CN117931786B - Digitalized multifunctional platform interaction management method, system and storage medium - Google Patents
Digitalized multifunctional platform interaction management method, system and storage medium Download PDFInfo
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Abstract
本发明公开了一种数字化的多功能平台交互管理方法、系统及存储介质，属于数据处理技术领域，包括：步骤S1：将数据信息分类为多个数据序列传输至数据管理模块；步骤S2：在数据序列中截取第一数据组并分割为第一子数据组，生成第一纠错码，将第一子数据组重新组合生成第二子数据组和对应的第二纠错码传输至中转单元，生成第三子数据组和对应的生成第三纠错码传输至处理单元；步骤S3：纠正第三子数据组的错误，以生成纠正数据并传输至管控单元；步骤S4：预测模型输出数据序列的预测结果传输至综合平台，向用户类型输出数据序列和预测结果。通过本发明可以对数据序列进行检测纠正和空间预测，从而提高数据序列的传输准确性和应用性。
The present invention discloses a digital multifunctional platform interactive management method, system and storage medium, which belongs to the field of data processing technology, including: step S1: classifying data information into multiple data sequences and transmitting them to a data management module; step S2: intercepting a first data group in the data sequence and dividing it into a first sub-data group, generating a first error correction code, recombining the first sub-data group to generate a second sub-data group and a corresponding second error correction code to transmit to a transfer unit, generating a third sub-data group and a corresponding third error correction code to transmit to a processing unit; step S3: correcting the error of the third sub-data group to generate correction data and transmit it to a control unit; step S4: the prediction result of the prediction model output data sequence is transmitted to a comprehensive platform, and the data sequence and the prediction result are output to the user type. Through the present invention, the data sequence can be detected, corrected and spatially predicted, thereby improving the transmission accuracy and applicability of the data sequence.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to a digital multifunctional platform interaction management method, a digital multifunctional platform interaction management system and a storage medium.
Background
In the business operation process, the business service promotion is an important proposition of business operation, the business needs to have successful operation awareness for business clients, the business needs to be hard in sharing with the business, the business service is digitized, so that business service is closed, business platform information is interacted, the whole business service life cycle is recorded, the subsequent data analysis, optimization improvement and the like are greatly achieved, the interaction management of the multifunctional platform can quickly and conveniently reach the business, the information is timely released, the traditional off-line bill is converted into on-line paperless office, the time is saved in digital circulation, one-user one-time information circulation is electronic, the quick flow approval and the optimization settlement are realized, the business satisfaction is improved, and the management efficiency of personnel in the business is improved.
For example, china patent application CN116796101A discloses a method for constructing a digital management platform, which comprises the following steps of obtaining related model data, classifying the related model data, publishing the classified model data, forming different functional components according to different use requirements, constructing a platform interface, and adding the generated functional components into the platform interface to realize platform multifunctional interface interaction. According to the technical scheme, on the basis of the prior art route, a VUE framework language is used, the advantage of the VUE framework is utilized, a digital platform is independently developed, developed functional components are added into the platform, various models are comprehensively managed, the functional components are customized for projects, and the comprehensive management capability and reusability of the platform are greatly improved. For example, chinese patent application CN116628071B discloses a data interaction method and system of a digital exhibition management platform, which relates to the technical field of data interaction, and constructs a digital exhibition management platform, including a data acquisition unit, a data transmission unit, a data management unit and a data interaction unit, wherein the data acquisition unit is used for acquiring an exhibition database, the data transmission unit is used for encrypting and transmitting the exhibition database to the data management unit, and the exhibition management parameter configuration is performed to obtain an exhibition management decision result, and the digital exhibition data interaction is performed based on the exhibition management decision result and the data interaction unit. The invention solves the technical problems of poor exhibition effect of the products to be exhibited and poor observation experience of the users to be exhibited due to lower intelligence degree of the exhibition layout of the digital exhibition in the prior art, realizes the connection management of the user platform, achieves the effect of improving the intelligence degree of the exhibition layout of the digital exhibition, and improves the exhibition effect of the products to be exhibited and the technical effect of the observation experience of the users to be exhibited.
