Index data processing method and device, equipment, storage medium and program product

Abstract

The present disclosure provides an indicator data processing method and apparatus, device, storage medium and program product, which can be applied to the fields of big data technology and financial technology. The indicator data processing method includes: using a data collection node, based on L predetermined indicators related to the promotion and use of the target technology, collecting N groups of historical indicator data corresponding to each predetermined indicator, and sending the historical indicator data to a first message middleware; using a data processing node, asynchronously obtaining historical indicator data from the first message middleware, processing the N groups of historical indicator data corresponding to each predetermined indicator, generating M groups of expected indicator data corresponding to each predetermined indicator, and sending the expected indicator data to a second message middleware; using a result analysis node, asynchronously obtaining historical indicator data from the first message middleware and obtaining expected indicator data from the second message middleware, and generating promotion and use analysis results for the target technology based on the N groups of historical indicator data and the M groups of expected indicator data corresponding to each predetermined indicator.

Description

Technical Field

[0001] The present disclosure relates to the fields of big data technology and financial technology, and specifically to an indicator data processing method, device, equipment, medium and program product. Background Art

[0002] With the outbreak of new technologies and digital development, new technologies and methods such as artificial intelligence, privacy computing, and VR are showing a state of continuous growth and are widely used in enterprises and institutions.

[0003] In the process of realizing the concept of the present disclosure, the inventors found that there are at least the following problems in the relevant technologies: in the process of introducing a new technology in the application system, it is necessary to consider the adaptability of the technology to the business, and not all technologies are suitable for the business system. Therefore, how to quantitatively consider the application value of new technologies in business systems is an urgent problem to be solved. Most of the existing methods are empirical methods. For example, most of them are simple assessments of overall scientific and technological capabilities or only one-time evaluations of the effectiveness of new technology applications or qualitative judgments. They are unable to accurately and objectively process the application adaptability of new technologies, and the existing methods need to obtain a large amount of system data according to human specifications for indiscriminate processing, and the processing efficiency is low. Summary of the Invention

[0004] In view of the above problems, the present disclosure provides an indicator data processing method, apparatus, device, medium and program product.

[0005] One aspect of the present disclosure provides an indicator data processing method, comprising:

[0006] Using a data collection node, based on L predetermined indicators related to the promotion and use of the target technology, N groups of historical indicator data corresponding to each predetermined indicator are collected, and the historical indicator data are sent to the first message middleware, wherein the N groups of historical indicator data correspond to N time windows within a predetermined historical time period, and the historical indicator data are used to represent the actual promotion and use effect of the target technology;

[0007] Using a data processing node, asynchronously obtain historical indicator data from the first message middleware, process N groups of historical indicator data corresponding to each predetermined indicator, generate M groups of expected indicator data corresponding to each predetermined indicator, and send the expected indicator data to the second message middleware, wherein the M groups of expected indicator data correspond to M target time windows among N time windows, M≤N, and the expected indicator data are used to represent the expected promotion and use effect of the target technology;

[0008] Using the result analysis node, historical indicator data is asynchronously obtained from the first message middleware and expected indicator data is obtained from the second message middleware, and based on N groups of historical indicator data and M groups of expected indicator data corresponding to each predetermined indicator, an analysis result for the promotion and use of the target technology is generated.

[0009] According to an embodiment of the present disclosure, wherein:

[0010] The L predetermined indicators include at least one required indicator and at least one optional indicator;

[0011] At least one necessary indicator includes at least: the resource consumption of the target technology;

[0012] At least one optional indicator includes at least: service coverage of the target technology, intellectual property coverage of the target technology, effect improvement coverage of the target technology and system error rate of the target technology.

[0013] According to an embodiment of the present disclosure, processing N groups of historical indicator data corresponding to each predetermined indicator to generate M groups of expected indicator data corresponding to each predetermined indicator includes:

[0014] generating a smoothing index corresponding to each predetermined indicator based on part or all of the N groups of first historical indicator data corresponding to each necessary indicator;

[0015] According to the smoothing index, N groups of historical indicator data corresponding to each predetermined indicator are smoothed to generate M groups of expected indicator data corresponding to each predetermined indicator.

[0016] According to an embodiment of the present disclosure, processing N groups of historical indicator data corresponding to each predetermined indicator to generate M groups of expected indicator data corresponding to each predetermined indicator includes:

[0017] generating an initial smoothing index corresponding to each predetermined indicator based on part or all of the N groups of first historical indicator data corresponding to each necessary indicator;

[0018] Adjusting the initial smoothing index based on part or all of the N groups of second historical indicator data corresponding to each optional indicator to generate an adjusted smoothing index corresponding to each optional indicator;

[0019] According to the initial smoothing index, N groups of first historical indicator data corresponding to each necessary indicator are smoothed, and according to the adjusted smoothing index, N groups of second historical indicator data corresponding to each optional indicator are smoothed to generate M groups of expected indicator data corresponding to each predetermined indicator.

[0020] According to an embodiment of the present disclosure, generating a promotion and utilization analysis result for a target technology based on N groups of historical indicator data and M groups of expected indicator data corresponding to each predetermined indicator includes:

[0021] From N sets of historical indicator data, read M sets of target historical indicator data corresponding to M target time windows;

[0022] Calculate the numerical deviations of M groups of target historical indicator data and M groups of expected indicator data corresponding to the same target time window, and obtain M groups of indicator deviation values ​​corresponding to each predetermined indicator, wherein the M groups of indicator deviation values ​​correspond to the M target time windows;

[0023] Based on the M groups of indicator deviation values ​​corresponding to each predetermined indicator, an analysis result of the promotion and use of the target technology is generated.

[0024] According to an embodiment of the present disclosure, generating a promotion and utilization analysis result for a target technology based on M groups of indicator deviation values ​​corresponding to each predetermined indicator includes:

[0025] The mean of the M groups of indicator deviation values ​​corresponding to each predetermined indicator is calculated to generate a first analysis result.

[0026] According to an embodiment of the present disclosure, generating a promotion and utilization analysis result for a target technology based on M groups of indicator deviation values ​​corresponding to each predetermined indicator includes:

[0027] Reading, from the M groups of indicator deviation values, multiple groups of reference indicator deviation values ​​corresponding to multiple reference time windows in the M target time windows;

[0028] A second analysis result is generated according to the numerical change trends of the multiple groups of reference indicator deviation values ​​corresponding to the respective predetermined indicators.

[0029] According to an embodiment of the present disclosure, the indicator deviation value is expressed as a positive deviation or a negative deviation;

[0030] Based on the M groups of indicator deviation values ​​corresponding to each predetermined indicator, the promotion and use analysis results for the target technology are generated, including:

[0031] Based on the number of multiple associated time windows in the M target time windows, a third analysis result is generated to characterize the correlation between the optional indicators and the necessary indicators, wherein, corresponding to the same associated time window, the indicator deviation values ​​corresponding to the optional indicators and the necessary indicators are simultaneously expressed as positive deviations.

