Synchronous data verification method and device, equipment, storage medium and program product

Abstract

The invention provides a synchronous data verification method and device, equipment, a storage medium and a program product, and relates to the technical field of data synchronization. The method comprises the following steps: acquiring synchronous data of a plurality of service systems; in the synchronous data, determining historical data corresponding to the historical time interval and real data corresponding to the to-be-predicted time interval; according to the historical data, performing data prediction by adopting a preset exponential smoothing algorithm model to obtain first prediction data; performing error compensation processing on the first prediction data according to the real data to obtain second prediction data; obtaining a synchronous data reasonable interval corresponding to the to-be-predicted time interval; and according to the synchronous data reasonable interval and the second prediction data, determining whether synchronous data exception exists or not. According to the method and the device, the problem that the synchronous data verification efficiency is relatively low due to the fact that the prior art mainly depends on manual auditing and the manual auditing is low in efficiency, low in accuracy and poor in timeliness is solved.

Description

Technical Field

[0001] The present application relates to the field of data synchronization technology, and in particular to a synchronization data verification method, device, equipment, storage medium and program product. Background Art

[0002] With the advancement of information technology, enterprise information system operation platforms are becoming increasingly complex. Different business units often use independent software systems to manage and process their own data. The demand for data interaction between information system operation platforms and these systems is increasing, and the accuracy of data synchronized between these platforms is extremely high. Therefore, determining the accuracy of data synchronized from other platforms is a current challenge.

[0003] Currently, most companies' internal information operation platforms can only use simple rules to detect whether the data is successfully synchronized. The accuracy of the data mainly relies on manual review by professionals. For example, if the synchronized data is zero but the status code returns 200, it will be considered successful.

[0004] However, synchronous data verification relies too much on manual review, which has low accuracy and poor timeliness in discovering failed or inaccurate data, resulting in low efficiency of synchronous data verification. Summary of the Invention

[0005] The present application provides a synchronous data verification method, device, equipment, storage medium and program product to solve the problem that the existing synchronous data verification relies too much on manual review with low accuracy, which in turn leads to low efficiency of synchronous data verification.

[0006] In a first aspect, the present application provides a synchronization data verification method, comprising:

[0007] Obtain synchronized data from multiple business systems;

[0008] In the synchronized data, the historical data corresponding to the historical time interval and the real data corresponding to the time interval to be predicted are determined; the historical time interval is determined by the time interval to be predicted, and the historical time interval has the same time length as the time interval to be predicted;

[0009] Based on the historical data, a preset exponential smoothing algorithm model is used to perform data forecasting to obtain first forecast data;

[0010] Performing error compensation processing on the first predicted data according to the real data to obtain second predicted data;

[0011] Obtain the reasonable interval of synchronization data corresponding to the time interval to be predicted;

[0012] Determine whether there is any synchronization data anomaly based on the reasonable interval of the synchronization data and the second prediction data.

[0013] In one possible design, synchronized data from multiple business systems is obtained, including:

[0014] Generate scheduled tasks according to the preset time sequence;

[0015] Send scheduled tasks to multiple business systems, where scheduled tasks are used to obtain synchronized data from the business systems;

[0016] Get the synchronization data of multiple business systems based on the response status of the business systems to the scheduled tasks.

[0017] In one possible design, based on the response status of the business system to the scheduled task, synchronized data from multiple business systems is obtained, including:

[0018] Determine whether the business system's response status to the scheduled task is an exception.

[0019] If the response status of the business system for the scheduled task is not an exception, the return information of the business system for the scheduled task is received, and the synchronization data of multiple business systems is obtained according to the return information;

[0020] If the response status of the business system to the scheduled task is a return exception, the retry mechanism is triggered; when the response status of the business system to the scheduled task is not a return exception when the number of retries is within the preset number of retries, the return information of the business system for the scheduled task is received, and the synchronization data of multiple business systems is obtained according to the return information; when the response status of the business system to the scheduled task is always a return exception when the number of retries is within the preset number of retries, the first system alarm information of the data synchronization exception is triggered.

[0021] In one possible design, based on historical data, a preset exponential smoothing algorithm model is used to perform data forecasting to obtain first forecast data, including:

[0022] Determining a preset triple exponential smoothing algorithm model; updating a smoothing coefficient of the triple exponential smoothing algorithm model according to a prediction result of the triple exponential smoothing algorithm model and actual data corresponding to the prediction result;

[0023] Based on historical data, a preset triple exponential smoothing algorithm model is used to perform data prediction to obtain first prediction data.

[0024] In one possible design, a preset triple exponential smoothing algorithm model is determined, including:

[0025] Get the period parameter of the time variable used to describe temporality;

[0026] According to the period parameters, the calculation formulas of the preset triple exponential smoothing algorithm model are determined.

