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

Abstract

The invention provides an index data processing method and device, equipment, a storage medium and a program product, and can be applied to the technical field of big data and the technical field of financial science and technology. The index data processing method comprises the following steps: acquiring N groups of historical index data corresponding to each predetermined index by using a data acquisition node based on L predetermined indexes related to promotion and use conditions of a target technology, and sending the historical index data to first message middleware; using a data processing node to asynchronously obtain historical index data from the first message middleware, processing N groups of historical index data corresponding to each predetermined index, generating M groups of expected index data corresponding to each predetermined index, and sending the expected index data to a second message middleware; and asynchronously obtaining the historical index data from the first message middleware and obtaining the expected index data from the second message middleware by using a result analysis node, and generating a popularization and use analysis result for the target technology based on the N groups of historical index data and the M groups of expected index data corresponding to each predetermined index.

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 sustained 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 related technology: in the process of introducing a new technology in the application system, it is necessary to consider the adaptability of the technology and 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] In one aspect of the present disclosure, a method for processing indicator data is provided, comprising:

[0006] Using the 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 characterize the actual promotion and use effect of the target technology;

[0007] Using the 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 in N time windows, M≤N, and the expected indicator data are used to characterize the expected promotion and use effect of the target technology;

[0008] By 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 necessary 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] Based on part or all of the N groups of first historical indicator data corresponding to each necessary indicator, generate a smoothing index corresponding to each predetermined 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] Based on part or all of the N groups of first historical indicator data corresponding to each necessary indicator, generate an initial smoothing index corresponding to each predetermined indicator;

[0018] Based on part or all of the N groups of second historical indicator data corresponding to each optional indicator, the initial smoothing index is adjusted 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 use 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 groups of historical indicator data, read M groups 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] According to the M groups of indicator deviation values ​​corresponding to each predetermined indicator, the promotion and use analysis results of the target technology are generated.

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

[0027] From the M groups of indicator deviation values, read 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 variation trends of the plurality of 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] According to 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 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 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, used 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 using the data collection node, and send the historical indicator data 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 characterize the actual promotion and use effect of the target technology;

[0037] A data processing module is used to use a data processing node to 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 in N time windows, M≤N, and the expected indicator data is 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 necessary 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 adjustment 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, and 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, so as 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, used 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 is used to 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;

[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 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, used for 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;

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

[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 above computer program or instructions are executed by a processor.

[0067] The fifth aspect of the present disclosure further provides a computer program product, including 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, because the data collection nodes have different processes for collecting indicators of each system, and the data processing nodes have different processes for processing data of each system, data collection and data processing cannot be performed synchronously. By setting message middleware between the data collection node and the data processing node, and between the data processing node and the result analysis node, the asynchronous processing of data collection, data processing, and result analysis based on the middleware mechanism is realized to decouple the processing processes of upstream and downstream nodes, avoid invalid data waiting for processing, and improve the operation efficiency of the system as a whole. Further, on the other hand, the data processing node and the result analysis node read the corresponding data from different message queues respectively. If different message middlewares are not distinguished, different types of data need to be marked and distinguished accordingly, which increases the complexity of data access. Through this method, the process of data processing is simplified, the efficiency of data access is improved, and data confusion calls between different processing services will not be caused, and the accuracy of data processing of different services is improved.

[0069] According to the embodiments of the present disclosure, historical indicator data of multiple evaluation indicators in multiple time windows are collected, and expected indicator data is generated based on the historical indicator data, and further analysis results are generated based on the historical indicator data and the expected indicator data. The generation of the analysis results is associated with the expected effects of each indicator, taking into account the potential correlation between the user's expected effects and actual effects of the technology application, and using the expected data as a reference standard to draw quantitative objective analysis conclusions, and the generated analysis results are more accurate and objective, and have a higher reference value. 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 A diagram schematically illustrates an application scenario of an indicator data processing method, apparatus, device, medium, and program product according to an embodiment of the present disclosure;

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

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

[0074] Figure 4 A flowchart of a method for generating a promotion and use analysis result for a target technology according to an embodiment of the present disclosure is schematically shown;

[0075] Figure 5 A structural block diagram of an indicator data processing device according to an embodiment of the present disclosure is schematically shown; 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 exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, 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 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 existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0079] All terms (including technical and scientific terms) used herein 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 using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to 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 (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.) involved 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 the scenario of using personal information for automated decision-making, the methods, devices, and systems provided by the embodiments of the present disclosure provide users with corresponding operation portals for users 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 a person'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: 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 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 to provide a medium for 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, wireless communication links, or optical fiber cables, etc.