However, in the above prior art, only the interaction mode of the multifunctional platform is set, in actual situations, the acquired data is transmitted through different communication ports, and there may be a phenomenon of data errors, so that the data is checked and corrected and the data sequence is spatially predicted on the premise of not affecting the data transmission rate, which is beneficial to data analysis and application.
Disclosure of Invention
In order to solve the problems, the invention provides a digital multifunctional platform interaction management method, a digital multifunctional platform interaction management system and a digital multifunctional platform interaction management storage medium, so as to solve the problems in the prior art.
In order to achieve the above object, the present invention provides a method for interaction management of a digital multifunctional platform, comprising:
The method comprises the steps that a collection module collects data information, the data information is classified into a plurality of data sequences based on preset label types, and all the data sequences are transmitted to a data management module;
The data management module intercepts first data groups in all the data sequences based on a preset first time period, divides the first data groups into a first sub-data group with a preset first number, sets a preset calculation process, generates first error correction codes based on the first sub-data groups, recombines the first sub-data groups to generate a second sub-data group with a preset second number, generates second error correction codes based on the second sub-data groups, transmits the second sub-data groups and the second error correction codes to a transfer unit in a first transmission mode, and the transfer unit recombines the second sub-data groups to generate a third sub-data group with a second number, generates a third error correction code based on the third sub-data group and transmits the third sub-data group and the third error correction code to a processing unit in a second transmission mode;
judging whether the third sub-data group has errors or not, correcting the errors of the third sub-data group to generate correction data of the first data group, transmitting the correction data to a management control unit, and transmitting all the third sub-data group to the management control unit if not, and repeatedly executing until the data sequence is transmitted to the management control unit;
The prediction model obtains the spatial correlation of the data sequence based on the tag type, outputs a prediction result of the data sequence based on the spatial correlation, transmits the prediction result to a comprehensive platform, sets different user types based on the tag type, and outputs the corresponding data sequence and the prediction result to the user type.
Further, determining whether the third sub-data group has an error includes the steps of:
Calculating the verification sum of all the second sub-data groups in a preset mode, and accumulating the verification sums of all the second sub-data groups to generate a first verification sum;
Acquiring the first sub-data group contained in the third sub-data group, calculating a first verification sum of the first sub-data group based on the number of the first sub-data groups, setting the first verification sum to be a first round of verification sum, calculating a second round of verification sum of the first round of verification sum and the next first sub-data group, repeatedly executing the steps until all the first sub-data groups in the third sub-data group are calculated, setting the calculation result to be the verification sum of the third sub-data group, and generating a fourth error correction code based on the verification sum of the third sub-data group;
The processing unit transmits the verification sums of all the third sub-data sets and the corresponding fourth error correction codes to the transfer unit, and adds the verification sums of all the third sub-data sets in the transfer unit to generate a second verification sum;
and comparing the sizes of the first verification sum and the second verification sum, if the first verification sum and the second verification sum are equal, judging that the third sub-data group has no error, and if the third verification sum and the second verification sum are not equal, judging that the third sub-data group has the error.
Further, outputting the predicted result of the data sequence includes the steps of:
The data prediction module acquires the data sequence with a preset second time period, wherein the data sequence comprises position information, a time sequence and a target variable, the prediction model performs spatial interpolation on the data sequence based on the time sequence, calculates regression coefficients of the data sequence to generate a regression function of the target variable in the data sequence, and predicts the prediction result of the target variable in future time based on the regression function;
Calculating the predicted result y of the target variable in future time based on a first formula: Wherein m is the number of the time series included in the future time, R (x; θ i) is the value of the ith Gaussian kernel function, x is the time series of the future time, θ i is the center point of the ith Gaussian kernel function, and β i is the ith regression coefficient;
Calculating the ith regression coefficient beta i based on a second formula, wherein the second formula is that
Wherein T is the number of the time series included in the second time period, β j is the j-th regression coefficient, Φ j is the j-th weight parameter, and ε i is the i-th error value.
