computer program is executed by at least one processor in the computer system to implement the flow steps of the embodiments of the method described above.
Accordingly, the present application also provides a computer-readable storage medium. The computer-readable storage medium may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, in which a computer program is stored, which, when executed by a processor, causes the processor to perform the steps of:
a computer program product which, when run on a computer, causes the computer to perform the steps of the processing method of digital transformation of said mechanism described in the embodiments above.
The computer readable storage medium may be an internal storage unit of the aforementioned device, such as a hard disk or a memory of the device. The computer readable storage medium may also be an external storage device of the apparatus, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. provided on the apparatus. Further, the computer readable storage medium may also include both an internal storage unit and an external storage device of the device.
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.
The storage medium is an entity and non-transitory storage medium, and may be various entity storage media capable of storing computer programs, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk.
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 application.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative. For example, the division of each unit is only one logic function division, and there may be another division manner in actual implementation. For example, various elements or components may be combined or may be integrated in another system or some features may be omitted, or not implemented.
The steps in the method of the embodiment of the application can be sequentially adjusted, combined and deleted according to actual needs. The units in the device of the embodiment of the application can be combined, divided and deleted according to actual needs. In addition, functional units in the embodiments of the present application 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, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in a storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing an electronic device (which may be a personal computer, a terminal, or a network device) to perform all or part of the steps of the method according to the embodiments of the present application.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, and these modifications or substitutions should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (10)

1. A mechanism digital transformation processing method is characterized by comprising the following steps:
acquiring preset quantization indexes of a plurality of preset mechanisms, and clustering all the preset mechanisms according to the preset quantization indexes to obtain a plurality of mechanism groups, wherein the preset quantization indexes describe indexes obtained by quantizing services of the preset mechanisms;
determining a test point mechanism from the mechanism group, performing digital transformation on the test point mechanism, and obtaining a test point index of the test point mechanism based on the digital transformation of the test point mechanism, wherein the test point mechanism describes a mechanism for performing digital transformation on the test point, and the test point index describes an index for quantifying the service of the test point mechanism;
determining a non-pilot mechanism from other mechanisms except the pilot mechanism, and acquiring a non-pilot index of the non-pilot mechanism, wherein the non-pilot mechanism is a mechanism which is not subjected to digital transformation, the non-pilot index describes an index for quantifying the service of the non-pilot mechanism, and the non-pilot index and the pilot index are benchmarking indexes;
and comparing the test point mechanism with the non-test point mechanism according to the test point index and the non-test point index to obtain a benchmarking result.
2. The method for processing digital transformation of mechanism according to claim 1, wherein obtaining the test point index of the test point mechanism based on the digital transformation of the test point mechanism comprises:
determining a time alignment time based on a preset time sequence method;
and acquiring a test point index corresponding to the test point mechanism within the benchmarking time according to the benchmarking time.
3. The method for processing digital transformation of organization according to claim 1, wherein said determining a trial organization from said organization group comprises:
acquiring a preset mechanism level identifier corresponding to a preset mechanism contained in the mechanism group, wherein the preset mechanism level identifier describes the level of the preset mechanism;
and determining the current test point mechanism for digital transformation from the mechanism group according to the sequence of the preset mechanism level identifications from low to high.
4. The method for processing digital transformation of organization according to claim 3, wherein said determining the current trial organization for digital transformation from said organization group comprises:
according to the preset mechanism level identification, randomly extracting a plurality of initial test point mechanisms from the preset mechanisms at the same level;
and confirming the initial trial mechanism, and taking the confirmed initial trial mechanism as a trial mechanism.
5. The method for processing mechanism digital transformation according to claim 1, wherein the clustering all the preset mechanisms according to the preset quantization index to obtain a plurality of mechanism groups comprises:
acquiring the clustering number input by a user;
and clustering all the preset mechanisms according to the preset quantization indexes and the clustering quantity to obtain mechanism groups with the number corresponding to the clustering quantity.
6. The method for processing digital transformation of mechanism according to claim 1, wherein said digitally transforming said trial mechanism comprises:
and displaying a preset digital service processing function to the pilot mechanism.
7. The method for processing digital transformation of an organization according to any one of claims 1-6, wherein the preset quantization index is a preset behavior index, wherein the preset behavior index describes an index corresponding to a behavior of the preset organization for processing the service.
8. A device for processing digital transformation of a mechanism, comprising:
the system comprises a clustering unit, a service processing unit and a service processing unit, wherein the clustering unit is used for acquiring preset quantization indexes of a plurality of preset mechanisms and clustering all the preset mechanisms according to the preset quantization indexes to obtain a plurality of mechanism groups, and the preset quantization indexes describe indexes obtained by quantizing the services of the preset mechanisms;
the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for determining a test point mechanism from the mechanism group, performing digital transformation on the test point mechanism and obtaining a test point index of the test point mechanism based on the digital transformation of the test point mechanism, the test point mechanism describes a mechanism used as a test point for performing digital transformation, and the test point index describes an index for quantifying the service of the test point mechanism;
a second obtaining unit, configured to determine a non-test-point mechanism from other mechanisms outside the test-point mechanism, and obtain a non-test-point index of the non-test-point mechanism, where the non-test-point mechanism is a mechanism that is not subjected to digital transformation, the non-test-point index describes an index that quantifies a service of the non-test-point mechanism, and the non-test-point index and the test-point index are benchmarking indexes;
and the comparison unit is used for comparing the test point mechanism with the non-test point mechanism according to the test point index and the non-test point index to obtain a benchmarking result.
9. A computer device, comprising a memory and a processor coupled to the memory; the memory is used for storing a computer program; the processor is adapted to run the computer program to perform the steps of the method according to any of claims 1-7.
10. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when being executed by a processor, realizes the steps of the method according to any one of claims 1 to 7.
CN202210959326.1A 2022-08-10 2022-08-10 Mechanism digital transformation processing method and device and related equipment Pending CN115204741A (en)

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