[0032] According to an embodiment of the present disclosure, wherein:

[0033] The data collection node is used to execute L collection processes so as to collect historical indicator data corresponding to the L predetermined indicators in parallel in a one-to-one correspondence;

[0034] The data processing node is used to execute L processing processes so as to process historical indicator data corresponding to the L predetermined indicators in parallel in a one-to-one correspondence.

[0035] Another aspect of the present disclosure provides an indicator data processing device, comprising:

[0036] a data collection module configured to, using a data collection node, collect N sets of historical indicator data corresponding to L predetermined indicators related to the promotion and use of the target technology, and send the historical indicator data to the first message middleware, wherein the N sets of historical indicator data correspond to N time windows within a predetermined historical time period, and the historical indicator data are used to represent the actual promotion and use effect of the target technology;

[0037] a data processing module, configured to asynchronously obtain historical indicator data from the first message-based middleware using a data processing node, process N groups of historical indicator data corresponding to each predetermined indicator, generate M groups of expected indicator data corresponding to each predetermined indicator, and send the expected indicator data to the second message-based middleware, wherein the M groups of expected indicator data correspond to M target time windows among N time windows, M≤N, and the expected indicator data are used to characterize the expected promotion and use effect of the target technology;

[0038] The result analysis module is used to use the result analysis node to asynchronously obtain historical indicator data from the first message middleware and expected indicator data from the second message middleware, and generate promotion and use analysis results for the target technology based on N groups of historical indicator data and M groups of expected indicator data corresponding to each predetermined indicator.

[0039] According to an embodiment of the present disclosure, wherein:

[0040] The L predetermined indicators include at least one required indicator and at least one optional indicator;

[0041] At least one necessary indicator includes at least: the resource consumption of the target technology;

[0042] At least one optional indicator includes at least: service coverage of the target technology, intellectual property coverage of the target technology, effect improvement coverage of the target technology and system error rate of the target technology.

[0043] According to an embodiment of the present disclosure, the data processing module includes:

[0044] a first generating unit, configured to generate a smoothing index corresponding to each predetermined indicator based on part or all of the N groups of first historical indicator data corresponding to each necessary indicator;

[0045] The first smoothing processing unit is used to perform smoothing processing on N groups of historical indicator data corresponding to each predetermined indicator according to a smoothing index, and generate M groups of expected indicator data corresponding to each predetermined indicator.

[0046] According to an embodiment of the present disclosure, the data processing module includes:

[0047] a second generating unit, configured to generate an initial smoothing index corresponding to each predetermined indicator based on part or all of the N groups of first historical indicator data corresponding to each necessary indicator;

[0048] an adjusting unit, configured to adjust the initial smoothing index based on part or all of the N groups of second historical indicator data corresponding to each optional indicator, to generate an adjusted smoothing index corresponding to each optional indicator;

[0049] The second smoothing processing unit is used to smooth the N groups of first historical indicator data corresponding to each necessary indicator according to the initial smoothing index, and to smooth the N groups of second historical indicator data corresponding to each optional indicator according to the adjusted smoothing index, to generate M groups of expected indicator data corresponding to each predetermined indicator.

[0050] According to an embodiment of the present disclosure, the result analysis module includes:

[0051] A reading unit, configured to read M groups of target historical indicator data corresponding to M target time windows from N groups of historical indicator data;

[0052] a deviation calculation unit, configured to calculate numerical deviations between M groups of target historical indicator data and M groups of expected indicator data corresponding to the same target time window, and obtain M groups of indicator deviation values ​​corresponding to each predetermined indicator, wherein the M groups of indicator deviation values ​​correspond to the M target time windows;

[0053] The result generating unit is used to generate the promotion and utilization analysis result for the target technology according to the M groups of indicator deviation values ​​corresponding to each predetermined indicator.

[0054] According to an embodiment of the present disclosure, the result generating unit includes:

[0055] The first result generating subunit is used to calculate the mean of the M groups of indicator deviation values ​​corresponding to each predetermined indicator to generate a first analysis result.

[0056] According to an embodiment of the present disclosure, the result generating unit includes:

[0057] a reading subunit, configured to read, from the M groups of indicator deviation values, multiple groups of reference indicator deviation values ​​corresponding to multiple reference time windows in the M target time windows;

[0058] The second result generating subunit is used to generate a second analysis result according to the numerical variation trends of the plurality of groups of reference indicator deviation values ​​corresponding to the respective predetermined indicators.

[0059] According to an embodiment of the present disclosure, the indicator deviation value is expressed as a positive deviation or a negative deviation;

[0060] The result generation unit includes:

[0061] The third result generating sub-unit is used to generate a third analysis result for characterizing the correlation between the optional indicator and the necessary indicator based on the number of multiple associated time windows in the M target time windows, wherein, corresponding to the same associated time window, the indicator deviation values ​​corresponding to the optional indicator and the necessary indicator are simultaneously expressed as positive deviations.

[0062] According to an embodiment of the present disclosure, wherein:

[0063] The data collection node is used to execute L collection processes so as to collect historical indicator data corresponding to the L predetermined indicators in parallel in a one-to-one correspondence;

[0064] The data processing node is used to execute L processing processes so as to process historical indicator data corresponding to the L predetermined indicators in parallel in a one-to-one correspondence.

[0065] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0066] The fourth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the computer program or instructions are executed by a processor.

[0067] A fifth aspect of the present disclosure further provides a computer program product, comprising a computer program or instructions, which implement the steps of the above method when executed by a processor.

[0068] According to the embodiments of the present disclosure, since the data collection nodes have different processes for collecting indicators for each system, and the data processing nodes have different processes for processing data for each system, data collection and data processing cannot be performed synchronously. By setting up message middleware between the data collection nodes and the data processing nodes, and between the data processing nodes and the result analysis nodes, respectively, based on the middleware mechanism, asynchronous processing of data collection, data processing, and result analysis is achieved, and the decoupling of the upstream and downstream node processing processes is achieved, thus avoiding invalid data waiting for processing, and improving the overall operating efficiency of the system. Furthermore, on the other hand, the data processing nodes and the result analysis nodes read corresponding data from different message queues respectively. If different message middlewares are not distinguished, different types of data need to be marked and distinguished for processing, which increases the complexity of data access. Through this method, the data processing process is simplified, the efficiency of data access is improved, and data confusion calls between different processing services will not be caused, thereby improving the accuracy of data processing for different services.

