Data processing method and device, equipment and medium

Abstract

The invention provides a data processing method which can be applied to the technical field of computers or the field of financial science and technology. The method comprises the following steps: acquiring data from a plurality of data sources; sorting the plurality of data sources based on an analytic hierarchy process to obtain a priority sequence of the plurality of data sources; and when the data source is not specified, calculating the data of the data source according to the priority order to judge the fluctuation condition of the data, and determining the data to be used based on the judgment result of the fluctuation condition of the data. The invention further provides a data processing device, equipment, a storage medium and a program product.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology or financial technology, and specifically, to a data processing method, device, equipment, medium and program product. Background Art

[0002] With the rapid development of the market and the intensification of competition, the accuracy and stability of data are particularly critical. However, when processing information from multiple data sources, we often face the challenges of data fluctuations and anomalies. If the data from a selected data source is abnormal and handled improperly, the wrong data may be used for important decisions, leading to adverse consequences.

[0003] Traditional data processing methods often evaluate the stability of each data source independently. However, this method will take up a lot of computing resources and reduce data processing efficiency when the data volume is large. In addition, this method cannot effectively integrate information from different data sources, which may ignore the key insights provided by certain data sources and affect data quality. Summary of the invention

[0004] In view of the above problems, embodiments of the present disclosure provide a data processing method, apparatus, device, medium, and program product.

[0005] According to a first aspect of the present disclosure, there is provided a data processing method, comprising: acquiring data from a plurality of data sources; sorting the plurality of data sources based on a hierarchical analysis method to acquire a priority order of the plurality of data sources; and when no data source is specified, calculating the data of the data source according to the priority order to determine a fluctuation of the data, and determining the data to be used based on a result of the determination of the fluctuation of the data.

[0006] According to an embodiment of the present disclosure, when a data source is specified, data from the specified data source is calculated to determine fluctuations in the data; and data to be used is determined based on the determination result of the fluctuations in the data.

[0007] According to an embodiment of the present disclosure, a method for sorting the multiple data sources based on the hierarchical analysis method and obtaining the priority order of the multiple data sources includes: establishing a hierarchical model, the hierarchical model includes a target layer, a criterion layer and a solution layer, wherein the criterion layer contains multiple criterion judgment parameters, and the solution layer includes the multiple data sources; constructing a comparison matrix based on the criterion judgment parameters of the criterion layer, analyzing and processing the comparison matrix, and obtaining the weight vector of the criterion judgment parameters; constructing a judgment matrix based on the criterion judgment parameters and the data sources, analyzing and processing the judgment matrix, and obtaining the weight vector of the data source corresponding to each criterion judgment parameter; calculating the data source score based on the weight vector of the criterion judgment parameter and the weight vector of the data source corresponding to each criterion judgment parameter; and arranging the data source scores from high to bottom to obtain the priority order of the data source.

[0008] According to an embodiment of the present disclosure, the method of calculating the data of the data source according to the priority order to determine the fluctuation of the data, and determining the data to be used based on the judgment result of the data fluctuation includes: judging the longitudinal fluctuation of the data of the i-th priority data source based on the data of the i-th priority data source, and when the longitudinal fluctuation is normal, determining the data to be used with the data of the i-th priority data source; or judging the longitudinal fluctuation of the data of the i-th priority data source based on the data of the i-th priority data source, and when the longitudinal fluctuation is abnormal, judging the lateral fluctuation of the data of the i-th priority data source based on the data of the i-th priority data source and the data of other data sources, and when the lateral fluctuation is normal, using the data of other data sources corresponding to the normal lateral fluctuation as the data to be used.

[0009] According to an embodiment of the present disclosure, data from an i-th priority data source is acquired based on a first time interval, wherein a method for determining data fluctuations of the i-th priority data source comprises: calculating a real-time longitudinal data change between current moment data of the i-th priority data source and previous moment data of the i-th priority data source, and when the real-time longitudinal data change is less than an average longitudinal data change, determining that the data fluctuation is normal, wherein the average longitudinal data change is calculated using historical data of the i-th priority data source acquired based on a second time interval.

[0010] According to an embodiment of the present disclosure, when the real-time longitudinal data change is greater than or equal to the average longitudinal data change, it is judged that the data fluctuation of the i-th priority data source is abnormal and a horizontal data comparison is triggered, wherein the horizontal data comparison includes: calculating the real-time horizontal data change of the current moment data of the i-th priority data source and the current moment data of the i+1-th priority data source; when the real-time horizontal data change is less than the average horizontal data change, it is judged that the horizontal data fluctuation is normal, and the current moment data of the i+1-th priority data source is used as the data to be used, wherein the average horizontal data change is calculated by obtaining historical data from the i-th priority data source and the i+1-th priority data source based on a second time interval.

[0011] According to an embodiment of the present disclosure, when the real-time lateral data change is greater than or equal to the average lateral data change, the real-time lateral data change of the current moment data of the i-th priority data source and the current moment data of data sources with increasing priority are calculated in sequence until the current moment data of the i+n-th priority data source is compared and it is determined that the lateral data fluctuation is normal, and the current moment data of the i+n-th priority data source is used as the data to be used.

[0012] According to an embodiment of the present disclosure, the method also includes: polling and calculating the real-time longitudinal data change, and when the real-time longitudinal data change is judged to be normal within a preset number of times, calculating the real-time horizontal data change based on the latest moment data of the i-th priority data source and the latest moment data of the i+1-th priority data source, and when the real-time horizontal data change is less than the average horizontal data change, judging that the data fluctuation is normal, wherein the average horizontal data change is calculated by obtaining historical data from the i-th priority data source and the i+1-th priority data source based on a second time interval.