[0027] In one possible design, after determining whether the synchronized data is abnormal based on the reasonable interval of the synchronized data and the second predicted data, the method further includes:

[0028] Execute task scheduling for the business system based on the abnormal results of synchronization data.

[0029] In one possible design, based on the abnormal results of the synchronized data, task scheduling is performed on the business system, including:

[0030] If it is determined that the synchronization data is abnormal, a second system alarm message of data synchronization abnormality is triggered;

[0031] If it is determined that there is no abnormality in the synchronized data, the synchronized data is stored.

[0032] In a second aspect, the present application provides a synchronous data verification device, comprising:

[0033] The first acquisition module is used to acquire synchronized data from multiple business systems;

[0034] A first determination module is configured to determine, in the synchronized data, historical data corresponding to a historical time interval and real data corresponding to a time interval to be predicted; the historical time interval is determined by the time interval to be predicted, and the historical time interval and the time interval to be predicted have the same length;

[0035] A prediction module, configured to perform data prediction based on historical data using a preset exponential smoothing algorithm model to obtain first prediction data;

[0036] a compensation module, configured to perform error compensation processing on the first prediction data according to the real data to obtain second prediction data;

[0037] The second acquisition module is used to obtain a reasonable interval of synchronization data corresponding to the time interval to be predicted;

[0038] The second determination module is used to determine whether there is an anomaly in the synchronization data according to a reasonable interval of the synchronization data and the second prediction data.

[0039] In a third aspect, the present application provides a synchronous data verification device, comprising: a memory, a processor;

[0040] Memory stores computer-executable instructions;

[0041] The processor executes the computer execution instructions stored in the memory, so that the processor executes a synchronous data verification method according to the first aspect of the invention.

[0042] In a fourth aspect, the present application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement a synchronous data verification method according to the first aspect of the invention.

[0043] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements a synchronous data verification method according to the invention content of the first aspect.

[0044] The present application provides a synchronization data verification method, device, equipment, storage medium and program product, which obtains synchronization data of multiple business systems; in the synchronization data, determines the historical data corresponding to the historical time interval and the real data corresponding to the time interval to be predicted; the historical time interval is determined by the time interval to be predicted, and the time length of the historical time interval is the same as that of the time interval to be predicted; based on the historical data, a preset exponential smoothing algorithm model is used to predict the data to obtain the first prediction data; based on the real data, the first prediction data is error compensated to obtain the second prediction data; a reasonable interval of synchronization data corresponding to the time interval to be predicted is obtained; based on the reasonable interval of synchronization data and the second prediction data, it is determined whether there is a synchronization data anomaly. Compared with the existing technology, the judgment of the legitimacy of synchronization data mainly relies on manual review, which is inefficient, inaccurate and timeless; synchronization data verification is mostly processed offline, and this static verification method is difficult to meet the real-time and dynamic requirements of synchronization data; at the same time, traditional verification methods are mostly based on fixed rules or scripts, which makes it difficult to comprehensively judge the legitimacy and accuracy of synchronization data. These problems have led to low efficiency in synchronization data verification. This application uses an exponential smoothing algorithm model to predict historical data, performs error compensation on the predicted data based on the real data corresponding to the time interval to be predicted, and determines whether the synchronized data is abnormal based on the reasonable interval of the synchronized data corresponding to the time interval to be predicted and the compensated predicted data, thereby effectively improving the accuracy of data synchronization and thus improving the efficiency of synchronized data verification. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 A schematic diagram of the system architecture of a synchronous data verification method provided in an embodiment of the present application;

[0047] Figure 2A schematic diagram of a synchronous data verification method provided in an embodiment of the present application Figure 1 ;

[0048] Figure 3 A schematic diagram of a synchronous data verification method provided in an embodiment of the present application Figure 2 ;

[0049] Figure 4 A schematic diagram of a synchronous data verification method provided in an embodiment of the present application Figure 3 ;

[0050] Figure 5 A schematic diagram of the structure of a synchronous data verification device provided in an embodiment of the present application;

[0051] Figure 6 A schematic diagram of the structure of a synchronous data verification device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0053] In the embodiments of the present application, words such as "first" and "second" are used to distinguish between identical or similar items with substantially the same functions and effects. Those skilled in the art will understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit differences. It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described in this application as "exemplary" or "for example" should not be interpreted as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way. In the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more.

[0054] It should be noted that the "at..." in the embodiments of the present application can be the instant when a certain situation occurs, or it can be a period of time after the situation occurs, and the embodiments of the present application do not specifically limit this. In addition, the synchronous data verification method provided in the embodiments of the present application is only an example, and the synchronous data verification method can also include more or less content.