[0069] According to the embodiments of the present disclosure, historical indicator data for multiple evaluation indicators over multiple time windows is collected, and expected indicator data is generated based on the historical indicator data. Furthermore, analysis results are generated based on the historical indicator data and the expected indicator data. The generation of the analysis results associates the expected effects of each indicator, taking into account the potential correlation between the user's expected effects and the actual effects of the technology application. Using the expected data as a reference standard, quantitative and objective analysis conclusions can be drawn, resulting in more accurate, objective, and referenceable analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0071] Figure 1 Schematically illustrates an application scenario diagram of the indicator data processing method, apparatus, device, medium, and program product according to an embodiment of the present disclosure;

[0072] Figure 2 The flowchart of the indicator data processing method according to the embodiment of the present disclosure is schematically shown;

[0073] Figure 3 A schematic diagram of a system principle of an indicator data processing method according to an embodiment of the present disclosure is shown;

[0074] Figure 4 Schematically shows a flow chart of a method for generating a promotion and use analysis result for a target technology according to an embodiment of the present disclosure;

[0075] Figure 5 A block diagram schematically illustrates a structure of an indicator data processing device according to an embodiment of the present disclosure; and

[0076] Figure 6 A block diagram of an electronic device suitable for implementing the indicator data processing method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0077] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0078] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0079] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0080] When expressions such as "at least one of A, B and C, etc." are used, they should generally be interpreted in accordance with the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0081] In the technical solution of the present invention, the user information involved (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0082] In scenarios where personal information is used for automated decision-making, the methods, devices, and systems provided by the embodiments of the present disclosure all provide users with corresponding operation portals for them to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision-making" here refers to the activity of automatically analyzing and evaluating an individual's behavioral habits, interests and hobbies, or economic, health, credit status, etc. through computer programs and making decisions. The expression "expert decision-making" here refers to the activity of making decisions by people who specialize in a certain field, have specialized experience, knowledge, and skills, and have reached a certain level of professionalism.

[0083] An embodiment of the present disclosure provides an indicator data processing method, including: utilizing a data collection node to collect N groups of historical indicator data corresponding to each predetermined indicator based on L predetermined indicators related to the promotion and use of the target technology, and sending the historical indicator data to a first message middleware; utilizing a data processing node to asynchronously obtain historical indicator data from the first message middleware, process the N groups of historical indicator data corresponding to each predetermined indicator, generate M groups of expected indicator data corresponding to each predetermined indicator, and send the expected indicator data to a second message middleware; utilizing a result analysis node to asynchronously obtain historical indicator data from the first message middleware and expected indicator data from the second message middleware, and generate a promotion and use analysis result for the target technology based on the N groups of historical indicator data and the M groups of expected indicator data corresponding to each predetermined indicator.

[0084] Figure 1 The application scenario diagram of the indicator data processing method, device, equipment, medium and program product according to the embodiments of the present disclosure is schematically shown.

[0085] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or optical fiber cables.

[0086] The user may use at least one of the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0087] The first terminal device 101 , the second terminal device 102 , and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0088] The server 105 may be a server that provides various services, such as a background management server (for example only) that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process received user requests and other data, and feed back processing results (e.g., web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0089] It should be noted that the indicator data processing method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the indicator data processing device provided in the embodiment of the present disclosure can generally be set in the server 105. The indicator data processing method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the indicator data processing device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0090] It should be noted that the indicator data processing method and apparatus, equipment, storage medium and program product of the embodiments of the present disclosure can be applied to the fields of big data technology and financial technology technology, and can also be used in any field other than the fields of big data technology and financial technology technology. The embodiments of the present disclosure do not limit the application fields of the above-mentioned indicator data processing method and apparatus, equipment, storage medium and program product.

[0091] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0092] The following will be based on Figure 1 The scene described by Figures 2 to 6 The indicator data processing method of the disclosed embodiment is described in detail.

[0093] Figure 2 The flowchart of the indicator data processing method according to the embodiment of the present disclosure is schematically shown; Figure 3The following schematically shows the system principle diagram of the indicator data processing method according to the embodiment of the present disclosure. Figure 2 、 Figure 3 This method will be described.

[0094] like Figure 3 As shown, the system to which the indicator data processing method of the embodiment of the present disclosure can be applied includes: a data acquisition node, a first message middleware, a data processing node, a second message middleware, and a result analysis node. Based on the system architecture, as shown in FIG. Figure 3 As shown, the method of this embodiment includes operations S201 to S203.

[0095] In operation S201, a data collection node is used to collect N sets of historical indicator data corresponding to L predetermined indicators related to the promotion and use of the target technology, and the historical indicator data is sent to the first message middleware. The N sets of historical indicator data correspond to N time windows within a predetermined historical time period, and the historical indicator data are used to represent the actual promotion and use effect of the target technology.

[0096] In operation S202, using a data processing node, historical indicator data is asynchronously obtained from the first message middleware, N groups of historical indicator data corresponding to each predetermined indicator are processed, M groups of expected indicator data corresponding to each predetermined indicator are generated, and the expected indicator data are sent to the second message middleware, where the M groups of expected indicator data correspond to M target time windows among N time windows, where M≤N, and the expected indicator data are used to represent the expected promotion and use effect of the target technology;

[0097] In operation S203, the result analysis node is used to asynchronously obtain historical indicator data from the first message middleware and expected indicator data from the second message middleware, and based on N groups of historical indicator data and M groups of expected indicator data corresponding to each predetermined indicator, an analysis result for the promotion and use of the target technology is generated.

[0098] According to an embodiment of the present disclosure, the data collection node, the data processing node, and the result analysis node may be a single service node or a service cluster including multiple nodes.

[0099] Since there are numerous business access channels and corresponding business processing systems in institutions, such as offline counters and online, in order to accurately collect various business quantitative indicators of new technologies in multiple business systems, distributed data collection nodes are deployed in each business system to collect indicator data from each business system.

[0100] The first and second message middleware can be various types of message middleware, such as various types of message queues. Message middleware is a supporting software system that provides synchronous or asynchronous, reliable message transmission for application systems in a network environment based on queue and message passing technologies.

[0101] According to embodiments of the present disclosure, when introducing a new technology into an application system, it is necessary to consider the compatibility of the technology with the business system. The target technology is the new technology to be analyzed. After it has been applied to multiple business systems for a period of time, the method of the present disclosure collects data on L (a positive integer) predetermined indicators related to the promotion and use of the target technology and processes the indicator data to generate the desired final result.

[0102] Among them, the predetermined indicators related to the promotion and use of the target technology can be indicators used to characterize multiple dimensions such as the breadth of use of the target technology, the effect of technological improvement, and resource consumption.

[0103] Furthermore, the collected indicator data can be N groups of historical indicator data corresponding to N time windows within a predetermined historical time period, for example, 30 groups of data corresponding to a time window of 1 day in the past month are collected, and each group corresponds to one day's data.

[0104] Furthermore, indicator data related to multiple predetermined indicators can be collected. Therefore, each predetermined indicator includes N sets of data corresponding to N time windows. For example, the data for indicator 1 includes 30 sets of data corresponding to the past 30 days, the data for indicator 2 also includes 30 sets of data corresponding to the past 30 days, and the data for indicator 3 also includes 30 sets of data corresponding to the past 30 days, and so on.