[0013] According to an embodiment of the present disclosure, calculating the data from the designated data source to determine the fluctuation of the data includes: calculating the real-time longitudinal data change between the data at the current moment and the data at the previous moment, and when the real-time longitudinal data change is less than the average longitudinal data change, determining that the data fluctuation is normal, wherein the average longitudinal data change is calculated based on historical data acquired at a second time interval.

[0014] According to an embodiment of the present disclosure, when the real-time longitudinal data change amount is greater than or equal to the average longitudinal data change amount, it is determined that the data fluctuation is abnormal and the current data is discarded.

[0015] The second aspect of the present disclosure provides a data processing device, including: an acquisition module, configured to acquire data from multiple data sources; a first calculation module, configured to sort the multiple data sources based on a hierarchical analysis method to acquire a priority order of the multiple data sources; and a second calculation module, configured to, when no data source is specified, calculate the data of the data source according to the priority order to determine the fluctuation of the data, and determine the data to be used based on the judgment result of the fluctuation of the data.

[0016] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the above-mentioned data processing method.

[0017] The fourth aspect of the present disclosure further provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the above-mentioned data processing method.

[0018] The fifth aspect of the present disclosure also provides a computer program product, including a computer program, which implements the above-mentioned data processing method when executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] 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:

[0020] Figure 1 The application scenario diagram of the data processing method according to the embodiment of the present disclosure is schematically shown.

[0021] Figure 2 The flowchart of the data processing method according to the embodiment of the present disclosure is schematically shown.

[0022] Figure 3 The flowchart schematically shows a method for sorting the multiple data sources based on the hierarchical analysis method and obtaining the priority order of the multiple data sources according to an embodiment of the present disclosure.

[0023] Figure 4 The figure schematically shows a schematic diagram of an AHP hierarchical structure model according to an embodiment of the present disclosure.

[0024] Figure 5A A schematic diagram of a comparison matrix according to an example of the present disclosure is schematically shown.

[0025] Figure 5B A schematic diagram of a judgment matrix according to an example of the present disclosure is schematically shown.

[0026] Figure 5C A schematic diagram of a judgment matrix according to another example of the present disclosure is schematically shown.

[0027] Figure 5D A schematic diagram of a judgment matrix according to another example of the present disclosure is schematically shown.

[0028] Figure 5E A schematic diagram of a judgment matrix according to another example of the present disclosure is schematically shown.

[0029] Figure 6 A schematic diagram of a weight matrix according to an example of the present disclosure is schematically shown.

[0030] Figure 7 A flowchart of a method for calculating the data of the data source according to the priority order to determine the fluctuation of the data, and determining the data to be used based on the judgment result of the fluctuation of the data according to an embodiment of the present disclosure is schematically shown.

[0031] Figure 8 The flowchart of the method for determining the data fluctuation of the i-th priority data source according to an embodiment of the present disclosure is schematically shown.

[0032] Fig. 9 The flowchart of the method for horizontal data comparison according to the embodiment of the present disclosure is schematically shown.

[0033] Fig.10 A flow chart of a method for determining data fluctuation according to other embodiments is schematically shown.

[0034] Fig.11 The flowchart schematically shows a method for determining data fluctuation when a specified data source exists according to an embodiment of the present disclosure.

[0035] Fig.12 The structure block diagram of a data processing device according to an embodiment of the present disclosure is schematically shown.

[0036] Fig.13 A block diagram of an electronic device suitable for implementing a data processing method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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.).

[0041] In today's fast-growing market, the accuracy and stability of data management are crucial to maintaining competitiveness. With the diversification of data sources, the information differences and volatility between various data sources increase the complexity of data processing. If these data are not properly managed and analyzed, their anomalies may lead to wrong business decisions, which will have a negative impact on the company's financial health and market position.

[0042] The traditional method used by most current data processing systems is to evaluate the stability of each single data source. This approach not only consumes a lot of computing resources when processing large-scale data, but also leads to slow processing speed and low efficiency. More importantly, this separate processing method lacks a grasp of the global perspective of the data, making it difficult to obtain a comprehensive analysis across data sources, which may cause key information to be missed.

[0043] Therefore, it is necessary to develop a more advanced data processing strategy that can realize all-round analysis and optimized processing of data.

[0044] In response to the above-mentioned problems existing in the prior art, an embodiment of the present disclosure provides a data processing method, comprising: acquiring data from multiple data sources; sorting the multiple data sources based on a hierarchical analysis method to acquire a priority order of the multiple data sources; and when no data source is specified, calculating the data of the data source according to the priority order to determine the fluctuation of the data, and determining the data to be used based on the judgment result of the fluctuation of the data.

[0045] It should be noted that the data processing method and device of the embodiments of the present disclosure can be used in the field of computer technology, such as data processing related aspects. On the other hand, the data processing method and device of the embodiments of the present disclosure can be used in any field other than the field of computer technology, such as the field of financial technology. The application field of the data processing method and device provided by the embodiments of the present disclosure is not limited.

[0046] Figure 1 The application scenario diagram of the data processing method according to the embodiment of the present disclosure is schematically shown.