[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 through 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 (only as an example) that provides support for 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 the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[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 only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

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

[0093] Figure 2 A flowchart of an indicator data processing method according to an 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 collection 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, 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 characterize the actual promotion and use effect of the target technology;

[0096] In operation S202, using the 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 in N time windows, M≤N, and the expected indicator data is used to characterize 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, a data collection node, a data processing node, and a result analysis node may be a single service node or a service cluster including multiple nodes.

[0099] Since there are many business access channels such as offline counters and online business access channels and their corresponding business processing systems in the organization, 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 of each business system.

[0100] The first message middleware and the 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 transmission technology.

[0101] According to the embodiments of the present disclosure, when introducing a new technology into an application system, the compatibility of the technology and the business system needs to be considered. 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 embodiments of the present disclosure collects data of L (positive integer) predetermined indicators related to the promotion and use of the target technology, and processes the indicator data to generate the final required results.

[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 may 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 are collected within the past month, and each group corresponds to one day's data.

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

[0105] Since historical indicator data is a true reflection of the actual application of the target technology in multiple business systems, the 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 node may be used to process N groups of historical indicator data corresponding to each predetermined indicator to generate M groups of expected indicator data corresponding to each predetermined indicator. The M groups of expected indicator data may correspond to M target time windows in N time windows, M≤N, that is, corresponding to all time windows or part of the N time windows in 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, or 20 groups of expected indicator data, 15 groups of expected indicator data, and so on are generated.

[0108] The expected indicator data is used to characterize the expected promotion and use effect of the target technology. The historical indicator data can be processed by a predetermined numerical processing algorithm so that the data distribution of the expected indicator data meets the promotion and use effect expected by the user. For example, if the user expects that after using the new technology, the ideal effect is that the data of each A indicator shows an upward trend period by period, and the data of the B indicator shows a downward trend period by period, the historical indicator data can be processed using the relevant linear interpolation method to obtain multiple groups of expected indicator data that meet the expected numerical change trend.

[0109] Further, in operation S203, based on N groups of historical indicator data corresponding to each predetermined indicator and M groups of expected indicator data, 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 the N groups of historical indicator data; generating an analysis result for the target technology 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.

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

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

[0112] According to the embodiments of the present disclosure, because the data collection nodes have different processes for collecting indicators of each system, and the data processing nodes have different processes for processing data of each system, data collection and data processing cannot be performed synchronously. By setting message middleware between the data collection node and the data processing node, and between the data processing node and the result analysis node, the asynchronous processing of data collection, data processing, and result analysis based on the middleware mechanism is realized to decouple the processing processes of upstream and downstream nodes, avoid invalid data waiting for processing, and improve the operation efficiency of the system as a whole. Further, on the other hand, the data processing node and the result analysis node read the corresponding data from different message queues respectively. If different message middlewares are not distinguished, different types of data need to be marked and distinguished accordingly, which increases the complexity of data access. Through this method, the process of data processing is simplified, the efficiency of data access is improved, and data confusion calls between different processing services will not be caused, and the accuracy of data processing of different services is improved.

[0113] According to the embodiments of the present disclosure, historical indicator data of multiple evaluation indicators in multiple time windows are collected, and expected indicator data is generated based on the historical indicator data, and further analysis results are generated based on the historical indicator data and the expected indicator data. The generation of the analysis results is associated with the expected effects of each indicator, taking into account the potential correlation between the user's expected effects and actual effects of the technology application, and using the expected data as a reference standard to draw quantitative objective analysis conclusions, and the generated analysis results are more accurate and objective, and have a higher reference value.

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

[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 may be provided in the first message middleware, corresponding one-to-one to L predetermined indicators. Each historical indicator data is marked with an indicator identifier. After the historical indicator data of the corresponding indicator is collected, it is placed in the message queue corresponding to the indicator according to the indicator identifier, so as to realize differentiated storage of different indicator data.

[0117] L message queues may also be provided 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 the expected indicator data of the corresponding indicator is obtained through processing, it is placed in the message queue corresponding to the indicator according to the indicator identifier, so as to realize differentiated storage of different indicator data.

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

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

[0120] For example, at least one optional indicator includes at least 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 effect 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 the processing errors of the second business system that applies the target technology to the total number of processing errors of the entire business system.

[0125] For example, each data collection node continuously collects various indicator data of the new technology to be tracked in each business system in the business field at a fixed time interval δ (time window length), summarizes the data of each predetermined indicator in N time windows, and stores it in the 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 The flowchart of the method for generating the promotion and use analysis results of the target technology according to the 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 for data alignment 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 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 28 groups of expected indicator data corresponding to the 3rd day to the 30th day.