[0105] Because historical indicator data is a true reflection of the actual application of the target technology in multiple business systems, historical indicator data can be used to characterize the actual promotion and use effect of the target technology within a predetermined historical time period.

[0106] In operation S202, the data processing nodes may be used to process N sets of historical indicator data corresponding to each predetermined indicator to generate M sets of expected indicator data corresponding to each predetermined indicator. The M sets of expected indicator data may correspond to M target time windows among N time windows, where M≤N, i.e., they may correspond to all or part of the N time windows within the predetermined historical time period.

[0107] For example, by processing 30 groups of historical indicator data in the past 30 days, 30 groups of expected indicator data are generated one by one. Alternatively, 20 groups of expected indicator data, 15 groups of expected indicator data, and so on are generated.

[0108] Expected indicator data is used to characterize the expected promotional impact of the target technology. This can be achieved by processing historical indicator data using a predetermined numerical processing algorithm to ensure that the distribution of the expected indicator data matches the user's desired promotional impact. For example, if a user expects the ideal effect of a new technology to be an upward trend in each indicator A and a downward trend in each indicator B, the historical indicator data can be processed using a related linear interpolation method to generate multiple sets of expected indicator data that conform to the expected numerical trends.

[0109] Further, in operation S203, based on N groups of historical indicator data and M groups of expected indicator data corresponding to each predetermined indicator, an analysis result for the promotion and use of the target technology is generated. For example, it can be: reading M groups of target historical indicator data corresponding to M target time windows from N groups of historical indicator data; by comparing and analyzing the M groups of target historical indicator data and the M groups of expected indicator data, such as deviation analysis, numerical distribution law comparison analysis, relative ratio analysis, etc., an analysis result for the target technology is generated.

[0110] According to an embodiment of the present disclosure, a first message-based middleware is provided between a data collection node and a data processing node. The data collection node, acting as a message provider, sends collected indicator data to the first message-based middleware. The data processing node, acting as a message consumer, asynchronously reads historical indicator data from the first message-based middleware for processing at appropriate times.

[0111] According to an embodiment of the present disclosure, a second message-based middleware is provided between the data processing node and the result analysis node. The data processing node, acting as a message provider, sends the processed expected indicator data to the second message-based middleware. The result analysis node, acting as a message consumer, asynchronously retrieves historical indicator data from the first message-based middleware and the expected indicator data from the second message-based middleware at appropriate times for processing.

[0112] According to the embodiments of the present disclosure, since the data collection nodes have different processes for collecting indicators for each system, and the data processing nodes have different processes for processing data for each system, data collection and data processing cannot be performed synchronously. By setting up message middleware between the data collection nodes and the data processing nodes, and between the data processing nodes and the result analysis nodes, respectively, based on the middleware mechanism, asynchronous processing of data collection, data processing, and result analysis is achieved, and the decoupling of the upstream and downstream node processing processes is achieved, thus avoiding invalid data waiting for processing, and improving the overall operating efficiency of the system. Furthermore, on the other hand, the data processing nodes and the result analysis nodes read corresponding data from different message queues respectively. If different message middlewares are not distinguished, different types of data need to be marked and distinguished for processing, which increases the complexity of data access. Through this method, the data processing process is simplified, the efficiency of data access is improved, and data confusion calls between different processing services will not be caused, thereby improving the accuracy of data processing for different services.

[0113] According to the embodiments of the present disclosure, historical indicator data for multiple evaluation indicators over multiple time windows is collected, and expected indicator data is generated based on the historical indicator data. Furthermore, analysis results are generated based on the historical indicator data and the expected indicator data. The generation of the analysis results associates the expected effects of each indicator, taking into account the potential correlation between the user's expected effects and the actual effects of the technology application. Using the expected data as a reference standard, quantitative and objective analysis conclusions can be drawn, resulting in more accurate, objective, and referenceable analysis results.

[0114] According to an embodiment of the present disclosure, further, the data collection node is used to execute L collection processes so that historical indicator data corresponding to L predetermined indicators are collected in parallel on a one-to-one basis; the data processing node is used to execute L processing processes so that historical indicator data corresponding to L predetermined indicators are processed in parallel on a one-to-one basis.

[0115] Based on the differences between indicators, the data collection processes of data collection nodes for different indicators are not unified, and the data processing processes of data processing nodes for different indicators are not unified. Therefore, by setting L collection processes, historical indicator data corresponding to the L predetermined indicators are collected in parallel one by one, and L processing processes are set to process the historical indicator data corresponding to the L predetermined indicators in parallel one by one, the overall data collection and data processing efficiency can be accelerated.

[0116] Furthermore, L message queues can be set up in the first message middleware, corresponding one-to-one to L predetermined indicators. Each historical indicator data is marked with an indicator identifier. After collecting the historical indicator data of the corresponding indicator, it is placed in the message queue corresponding to the indicator according to the indicator identifier to achieve differentiated storage of different indicator data.

[0117] L message queues can also be set up in the second message middleware, corresponding one-to-one to the L predetermined indicators. Each expected indicator data is marked with an indicator identifier. After processing the expected indicator data of the corresponding indicator, it is placed in the message queue corresponding to the indicator according to the indicator identifier to achieve differentiated storage of different indicator data.

[0118] According to an embodiment of the present disclosure, the L predetermined indicators include at least one required indicator and at least one optional indicator. The required and optional indicators are determined based on user business needs. Indicators that users pay more attention to are designated as required indicators, while indicators that users pay less attention to are designated as optional indicators.

[0119] Specifically, for example, the at least one necessary indicator includes, but is not limited to, the resource consumption of the target technology, such as the software and hardware resources and technical capital investment consumed by the application of the target technology. The resource consumption of the target technology can include the direct and indirect resource investment of the target technology in various service channels.

[0120] For example, at least one optional indicator includes but is not limited to: service coverage of the target technology, intellectual property coverage of the target technology, effect improvement coverage of the target technology, and system error rate of the target technology.

[0121] Among them, the service coverage of the target technology is: the proportion of target services that apply the target technology in the total number of services.

[0122] The intellectual property coverage of the target technology is: the proportion of target intellectual property rights involving the target technology in the total number of intellectual property rights.

[0123] The coverage of the target technology's performance improvement is: the proportion of the first business systems whose performance has been improved due to the application of the target technology in the total number of business systems.

[0124] The system error rate of the target technology is: the proportion of processing errors of the second business system that applies the target technology to the total number of processing errors of all business systems.

[0125] For example, each data collection node continuously collects various indicator data of the new technology to be tracked in each business system at a fixed time interval δ (time window length), summarizes the data of each predetermined indicator in N time windows, and stores it in a time series data table, as shown in Table 1 below.

[0126] Table 1

[0127]

[0128] Among them, corresponding to the nth time window, t n =t1+nδ.

[0129] Figure 4 A flowchart of a method for generating promotion and use analysis results for a target technology according to an embodiment of the present disclosure is schematically shown.