[0055] With the development of information technology, enterprise information system operation platforms have become increasingly complex. Different business units often use independent software systems to manage and process their own data. The demand for data interaction between information system operation platforms and these systems is increasing, and the accuracy of data synchronization between these platforms is very high.

[0056] However, as business scenarios evolve, the amount of data that needs to be synchronized daily increases. The accuracy of synchronized data primarily relies on manual review and simple rule-based verification, which increases labor costs. Therefore, enterprises need an information system operation platform that can automatically and intelligently synchronize data and determine its legitimacy. This will improve the timeliness and accuracy of data acquisition and management, while reducing labor costs.

[0057] Therefore, how to determine the accuracy of data synchronized from other platforms is a current challenge. Currently, most companies' internal information operation platforms have the following main problems with automated data synchronization:

[0058] On the one hand, the success or failure of data synchronization can only be checked using simple rules. Data accuracy relies primarily on manual review by professionals, but manual review is inefficient: for example, if the synchronized data is zero but the status code returns 200, it will be considered successful. When there are too many data indicators, it often leads to high labor costs and is prone to missed or misjudged reviews, reducing the accuracy of synchronized data. Furthermore, manual review can delay the detection of failed or inaccurate data, which also leads to inefficient synchronization data verification.

[0059] On the one hand, at present, the synchronization data verification of the internal information operation platform of the enterprise usually relies mostly on offline processing, which is unable to monitor the data status in real time and dynamically adjust the strategy. When the data differences between different months are large, it is often impossible to effectively identify and judge. This static verification method is difficult to meet the real-time and dynamic requirements of synchronization data.

[0060] On the other hand, traditional verification methods are mostly based on fixed rules or scripts, which make it difficult to comprehensively judge the legitimacy and accuracy of synchronized data. Existing technologies often cannot effectively identify and judge synchronized data. At the same time, this method can usually only provide simple result output, and cannot provide good feedback processing for abnormal situations, and cannot maintain the stable operation of the system.

[0061] Based on this, the embodiments of the present application provide a synchronous data verification method, device, equipment, storage medium and program product, which can be used in the field of data synchronization technology, aiming to solve the above technical problems of the prior art.

[0062] The inventive concept of this application lies in: To address the above-mentioned issues, while researching the efficiency of synchronized data verification, the inventors discovered that existing verification methods rely on manual review, typically conducted offline, and primarily on fixed rules or scripts. Manual review is costly, lacks timely problem detection, and results in low verification accuracy. Offline processing also struggles to meet the real-time and dynamic requirements of synchronized data. Furthermore, the inflexibility of the verification method makes it difficult to comprehensively and effectively identify and address issues. Based on this, the inventors analyze historical data using a cubic exponential smoothing algorithm model to predict the time interval for the data to be synchronized. They then correct the predicted data using error compensation parameters, thereby improving prediction accuracy. By adjusting the smoothing coefficient, they flexibly adjust the weighting between the previous period's actual value and the predicted value, thereby dynamically adjusting the model's adaptability and accuracy. Finally, by generating a reasonable range for the predicted value, the synchronized data is automatically determined to be abnormal. For abnormal data, an alarm process is automatically triggered, thereby achieving automated operation and maintenance, reducing the workload of maintenance personnel. Based on this, the present application proposes a synchronized data verification method to further improve the efficiency of synchronized data verification.

[0063] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0064] Figure 1 A schematic diagram of the system architecture of a synchronous data verification method provided in an embodiment of the present application. Figure 1 In the above architecture, the above architecture includes at least one of a data acquisition device 101, a processing device 102 and a display device 103.

[0065] It is understood that the structure illustrated in the embodiment of this application does not constitute a specific limitation on the architecture of the dish label processing system. In other feasible embodiments of this application, the above architecture may include more or fewer components than shown in the figure, or combine certain components, split certain components, or arrange components differently. The specific configuration can be determined according to the actual application scenario and is not limited here. Figure 1 The components shown can be implemented in hardware, software, or a combination of software and hardware.

[0066] In the specific implementation process, the data acquisition device 101 may include an input / output interface and a communication interface. The data acquisition device 101 may be connected to the processing device through the input / output interface or the communication interface to obtain synchronous data of multiple business systems.

[0067] The processing device 102 can determine the historical data corresponding to the historical time interval and the real data corresponding to the time interval to be predicted in the synchronized data of multiple business systems, and determine whether there is any synchronization data anomaly based on the error compensation processing between the historical data and the real data.

[0068] The display device 103 may also be a touch screen display or a screen of a terminal device, which is used to trigger alarm information or store synchronization data while displaying the above content to achieve interaction with the user.

[0069] It should be understood that the above-mentioned processing device can be implemented by a processor reading instructions in a memory and executing the instructions, or it can be implemented by a chip circuit.

