Data development method, device, equipment, storage medium and product

Abstract

The application discloses a data development method and device, equipment, storage medium and product, relates to the cloud computing technical field, and the method comprises the steps that a storage space is divided into a plurality of node spaces, the storage path of to-be-stored data is determined according to a time and space algorithm, the corresponding relationship between the to-be-stored data and the node spaces is determined according to the storage path, resource scheduling is carried out according to the corresponding relationship and the priority parameter of each storage path, and the to-be-stored data is stored into the node spaces; the current virtual space statement value is determined according to a data development statement and a virtual mapping relationship between a virtual space and the node spaces; the decision data of the data development statement is determined according to the current virtual space statement value, and data development is carried out according to the decision data. The application avoids the resource preemption problem from the aspects of time, space and resource optimization scheduling, guarantees the efficient and high-quality execution of the data development task of the data middle platform, and improves the scheduling capability from the resource guarantee aspect.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cloud computing, and particularly relates to a data development method and device, equipment, storage medium and product. BACKGROUND

[0002] In an existing data development scheme based on a data middle platform, an independent space required for storage is divided, a same kind and parallel node space and a virtual node space are divided in the independent space, a node association of a storage resource is determined based on a virtual identification information of a predefined mapping relationship of the storage resource, and a virtual backup mapping storage of a virtual identification of the storage resource is generated by again performing a virtual identification predefined mapping relationship on the data development execution after the data development execution is completed.

[0003] When solving the resource preemption problem of the data middle platform, the prior art scheme avoids data disorder by processing user task logic submitted in the same time period, or performs data isolation by interface encapsulation of the technical architecture. However, the huge resources of the existing development middle platform cannot be isolated at each resource point when the resources are utilized, and the scheduling capability is reduced due to mutual influence caused by resource preemption, and at the same time, the collaborative development and use of multiple users cannot be performed at the same time, resulting in low development quality and low development efficiency. SUMMARY

[0004] The main purpose of the present application is to provide a data development method, device, equipment, storage medium and product, and to solve the technical problem that each resource point in the data middle platform cannot be isolated and cannot be developed collaboratively by multiple users, resulting in low development quality and low efficiency.

[0005] To achieve the above purpose, the present application provides a data development method, which comprises:

[0006] According to a cold and hot storage algorithm, a storage space is divided into a plurality of node spaces, and the node spaces include a virtual space;

[0007] According to a preset time algorithm and a preset space algorithm, a storage path of to-be-stored data is determined, and a corresponding relationship between the to-be-stored data and the node spaces is determined according to the storage path, and an attribute in the storage path includes a cold and hot storage classification;

[0008] According to the corresponding relationship and a priority parameter corresponding to each storage path, resource scheduling is performed, and the to-be-stored data is stored in the node spaces;

[0009] According to a received data development statement and a virtual mapping relationship between the virtual space and the node spaces, a current virtual space statement value is determined;

[0010] Determine decision data of the data development sentence according to the current virtual space sentence value, and develop data according to the decision data.

[0011] In an embodiment, the attributes in the storage path include a resource pool, a resource pool area, a cold and hot storage classification, a compression type, a data directory, a time period, and a file name.

[0012] The step of determining the storage path of the to-be-stored data according to the preset time algorithm and the preset space algorithm comprises:

[0013] Determine a resource pool, a resource pool area, and a first cold and hot storage classification according to the preset space algorithm.

[0014] Determine a second cold and hot storage classification, a compression type, and a time period according to the preset time algorithm.

[0015] Encrypt and fuse the first cold and hot storage classification and the second cold and hot storage classification according to a preset weight to obtain a cold and hot storage classification.

[0016] Encrypt the resource pool, the resource pool area, the cold and hot storage classification, and the compression type according to a multi-level linear congruential algorithm to obtain a plurality of encrypted ciphertexts, and generate a storage path according to the plurality of encrypted ciphertexts, the data directory, the time period, and the file name.

[0017] In an embodiment, the step of determining a resource pool, a resource pool area, and a first cold and hot storage classification according to the preset space algorithm comprises:

[0018] Collect spatial feature attributes of the to-be-stored data.

[0019] Determine information entropy of each spatial feature attribute to obtain an uncertainty measure corresponding to each spatial feature attribute.

[0020] Determine an information gain value of each spatial feature attribute according to the information entropy.

[0021] Determine optimal spatial feature attributes and model parameters corresponding to a decision tree algorithm according to each spatial feature attribute and the information gain value.