[0130] Figure 4 As shown, the method of this embodiment includes operations S401 to S403.

[0131] In operation S401, M groups of target historical indicator data corresponding to M target time windows are read from N groups of historical indicator data. Since the subsequent calculation of indicator deviations needs to calculate the deviations corresponding to the same time window respectively, this operation is used to align the data so that the historical indicator data and the expected indicator data contain the same number of time windows. For example: N groups of historical indicator data include 30 groups of historical indicator data corresponding to the past 30 days, and the M target time windows are set to 28 time windows from the 3rd day to the 30th day. Then the M groups of target historical indicator data corresponding to the M target time windows are the 28 groups of historical indicator data corresponding to the 30th day. The M groups of expected indicator data corresponding to the M target time windows are also the 28 groups of expected indicator data corresponding to the 30th day.

[0132] In operation S402, the numerical deviations of M groups of target historical indicator data and M groups of expected indicator data corresponding to the same target time window are calculated to obtain M groups of indicator deviation values ​​corresponding to each predetermined indicator, wherein the M groups of indicator deviation values ​​correspond to M target time windows.

[0133] For example, based on 28 groups of historical indicator data corresponding to the 3rd to the 30th day, and 28 groups of expected indicator data corresponding to the 3rd to the 30th day, the numerical deviation corresponding to each time window is calculated respectively, such as the numerical deviation corresponding to the 3rd day, the numerical deviation corresponding to the 4th day, the numerical deviation corresponding to the 5th day... the numerical deviation corresponding to the 30th day, and 28 groups of numerical deviations corresponding to the 30th day are obtained.

[0134] In operation S403 , a promotion and utilization analysis result for the target technology is generated based on the M groups of indicator deviation values ​​corresponding to the predetermined indicators.

[0135] According to an embodiment of the present disclosure, the above method collects historical indicator data for multiple evaluation indicators in multiple time windows, generates expected indicator data based on the historical indicator data, further calculates the deviation between the historical indicator data and the expected indicator data, and generates analysis results based on the deviation. This method can continuously and dynamically monitor data during the application of new technologies, analyze and process the adaptation of technologies at different time stages based on indicator data from specific historical time periods, and continuously and dynamically evaluate changes in adaptability, providing effective and quantifiable technical support for overall decision-making and judgment on the effectiveness of the new technology in business systems.

[0136] According to an embodiment of the present disclosure, calculating the numerical deviations of M groups of target historical indicator data and M groups of expected indicator data corresponding to the same target time window includes:

[0137] First, based on the expected change trend of the indicator set for the predetermined indicator, the deviation correction coefficient corresponding to each predetermined indicator is determined;

[0138] Secondly, according to the deviation correction coefficient, the numerical deviations of M groups of target historical indicator data and M groups of expected indicator data corresponding to the same target time window are calculated.

[0139] According to the embodiments of the present disclosure, based on the same expected effect, different indicators show different numerical change trends, some indicators show a numerical growth trend, and some indicators show a numerical decline trend. For example, under the premise of expecting to achieve a relatively good promotion effect, the indicator values ​​of the target technology's resource consumption and system error rate should show a downward trend over time; conversely, the indicator values ​​of the target technology's service coverage and intellectual property coverage should show an upward trend over time. In order to eliminate this difference, the deviations of all indicators are calculated based on the same data benchmark to avoid large calculation errors, and the deviation correction coefficient is used to correct the deviation.

[0140] The numerical deviation between the historical indicator data and the expected indicator data corresponding to the nth target time window: D = (-1) k (a n -a ′ n ). Where k is the deviation correction coefficient. a n is the historical indicator data corresponding to the nth target time window, a ′ n is the expected indicator data corresponding to the nth target time window.

[0141] The method for determining k is: when the expected change trend of the indicator set for the predetermined indicator is an increase in value, k=0; conversely, when the expected change trend of the indicator set for the predetermined indicator is a decrease in value, k=1.

[0142] Table 2 below shows an example of a deviation record table D for a certain indicator a. a .

[0143] Table 2

[0144]

[0145] The numerical deviations in the table are calculated starting from the third time window.

[0146] According to an embodiment of the present disclosure, based on the M groups of indicator deviation values ​​corresponding to each predetermined indicator, an analysis result for the promotion and use of the target technology is generated, which may include analysis of various situations, for example, overall deviation analysis, local deviation analysis, correlation deviation analysis, etc. for each indicator.

[0147] Specifically: The methods for conducting overall deviation analysis on each indicator include:

[0148] The mean of the M groups of indicator deviation values ​​corresponding to each predetermined indicator is calculated to generate a first analysis result, wherein the first analysis result is used to characterize the overall impact of the promotion and use of the target technology on each predetermined indicator.

[0149] For example, calculate the mean of all deviations of the deviation values ​​of the M groups of indicators. If the result is greater than 0, the output result is: the overall expected growth of the indicator is higher than expected; if the result is less than or equal to 0, the output result is: the overall expected growth of the indicator is lower than expected.

[0150] According to an embodiment of the present disclosure, a method for performing local deviation analysis on each indicator includes:

[0151] First, multiple groups of reference indicator deviation values ​​corresponding to multiple reference time windows in the M target time windows are read from the M groups of indicator deviation values.

[0152] Because not all periodic indicators are of recent interest to users, multiple reference time windows can be pre-configured to represent any desired time windows, such as the most recent time windows of interest. Through flexible configuration, data corresponding to unimportant time windows can be filtered.

[0153] Then, a second analysis result is generated according to the numerical variation trend of the multiple groups of reference indicator deviation values ​​corresponding to each predetermined indicator, wherein the second analysis result is used to characterize the local impact of the promotion and use of the target technology on each predetermined indicator.

[0154] For example, if the deviations of multiple reference time windows are calculated to be greater than 0 over time, the output result is: the indicator has high expected growth in the local period; conversely, if the result is less than or equal to 0, the output result is: the indicator has low expected growth in the local period.

[0155] According to an embodiment of the present disclosure, further, the indicator deviation value is expressed as a positive deviation (the deviation value is greater than or equal to 0) or a negative deviation (the deviation value is less than 0).

[0156] Specifically: The method of performing correlation deviation analysis on each indicator includes:

[0157] Based on the number of multiple associated time windows in the M target time windows, a third analysis result is generated to characterize the correlation between the optional indicators and the necessary indicators, wherein, corresponding to the same associated time window, the indicator deviation values ​​corresponding to the optional indicators and the necessary indicators are simultaneously expressed as positive deviations.

[0158] Specifically, each optional indicator is traversed, and the correlation between each optional indicator and the necessary indicator is calculated respectively.

[0159] For example, the calculation method of the correlation degree SUPPORT(invt,b) between the optional indicator b and the required indicator invt is as follows: Formula (1).

[0160]

[0161] Among them, N(invt deviation value is greater than 0 ∪b deviation value is greater than 0) represents the number of associated time windows in which the deviation values ​​of the optional indicator b and the necessary indicator invt are both greater than 0 in the M target time windows.