[0070] In addition, the network architecture and business scenarios described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Ordinary technicians in this field can know that with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0071] The technical solution of this application is described in detail below with reference to specific embodiments:

[0072] Figure 2 A schematic diagram of a synchronous data verification method provided in an embodiment of the present application Figure 1 ,like Figure 2 As shown, the method includes:

[0073] S201. Acquire synchronous data of multiple business systems.

[0074] S202. Determine historical data corresponding to a historical time interval and real data corresponding to a time interval to be predicted in the synchronized data; the historical time interval is determined by the time interval to be predicted, and the historical time interval and the time interval to be predicted have the same length.

[0075] Ensure that the historical time interval is the same length as the time interval to be predicted. For example, if the time interval to be predicted is next week, the historical time interval should be the same week in the past.

[0076] The real data corresponding to the time interval to be predicted are the actual values ​​that have not been predicted.

[0077] Among them, historical data should be consistent with the data of the time period to be predicted in terms of type, statistical caliber, etc. to ensure the accuracy and reliability of the prediction model.

[0078] S203: Based on the historical data, a preset exponential smoothing algorithm model is used to perform data prediction to obtain first prediction data.

[0079] Specifically, based on historical data, data prediction is performed through a cubic exponential smoothing algorithm model to obtain first prediction data.

[0080] S204: Perform error compensation processing on the first predicted data according to the real data to obtain second predicted data.

[0081] Specifically, based on the real data, target data with the same time interval as the real data is found in the first predicted data, the difference between the target data and the real data is determined, the error compensation parameter is determined based on the difference, and the first predicted data is error compensated according to the error compensation parameter to obtain the second predicted data.

[0082] It should be noted that the error compensation parameter is used to compensate the error of the prediction data corresponding to the time interval to be predicted obtained by the weighted exponential smoothing algorithm model, thereby correcting the prediction data, reducing the prediction error as much as possible, and improving the accuracy of the final prediction data.

[0083] S205: Obtain a reasonable interval of synchronization data corresponding to the time interval to be predicted.

[0084] S206: Determine whether there is an anomaly in the synchronization data based on the reasonable interval of the synchronization data and the second prediction data.

[0085] It should be noted that, after step S206, the following steps are also included:

[0086] S207: Execute task scheduling for the business system based on the abnormal result of the synchronization data.

[0087] Specifically, if it is determined that the synchronization data is abnormal, a second system alarm message of data synchronization abnormality is triggered.

[0088] For example, if an anomaly is detected in the synchronized data, the relevant personnel will be notified via email or text message for processing.

[0089] Specifically, if it is determined that there is no abnormality in the synchronization data, the synchronization data is stored.

[0090] For example, if it is determined that there is no abnormality in the synchronized data, the synchronized data is stored in the corresponding data table and a log is recorded. At the same time, the prediction table is updated according to the current data to generate a new prediction value.

[0091] It should be noted that in the process of synchronizing data on the information system operation platform, as the amount of synchronized data increases, the accuracy of the exponential smoothing algorithm model in judging the accuracy of synchronized data is continuously improving, which not only reduces the workload of operation and maintenance personnel but also improves the efficiency of synchronized data verification.

[0092] It should also be noted that when the call to synchronize data fails, an error retry mechanism will be provided. If multiple failures occur, SMS or email notifications will be sent to relevant personnel in a timely manner to ensure the timeliness and accuracy of the synchronized data.

[0093] The present embodiment provides a synchronization data verification method, which obtains synchronization data from multiple business systems; in the synchronization data, determines the historical data corresponding to the historical time interval and the real data corresponding to the time interval to be predicted; the historical time interval is determined by the time interval to be predicted, and the time length of the historical time interval is the same as that of the time interval to be predicted; based on the historical data, a preset exponential smoothing algorithm model is used to predict the data to obtain the first prediction data; based on the real data, the first prediction data is error compensated to obtain the second prediction data; a reasonable interval of synchronization data corresponding to the time interval to be predicted is obtained; based on the reasonable interval of synchronization data and the second prediction data, it is determined whether there is a synchronization data anomaly. Compared with the existing technology, the judgment of the legitimacy of synchronization data mainly relies on manual review, which is inefficient, inaccurate and timeless; synchronization data verification is mostly processed offline, and this static verification method is difficult to meet the real-time and dynamic requirements of synchronization data; at the same time, traditional verification methods are mostly based on fixed rules or scripts, which makes it difficult to comprehensively judge the legitimacy and accuracy of synchronization data. These problems have led to low efficiency in synchronization data verification. This application uses an exponential smoothing algorithm model to predict historical data, performs error compensation on the predicted data based on the real data corresponding to the time interval to be predicted, and determines whether the synchronized data is abnormal based on the reasonable interval of the synchronized data corresponding to the time interval to be predicted and the compensated predicted data, thereby effectively improving the accuracy of data synchronization and thus improving the efficiency of synchronized data verification.