[0047] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a data processing method including terminal devices 101, 102, 103, a network 104 and servers 105, 106. The network 104 is used to provide a medium for a communication link between the terminal devices 101, 102, 103 and the servers 105, 106. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0048] Users can use terminal devices 101, 102, 103 to interact with servers 105, 106 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0049] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0050] Servers 105 and 106 may be servers that provide various services, such as background management servers (only as an example) that provide support for websites browsed by users using terminal devices 101, 102, and 103. The background management server may analyze and process received data such as user requests, and feed back processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0051] It should be noted that the data processing method provided in the embodiment of the present disclosure can generally be executed by the servers 105 and 106. Accordingly, the data processing device provided in the embodiment of the present disclosure can generally be set in the servers 105 and 106. The 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 servers 105 and 106 and can communicate with the terminal devices 101, 102, 103 and / or the servers 105 and 106. Accordingly, the 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 servers 105 and 106 and can communicate with the terminal devices 101, 102, 103 and / or the servers 105 and 106.

[0052] 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.

[0053] The following will be based on Figure 1 The scene described by Figure 2 to Figure 11 The data processing method of the disclosed embodiment is described in detail.

[0054] Figure 2 The flowchart of the data processing method according to the embodiment of the present disclosure is schematically shown.

[0055] like Figure 2 As shown, the data processing method of this embodiment at least includes operations S210 to S240 , and the data processing method can be executed by the servers 105 and 106 .

[0056] It should be noted that although Figure 2 The steps in the figure are shown in sequence as indicated by the arrows, but the steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of the steps, and they can be executed in other orders. Moreover, at least some of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0057] In operation S210, data from multiple data sources is obtained. In some examples, multiple data sources come from multiple suppliers. In some examples, data can be obtained from each supplier based on a preset time interval. For example, when providing real-time quotes for foreign exchange, foreign exchange settlement and sale, foreign exchange, precious metals, derivatives, etc., price data can be obtained based on multiple institutions based on preset time intervals. For example, prices can be obtained from data source a, data source b, and data source c once every 5 hours, 2 hours, or 1 hour in a day; prices can also be obtained from data source a once every 5 hours in a day, prices can be obtained from data source b once every 3 hours, and prices can be obtained from data source c once every 2 hours. In other examples, in order to monitor potential network attacks, such as distributed denial of service attacks, flow data can be obtained regularly from different network intrusion detection system suppliers. For example, flow data can be obtained from data source A, data source B, and data source C every 15 minutes; flow data can also be obtained from data source A, data source B, and data source C every 30 minutes; flow data can also be obtained from data source A, data source B, and data source C every 1 hour for real-time analysis and response.

[0058] In operation S220, the plurality of data sources are sorted based on the analytic hierarchy process to obtain the priority order of the plurality of data sources. In the embodiment of the present disclosure, the analytic hierarchy process is used to implement the priority sorting of the plurality of data sources. The analytic hierarchy process establishes a hierarchical structure model of the target layer, the standard layer and the solution layer, and then gives different importance to each standard, so as to quantitatively evaluate each candidate solution and sort it.

[0059] In operation S230, it is determined whether a designated data source exists.

[0060] When the data source is not specified, operation S240 is performed.

[0061] In operation S240, the data of the data source is calculated according to the priority order to determine the fluctuation of the data, and the data to be used is determined based on the determination result of the fluctuation of the data.

[0062] Continue to refer to Figure 2 , when the data source is specified, operations S250 to S260 are performed.

[0063] In operation S250, the data from the designated data source is calculated to determine fluctuation of the data.

[0064] In operation S260, data to be used is determined based on the determination result of the fluctuation condition of the data.

[0065] It should be understood that in some cases, a data source can be specified and the corresponding data can be obtained. In this case, only the fluctuation of the data of the specified data source is determined. When the data source is not specified, data can be screened based on the priority order to reduce unnecessary resource consumption and efficiency reduction caused by synchronous processing of multiple data sources.

[0066] In the embodiment of the present disclosure, multiple data sources are sorted by hierarchical analysis method, and data fluctuation is judged based on the sorting priority to determine the data source data to be used, which can at least partially solve the problem of consuming a large amount of computing resources when analyzing and comparing each data source data when there are multiple data sources in the prior art. And it makes the data provided to customers continuous and stable, which is conducive to maintaining business stability and security.

[0067] For example, in an embodiment of obtaining data from multiple real-time quotation data sources, due to the pioneering use of multiple quotation data sources and multi-level comparative risk prevention and control methods, the risk of customer prices deviating from market conditions can be effectively avoided, ensuring that the bank provides continuous and stable quotations to the market, thereby improving customer satisfaction and maintaining the bank's market reputation.

[0068] For example, in the embodiment of obtaining data from multiple network intrusion traffic data sources, the use of multiple data sources and multi-level security risk assessment and prevention methods can effectively enhance network security protection capabilities. The method can detect, analyze and compare intrusion traffic from different angles and levels, so it can more accurately identify potential security threats and abnormal behaviors, thereby improving defense efficiency and accuracy, and enhancing customer trust and satisfaction.

[0069] Figure 3 The flowchart schematically shows a method for sorting the multiple data sources based on the hierarchical analysis method and obtaining the priority order of the multiple data sources according to an embodiment of the present disclosure.

[0070] like Figure 3 As shown, the method of sorting the multiple data sources based on the hierarchical analysis method and obtaining the priority order of the multiple data sources in this embodiment includes operations S310 to S350.

[0071] In operation S310, a hierarchical model is established, wherein the hierarchical model includes a target layer, a criterion layer, and a solution layer. The criterion layer includes a plurality of criterion judgment parameters, and the solution layer includes the plurality of data sources. Figure 4 As shown, it schematically shows the schematic diagram of the AHP hierarchical structure model in the scenario of foreign exchange product quotation. Figure 4,The criterion judgment parameters can include cost, stability, market share, and company operating conditions.,Multiple data sources can include four financial information service institutions, A, B, C, and D.