[0162] The calculation results of multiple optional indicators can be sorted, and the top indicators are selected to output the third analysis result: these three indicators have a strong correlation with the optional indicators.

[0163] According to an embodiment of the present disclosure, a method for processing N groups of historical indicator data corresponding to each predetermined indicator to generate M groups of expected indicator data corresponding to each predetermined indicator may include using various time series prediction algorithms to predict the corresponding values ​​of each indicator in the next indicator collection window.

[0164] The time series prediction algorithms that can be used may include a variety of optional time series prediction algorithms, such as exponential smoothing method, autoregressive algorithm, moving average model algorithm, autoregressive sliding average model algorithm, vector autoregressive algorithm, etc.

[0165] Furthermore, the exponential smoothing method can be used to process N groups of historical indicator data to generate M groups of expected indicator data corresponding to each predetermined indicator. Specifically, it includes:

[0166] First, based on part or all of the N groups of first historical indicator data corresponding to the necessary indicators, a smoothing index corresponding to each predetermined indicator is generated;

[0167] Afterwards, the N groups of historical indicator data corresponding to each predetermined indicator are smoothed according to the smoothing index to generate M groups of expected indicator data corresponding to each predetermined indicator.

[0168] According to an embodiment of the present disclosure, a smoothing index corresponding to each predetermined indicator is generated based on processing the historical indicator data of the necessary indicator. Each indicator corresponds to its own dedicated smoothing index. The dedicated smoothing indexes corresponding to different indicators can be the same or different.

[0169] When the exclusive smoothing indexes corresponding to different indicators are the same, for any indicator a, its exclusive smoothing index α a It takes a value between 0 and 1. It can be to process part or all of the N sets of first historical indicator data corresponding to the necessary indicator invt. For example, the three sets of historical indicator data invt1, invt2, and invt3 corresponding to the first three time windows are taken to calculate the exclusive smoothing index α. a The specific method can be the algorithm of the following formula (2):

[0170]

[0171] According to an embodiment of the present disclosure, M groups of expected indicator data correspond one-to-one to M target time windows.

[0172] Smoothing the N sets of historical indicator data corresponding to each predetermined indicator generates M sets of expected indicator data corresponding to each predetermined indicator, including generating expected indicator data corresponding to each target time window. The M sets of expected indicator data may correspond to the M target time windows among the N time windows, or may correspond to all or part of the N time windows within the predetermined historical time period.

[0173] For any n-th target time window, generating the n-th set of expected indicator data corresponding to the n-th target time window includes:

[0174] Based on the n-1 groups of historical indicator data corresponding to the first n-1 target time windows, the nth group of expected indicator data corresponding to the nth target time window is calculated.

[0175] For any indicator a, at t n The expected value of the time window a ′ n Refer to the following formula (3):

[0176]

[0177] Using the above algorithm, some window data may be missing. For example, data for the first two periods are missing (the smoothing index is calculated starting from the third period). Therefore, the M sets of expected indicator data may correspond to M of the N time windows (N-2, counting starting from the third period). Alternatively, the data for the first two periods can be padded (for example, with historical measured values), and the resulting M sets of expected indicator data may correspond to all of the N time windows.

[0178] After completing the data, examples of predicted values ​​calculated according to the above-mentioned prediction value calculation method are shown in Table 3 below.

[0179] Table 3

[0180] Indicator a\time <![CDATA[t1]]> <![CDATA[t2]]> <![CDATA[t3]]> … Expected value of indicator a <![CDATA[a1]]> <![CDATA[a2]]> <![CDATA[a′3=α a a2+a a (1-a a )a1]]> …

[0181] According to the embodiments of the present disclosure, considering that various indicators of new technologies fluctuate due to various factors (such as seasonality, cyclicality, and other technical factors), and that the data distribution of each indicator across multiple time windows is potentially correlated, rather than isolated data, the processing logic of the exponential smoothing method takes into account the potential correlation of multi-period indicator data, more accurately reflecting the true objective distribution of each indicator and minimizing the influence of random factors.

[0182] Furthermore, the generation of the smoothing index is based on the processing of the data of the necessary indicators. In this way, the data distribution pattern of the generated period indicator data is similar to the data distribution pattern of the necessary indicators, which is in line with the real needs of the actual business and user expectations, and realizes the use of the most appropriate numerical processing method to make the indicator data distribution pattern meet user expectations and meet actual business needs.

[0183] According to the embodiments of the present disclosure, with reference to the aforementioned embodiments, each indicator corresponds to its own dedicated smoothing index. The dedicated smoothing indexes corresponding to different indicators can be the same or different.

[0184] When the dedicated smoothing exponents corresponding to different indicators are different, processing N groups of historical indicator data corresponding to each predetermined indicator to generate M groups of expected indicator data corresponding to each predetermined indicator includes:

[0185] First, based on part or all of the N sets of first historical indicator data corresponding to each necessary indicator, an initial smoothing index corresponding to each predetermined indicator is generated; the specific method thereof can be referred to the algorithm of formula (2) in the aforementioned embodiment, and will not be described in detail here.

[0186] Subsequently, the initial smoothed index is adjusted based on some or all of the N sets of second historical indicator data corresponding to each selectable indicator, generating an adjusted smoothed index corresponding to each selectable indicator. Here, the smoothed index is adjusted using the actual historical data of each selectable indicator, making the smoothed index more objective and reasonable by combining factors such as the necessary indicators and the changing trends of the indicator data itself. Specific methods for adjusting the smoothed index include, for example, using various trial and error evaluation methods.

[0187] Then, based on the initial smoothing index, the N sets of first historical indicator data corresponding to each required indicator are smoothed, and based on the adjusted smoothing index, the N sets of second historical indicator data corresponding to each optional indicator are smoothed to generate M sets of expected indicator data corresponding to each predetermined indicator. The specific method can be found in the algorithm of formula (3) in the previous embodiment. This will not be repeated here.

[0188] Based on the above-mentioned index data processing method, the present disclosure also provides an index data processing device. Figure 5 The device is described in detail.

[0189] Figure 5 The structural block diagram of the indicator data processing device according to an embodiment of the present disclosure is schematically shown.

[0190] like Figure 5 As shown, the indicator data processing device 500 of this embodiment includes a data acquisition module 501 , a data processing module 502 , and a result analysis module 503 .