Further, the first sub-data group reassembly comprises the steps of:
Equally dividing the first data set into the first sub-data sets of the first number based on a preset time interval, sequentially combining the first sub-data sets based on the second number to generate a plurality of combined results, extracting two combined results from the combined results to generate the second sub-data set and the third sub-data set, wherein the last first sub-data set in the third sub-data set is identical to the first sub-data set in the second sub-data set.
Further, generating correction data for the first data set comprises the steps of:
And restoring the second error correction code in the transfer unit to the second sub-data group based on the inverse operation of the preset calculation process, respectively summarizing and comparing all the second sub-data group and the third sub-data group, obtaining the error position of the third sub-data group, calculating the correction data of the third sub-data group in the processing unit based on the error position and the second sub-data group, and setting all the corrected third sub-data group as the correction data of the first data group.
The invention also provides a digitalized multifunctional platform interaction management system, which is used for realizing the digitalized multifunctional platform interaction management method, and mainly comprises the following steps:
The data management module is used for managing the data of the data information according to the data sequence, and acquiring the data information;
The data management module is used for intercepting a first data group in all the data sequences based on a preset first time period, dividing the first data group into a preset first number of first sub-data groups, generating a first error correction code based on the first sub-data groups, recombining the first sub-data groups to generate a preset second number of second sub-data groups, generating a second error correction code based on the second sub-data groups, transmitting the second sub-data groups and the second error correction code to a transfer unit in a first transmission mode, recombining the second sub-data groups to generate a second number of third sub-data groups, generating a third error correction code based on the third sub-data groups, and transmitting the third sub-data groups and the third error correction code to a processing unit in a second transmission mode;
The data correction module is used for judging whether the first data set has errors, correcting the errors of the data sequence to generate correction data of the first data set, transmitting the correction data to a management control unit, and transmitting the first data set to the management control unit under the condition that the first data set has errors, and repeatedly executing the steps until the data sequence is transmitted to the management control unit;
the data prediction module is used for obtaining the spatial correlation of the data sequence based on the tag type by a prediction model, outputting a prediction result of the data sequence based on the spatial correlation, transmitting the prediction result to a comprehensive platform, setting different user types based on the tag type, and outputting the corresponding data sequence and the prediction result to the user type by the comprehensive platform.
The invention also provides a computer storage medium which stores program instructions, wherein the program instructions control equipment where the computer storage medium is located to execute the digitalized multifunctional platform interaction management method when running.
Compared with the prior art, the invention has the following beneficial effects:
The invention firstly carries out verification and correction on the collected data sequence through the data management module, can effectively improve the accuracy of data transmission, wherein the transfer unit is used for comparing the first verification sum with the second verification sum to judge whether the data sequence has errors, therefore, the verification sum of the data groups is not required to be added in the transmission process, the reduction of the transmission efficiency can be avoided, then the error correction codes corresponding to the data groups are arranged for transmission, the first sub-data groups in the first data groups are arranged and combined to generate the second sub-data group and the third sub-data group with different combination results, and the second sub-data group and the third sub-data group are transmitted to the processing unit of the data management module through different transmission modes, so that the data channel interference among the same transmission modes can be avoided, and finally, the prediction result is output after the spatial interpolation is carried out on the data sequence through setting the prediction model, thereby being beneficial to the analysis and the application of the data sequence.
Drawings
FIG. 1 is a flow chart of steps of a method for interactive management of a digital multifunctional platform according to the present invention;
FIG. 2 is a flow chart of a data management module according to the present invention;
FIG. 3 is a schematic diagram of correcting a third sub-data set according to the present invention;
FIG. 4 is a block diagram of a digital multi-functional platform interaction management system according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
It will be understood that the terms "first," "second," and the like, as used herein, may be used to describe various elements, but these elements are not limited by these terms unless otherwise specified. These terms are only used to distinguish one element from another element. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script, without departing from the scope of this disclosure.