[0072] In operation S320, a comparison matrix is ​​constructed based on the criterion judgment parameters of the criterion layer, and the comparison matrix is ​​analyzed and processed to obtain a weight vector of the criterion judgment parameters. The comparison matrix of the criterion layer is used to reflect the relative importance or superiority of different criteria. In one example, a comparison matrix MZ is constructed, where M represents the target layer, Z represents the criterion layer, and there are four criterion judgment parameters Z1, Z2, Z3, and Z4. The above four criterion judgment parameters are assigned based on expert experience and then compared in pairs to obtain a paired comparison matrix, such as Figure 5A As shown. By processing the comparison matrix using statistical analysis software such as matlab or SPSSAU software, the first weight vector w can be calculated. Furthermore, the consistency ratio of the comparison matrix can be obtained based on the second weight vector. When the consistency ratio is <0.1, it means that the consistency of the comparison matrix is ​​good. Through the consistency check, the setting of the criterion judgment parameters has a high modeling quality and the credibility of the calculation results. In one example, after the consistency check, the weight vector w of the MZ comparison matrix is ​​(0.1228, 0.3396, 0.1627, 0.3750) T .

[0073] In operation S330, a judgment matrix is ​​constructed based on the criterion judgment parameter and the data source, and the judgment matrix is ​​analyzed and processed to obtain a weight vector of the data source corresponding to each criterion judgment parameter. Similar to operation S320. The above judgment matrix is ​​used to reflect the relative importance or superiority of different data sources for a certain criterion judgment parameter condition target. In one example, judgment matrices Z1-P, Z2-P, Z3-P and Z4-P are constructed, where Z1, Z2, Z3, and Z4 represent four criterion judgment parameters, and P represents different data sources. In one example, the data source may include A, B, C, and D. For a criterion judgment parameter, the superiority of different data sources is assigned based on expert experience and then compared pairwise to obtain four judgment matrices corresponding to the four criterion judgment parameters Z1, Z2, Z3, and Z4, respectively, as shown in FIG. Figure 5B-5E As shown. By processing the judgment matrix using statistical analysis software, such as matlab or SPSSAU software, the second weight vector w can be calculated. Further, the consistency ratio of the judgment matrix can be obtained based on the second weight vector. When the consistency ratio is less than 0.1, it means that the consistency of the judgment matrix is ​​good. Through the consistency check, the contribution setting of the data source has a high modeling quality and the credibility of the calculation results. In an example, Figure 5B-5EAs shown, after passing the consistency check, the weight vectors of Z1-P, Z2-P, Z3-P and Z4-P are:

[0074] w=(0.1409, 0.2817, 0.3373, 0.2401) T , w=(0.1332, 0.2676, 0.0980, 0.5013) T ,

[0075] w=(0.1810, 0.3199, 0.1407, 0.3584) T , w=(0.2032, 0.4262, 0.0879, 0.2827) T .

[0076] In operation S340, a data source score is calculated based on the weight vector of the criterion judgment parameter and the weight vector of the data source corresponding to each criterion judgment parameter. A weight matrix can be obtained based on the weight vector of the criterion judgment parameter and the weight vector of the data source corresponding to each criterion judgment parameter, and the data source score is calculated accordingly. Figure 6 As shown, it summarizes the Figure 4 and Figure 5A-5E The weight vectors of the comparison matrix and judgment matrix are constructed accordingly. Further, the scores of each data source are calculated. For example, the score of data source A is:

[0077] 0.1228*0.1409+0.3396*0.1332+0.1627*0.1810+0.3750 / 80.2032=0.168.

[0078] Similarly, the scores of data source B, data source C, and data source D are calculated to be 0.337, 0.131, and 0.364, respectively.

[0079] In operation S350, the data sources are ranked from high to low to obtain the priority order of the data sources. Figure 6 In the example of , it can be seen from the calculation that data source D has the highest score, followed by data source B, data source C and data source A. Therefore, the priority order of data sources for the product quotation is data source D, data source B, data source C and data source A.

[0080] According to an embodiment of the present invention, by establishing a hierarchical model and performing systematic analysis on the data, each data source can be fairly evaluated according to the importance of different criteria, which can help decision makers make the best choice among many alternatives, thereby improving the accuracy, efficiency and effectiveness of decision-making.

[0081] Figure 7A flowchart of a method for calculating the data of the data source according to the priority order to determine the fluctuation of the data, and determining the data to be used based on the judgment result of the fluctuation of the data according to an embodiment of the present disclosure is schematically shown.

[0082] like Figure 7 As shown, the method of this embodiment of calculating the data of the data source according to the priority order to determine the fluctuation of the data, and determining the data to be used based on the judgment result of the fluctuation of the data includes at least operations S710 to S720.

[0083] In operation S710, it is determined whether the vertical fluctuation of the data of the i-th priority data source is normal based on the data of the i-th priority data source.

[0084] When the longitudinal fluctuation is normal, operation S720 is performed.

[0085] In operation S720, the data to be used is determined based on the i-th priority data source data.

[0086] In the embodiments of the present disclosure, after determining the priority of the data source, the data stability of the data source with a higher priority can be judged first. When its stability cannot meet the requirements, other data sources with lower priority can be screened. The vertical fluctuation refers to the fluctuation calculated by comparing the current round of data with the previous round of data of the same data source. When the vertical fluctuation is normal, it means that the data of the data source remains stable.