[0132] In operation S402, 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 day to the 30th day and 28 groups of expected indicator data corresponding to the 3rd day 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 according to the M groups of indicator deviation values ​​corresponding to each predetermined indicator.

[0135] According to the embodiments of the present disclosure, the above method collects historical indicator data of 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. The data in the application process of new technologies can be continuously and dynamically monitored. Based on the indicator data of a specific historical time period, the adaptation of the technology at different time stages is analyzed and processed, which can realize the continuous dynamic evaluation of the changes in the adaptability and provide effective and quantifiable technical support for the overall decision-making to judge the effect of the use of the new technology in the business system.

[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] Firstly, based on the expected change trend of the indicator set for the predetermined indicator, a 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 present different numerical change trends, some indicators present numerical growth trends, and some indicators present numerical decline trends. For example, under the premise of expecting to achieve a relatively good promotion effect, the indicator values ​​of the resource consumption and system error rate of the target technology should show a downward trend over time; conversely, the indicator values ​​of the service coverage and intellectual property coverage of the target technology should show an upward trend over time. In order to eliminate this difference, the deviations of all indicators are calculated on the same data basis 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. 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 as follows: 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 overall deviation analysis of 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 group 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] Since not all periodic indicators are recent focus points of users, multiple reference time windows can be pre-configured time windows that any user wants to focus on, such as the last few time windows that users focus on. Through flexible configuration, data corresponding to time windows that are not of concern can be filtered.

[0153] Then, according to the numerical variation trend of the multiple groups of reference indicator deviation values ​​corresponding to each predetermined indicator, a second analysis result is generated, 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 higher expected growth in the local period; conversely, if the result is less than or equal to 0, the output result is: the indicator has lower 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 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 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 calculating the correlation degree SUPPORT(invt,b) between the optional index b and the required index invt is referred to the following 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 are strongly correlated 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, 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 each necessary indicator, a smoothing index corresponding to each predetermined indicator is generated;

[0167] Afterwards, according to the smoothing index, the 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.

[0168] According to an embodiment of the present disclosure, the smoothing index corresponding to each predetermined indicator is generated based on the processing of 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 groups of first historical indicator data corresponding to the necessary indicator invt. For example, take the three groups of historical indicator data invt1, invt2, invt3 corresponding to the first three time windows to calculate the exclusive smoothing index α a The specific method can adopt 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 groups of historical indicator data corresponding to each predetermined indicator, generating M groups of expected indicator data corresponding to each predetermined indicator, including generating expected indicator data corresponding to each target time window respectively. The M groups of expected indicator data may correspond to the M target time windows in the N time windows, and may correspond to all time windows or part of the time windows in the N time windows in the predetermined historical time period.

[0173] Wherein, for any nth target time window, generating the nth group of expected indicator data corresponding to the nth 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) algorithm:

[0176]

[0177] When using the above algorithm, some window data may be missing, for example, the first two periods of data are missing (the smoothing index is calculated from the third period), so the M groups of expected index data may correspond to M of the N time windows (N-2, counting from the third period). Alternatively, the first two periods of data may be supplemented (for example, with historical measured values), and the obtained M groups of expected index data may correspond to all of the N time windows.

[0178] After completing the data, an example of the predicted value calculated according to the above-mentioned predicted value calculation method is 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 the various indicators of new technologies will fluctuate with various factors (seasonality, periodicity, other technical factors, etc.), the data distribution of each indicator in multiple time windows has potential correlation, not isolated multiple data. The processing logic of the exponential smoothing method will take into account the potential correlation of multi-period indicator data, which is more in line with the real objective distribution of each indicator and minimizes the impact of accidental factors.

[0182] Furthermore, the generation of the smoothing index is based on the data of the necessary indicators. In this way, the data distribution law of the generated period indicator data is similar to the data distribution law of the necessary indicators, which meets 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 law meet user expectations and meet actual business needs.

[0183] According to the embodiments of the present disclosure, it can be known from the above embodiments that 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 exclusive smoothing indexes corresponding to different indicators are different, the N groups of historical indicator data corresponding to each predetermined indicator are processed to generate M groups of expected indicator data corresponding to each predetermined indicator, including:

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

[0186] Afterwards, based on part or all of the N groups of second historical indicator data corresponding to each optional indicator, the initial smoothing index is adjusted to generate an adjusted smoothing index corresponding to each optional indicator. Here, the smoothing index is adjusted using the historical real data of each optional indicator, so that the smoothing index is derived by combining the necessary indicators and the comprehensive factors of the trend of the indicator data itself, which is more objective and reasonable. The specific adjustment method, for example, can be to adjust the smoothing index using various trial calculation evaluation methods.