[0191] The data collection module 501 is configured to use a data collection node to collect N sets of historical indicator data corresponding to L predetermined indicators related to the promotion and use of the target technology, and send the historical indicator data to the first message middleware, wherein the N sets of historical indicator data correspond to N time windows within a predetermined historical time period, and the historical indicator data are used to represent the actual promotion and use effect of the target technology;

[0192] A data processing module 502 is configured to asynchronously obtain historical indicator data from the first message-based middleware using a data processing node, process N groups of historical indicator data corresponding to each predetermined indicator, generate M groups of expected indicator data corresponding to each predetermined indicator, and send the expected indicator data to the second message-based middleware, wherein the M groups of expected indicator data correspond to M target time windows among N time windows, where M≤N, and the expected indicator data is used to represent the expected promotion and use effect of the target technology;

[0193] The result analysis module 503 is used to use the result analysis node to asynchronously obtain historical indicator data from the first message middleware and expected indicator data from the second message middleware, and generate promotion and use analysis results for the target technology based on N groups of historical indicator data and M groups of expected indicator data corresponding to each predetermined indicator.

[0194] According to an embodiment of the present disclosure, wherein: the L predetermined indicators include at least one necessary indicator and at least one optional indicator;

[0195] At least one necessary indicator includes at least: the resource consumption of the target technology;

[0196] At least one optional indicator includes at least: service coverage of the target technology, intellectual property coverage of the target technology, effect improvement coverage of the target technology and system error rate of the target technology.

[0197] According to an embodiment of the present disclosure, the data processing module 502 includes a first generating unit and a first smoothing processing unit.

[0198] The first generating unit is used to generate a smoothing index corresponding to each predetermined indicator based on part or all of the N groups of first historical indicator data corresponding to each necessary indicator; the first smoothing processing unit is used to smooth the N groups of historical indicator data corresponding to each predetermined indicator according to the smoothing index to generate M groups of expected indicator data corresponding to each predetermined indicator.

[0199] According to an embodiment of the present disclosure, the data processing module 502 includes a second generating unit, an adjusting unit, and a second smoothing processing unit.

[0200] The second generating unit is used to generate an initial smoothing index corresponding to each predetermined indicator based on part or all of the N groups of first historical indicator data corresponding to each necessary indicator; the adjusting unit is used to adjust the initial smoothing index based on part or all of the N groups of second historical indicator data corresponding to each optional indicator, and generate an adjusted smoothing index corresponding to each optional indicator; the second smoothing processing unit is used to smooth the N groups of first historical indicator data corresponding to each necessary indicator according to the initial smoothing index, and to smooth the N groups of second historical indicator data corresponding to each optional indicator according to the adjusted smoothing index, and generate M groups of expected indicator data corresponding to each predetermined indicator.

[0201] According to an embodiment of the present disclosure, the result analysis module 503 includes a reading unit, a deviation calculation unit, and a result generation unit.

[0202] A reading unit is used to read M groups of target historical indicator data corresponding to M target time windows from N groups of historical indicator data; a deviation calculation unit is used to calculate the numerical deviations of the M groups of target historical indicator data and the M groups of expected indicator data corresponding to the same target time window, and obtain M groups of indicator deviation values ​​corresponding to each predetermined indicator, wherein the M groups of indicator deviation values ​​correspond to the M target time windows; a result generation unit is used to generate an analysis result for the promotion and use of the target technology based on the M groups of indicator deviation values ​​corresponding to each predetermined indicator.

[0203] According to an embodiment of the present disclosure, the result generating unit includes a first result generating subunit, which is used to calculate the mean of M groups of indicator deviation values ​​corresponding to each predetermined indicator to generate a first analysis result.

[0204] According to an embodiment of the present disclosure, the result generating unit includes a reading subunit and a second result generating subunit.

[0205] The reading subunit is used to read multiple groups of reference indicator deviation values ​​corresponding to multiple reference time windows in the M target time windows from the M groups of indicator deviation values; the second result generating subunit is used to generate a second analysis result based on the numerical change trend of the multiple groups of reference indicator deviation values ​​corresponding to each predetermined indicator.

[0206] According to an embodiment of the present disclosure, the indicator deviation value is expressed as a positive deviation or a negative deviation;

[0207] The result generation unit includes a third result generation sub-unit, which is used to generate a third analysis result for characterizing the correlation between the optional indicator and the necessary indicator based on the number of multiple associated time windows in the M target time windows, wherein, corresponding to the same associated time window, the indicator deviation values ​​corresponding to the optional indicator and the necessary indicator are simultaneously expressed as positive deviations.

[0208] According to an embodiment of the present disclosure, the data collection node is used to execute L collection processes so that historical indicator data corresponding to L predetermined indicators are collected in parallel on a one-to-one basis; the data processing node is used to execute L processing processes so that historical indicator data corresponding to L predetermined indicators are processed in parallel on a one-to-one basis.

[0209] According to an embodiment of the present disclosure, any multiple modules among the data acquisition module 501, the data processing module 502, and the result analysis module 503 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the data acquisition module 501, the data processing module 502, and the result analysis module 503 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable method of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in an appropriate combination of any of them. Alternatively, at least one of the data acquisition module 501, the data processing module 502, and the result analysis module 503 can be at least partially implemented as a computer program module, which can perform the corresponding function when the computer program module is executed.

[0210] Figure 6 A block diagram of an electronic device suitable for implementing the indicator data processing method according to an embodiment of the present disclosure is schematically shown.

[0211] like Figure 6 As shown, the electronic device 600 according to an embodiment of the present disclosure includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 into a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include an onboard memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for executing different actions of the method flow according to an embodiment of the present disclosure.

[0212] Various programs and data required for the operation of the electronic device 600 are stored in the RAM 603. The processor 601, ROM 602, and RAM 603 are connected to each other via a bus 604. The processor 601 executes the various operations of the method flow according to the embodiment of the present disclosure by executing the programs in the ROM 602 and / or RAM 603. It should be noted that the programs may also be stored in one or more memories other than the ROM 602 and RAM 603. The processor 601 may also execute the various operations of the method flow according to the embodiment of the present disclosure by executing the programs stored in the one or more memories.

[0213] According to an embodiment of the present disclosure, electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to bus 604. Electronic device 600 may further include one or more of the following components connected to I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and speakers; a storage section 608 including a hard disk; and a communication section 609 including a network interface card such as a LAN card or a modem. Communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. Removable media 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed in drive 610 as needed, so that computer programs read from the removable media can be installed into storage section 608 as needed.

[0214] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when executed, implements the method according to the embodiments of the present disclosure.

[0215] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, such as but not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 602 and / or RAM 603 described above and / or one or more memories other than ROM 602 and RAM 603.

[0216] The embodiments of the present disclosure also include a computer program product, which includes a computer program containing program code for executing the method shown in the flowchart. When the computer program product is executed in a computer system, the program code is used to enable the computer system to implement the indicator data processing method provided by the embodiments of the present disclosure.

[0217] The computer program executes the above functions defined in the system / device of the embodiment of the present disclosure when the processor 601 executes the computer program. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0218] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 609, and / or installed from a removable medium 611. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0219] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 609, and / or installed from a removable medium 611. When the computer program is executed by the processor 601, the above-described functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the systems, devices, means, modules, units, etc. described above can be implemented by computer program modules.

[0220] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect via the Internet).