[0022] Determine a resource pool, a resource pool area, and a first cold and hot storage classification according to a decision tree algorithm, the model parameters, and the optimal spatial feature attributes.

[0023] In an embodiment, the step of determining a second cold and hot storage classification, a compression type, and a time period according to the preset time algorithm comprises:

[0024] Collect time feature attributes of the to-be-stored data.

[0025] determine the hidden state and the output vector of the current time step corresponding to the sequence of each data storage unit in the storage space according to the recurrent neural network algorithm, the time characteristic attribute, the input vector of the current time step and the hidden state of the previous time step;

[0026] determine the second cold and hot storage classification, the compression type and the time period according to the hidden state and the output vector of the current time step.

[0027] In an embodiment, the step of encrypting the resource pool, the resource pool area, the cold and hot storage classification and the compression type according to the multi-level linear congruential algorithm to obtain a plurality of encrypted ciphertexts comprises:

[0028] respectively convert the resource pool, the resource pool area, the cold and hot storage classification and the compression type into a digital sequence;

[0029] determine the encrypted resource pool, the encrypted resource pool area, the encrypted cold and hot storage classification and the encrypted compression type according to the digital sequence, a preset constant and the size of the encryption character set.

[0030] In an embodiment, after the step of determining the decision data of the data development statement according to the current virtual space statement value and performing data development according to the decision data, the data development device further comprises:

[0031] add a resource extraction marker point to the decision data;

[0032] remove the node resource in the storage space that has the resource extraction marker point.

[0033] In addition, to achieve the above-mentioned purpose, the present application further provides a data development device, which comprises:

[0034] a storage space segmentation module configured to segment a storage space into a plurality of node spaces according to a cold and hot storage algorithm, wherein the node spaces comprise virtual spaces;

[0035] a storage path determination module configured to determine a storage path of to-be-stored data according to a preset time algorithm and a preset space algorithm, and determine a correspondence between the to-be-stored data and the node spaces according to the storage path, wherein an attribute in the storage path comprises a cold and hot storage classification;

[0036] a resource scheduling module configured to perform resource scheduling according to the correspondence and a priority parameter corresponding to each storage path, and store the to-be-stored data into the node spaces;

[0037] a statement value determination module configured to determine a current virtual space statement value according to a received data development statement and a virtual mapping relationship between the virtual spaces and the node spaces.

[0038] a data development module configured to determine decision data of the data development statement according to the current virtual space statement value, and to develop data according to the decision data.

[0039] In addition, to achieve the above object, the present application further provides a data development device, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the data development method as described above.

[0040] In addition, to achieve the above object, the present application further provides a storage medium, which is a computer readable storage medium, and the storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the data development method as described above.

[0041] In addition, to achieve the above object, the present application further provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the steps of the data development method as described above.

[0042] The present application provides a data development method, which divides a storage space into a plurality of node spaces according to a hot and cold storage algorithm, determines a storage path of to-be-stored data according to a time and space algorithm, determines a corresponding relationship between the to-be-stored data and the node spaces according to the storage path, performs resource scheduling according to the corresponding relationship and a priority parameter corresponding to each storage path, and stores the to-be-stored data into the node spaces; determines a current virtual space statement value according to a received data development statement and a virtual mapping relationship between a virtual space and the node spaces; determines decision data of the data development statement according to the current virtual space statement value, and develops data according to the decision data. The present application avoids resource preemption from three aspects of time, space and resource optimization scheduling, guarantees the efficient and high-quality execution of data development tasks of a data middle platform, and improves the scheduling capability from the aspect of resource guarantee. BRIEF DESCRIPTION OF DRAWINGS

[0043] The accompanying drawings, which are incorporated herein and constitute part of the specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced here. Obviously, for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0045] Figure 1A flowchart provided by the first embodiment of the data development method of the present application;

[0046] Figure 2 A unit structure diagram in the data development system of the present application;

[0047] Figure 3 A storage unit structure diagram in the data development method of the present application;

[0048] Figure 4 A flowchart provided by the second embodiment of the data development method of the present application;

[0049] Figure 5 A calculation decision tree diagram of the resource pool in the data development method of the present application;

[0050] Figure 6 A calculation decision tree diagram of the resource pool area in the data development method of the present application;

[0051] Figure 7 A calculation decision tree diagram of the first cold and hot storage classification in the data development method of the present application;

[0052] Figure 8 A structure diagram of the simple circulation network in the data development method of the present application;

[0053] Figure 9 An update flowchart of the simple circulation network in the data development method of the present application;

[0054] Figure 10 An example diagram of the space-time fusion in the data development method of the present application;

[0055] Figure 11 A module structure diagram of the data development device of the present application;

[0056] Figure 12 A device structure diagram of the hardware running environment involved in the data development method of the present application.