[0187] Then, according to the initial smoothing index, the N groups of first historical indicator data corresponding to each necessary indicator are smoothed, and according to the adjusted smoothing index, the 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. The specific method can refer to the algorithm of formula (3) in the above embodiment. It 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 collection module 501 , a data processing module 502 , and a result analysis module 503 .

[0191] The data collection module 501 is used to use the 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 send the historical indicator data 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 characterize the actual promotion and use effect of the target technology;

[0192] The data processing module 502 is used to use the data processing node to 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 in N time windows, M≤N, and the expected indicator data is used to characterize 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 to 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 to 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 a promotion and utilization analysis result for the target technology according to 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] A reading subunit is used to read multiple groups of reference indicator deviation values ​​corresponding to multiple reference time windows in M ​​target time windows from M groups of indicator deviation values; a second result generating subunit is used to generate a second analysis result according to the numerical change trend of 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 the historical indicator data corresponding to the L predetermined indicators are collected in parallel in a one-to-one correspondence; the data processing node is used to execute L processing processes so that the historical indicator data corresponding to the L predetermined indicators are processed in parallel in a one-to-one correspondence.

[0209] According to an embodiment of the present disclosure, any multiple modules in the data acquisition module 501, the data processing module 502, and the result analysis module 503 can be combined in 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 way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware or in any 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, and when the computer program module is run, the corresponding function can be 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 special-purpose 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 performing different actions of the method flow according to an embodiment of the present disclosure.

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

[0213] According to an embodiment of the present disclosure, the electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to the bus 604. The electronic device 600 may further include one or more of the following components connected to the input / output (I / O) interface 605: an input portion 606 including a keyboard, a mouse, etc.; an output portion 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 608 including a hard disk, etc.; and a communication portion 609 including a network interface card such as a LAN card, a modem, etc. The communication portion 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the input / output (I / O) interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage portion 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 without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0215] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is 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, which may be used by or in combination with an instruction execution system, an apparatus or a 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 embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the indicator data processing method provided by the embodiment of the present disclosure.

[0217] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 601. 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 rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 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, etc., or any suitable combination of the above.

[0219] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the processor 601, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, apparatus, module, unit, etc. described above can be implemented by a computer program module.

[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 computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of 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 through the Internet).

[0221] The flow charts and block diagrams in the accompanying drawings illustrate the possible 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 flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order 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 flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0222] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations and / or combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways without departing from the spirit and teachings of the present disclosure. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0223] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of 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 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 characterize the actual promotion and use effect 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 in 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 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: 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.

3. The method according to claim 2, characterized in that Processing the 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: Based on part or all of the N groups of first historical indicator data corresponding to each of the necessary indicators, generating a smoothing index corresponding to each of the predetermined indicators; According to the smoothing index, the 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.

4. The method according to claim 2, 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: Based on part or all of the N groups of first historical indicator data corresponding to each of the necessary indicators, generate an initial smoothing index corresponding to each of the predetermined indicators; Based on part or all of the N groups of second historical indicator data corresponding to each of the optional indicators, the initial smoothing index is adjusted 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.

5. The method according to claim 2, characterized in that: Based on the N groups of historical indicator data and the M groups of expected indicator data corresponding to each of the predetermined indicators, generating a promotion and use analysis result for the target technology includes: Reading, from the N groups of historical indicator data, M groups of target historical indicator data corresponding to the M target time windows; 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 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; According to 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.

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

7. The method according to claim 5, characterized in that Generating the promotion and use analysis results for the target technology according to the M groups of indicator deviation values ​​corresponding to each of the predetermined indicators includes: From the M groups of indicator deviation values, read 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 variation trends of the plurality of groups of reference indicator deviation values ​​corresponding to the respective predetermined indicators.

8. The method according to claim 5, characterized in that The indicator deviation value is expressed as a positive deviation or a negative deviation; Generating the promotion and use analysis results 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 degree of association 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.

9. The method according to claim 1, characterized in that: 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 the historical indicator data corresponding to the L predetermined indicators in parallel in a one-to-one correspondence.

10. An indicator data processing device, characterized in that: The device comprises: A data collection module, used to use a data collection node to collect N groups of historical indicator data corresponding to each of 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 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; A data processing module, used to use a data processing node to asynchronously obtain the historical indicator data from the first message middleware, 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 in the N time windows, M≤N, and the expected indicator data is used to characterize the expected promotion and use effect of the target technology; A result analysis module is used to utilize 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.

11. An electronic device, comprising: one or more processors; a memory for storing one or more computer programs, The method 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 9.

12. 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 9 are implemented.

13. 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 9 are implemented.

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