[0094] Figure 3 A schematic diagram of a synchronous data verification method provided in an embodiment of the present application Figure 2 ,like Figure 3 As shown, the specific implementation steps of the above S201 include:

[0095] S301: Generate a scheduled task according to a preset time sequence.

[0096] Among them, the data synchronized from the information system operation platform to other business systems are a set of single or multiple time-ordered data in a continuous period. Such data has quantitative characteristics and can be called a time series.

[0097] S302: Send the scheduled task to multiple business systems, where the scheduled task is used to obtain synchronization data of the business systems.

[0098] In this embodiment, synchronized data from each business system is obtained, and a scheduled task is generated according to a preset time sequence. The scheduled task is sent to multiple business systems, and the data from each business system is obtained and processed by the scheduled task.

[0099] This includes collecting data in the form of API, KAFKA, HTTP, etc.

[0100] S303: Acquire synchronization data of multiple business systems according to the response status of the business systems to the scheduled tasks.

[0101] Specifically, it is determined whether the response status of the business system to the scheduled task is a return exception.

[0102] Optionally, if the response status of the business system to the scheduled task is not a return exception, the return information of the business system to the scheduled task is received, and the synchronization data of the multiple business systems is obtained according to the return information.

[0103] Optionally, if the response status of the business system to the scheduled task is a return exception, the retry mechanism is triggered; when the response status of the business system to the scheduled task is not a return exception when the number of retries is within the preset number of retries, the return information of the business system to the scheduled task is received, and the synchronization data of multiple business systems is obtained based on the return information; when the response status of the business system to the scheduled task is always a return exception when the number of retries is within the preset number of retries, the first system alarm information of the data synchronization exception is triggered.

[0104] For example, when a scheduled task returns an exception, a retry mechanism will be triggered to resynchronize the data. When the number of retries exceeds the threshold, a system alarm will be triggered, and relevant personnel will be notified by email or text message to handle the problem in a timely manner and synchronize the data.

[0105] It's important to note that scheduled tasks automatically retrieve data from multiple business systems and trigger a retry mechanism when synchronization fails. When the number of retries exceeds a preset threshold, an alert is automatically sent to relevant personnel. This mechanism efficiently and stably synchronizes data from multiple business systems, ensuring reliable and timely synchronization, reducing manual intervention, and improving the automation level of system operations. Furthermore, it handles data synchronization anomalies on the information system operations platform in real time, ensuring platform stability and reliability.

[0106] In this embodiment, data from multiple business systems is automatically acquired based on scheduled tasks, and a retry mechanism is triggered when synchronization fails. When the number of retries exceeds a preset threshold, an alert is automatically sent to relevant personnel, ensuring the reliability and timeliness of data synchronization and improving the efficiency of synchronized data verification.

[0107] Figure 4 A schematic diagram of a synchronous data verification method provided in an embodiment of the present application Figure 3 ,like Figure 4 As shown, the specific implementation steps of the above S203 include:

[0108] S401. Determine a preset cubic exponential smoothing algorithm model; update the smoothing coefficient of the cubic exponential smoothing algorithm model according to the prediction result of the cubic exponential smoothing algorithm model and the real data corresponding to the prediction result.

[0109] Among them, the exponential smoothing method is used based on historical data to predict the data to be synchronized, and the prediction of the next step is regarded as the prediction of future periods.

[0110] Specifically, the forecast value for the next period is calculated using the basic formula of the exponential smoothing method.

[0111] Among them, the basic formula of exponential smoothing is:

[0112] F t+1 =αX t +(1-α)F t

[0113] 0≤α≤1

[0114] Among them, X is the actual demand; F is the forecast; X t is the actual value of period t; F t is the predicted value of period t; F t+1 is the predicted value of the t+1 period; α is the smoothing coefficient, which ranges from 0 to 1 and is used to adjust the weight of the actual value of the previous period and the predicted value.

[0115] Among them, F t+1 Part of it comes from the actual value of the previous period, and the rest comes from the predicted value of the previous period, that is, F t+1 It is the weighted average of the previous period's actual value and the forecast value.

[0116] Specifically, when the basic formula of exponential smoothing is used to express that the forecast value of the next period is equal to the forecast value of the current period plus an adjustment term based on the error, another expression of the basic formula of exponential smoothing is:

[0117] F t+1 =F t +α(X t -Ft )

[0118] 0≤α≤1

[0119] The adjustment term is α times the difference between the actual value and the predicted value.