[0087] In other cases, when the longitudinal fluctuation is abnormal, operation S730 is performed.

[0088] In operation S730, the lateral fluctuation of the data of the i-th priority data source is determined based on the data of the i-th priority data source and the data of other data sources. When the lateral fluctuation is normal, the data of other data sources corresponding to the normal lateral fluctuation is used as the data to be used. In the embodiment of the present disclosure, when the longitudinal fluctuation is abnormal, the credibility of the current data source data is in doubt. In order to ensure the stability and reliability of the data and guarantee the business security, the lateral fluctuation is determined. Among them, the lateral fluctuation refers to the fluctuation calculated by comparing the current round of data of the same data source with the previous round of data of another data source. When the lateral fluctuation is stable, it means that the fluctuation of the current data source is relatively stable compared with the overall fluctuation of each data source. However, since the current data source fluctuates unstably in this data source, at this time, the data of other data sources used for comparison are used as the data to be used. For example, if the data of the i-th priority data source is determined to be unstable in the judgment of the longitudinal fluctuation, but is determined to be stable when the lateral fluctuation is determined with the j-th priority data source, the j-th priority data source is used as the data source of the new data to be used.

[0089] In an embodiment of the present disclosure, data from an i-th priority data source may be acquired based on a first time interval.

[0090] According to an embodiment of the present invention, by judging and selecting data sources, especially in cases involving data volatility, it is possible to dynamically evaluate and select data sources and adapt to real-time data changes, thereby enhancing the system's ability to respond to emergencies and significantly reducing errors and deviations caused by data quality issues.

[0091] Figure 8 The flowchart of the method for determining the data fluctuation of the i-th priority data source according to an embodiment of the present disclosure is schematically shown.

[0092] like Figure 8 As shown, the method for determining the data fluctuation of the i-th priority data source in this embodiment at least includes operations S810 to S830.

[0093] In operation S810, a real-time longitudinal data change amount between the current moment data of the i-th priority data source and the previous moment data of the i-th priority data source is calculated.

[0094] In operation S820, it is determined whether the real-time longitudinal data change is less than the average longitudinal data change, wherein the average longitudinal data change is calculated based on the historical data of the i-th priority data source acquired at the second time interval.

[0095] When the real-time longitudinal data change amount is less than the average longitudinal data change amount, operation S830 is performed.

[0096] In operation S830, it is determined whether the data fluctuation is normal.

[0097] In one example, the longitudinal fluctuation variation can be calculated based on the following formula: Among them, R 纵 Represents the longitudinal fluctuation change, Pi T Pi is the data of the current round of data of the i-th priority data source, T-1 is the previous round of data from the i-th priority data source. The average vertical fluctuation change can be calculated based on the following formula: Among them, R 纵1 represents the real-time longitudinal data change calculated for the first time within the second time interval, R 纵2 represents the real-time longitudinal data change calculated for the second time within the second time interval, and so on. 纵n Represents the real-time longitudinal data change amount calculated for the nth time within the second time interval. In some examples, the second time interval may be one day, and correspondingly, the historical data of the i-th priority data source may be multiple data obtained by the data source on the previous day.

[0098] In other cases, when the real-time longitudinal data change amount is greater than or equal to the average longitudinal data change amount, operation S840 is performed.

[0099] In operation S840, it is determined that the data fluctuation of the i-th priority data source is abnormal and a horizontal data comparison is triggered.

[0100] Fig. 9 The flowchart of the method for horizontal data comparison according to the embodiment of the present disclosure is schematically shown.

[0101] like Fig. 9 As shown, the method for horizontal data comparison in this embodiment at least includes operations S910 to S930.

[0102] In operation S910, the real-time horizontal data change amount between the current moment data of the i-th priority data source and the current moment data of the i+1-th priority data source is calculated. In one example, the real-time horizontal data change amount can be calculated based on the following formula:

[0103] Among them, R 横 Represents the real-time horizontal data change, Pi T Pj is the data of the current round of the i-th priority data source, T-1 The data source of this round is the j-th priority data.

[0104] In operation S920, it is determined whether the real-time horizontal data change is less than the average horizontal data change. The average horizontal data change is calculated based on the historical data from the i-th priority data source and the i+1-th priority data source acquired based on the second time interval. In one example, the average horizontal data change can be calculated based on the following formula:

[0105] Among them, R 横1 represents the real-time horizontal data change calculated for the first time within the second time interval, R 横2 Represents the real-time horizontal data change calculated for the second time within the second time interval, and so on. 横n represents the real-time horizontal data change amount calculated for the nth time within the second time interval. In one example, the average horizontal data change amount can be calculated based on multiple real-time horizontal data change amounts of data from different data sources on the previous day.

[0106] When the real-time horizontal data change amount is less than the average horizontal data change amount, operation S930 is performed.

[0107] In operation S930, it is determined that the lateral data fluctuation is normal, and the current moment data of the i+1th priority data source is used as the data to be used. Since the lateral data fluctuation is normal, the data of the i+1th priority data source used for comparison is relatively stable. Therefore, when the reliability of the i-th priority data source is in doubt, the current moment data of the i+1th priority data source, which is relatively more stable and has a higher priority, can be used as the data to be used.

[0108] In some embodiments, when the real-time lateral data change amount is greater than or equal to the average lateral data change amount, operation S940 is performed.