[0057] The object, function features and advantages of the present application will be further explained with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0058] It should be understood that the specific embodiments described herein are merely intended to explain the technical solutions of the present application, and not to limit the present application.

[0059] In order to better understand the technical solutions of the present application, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0060] The main solution of the embodiment of the application is: according to a cold and hot storage algorithm, a storage space is divided into a plurality of node spaces, the node spaces including virtual spaces; according to a preset time algorithm and a preset space algorithm, a storage path of to-be-stored data is determined, and a corresponding relationship between the to-be-stored data and the node spaces is determined according to the storage path, an attribute in the storage path including cold and hot storage classification; according to the corresponding relationship and a priority parameter corresponding to each storage path, resource scheduling is performed, and the to-be-stored data is stored into the node spaces; according to a received data development statement and a virtual mapping relationship between the virtual spaces and the node spaces, a current virtual space statement value is determined; according to the current virtual space statement value, decision data of the data development statement is determined, and data development is performed according to the decision data.

[0061] Since the prior art solution solves the problem of resource preemption of the data middle platform by avoiding data disorder through logical processing of user tasks submitted in the same time period, or by encapsulating an application programming interface (API) through a technical architecture to isolate data. However, the huge resources of the existing development technology middle platform cannot be isolated at each resource point during resource utilization, and are affected by each other due to resource preemption, resulting in a decline in scheduling capability, and the simultaneous collaborative development and use of multiple users is not possible, resulting in low development quality and reduced development efficiency.

[0062] The application provides a solution by dividing a storage space into a plurality of node spaces according to a cold and hot storage algorithm, determining a storage path of to-be-stored data according to a time and space algorithm, determining a corresponding relationship between the to-be-stored data and the node spaces according to the storage path, performing resource scheduling according to the corresponding relationship and a priority parameter corresponding to each storage path, and storing the to-be-stored data into the node spaces; determining a current virtual space statement value according to a received data development statement and a virtual mapping relationship between virtual spaces and node spaces; determining decision data of the data development statement according to the current virtual space statement value, and performing data development according to the decision data. The application avoids the problem of resource preemption from the aspects of time, space, and resource optimization scheduling, and ensures the efficient and high-quality execution of data development tasks of the data middle platform, thereby improving the scheduling capability from the aspect of resource guarantee.

[0063] It should be noted that the execution subject of the method of the embodiment can be a computing service device with data development, network communication, and program running functions, such as a tablet computer, a personal computer, a mobile phone, and the like; or a data development device based on a data middle platform with the same or similar functions. The embodiments of the application and the following embodiments will be described by taking the data development device based on the data middle platform as an example.

[0064] Based on this, the data development method provided in the embodiments of the present application comprises the steps of Figure 1 , Figure 1 The flowchart of the first embodiment of the data development method of the present application is shown in the figure.

[0065] In this embodiment, the data development method comprises steps S10-S50:

[0066] Step S10, according to the cold and hot storage algorithm, the storage space is divided into multiple node spaces, and the node space includes a virtual space.

[0067] It should be noted that the data center is a concept based on modern data technology and architecture, aiming to build a unified data platform to solve the problems of internal data islands, data dispersion and low data quality. First of all, refer to Figure 2 The units contained in the data development system based on the data center are described, including data acquisition unit, data processing unit, decision model unit, data analysis unit, intelligent control unit, data storage unit, data display unit and data determination unit, etc. These modules together constitute the basic framework of the data center, and the unit modules cooperate with each other to ensure that the enterprise can efficiently manage and utilize its data resources. Among them, the relationship between the data storage unit and the storage space, the virtual space and the node space can be referred to Figure 3 .