[0120] Furthermore, by adjusting the smoothing coefficient α, the weighting of the previous period's actual and forecast values ​​can be adjusted: the larger α, the greater the weight of the previous period's actual value and the smaller the weight of the previous period's forecast. This makes the forecast model more sensitive and more responsive to actual changes. However, it also increases its susceptibility to random factors, leading to greater fluctuations in the supply chain. The smaller α, the smaller the weight of the previous period's actual value and the larger the weight of the previous period's forecast. This allows more fluctuations to be filtered out as "noise," resulting in a more stable forecast.

[0121] It should be noted that by adjusting the smoothing coefficient α, the weighting of the previous period's actual value and the forecast value can be flexibly adjusted to make the forecast model more sensitive or stable. This dynamic adjustment capability enhances the adaptability and accuracy of the model, enabling it to better adapt to different data change patterns.

[0122] It should also be noted that some of the synchronized business system data in certain months have significant differences from the data in other months. In order to improve the accuracy of the forecast, when using the triple exponential smoothing algorithm for this part of the synchronized data, it is necessary to add a quantity to the triple exponential smoothing method to describe the temporal nature and the corresponding equation of the cumulative formula.

[0123] Furthermore, a period parameter of a time variable used to describe temporality is obtained.

[0124] Among them, the length of the cycle can be set differently according to different dimensions of daily, weekly, monthly and annual reports.

[0125] Furthermore, each calculation formula of the preset triple exponential smoothing algorithm model is determined according to the period parameter.

[0126] Specifically, the formula for calculating the smoothing value is:

[0127] s i =α(x i -p i-k )+(1-α)(s i-1 -t i-1 )

[0128] Among them, s i is the smoothed value of the i-th period; x i is the observation value of the i-th period; p i-k is the predicted value for the ikth period; s i-1 is the smoothed value of the i-1th period; t i-1 is the trend term of the i-1th period.

[0129] Among them, this formula combines the current observation value x i Compared with the forecast value p of the previous period i-k The difference between the two periods, and the smoothed value s of the previous period i-1 and the trend term t i-1 The weighted sum of .

[0130] Specifically, the formula for calculating the trend term is:

[0131] t i =β(s i -s i-1 )+(1-β)t i-1

[0132] Among them, t i is the trend term of the i-th period; s i is the smoothed value of the i-th period; β is another smoothing coefficient, which is used to adjust the weight between the current smoothed value and the smoothed value of the previous period.

[0133] The formula is based on the current smoothing value s i and the smoothed value s of the previous period i-1 The difference between the two periods, and the trend term t of the previous period i-1 The weighted sum of .

[0134] Specifically, the formula for calculating the predicted value is:

[0135] p i =γ(x i -s i )+(1-γ)p i-k

[0136] Among them, p i is the forecast value of the i-th period; k is the period parameter; p i-k is the forecast value for the ikth period; γ is another smoothing coefficient used to adjust the weight between the current observation value and the forecast value.

[0137] The formula is based on the current observation value x i With smoothing value s i The difference between the two periods, and the forecast value p of the previous period i-k The weighted sum of .

[0138] It should be noted that these three formulas together constitute a complete exponential smoothing model for processing time series data with trends and seasonality. Formula 1 calculates the smoothed value, Formula 2 calculates the trend term, and Formula 3 calculates the predicted value. By adjusting the values of α, β, and γ, the prediction performance of the model can be optimized to make it more adaptable to different data change patterns. This model has a wide range of applications in time series prediction, can effectively handle complex data features, and improve the accuracy of prediction.

[0139] It should also be noted that all exponential smoothing methods are based on recurrence relations, which means that initial values need to be set before they can be used. However, the choice of initial values is not particularly important. The exponential decay law shows that the "memory" ability of all exponential smoothing methods is very short. After only a few time steps, the influence of the initial values will become negligible.

[0140] For example, some reasonable initial values are as follows:

[0141] s0 = x0 or

[0142] 1 < n < 5 and t0 = 0 or t0 = x1 - x0

[0143] S402. According to historical data, use a preset cubic exponential smoothing algorithm model for data prediction to obtain the first predicted data.

[0144] In this embodiment, based on the cubic exponential smoothing algorithm model, historical data is analyzed to predict the time interval of the data to be synchronized, and the predicted data is corrected through error compensation parameters, improving the accuracy of data synchronization. More accurately judge the legality and accuracy of the data, and then improve the efficiency of synchronizing data verification.

[0145] Figure 5 The structural schematic diagram of a synchronizing data verification device provided by an embodiment of the present application is as Figure 5 shown. The device includes: a first acquisition module 51, a first determination module 52, a prediction module 53, a compensation module 54, a second acquisition module 55, and a second determination module 56.