[0109] In operation S940, the real-time lateral data change of the current moment data of the i-th priority data source and the current moment data of data sources with increasing priority positions are calculated in sequence until the current moment data of the i+n-th priority data source is compared and it is determined that the lateral data fluctuation is normal, and the current moment data of the i+n-th priority data source is used as the data to be used.

[0110] For example, when the real-time horizontal data change between the current moment data of the 1st priority data source and the current moment data of the 2nd priority data source is calculated, and the real-time horizontal data change is greater than or equal to the average horizontal data change, the real-time horizontal data change between the current moment data of the 1st priority data source and the current moment data of the 3rd priority data source is continued to be calculated. When the real-time horizontal data change is less than the average horizontal data change, the current moment data of the 3rd priority data source is used as the data to be used. When the real-time horizontal data change is greater than or equal to the average horizontal data change, the real-time horizontal data change between the current moment data of the 1st priority data source and the current moment data of the 4th priority data source is continued to be calculated, and so on. Among them, when the real-time horizontal data change is greater than or equal to the average horizontal data change at a certain time, an early warning prompt can be triggered. It can be manually selected whether to intervene in the processing to improve the timeliness of the processing.

[0111] In other embodiments, for a certain data source, even if the real-time longitudinal data changes calculated multiple times are normal, the horizontal data fluctuation situation can be judged to further improve the reliability and validity of the data.

[0112] Fig.10 A flow chart of a method for determining data fluctuation according to other embodiments is schematically shown.

[0113] like Fig.10 As shown, the method for determining data fluctuation in some other embodiments includes operation S1010.

[0114] In operation S1010, the real-time longitudinal data change is calculated by polling. When the real-time longitudinal data change is judged to be normal within the preset number of times, the real-time horizontal data change is calculated based on the latest moment data of the i-th priority data source and the latest moment data of the i+1-th priority data source. When the real-time horizontal data change is less than the average horizontal data change, the data fluctuation is judged to be normal. Similar to operation S920, the average horizontal data change is calculated based on the historical data from the i-th priority data source and the i+1-th priority data source obtained based on the second time interval. Among them, the preset number of times can be set based on expert experience. For example, when 5 rounds of calculations of the real-time longitudinal data change are normal, the calculation of the horizontal data fluctuation is triggered. Further strengthen the confirmation of data stability.

[0115] According to an embodiment of the present disclosure, when there is a specified data source, it can be based on Fig.11 The method shown determines the fluctuation of the data.

[0116] Fig.11The flowchart schematically shows a method for determining data fluctuation when a specified data source exists according to an embodiment of the present disclosure.

[0117] like Fig.11 As shown, the method for determining data fluctuation when a designated data source exists in this embodiment at least includes operations S1110 to S1130.

[0118] In operation S1110, a real-time longitudinal data change amount between current moment data and previous moment data is calculated.

[0119] In operation S1120, it is determined whether the real-time longitudinal data change amount is smaller than the average longitudinal data change amount.

[0120] When the real-time longitudinal data variation is less than the average longitudinal data variation, operation S1130 is performed, where, similar to operation S820, the average longitudinal data variation is calculated based on the historical data of the data source acquired at the second time interval.

[0121] In operation S1130, it is determined whether the data fluctuation is normal.

[0122] In other cases, when the real-time longitudinal data change amount is greater than or equal to the average longitudinal data change amount, operation S1140 is performed.

[0123] In operation S1140, it is determined that the data fluctuation is abnormal, and the current data is discarded.

[0124] It should be understood that after the current data is removed, in determining the next round of longitudinal data fluctuations, the data from the previous round of removed data can be used as comparison data and compared with the current data to calculate the real-time longitudinal data change.

[0125] Based on the above data processing method, the present disclosure also provides a data processing device. Fig.12 The device is described in detail.

[0126] Fig.12 The structure block diagram of a data processing device according to an embodiment of the present disclosure is schematically shown.

[0127] like Fig.12 As shown, the data processing device 1200 of this embodiment includes an acquisition module 1210 , a first calculation module 1220 and a second calculation module 1230 .

[0128] The acquisition module 1210 is configured to acquire data from multiple data sources.

[0129] The first calculation module 1220 is configured to sort the multiple data sources based on the hierarchical analysis method to obtain the priority order of the multiple data sources.

[0130] The second calculation module 1230 is configured to calculate the data of the data source according to the priority order to determine the fluctuation of the data when the data source is not specified, and determine the data to be used based on the judgment result of the fluctuation of the data.

[0131] According to the apparatus of the embodiment of the present disclosure, when a data source is specified, data from the specified data source is calculated to determine fluctuations of the data; and data to be used is determined based on the determination result of the fluctuations of the data.

[0132] According to the device of the embodiment of the present disclosure, the method for sorting the multiple data sources based on the hierarchical analysis method and obtaining the priority order of the multiple data sources includes: establishing a hierarchical model, the hierarchical model includes a target layer, a criterion layer and a solution layer, wherein the criterion layer contains multiple criterion judgment parameters, and the solution layer includes the multiple data sources; constructing a comparison matrix based on the criterion judgment parameters of the criterion layer, analyzing and processing the comparison matrix, and obtaining the weight vector of the criterion judgment parameters; constructing a judgment matrix based on the criterion judgment parameters and the data sources, analyzing and processing the judgment matrix, and obtaining the weight vector of the data source corresponding to each criterion judgment parameter; calculating the data source score based on the weight vector of the criterion judgment parameter and the weight vector of the data source corresponding to each criterion judgment parameter; and arranging the data source scores from high to bottom to obtain the priority order of the data source.