As shown in fig. 1, a digitalized multifunctional platform interaction management method includes:
step S1, a collection module collects data information, the data information is classified into a plurality of data sequences based on preset label types, and all the data sequences are transmitted to a data management module.
Specifically, in this embodiment, the digital market module is used as the field to perform interactive management analysis on the multifunctional platform, the data information is collected through different data acquisition modes, the data acquisition modes refer to acquisition ways for acquiring the data information, including and not limited to web page input, applet input, system input and the like, the data information refers to various complete data to be acquired, including and not limited to customer basic information, store entering frequency and consumption satisfaction in customer data, tenant position, business unit price and sale comparison in merchant data, lease rate, recruitment rate, business ratio, target achievement rate and the like in operation data, and energy consumption cost, popularization cost, management and control cost and the like in management data, and the integrity of the multifunctional platform can be effectively realized by acquiring various data related to the market. For example, the customer data may be acquired by binding member information of the customer through an applet and recording the condition of the customer's business entry based on the member information.
The tag type includes, but is not limited to, customer data, merchant data, operation data and management data, and the tag type can be used for summarizing data information of the same tag type collected in different data acquisition modes to generate a data sequence, wherein the data sequence is time sequence data containing different tag types, for example, summarizing customer member information acquired in different merchants can reduce the frequency of logging in members when a customer enters different merchants.
And S2, intercepting a first data group in all data sequences based on a preset first time period by the data management module, dividing the first data group into a preset first number of first sub-data groups, setting a preset calculation process, generating a first error correction code based on the first sub-data groups, recombining the first sub-data groups to generate a preset second number of second sub-data groups, generating a second error correction code based on the second sub-data groups, transmitting the second sub-data groups and the second error correction code to the transfer unit in a first transmission mode, recombining the second sub-data groups to generate a second number of third sub-data groups by the transfer unit, generating a third error correction code based on the third sub-data groups, and transmitting the third sub-data groups and the third error correction code to the processing unit in a second transmission mode.
Specifically, in this embodiment, the data management module includes an input unit, a transfer unit and a processing unit, where the first time period is set to 24 hours, the data sequence is divided into a plurality of first data groups by the first time period, for example, the time sequence length included in the collected data sequence is 72 hours, then the data sequence may be divided into 3 first data groups, the preset calculation process refers to the result of adjacent exclusive-or operation of the first data group and the second first data group in the data groups, the adjacent exclusive-or operation refers to the result of exclusive-or operation of the first data group and the second data group in the data groups, the exclusive-or operation is performed by the operation result and the third first data group, and so on, until all the first data groups in the data groups are completed exclusive-or operation, and the last operation result is set to the error correction code of the data group, the first number is set to 5, the first data groups are divided into 5 first data groups, by the same method, the second error correction code and the third error correction code are both the result of adjacent exclusive-or operation performed by the corresponding second data groups and the third data groups, the first data groups in the data groups and the second data groups are all the first data groups, the error correction units are read from the first data groups, and the error correction units are combined to the first data groups, and the error correction units are simultaneously, and the error correction units are generated, and the error correction codes are transmitted to the error correction units are transmitted to the data groups.
And step S3, judging whether the third sub-data group has errors, correcting the errors of the third sub-data group to generate correction data of the first data group, transmitting the correction data to the control unit, and if not, transmitting all the third sub-data group to the control unit, and repeatedly executing until the data sequence is transmitted to the control unit.
Specifically, in this embodiment, when all data information is collected and classified, detection and verification are required for accuracy of the data information, and because different communication ports are used to collect the data information, data may be lost or transmitted in error, so that the data sequence is transmitted to the management and control unit after verification, and accuracy of data transmission can be improved.