[0221] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0222] Those skilled in the art will appreciate that the features described in the various embodiments and / or claims of this disclosure may be combined and / or coupled in various ways, even if such combinations and / or couplings are not explicitly described in this disclosure. In particular, the features described in the various embodiments and / or claims of this disclosure may be combined and / or coupled in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or couplings are intended to fall within the scope of this disclosure.

[0223] The embodiments of the present disclosure are described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be used in combination to advantage. The scope of the present disclosure is defined by the appended claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for processing index data, characterized in that: The method comprises: Using a data collection node, based on L predetermined indicators related to the promotion and use of the target technology, N groups of historical indicator data corresponding to each of the predetermined indicators are collected, and the historical indicator data are sent to a first message middleware, wherein the N groups of historical indicator data correspond to N time windows within a predetermined historical time period, and the historical indicator data are used to characterize the actual promotion and use effect of the target technology; the L predetermined indicators include at least one necessary indicator and at least one optional indicator; the at least one necessary indicator includes at least: the resource consumption of the target technology; the at least one optional indicator includes at least: the service coverage of the target technology, the intellectual property coverage of the target technology, the effect improvement coverage of the target technology, and the system error rate of the target technology; Utilizing a data processing node, asynchronously obtaining the historical indicator data from the first message middleware, processing N groups of historical indicator data corresponding to each of the predetermined indicators, generating M groups of expected indicator data corresponding to each of the predetermined indicators, and sending the expected indicator data to the second message middleware, wherein the M groups of expected indicator data correspond to M target time windows among the N time windows, M≤N, and the expected indicator data are used to characterize the expected promotion and use effect of the target technology; Utilizing the result analysis node, the historical indicator data is asynchronously obtained from the first message middleware and the expected indicator data is obtained from the second message middleware, and based on the N groups of historical indicator data and M groups of expected indicator data corresponding to each of the predetermined indicators, an analysis result for the promotion and use of the target technology is generated.

2. The method according to claim 1, characterized in that Processing N groups of historical indicator data corresponding to each of the predetermined indicators to generate M groups of expected indicator data corresponding to each of the predetermined indicators includes: generating a smoothing index corresponding to each of the predetermined indicators based on part or all of the N groups of first historical indicator data corresponding to each of the necessary indicators; According to the smoothing index, N groups of historical indicator data corresponding to each of the predetermined indicators are smoothed to generate M groups of expected indicator data corresponding to each of the predetermined indicators.

3. The method according to claim 1, characterized in that Processing N groups of historical indicator data corresponding to each of the predetermined indicators to generate M groups of expected indicator data corresponding to each of the predetermined indicators includes: generating an initial smoothing index corresponding to each of the predetermined indicators based on part or all of the N groups of first historical indicator data corresponding to each of the necessary indicators; Adjusting the initial smoothing index based on part or all of the N groups of second historical indicator data corresponding to each of the optional indicators to generate an adjusted smoothing index corresponding to each of the optional indicators; According to the initial smoothing index, N groups of first historical indicator data corresponding to each of the necessary indicators are smoothed, and according to the adjusted smoothing index, N groups of second historical indicator data corresponding to each of the optional indicators are smoothed to generate M groups of expected indicator data corresponding to each of the predetermined indicators.

4. The method according to claim 1, wherein Generating a promotion and utilization analysis result for the target technology based on the N groups of historical indicator data and the M groups of expected indicator data corresponding to each of the predetermined indicators includes: Reading, from the N groups of historical indicator data, M groups of target historical indicator data corresponding to the M target time windows; Calculating the numerical deviations of the M groups of target historical indicator data and the M groups of expected indicator data corresponding to the same target time window to obtain M groups of indicator deviation values ​​corresponding to each of the predetermined indicators, wherein the M groups of indicator deviation values ​​correspond to the M target time windows; Based on the M groups of indicator deviation values ​​corresponding to each of the predetermined indicators, a promotion and utilization analysis result for the target technology is generated.

5. The method according to claim 4, characterized in that Generating a promotion and utilization analysis result for the target technology according to the M groups of indicator deviation values ​​corresponding to each of the predetermined indicators includes: Calculate the mean of the M groups of indicator deviation values ​​corresponding to each of the predetermined indicators to generate a first analysis result.

6. The method according to claim 4, characterized in that Generating a promotion and utilization analysis result for the target technology according to the M groups of indicator deviation values ​​corresponding to each of the predetermined indicators includes: Reading, from the M groups of indicator deviation values, multiple groups of reference indicator deviation values ​​corresponding to multiple reference time windows in the M target time windows; A second analysis result is generated according to the numerical change trends of the multiple groups of reference indicator deviation values ​​corresponding to the respective predetermined indicators.

7. The method according to claim 4, characterized in that The indicator deviation value is expressed as a positive deviation or a negative deviation; Generating a promotion and utilization analysis result for the target technology according to the M groups of indicator deviation values ​​corresponding to each of the predetermined indicators includes: Based on the number of multiple associated time windows in the M target time windows, a third analysis result is generated to characterize the correlation between the optional indicator and the necessary indicator, wherein, corresponding to the same associated time window, the indicator deviation values ​​corresponding to the optional indicator and the necessary indicator simultaneously appear as positive deviations.

8. The method according to claim 1, wherein: The data collection node is used to execute L collection processes so as to collect historical indicator data corresponding to the L predetermined indicators in parallel in a one-to-one correspondence; The data processing node is used to execute L processing processes so as to process historical indicator data corresponding to the L predetermined indicators in parallel in a one-to-one correspondence.

9. An indicator data processing device, characterized in that: The device comprises: A data collection module is configured to utilize a data collection node to collect, based on L predetermined indicators related to the promotion and use of a target technology, N groups of historical indicator data corresponding to each of the predetermined indicators, and send the historical indicator data to a first message middleware, wherein the N groups of historical indicator data correspond to N time windows within a predetermined historical time period, and the historical indicator data are used to characterize the actual promotion and use effect of the target technology; the L predetermined indicators include at least one required indicator and at least one optional indicator; the at least one required indicator includes at least: resource consumption of the target technology; the at least one optional indicator includes at least: service coverage of the target technology, intellectual property coverage of the target technology, effect improvement coverage of the target technology, and system error rate of the target technology; a data processing module, configured to asynchronously obtain the historical indicator data from the first message middleware using a data processing node, process N groups of historical indicator data corresponding to each of the predetermined indicators, generate M groups of expected indicator data corresponding to each of the predetermined indicators, and send the expected indicator data to the second message middleware, wherein the M groups of expected indicator data correspond to M target time windows among the N time windows, M≤N, and the expected indicator data are used to characterize the expected promotion and use effect of the target technology; A result analysis module is used to use a result analysis node to asynchronously obtain the historical indicator data from the first message middleware and the expected indicator data from the second message middleware, and generate an analysis result for the promotion and use of the target technology based on N groups of historical indicator data and M groups of expected indicator data corresponding to each of the predetermined indicators.

10. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

12. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

Images