[0133] According to the device of the embodiment of the present disclosure, the method for calculating the data of the data source according to the priority order to determine the fluctuation of the data, and determining the data to be used based on the judgment result of the data fluctuation includes: judging the longitudinal fluctuation of the data of the i-priority data source based on the data of the i-priority data source, when the longitudinal fluctuation is normal, determining the data to be used with the data of the i-priority data source; or judging the longitudinal fluctuation of the data of the i-priority data source based on the data of the i-priority data source, when the longitudinal fluctuation is abnormal, judging the lateral fluctuation of the data of the i-priority data source based on the data of the i-priority data source and the data of other data sources, when the lateral fluctuation is normal, using the data of other data sources corresponding to the normal lateral fluctuation as the data to be used.

[0134] According to the device of an embodiment of the present disclosure, data from an i-th priority data source is acquired based on a first time interval, wherein a method for determining data fluctuations of the i-th priority data source comprises: calculating a real-time longitudinal data change between current moment data of the i-th priority data source and previous moment data of the i-th priority data source, and when the real-time longitudinal data change is less than an average longitudinal data change, determining that the data fluctuation is normal, wherein the average longitudinal data change is calculated using historical data of the i-th priority data source acquired based on a second time interval.

[0135] According to the device of an embodiment of the present disclosure, when the real-time longitudinal data change is greater than or equal to the average longitudinal data change, it is judged that the data fluctuation of the i-th priority data source is abnormal and a horizontal data comparison is triggered, wherein the horizontal data comparison includes: calculating the real-time horizontal data change of the current moment data of the i-th priority data source and the current moment data of the i+1-th priority data source; when the real-time horizontal data change is less than the average horizontal data change, it is judged that the horizontal data fluctuation is normal, and the current moment data of the i+1-th priority data source is used as the data to be used, wherein the average horizontal data change is calculated by obtaining historical data from the i-th priority data source and the i+1-th priority data source based on a second time interval.

[0136] According to the device of an embodiment of the present disclosure, when the real-time lateral data change is greater than or equal to the average lateral data change, the real-time lateral data change of the current moment data of the i-th priority data source and the current moment data of data sources with increasing priority are calculated in sequence until the current moment data of the i+n-th priority data source is compared and it is determined that the lateral data fluctuation is normal, and the current moment data of the i+n-th priority data source is used as the data to be used.

[0137] According to the device of the embodiment of the present disclosure, the real-time longitudinal data change can also be polled and calculated. When the real-time longitudinal data change is judged to be normal within a preset number of times, the real-time horizontal data change is calculated based on the latest moment data of the i-th priority data source and the latest moment data of the i+1-th priority data source. When the real-time horizontal data change is less than the average horizontal data change, the data fluctuation is judged to be normal, wherein the average horizontal data change is calculated based on the historical data from the i-th priority data source and the i+1-th priority data source obtained based on the second time interval.

[0138] According to the device of an embodiment of the present disclosure, the data from the designated data source is calculated to determine the fluctuation of the data, including: calculating the real-time longitudinal data change between the data at the current moment and the data at the previous moment, and when the real-time longitudinal data change is less than the average longitudinal data change, determining that the data fluctuation is normal, wherein the average longitudinal data change is calculated based on historical data acquired at a second time interval.

[0139] According to the device of the embodiment of the present disclosure, when the real-time longitudinal data change amount is greater than or equal to the average longitudinal data change amount, it is determined that the data fluctuation is abnormal and the current data is discarded.

[0140] According to an embodiment of the present disclosure, any multiple modules of the acquisition module 1210, the first calculation module 1220 and the first calculation module 1230 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 acquisition module 1210, the first calculation module 1220 and the first calculation module 1230 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 acquisition module 1210, the first calculation module 1220 and the first calculation module 1230 can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be performed.

[0141] Fig.13 A block diagram of an electronic device suitable for implementing a data processing method according to an embodiment of the present disclosure is schematically shown.

[0142] like Fig.13As shown, the electronic device 1300 according to an embodiment of the present disclosure includes a processor 1301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1302 or a program loaded from a storage part 1308 into a random access memory (RAM) 1303. The processor 1301 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 1301 may also include an onboard memory for caching purposes. The processor 1301 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.

[0143] In RAM 1303, various programs and data required for the operation of electronic device 1300 are stored. Processor 1301, ROM 1302 and RAM 1303 are connected to each other through bus 1304. Processor 1301 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 1302 and / or RAM 1303. It should be noted that the program can also be stored in one or more memories other than ROM 1302 and RAM 1303. Processor 1301 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.

[0144] According to an embodiment of the present disclosure, the electronic device 1300 may further include an input / output (I / O) interface 1305, which is also connected to the bus 1304. The electronic device 1300 may further include one or more of the following components connected to the I / O interface 1305: an input portion 1306 including a keyboard, a mouse, etc.; an output portion 1307 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 1308 including a hard disk, etc.; and a communication portion 1309 including a network interface card such as a LAN card, a modem, etc. The communication portion 1309 performs communication processing via a network such as the Internet. A drive 1310 is also connected to the I / O interface 1305 as needed. A removable medium 1311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1310 as needed, so that a computer program read therefrom is installed into the storage portion 1308 as needed.

[0145] 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.

[0146] According to an embodiment of the present disclosure, a 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 1302 and / or RAM 1303 described above and / or one or more memories other than ROM 1302 and RAM 1303.

[0147] 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 item recommendation method provided by the embodiment of the present disclosure.