And S4, the prediction model acquires the spatial correlation of the data sequence based on the tag type, outputs a prediction result of the data sequence based on the spatial correlation, transmits the prediction result to the comprehensive platform, sets different user types based on the tag type, and outputs the corresponding data sequence and the prediction result to the user type.
Specifically, in the present embodiment, the prediction model is used for analyzing and predicting the data sequence corresponding to each tag type, the spatial correlation is the correlation among the time sequence, the location information and the target variable contained in the data sequence, for example, the frequency of arrival of the customer P1 at the urban area a at the store C1 is 10 times, the frequency of arrival of the customer P2 at the urban area B at the store C1 is 2 times, the location information of the customer data at the urban area a and the urban area B is the time sequence, the location information at the time 2024/01/01/11:00 is the target variable, and for example, the frequency of arrival at the store C1 at the time 2024/01/01/10:00 is 1000 yuan, the business of the business at the F3 layer 301 at the store C1 at the time 2024/01/01/10:00 is 100 yuan, the business of the business at the F3 layer 301 at the store C1 is 100 yuan, the business 1 layer 101 and the business 3 layer is the location information at the time 2024/01/10:00 is the target variable; the prediction result is the numerical value of the target variable in the data sequence at the future time, the data sequence is analyzed and predicted through a prediction model, the numerical value of the target variable at the future time can be predicted and output, and the analysis and planning propaganda of the market are facilitated, for example, the number of customers and the store entering frequency of the urban area A in the prediction result are both larger than those of the urban area B, the propaganda operation of the market can be carried out in the urban area A, the comprehensive platform is a platform for realizing multifunctional data interaction management, the data sequence corresponding to the customer data, the merchant data, the operation data and the management data are subjected to digital interaction, the method can simply and accurately browse and analyze the data, share the data value of the whole link and co-construct the digital ecology of the whole scene.
Determining whether the third sub-data group has an error includes the steps of:
and calculating the verification sum of all the second sub-data groups in a preset mode, and accumulating the verification sums of all the second sub-data groups to generate a first verification sum.
The first sub data group included in the third sub data group is acquired, the checksum between the first sub data group and the adjacent first sub data group is calculated based on the number of the first sub data groups and set to be the first round checksum, the checksum between the first round checksum and the next first sub data group is calculated and set to be the second round checksum, the steps are repeatedly executed until all the first sub data groups in the third sub data group are calculated, the calculation result is set to be the checksum of the third sub data group, and a fourth error correction code is generated based on the checksum of the third sub data group.
The processing unit transmits the checksum of all third sub-data sets and the corresponding fourth error correction code to the transfer unit, and adds the checksum of all third sub-data sets in the transfer unit to generate a second checksum.
And comparing the sizes of the first verification sum and the second verification sum, if the sizes are equal, judging that the third sub-data group has no error, and if the sizes are not equal, judging that the third sub-data group has the error.
Specifically, in this embodiment, the verification sum is a result generated by calculating the corresponding data set by a preset manner, for example, the preset manner is set to be a product result of a certain polynomial and the corresponding data set, whether the corresponding data set has an error can be judged by comparing the verification sums, after the transfer unit receives the second sub-data set, the verification sums of the second sub-data sets are calculated respectively, and the verification sums of all the second sub-data sets are accumulated to generate a first verification sum, where the first verification sum refers to that the second sub-data set contains values of all the first sub-data sets, after the processing unit receives the third sub-data set, the second verification sum of all the third sub-data sets needs to be calculated, for example, as shown in fig. 2, the third sub-data group h31 includes three first sub-data groups h1, h2 and h3, the checksum E1 of the first sub-data group h1 is calculated by a preset manner, the checksum E1 and the checksum E2 of the first sub-data group h2 are calculated by a preset manner as a first round checksum E2, the checksum E2 and the checksum E3 of the first sub-data group h3 are calculated by a preset manner as a second round checksum E3, the second round checksum E3 is the checksum of the third sub-data group h31, the checksum E5 of the third sub-data group h32 is calculated by the same method, and the sums E3 and E5 are accumulated to generate a second checksum; comparing the first verification sum with the second verification sum to determine whether the first verification sum is equal to the second verification sum, and if not, indicating that an error occurs when the second sub-data set of the transfer unit is transmitted to the processing unit.