[0068] It can be understood that the storage space can be divided, and the cold and hot storage algorithm is added when dividing, and the storage space is divided according to the cold and hot storage, which is better than the contribution level space, and the storage space is divided into storage node space and virtual space, and each space is divided into multiple node spaces. The multiple node spaces are associated according to the assigned label vector; the virtual space includes the assigned label vector value corresponding to the storage node space, the data synchronization amount corresponding to the storage node space, and the temporary storage space; based on the pre-allocated space, a certain space can be pre-allocated before data insertion to avoid frequent data reorganization, and reasonable space pre-allocation can be made according to the prediction of data growth. Based on the basis of multiple divided node spaces, data shards can be stored, and different data can be stored in different locations, which can reduce the influence of single data insertion or deletion operation on the whole data. Among them, based on the convolutional neural network or the recurrent neural network, the individual data is independently divided according to the data characteristics and the required division characteristics, and the distributed algorithm is associated based on the correlation or association degree between individual data.

[0069] Step S20, according to the preset time algorithm and the preset space algorithm, determine the storage path of the to-be-stored data, and determine the correspondence between the to-be-stored data and the node space according to the storage path, the attributes in the storage path include cold and hot storage classification.

[0070] It can be understood that the data in the data center is loaded with data, and the independent data set is divided into independent individual data, each data set or row of independent data is an individual, and the data is classified, analyzed and planned, and the characteristic attributes and correlation relationships that need to be grouped are determined, and then according to the analysis and planning results, the data set is divided according to the consistency double Hash Hash algorithm, the corresponding relationship between the data to be stored and the node space is determined according to the storage path, and the best storage strategy of each data is obtained. Individual data can be dispersed to different node spaces to avoid data collision probability. However, this step does not perform landing execution, and this step is the basis model data for subsequent global scheduling execution and data encryption service.

[0071] It should be understood that considering the multi-resource pool off-site storage, the data storage uses both time and space algorithms to combine data segmentation, determine the storage path of the data to be stored (including the attribute of cold and hot storage classification), the data time attribute uses a recurrent neural network algorithm to model, and the data space attribute uses a decision tree algorithm to model.

[0072] Step S30, according to the corresponding relationship and the priority parameter corresponding to each storage path, the resource scheduling is performed, and the data to be stored is stored into the node space.

[0073] It can be understood that after obtaining the best storage strategy of each data, when the global scheduling task is executed, unified scheduling control can be performed according to the corresponding relationship, so that the response file is generated in the corresponding storage area. At the same time, the task priority can be controlled according to the priority parameter corresponding to each storage path, and the business characteristics are guaranteed to reasonably use the resources. The scheduling control rule is controlled by the following four-bit code to adapt to different business task scenarios.

[0074]

[0075] For example, the following is the first global resource scheduling processing flow, which is suitable for processing tasks according to the day cycle.

[0076] Scheduling control code: 1211;

[0077] Get storage location secret key: a = 5, b = 3, n = 26, perform linear congruence equation decryption;

[0078] Parse array one "resource pool", set global parallel task here, and schedule tasks between resource pools independently and in parallel;

[0079] Parse array two "resource pool area", get resource pool pod name (pod refers to the smallest deployable computing unit). Get resource pool resource information;

[0080] Pull up the idle resource task in the resource pool;

[0081] Analyzing array three "cold and hot storage classification", preferentially executing hot storage task;

[0082] Analyzing array four "compression type", preferentially executing uncompressed file task;

[0083] The daily cycle task is executed according to the above scheduling execution principle, the tasks of each resource pool are parallel, the timeliness of daily task is guaranteed, and the daily task is executed on the hot storage, so that the calculation speed is increased. However, the performance of the hot storage and the resource pool is relatively large in resource consumption, and the scheduling control scene is suitable for the business scene with high timeliness.

[0084] For example, the following is the second global resource scheduling process, which is suitable for processing tasks in a day-month cycle.

[0085] Scheduling control code: 2322;

[0086] Obtaining storage location secret key: a=5, b=3, n=26, performing linear congruence equation decryption;

[0087] Analyzing array one "resource pool", setting global serial task, and scheduling tasks between resource pools are executed in sequence;

[0088] Analyzing array two "resource pool area", obtaining resource pool pod name, and obtaining resource pool resource information;

[0089] The tasks in each area of the resource pool are executed in sequence;

[0090] Analyzing array three "cold and hot storage classification", preferentially executing cold storage task;

[0091] Analyzing array four "compression type", preferentially executing compressed file;

[0092] The month cycle task has low timeliness requirement, and is executed according to the above scheduling execution principle, the tasks of each resource pool are serial, the resource consumption is saved to the maximum extent, and each task is executed on the cold storage, so that the calculation speed is relatively low, but the expensive hot storage resource is not occupied. The compressed file has slow calculation speed but small resource consumption. This scene is suitable for the task scene with low timeliness, saves resources to the maximum extent, and reduces investment.