[0148] 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 1301. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0149] 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 1309, and / or installed from the removable medium 1311. 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.

[0150] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 1309, and / or installed from the removable medium 1311. When the computer program is executed by the processor 1301, 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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 data processing method, characterized in that: The method comprises: Obtain data from multiple data sources; Sorting the multiple data sources based on the hierarchical analysis method to obtain the priority order of the multiple data sources; and When the data source is not specified, the data of the data source is calculated according to the priority order to determine the fluctuation of the data, and the data to be used is determined based on the determination result of the fluctuation of the data.

2. The method according to claim 1, characterized in that: When a data source is specified, calculating the data from the specified data source to determine fluctuations of the data; and The data to be used is determined based on the determination result of the fluctuation condition of the data.

3. The method according to claim 1 or 2, wherein: The method of sorting the multiple data sources based on the hierarchical analysis method to obtain the priority order of the multiple data sources includes: Establishing a hierarchical model, the hierarchical model includes a target layer, a criterion layer and a solution layer, wherein the criterion layer includes a plurality of criterion judgment parameters, and the solution layer includes the plurality of data sources; constructing a comparison matrix based on the criterion judgment parameters of the criterion layer, analyzing and processing the comparison matrix, and obtaining a weight vector of the criterion judgment parameters; Constructing a judgment matrix based on the criterion judgment parameters and the data source, analyzing and processing the judgment matrix, and obtaining a weight vector of the data source corresponding to each criterion judgment parameter; Calculating a data source score based on the weight vector of the criterion judgment parameter and the weight vector of the data source corresponding to each criterion judgment parameter; and The data sources are ranked from highest to lowest in terms of their scores to obtain a priority order of the data sources.

4. The method according to claim 1, characterized in that: The method of calculating the data of the data source according to the priority order to determine the fluctuation of the data, and determining the data to be used based on the determination result of the fluctuation of the data includes: Based on the data of the i-th priority data source, determine the vertical fluctuation of the i-th priority data source data, and when the vertical fluctuation is normal, determine the data to be used based on the i-th priority data source data; or The vertical fluctuation of the data of the i-priority data source is judged based on the data of the i-priority data source; when the vertical fluctuation is abnormal, the horizontal fluctuation of the data of the i-priority data source is judged based on the data of the i-priority data source and the data of other data sources; when the horizontal fluctuation is normal, the data of other data sources corresponding to the normal horizontal fluctuation are used as the data to be used.

5. The method according to claim 4, characterized in that The data from the i-th priority data source is acquired based on the first time interval, wherein the method for determining the data fluctuation of the i-th priority data source includes: Calculate the real-time longitudinal data change between the current moment data of the i-th priority data source and the previous moment data of the i-th priority data source, and when the real-time longitudinal data change is less than the average longitudinal data change, judge that the data fluctuation is normal, wherein the average longitudinal data change is calculated based on the historical data of the i-th priority data source obtained based on the second time interval.

6. The method according to claim 5, characterized in that When the real-time longitudinal data change is greater than or equal to the average longitudinal data change, it is determined that the data fluctuation of the i-th priority data source is abnormal and a horizontal data comparison is triggered, wherein the horizontal data comparison includes: Calculate the real-time horizontal data change between the current moment data of the i-th priority data source and the current moment data of the i+1-th priority data source; When the real-time lateral data change is less than the average lateral data change, the lateral data fluctuation is judged to be normal, and the current time data of the i+1th priority data source is used as the data to be used, wherein the average lateral data change is calculated based on the historical data from the i-th priority data source and the i+1-th priority data source acquired based on the second time interval.

7. The method according to claim 6, characterized in that When the real-time lateral data change is greater than or equal to the average lateral data change, the real-time lateral data change of the current moment data of the i-th priority data source and the current moment data of data sources with increasing priority are calculated in sequence until the current moment data of the i+n-th priority data source is compared and it is determined that the lateral data fluctuation is normal, and the current moment data of the i+n-th priority data source is used as the data to be used.

8. The method according to claim 5, characterized in that The method further comprises: The real-time longitudinal data change is calculated by polling, and when it is judged that the real-time longitudinal data change is normal within a preset number of times, the real-time horizontal data change is calculated based on the latest time data of the i-th priority data source and the latest time data of the i+1-th priority data source, and when the real-time horizontal data change is less than the average horizontal data change, the data fluctuation is judged to be normal, wherein the average horizontal data change is calculated by historical data from the i-th priority data source and the i+1-th priority data source obtained based on a second time interval.

9. The method according to claim 2, characterized in that: The calculating the data from the designated data source to determine the fluctuation of the data includes: Calculate the real-time longitudinal data change between the current moment data and the previous moment data. When the real-time longitudinal data change is less than the average longitudinal data change, determine that the data fluctuation is normal, wherein the average longitudinal data change is calculated based on the historical data obtained at the second time interval.

10. The method according to claim 9, characterized in that When the real-time longitudinal data variation is greater than or equal to the average longitudinal data variation, it is determined that the data fluctuation is abnormal and the current data is discarded.

11. A data processing device, characterized in that: The device comprises: an acquisition module configured to acquire data from a plurality of data sources; A first calculation module is configured to sort the multiple data sources based on the hierarchical analysis method to obtain the priority order of the multiple data sources; and The second calculation module is configured to calculate the data of the data source according to the priority order to determine the fluctuation of the data when no data source is specified, and determine the data to be used based on the judgment result of the fluctuation of the data.

12. An electronic device comprising: one or more processors; a storage device 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 10.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

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