The prediction result of the output data sequence comprises the following steps:
the data prediction module acquires a data sequence with a preset second time period, the data sequence comprises position information, a time sequence and a target variable, the prediction model performs spatial interpolation on the data sequence based on the time sequence, and calculates regression coefficients of the data sequence to generate a regression function of the target variable in the data sequence, and a prediction result of the target variable in future time is predicted based on the regression function.
Calculating a predicted result y of the target variable in the future time based on a first formula: Where m is the number of time-series-included future times, R (x; θ i) is the value of the ith Gaussian kernel function, x is the time-series of future times, θ i is the center point of the ith Gaussian kernel function, and β i is the ith regression coefficient.
Calculating an ith regression coefficient beta i based on a second formula of
Wherein T is the number of time series included in the second time period, β j is the jth regression coefficient, Φ j is the jth weight parameter, and ε i is the ith error value.
Specifically, in this embodiment, in order to more comprehensively analyze and collect the transmitted data sequence, the collected data sequence may be used as historical data, the target variable in the data sequence is predicted by the prediction model, the data sequence in the second time period corresponding to the tag type is input into the prediction model, the time sequence refers to the time distance of the collected data sequence, for example, the data sequence includes (time sequence, location information, target variable) three-dimensional information features, the time distance of the data sequence is 1 hour, that is, the time sequence of the data sequence is every 1 hour, the second time period is set to 30×24=720 hours, the tag type is set to merchant data, the data sequences with 720 time sequence lengths are obtained, the target variable of each time sequence is spatially interpolated based on the location information of the data sequence, the spatial interpolation may be performed by adopting a kriging method or a cubic spline function method, and the spatial regression analysis may be performed by the spatial interpolation to generate a regression function, where the regression function refers to a formula of the prediction model for outputting the prediction result.
The first formula refers to linear regression analysis of m Gaussian kernel functions, wherein the Gaussian kernel functions refer to monotone functions of Euclidean distances from any point x to a central point in a space, and the calculation formula of the ith Gaussian kernel function R (x; theta i) is as follows: wherein θ i is the center point of the ith gaussian kernel function, α i is the width parameter of the gaussian kernel function, and i is the operation symbol for calculating the spatial distance, and when x is far from the center point, the function has a small value.
The ith regression coefficient β i can be calculated by a second formula from the regression coefficient generated by the collected data sequence, the error value refers to the difference between the target variable value in the collected data sequence and the prediction result of the regression function, which is simply understood as that the collected data sequence is used as historical data, the regression function is set according to the gaussian kernel function, and the target variable in the future time is spatially predicted.
The first sub-data set reassembly comprises the steps of:
Equally dividing the first data group into a first number of first sub-data groups based on a preset time interval, sequentially combining the first sub-data groups based on a second number to generate a plurality of combined results, extracting two combined results from the combined results to generate a second sub-data group and a third sub-data group, wherein the last first sub-data group in the third sub-data group is identical to the first sub-data group in the second sub-data group.
Specifically, in this embodiment, the first data set may be equally divided by the set time interval, as shown in fig. 2, the first data set h11 is divided into 5 first sub-data sets h1, h2, h3, h4 and h5, and two combination results are extracted to be respectively the second sub-data sets h21 and h22 and the third sub-data sets h31 and h32, where the last first sub-data set h3 in the third sub-data set h31 is the same as the first sub-data set h3 in the second sub-data set h22, and by the above combination manner, the second sub-data set may be used for correction when an error occurs in the third sub-data set.
Generating correction data for the first data set comprises the steps of:
And restoring the second error correction code in the transfer unit to a second sub-data group based on the inverse operation of a preset calculation process, respectively summarizing and comparing all the second sub-data group and the third sub-data group, obtaining the error position of the third sub-data group, calculating the correction data of the third sub-data group in the processing unit based on the error position and the second sub-data group, and setting all the corrected third sub-data group as the correction data of the first data group.