[0146] The first acquisition module 51 is used to acquire the synchronizing data of multiple service systems;

[0147] The first determination module 52 is used to determine the historical data corresponding to the historical time interval and the real data corresponding to the time interval to be predicted in the synchronizing data; the historical time interval is determined by the time interval to be predicted, and the historical time interval has the same time length as the time interval to be predicted;

[0148] The prediction module 53 is used to perform data prediction based on historical data using a preset exponential smoothing algorithm model to obtain first prediction data;

[0149] a compensation module 54 for performing error compensation processing on the first prediction data according to the real data to obtain second prediction data;

[0150] The second acquisition module 55 is used to obtain a reasonable interval of synchronization data corresponding to the time interval to be predicted;

[0151] The second determining module 56 is configured to determine whether there is an anomaly in the synchronization data according to the reasonable interval of the synchronization data and the second prediction data.

[0152] In one possible design, synchronized data from multiple business systems is obtained, including:

[0153] The first acquisition module 51 is further configured to generate a scheduled task according to a preset time sequence;

[0154] Send scheduled tasks to multiple business systems, where scheduled tasks are used to obtain synchronized data from the business systems;

[0155] Get the synchronization data of multiple business systems based on the response status of the business systems to the scheduled tasks.

[0156] In one possible design, based on the response status of the business system to the scheduled task, synchronized data from multiple business systems is obtained, including:

[0157] The first acquisition module 51 is further used to determine whether the response status of the business system to the scheduled task is a return exception;

[0158] If the response status of the business system for the scheduled task is not an exception, the return information of the business system for the scheduled task is received, and the synchronization data of multiple business systems is obtained according to the return information;

[0159] If the response status of the business system to the scheduled task is a return exception, the retry mechanism is triggered; when the response status of the business system to the scheduled task is not a return exception when the number of retries is within the preset number of retries, the return information of the business system for the scheduled task is received, and the synchronization data of multiple business systems is obtained according to the return information; when the response status of the business system to the scheduled task is always a return exception when the number of retries is within the preset number of retries, the first system alarm information of the data synchronization exception is triggered.

[0160] In one possible design, based on historical data, a preset exponential smoothing algorithm model is used to perform data forecasting to obtain first forecast data, including:

[0161] The prediction module 53 is further used to determine a preset triple exponential smoothing algorithm model; the smoothing coefficient of the triple exponential smoothing algorithm model is updated according to the prediction result of the triple exponential smoothing algorithm model and the real data corresponding to the prediction result;

[0162] Based on historical data, a preset triple exponential smoothing algorithm model is used to perform data prediction to obtain first prediction data.

[0163] In one possible design, a preset triple exponential smoothing algorithm model is determined, including:

[0164] A prediction module 53 for obtaining period parameters of a time variable used to describe temporality;

[0165] According to the period parameters, the calculation formulas of the preset triple exponential smoothing algorithm model are determined.

[0166] In one possible design, after determining whether the synchronized data is abnormal based on the reasonable interval of the synchronized data and the second predicted data, the method further includes:

[0167] Execute task scheduling for the business system based on the abnormal results of synchronization data.

[0168] In one possible design, based on the abnormal results of the synchronized data, task scheduling is performed on the business system, including:

[0169] If it is determined that the synchronization data is abnormal, a second system alarm message of data synchronization abnormality is triggered;

[0170] If it is determined that there is no abnormality in the synchronized data, the synchronized data is stored.

[0171] A synchronous data verification device provided in this embodiment can execute a synchronous data verification method of the above embodiment. Its implementation principle and technical effects are similar and will not be described in detail in this embodiment.

[0172] In a specific implementation of the aforementioned synchronous data verification method, each module may be implemented as a processor, and the processor may execute computer-executable instructions stored in a memory, so that the processor executes the aforementioned synchronous data verification method.

[0173] Figure 6 This is a structural diagram of a synchronous data verification device provided by an embodiment of the present application. Figure 6 As shown, the synchronous data verification device 60 includes: at least one processor 61 and a memory 62. The synchronous data verification device 60 also includes a communication component 63. 61, the memory 62 and the communication component 63 are connected via a second bus 64.

[0174] In a specific implementation process, at least one processor 61 executes the computer execution instructions stored in the memory 62, so that at least one processor 61 executes a synchronization data verification method executed by the synchronization data verification device side as described above.

[0175] The specific implementation process of the processor 61 can be found in the above-mentioned method embodiment. Its implementation principle and technical effects are similar and will not be repeated here in this embodiment.

[0176] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly implemented by a hardware processor or implemented by a combination of hardware and software modules in the processor.

[0177] The memory may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk storage.

[0178] The second bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be classified as address buses, data buses, control buses, etc. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0179] The above-mentioned functions implemented by the synchronous data verification device and the main control device have introduced the solutions provided by the embodiments of the present invention. It can be understood that in order to implement the above-mentioned functions, the synchronous data verification device or the main control device includes hardware structures and / or software modules corresponding to the execution of each function. In combination with the units and algorithm steps of the various examples described in the embodiments disclosed in the embodiments of the present invention, the embodiments of the present invention can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the technical solution of the embodiments of the present invention.