[0093] In step S40, the current virtual space statement value is determined according to the received data development statement and the virtual mapping relationship between the virtual space and the node space.

[0094] It can be understood that the individual storage resources in each node space can be virtually identified and a predefined mapping relationship can be defined, and the target available area of the storage resources can be determined based on the predefined mapping relationship information. The target available area is the remaining available value of the current virtual node space, and when the remaining available value is lower than 20% of the total available area, the virtual node space no longer receives data development statements. The virtual backup mapping storage of the virtual identification of the storage resources is completed based on the link of the virtual space and the node space mapping relationship.

[0095] It should be understood that the data development statement transmitted by the development unit is transmitted to the operation unit based on the data processing module, and the data development statement transmitted by the data processing module is received by the operation unit and executed. Based on the development data node determined by the data development statement, the virtual space node mapping data is determined by the virtual mapping relationship between the virtual space and the node space, and the virtual arrangement storage is performed based on the virtual space node mapping data, the data development statement and the statement timestamp, and the current virtual space statement value is fed back.

[0096] The development unit includes an identity verification module, and the development unit includes a multi-user online development interface, a multi-user visual synchronization feedback interface and a multi-user multi-party determination. The identity verification module includes the following steps: a. reading user information by inputting an identity token, and verifying the identity by the identity information stored in the login system. If the verification fails, the operation is refused, and if the verification is passed, the operation step b is entered; b. After the verification is passed, the username and password information are read, and the identity verification is completed after the reading verification is passed, and the login is completed.

[0097] Step S50, determining decision data of the data development statement according to the current virtual space statement value, and developing data according to the decision data.

[0098] It can be understood that the current virtual space statement value can be fed back to the development user, and each data development statement can be displayed, and the decision of the data development statement can be finally completed based on the multi-party determination and the decision model, and the data development execution can be completed based on the decision data by the virtual space mapping. The virtual space mapping determines the decision data development statement based on the corresponding storage node space data synchronization amount, and synchronously stores the individual data in the space.

[0099] In a possible implementation, after step S50, steps S60-S70 can also be included:

[0100] Step S60, adding a resource extraction marker point to the decision data.

[0101] It can be understood that the virtual identification pre-defined mapping relationship is performed again after the data development execution is completed, and the virtual backup mapping storage generation of the storage resource virtual identification is completed. The resource collection classification is performed again on the resource data collected and integrated by the data acquisition processing system, and the extraction marking of the resource is performed by the resource extraction marking point.

[0102] In step S70, the node resource with the resource extraction marking point in the storage space is removed.

[0103] It can be understood that the node resource with the extraction marking is compared with the database to remove the new marking point and repeated marking, and finally integrated and uploaded by the resource integration pre-storage. The completed data is marked again, based on independent scheduling arrangement management, when the data isolation and scheduling ability are improved, not only the resource use and occupancy rate of redundant data are removed, the data resource utilization is improved, and the accurate control of the resource data of the whole management system is realized.

[0104] The embodiment provides a data development method, according to the cold and hot storage algorithm, the storage space is divided into a plurality of node spaces, according to the time and space algorithm, the storage path of the to-be-stored data is determined, and according to the storage path, the corresponding relationship between the to-be-stored data and the node space is determined, according to the corresponding relationship and the priority parameter corresponding to each storage path, the resource scheduling is performed, and the to-be-stored data is stored into the node space; according to the received data development statement and the virtual mapping relationship between the virtual space and the node space, the current virtual space statement value is determined; according to the current virtual space statement value, the decision data of the data development statement is determined, and according to the decision data, the data development is performed. The embodiment avoids the resource preemption problem from the aspects of time, space and resource optimization scheduling, guarantees the efficient and high-quality execution of the data development task of the data middle platform, and improves the scheduling ability from the resource guarantee aspect. While the resources are isolated, the embodiment also provides a specific scheme for optimizing the resource distribution, and also considers the safety and performance problems. The embodiment also provides a rule assumption for scheduling execution, and enriches the innovation ability of the data middle platform construction and development.

[0105] Based on the first embodiment of the application, in the second embodiment of the application, the same or similar contents as the above embodiment one can refer to the above introduction, and will not be described hereinafter. On this basis, please refer to Figure 4 The attributes in the storage path include a resource pool, a resource pool area, a cold and hot storage classification, a compression type, a data directory, a time period and a file name; the data development method further includes steps S201-S204:

[0106] In step S201, the resource pool, the resource pool area and the first cold and hot storage classification are determined according to a preset space algorithm.