Specifically, in this embodiment, for example, as shown in fig. 3, when errors occur in three first sub-data groups in the third sub-data group h31, the second error correction code g22 in the second sub-data group h22 and the third error correction code g31 in the third sub-data group h31 are used to perform exclusive-or inverse operation to generate the first sub-data group h3 of the third sub-data group h31, then the corrected first sub-data group h3 in the third sub-data group h31 and the third error correction code g31 are used to perform exclusive-or inverse operation to generate the first sub-data group h2 of the third sub-data group h31, and finally the corrected first sub-data group h2 in the third sub-data group h31 and the third error correction code g31 are used to perform exclusive-or inverse operation to generate the first sub-data group h1 of the third sub-data group h31, that is, the corrected third sub-data group h31 and the third sub-data group h32 where no errors occur are set as data of the first data group.
The invention firstly carries out verification and correction on the collected data sequence through the data management module, can effectively improve the accuracy of data transmission, wherein the transfer unit is used for comparing the first verification sum with the second verification sum to judge whether the data sequence has errors, therefore, the verification sum of the data groups is not required to be added in the transmission process, the reduction of the transmission efficiency can be avoided, then the error correction codes corresponding to the data groups are arranged for transmission, the first sub-data groups in the first data groups are arranged and combined to generate the second sub-data group and the third sub-data group with different combination results, and the second sub-data group and the third sub-data group are transmitted to the processing unit of the data management module through different transmission modes, so that the data channel interference among the same transmission modes can be avoided, and finally, the prediction result is output after the spatial interpolation is carried out on the data sequence through setting the prediction model, thereby being beneficial to the analysis and the application of the data sequence.
Particularly, the invention can verify and correct the data sequence and simultaneously spatially predict the target variable in the data sequence, thereby avoiding data errors during the digital interaction of the multifunctional platform.
As shown in fig. 4, the present invention further provides a digitalized multifunctional platform interaction management system, where the system is used for implementing the digitalized multifunctional platform interaction management method, and the system mainly includes:
the data management module is used for managing the data of the data information, and the data management module is used for managing the data of the data information.
The data management module is used for intercepting a first data group in all data sequences based on a preset first time period, dividing the first data group into a first sub-data group with a preset first number, generating a first error correction code based on the first sub-data group, recombining the first sub-data group to generate a second sub-data group with a preset second number, generating a second error correction code based on the second sub-data group, transmitting the second sub-data group and the second error correction code to the transfer unit in a first transmission mode, recombining the second sub-data group to generate a third sub-data group with a second number by the transfer unit, generating a third error correction code based on the third sub-data group, and transmitting the third sub-data group and the third error correction code to the processing unit in a second transmission mode.
The data correction module is used for judging whether the first data set has errors or not, correcting the errors of the data sequence to generate correction data of the first data set, transmitting the correction data to the control unit if yes, and transmitting the first data set to the control unit if no, and repeatedly executing the steps until the data sequence is transmitted to the control unit.
The data prediction module is used for obtaining the spatial correlation of the data sequence based on the tag type by the prediction model, outputting the prediction result of the data sequence based on the spatial correlation, transmitting the prediction result to the comprehensive platform, setting different user types based on the tag type, and outputting the corresponding data sequence and the prediction result to the user type by the comprehensive platform.
The invention also provides a computer storage medium which stores program instructions, wherein the equipment where the computer storage medium is located is controlled to execute the digitalized multifunctional platform interaction management method when the program instructions run.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of computer programs, which may be stored on a non-transitory computer readable storage medium, and which, when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the foregoing embodiments may be arbitrarily combined, and for brevity, all of the possible combinations of the technical features of the foregoing embodiments are not described, however, they should be considered as the scope of the disclosure as long as there is no contradiction between the combinations of the technical features.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (6)
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