[0180] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the computer-readable storage medium is used to implement the above-mentioned synchronous data verification method.

[0181] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile memory device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0182] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in a synchronous data verification device or a main control device.

[0183] The present application also provides a computer program product, which includes: a computer program, which is stored in a readable storage medium, at least one processor of the synchronization data verification device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the synchronization data verification device executes the solution provided by any of the above embodiments.

[0184] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0185] So far, the technical solution of the present application has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the scope of protection of the present application is obviously not limited to these specific embodiments. The above embodiments are only used to illustrate the technical solution of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, ordinary technicians in this field should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solution to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A synchronous data verification method, characterized in that: include: Obtain synchronized data from multiple business systems; In the synchronized data, historical data corresponding to a historical time interval and real data corresponding to a time interval to be predicted are determined; the historical time interval is determined by the time interval to be predicted, and the historical time interval and the time interval to be predicted have the same length; Based on the historical data, a preset exponential smoothing algorithm model is used to perform data forecasting to obtain first forecast data; performing error compensation processing on the first prediction data according to the real data to obtain second prediction data; Obtaining a reasonable interval of synchronization data corresponding to the time interval to be predicted; Determine whether there is an anomaly in the synchronization data based on the reasonable interval of the synchronization data and the second prediction data.

2. The method according to claim 1, characterized in that The obtaining of synchronized data of multiple business systems includes: Generate scheduled tasks according to the preset time sequence; Sending the scheduled task to the multiple business systems, wherein the scheduled task is used to obtain synchronization data of the business systems; Acquire synchronization data of the multiple business systems according to the response status of the business system to the scheduled task.

3. The method according to claim 2, characterized in that The acquiring, according to the response status of the business system to the scheduled task, the synchronization data of the multiple business systems includes: Determine whether the response status of the business system to the scheduled task is a return exception; If the response status of the business system to the scheduled task is not a return exception, receiving the return information of the business system to the scheduled task, and obtaining the synchronization data of the multiple business systems according to the return information; If the response status of the business system to the scheduled task is a return exception, a retry mechanism is triggered; when the response status of the business system to the scheduled task is not a return exception when the number of retries is within the preset number of retries, the return information of the business system to the scheduled task is received, and the synchronization data of the multiple business systems is obtained according to the return information; when the response status of the business system to the scheduled task is always a return exception when the number of retries is within the preset number of retries, the first system alarm information of data synchronization abnormality is triggered.

4. The method according to any one of claims 1 to 3, characterized in that The method of performing data forecasting based on the historical data using a preset exponential smoothing algorithm model to obtain first forecast data includes: Determining a preset triple exponential smoothing algorithm model; updating a smoothing coefficient of the triple exponential smoothing algorithm model according to a prediction result of the triple exponential smoothing algorithm model and real data corresponding to the prediction result; Based on the historical data, the preset cubic exponential smoothing algorithm model is used to perform data prediction to obtain the first predicted data.

5. The method according to claim 4, characterized in that The method of determining a preset triple exponential smoothing algorithm model includes: Get the period parameter of the time variable used to describe temporality; According to the period parameters, each calculation formula of the preset triple exponential smoothing algorithm model is determined.

6. The method according to any one of claims 1 to 3, characterized in that After determining whether there is an anomaly in the synchronization data according to the reasonable interval of the synchronization data and the second predicted data, the method further includes: According to the abnormal result of the synchronization data, task scheduling is performed on the business system.

7. The method according to claim 6, characterized in that The executing task scheduling for the business system according to the abnormal result of the synchronization data includes: If it is determined that the synchronization data is abnormal, a second system alarm message of data synchronization abnormality is triggered; If it is determined that there is no abnormality in the synchronization data, the synchronization data is stored.

8. A synchronous data verification device, characterized in that: include: The first acquisition module is used to acquire synchronized data from multiple business systems; a first determining module configured to determine, in the synchronized data, historical data corresponding to a historical time interval and real data corresponding to a time interval to be predicted; wherein the historical time interval is determined by the time interval to be predicted, and the historical time interval has the same time length as the time interval to be predicted; A prediction module, configured to perform data prediction based on the historical data using a preset exponential smoothing algorithm model to obtain first prediction data; a compensation module, configured to perform error compensation processing on the first prediction data according to the real data to obtain second prediction data; The second acquisition module is used to obtain a reasonable interval of synchronization data corresponding to the time interval to be predicted; The second determination module is used to determine whether there is a synchronization data anomaly based on the reasonable interval of the synchronization data and the second prediction data.

9. A synchronous data verification device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

11. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 7 when executed by a processor.

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