[0107] It should be noted that the core attributes of the storage path of a file mainly include the following: / resource pool / resource pool area / cold and hot storage classification / compression type / data directory / time period / file name, etc. Among them, the resource pool, resource pool area, and cold and hot storage classification are determined by spatial mathematical modeling calculation of a space algorithm (for example, a decision tree algorithm). The data directory and file name are provided by data governance data asset management.

[0108] In a possible implementation, step S201 can include steps S2011-S2015:

[0109] Step S2011, collect the spatial feature attributes of the data to be stored.

[0110] It can be understood that for spatial dimension data attribute sample input, a decision tree algorithm is used for spatial attribute modeling, and the regional attributes, storage frequency, reading frequency, modification frequency, compression frequency, and storage size per cycle of the data are collected as input parameters, and the resource pool, resource pool storage area, and cold and hot storage classification of the file are output.

[0111] Exemplarily, the sample data is as follows:

[0112]

[0113]

[0114] The positive sample is as follows.

[0115]

[0116] Step S2012, determine the information entropy of each spatial feature attribute, and obtain the uncertainty measure corresponding to each spatial feature attribute.

[0117] It can be understood that the information entropy of each spatial feature attribute can be calculated according to the following formula:

[0118]

[0119] Wherein, pi represents the proportion of the i-th spatial feature attribute in the data to be stored, n represents the number of spatial feature attributes, and log represents the calculation of logarithm. Thus, the uncertainty measure corresponding to each spatial feature attribute can be obtained, as shown in the following table.

[0120]

[0121] Step S2013, determine the information gain value of each spatial feature attribute according to the information entropy.

[0122] It can be understood that the information gain value of each spatial feature attribute can be calculated in combination with the information entropy and the following formula:

[0123]

[0124] wherein k represents the number of subsets of data to be stored, H(D) represents the information entropy of the data to be stored, |Di| represents the sample quantity of the i-th subset, |D| represents the total sample quantity of the data to be stored, and H(Di) represents the information entropy of the i-th subset. The greater the information gain, the better the classification effect brought by selecting the spatial feature attribute for partitioning.

[0125] Step S2014, determining the optimal spatial feature attribute and the model parameter corresponding to the decision tree algorithm according to each spatial feature attribute and the information gain value.

[0126] It can be understood that after training by the data samples, the optimal spatial data attribute and the model parameter corresponding to the decision tree algorithm can be determined according to the decision tree rule in the decision tree algorithm.

[0127] Step S2015, determining the resource pool, resource pool area and first cold and hot storage classification according to the decision tree algorithm, the model parameter and the optimal spatial feature attribute.

[0128] It can be understood that the resource pool, resource pool area and first cold and hot storage classification can be respectively determined according to the decision tree algorithm, the model parameter and the optimal spatial feature attribute. The calculation decision tree of the resource pool can refer to FIG. Figure 5 The calculation decision tree of the resource pool area can refer to FIG. Figure 6 The calculation decision tree of the first cold and hot storage classification can refer to FIG. Figure 7 The data is classified by region and space, the optimal spatial partition attribute is determined, and the result content shown in the following table is output.

[0129]

[0130] Step S202, determining the second cold and hot storage classification, compression type and time period according to a preset time algorithm.

[0131] It can be understood that the cold and hot storage classification, compression type and time period in the attribute of the storage path can be determined by modeling and calculation of a time algorithm (for example, a recurrent neural network algorithm).

[0132] In a feasible implementation, step S202 can include steps S2021-S2023:

[0133] Step S2021, collecting the time feature attribute of the data to be stored.

[0134] It can be understood that, for the input of data attribute samples in the time dimension, based on the recurrent neural network algorithm, independent data segmentation is performed according to the data characteristics and the required segmentation time sequence characteristics, and the time period of data collection, the storage size, the reading frequency, the compression type, the output cold and hot storage classification, the compression type and the time period and other time characteristic attributes are collected.

[0135] In step S2022, the hidden state and the output vector of the current time step corresponding to each data storage unit in the storage space are determined according to the recurrent neural network algorithm, the time characteristic attribute, the input vector of the current time step and the hidden state of the previous time step.

[0136] It can be understood that, assuming that the sequence of each data storage unit is denoted as RNN, the recurrent neural network (RNN) is a neural network architecture that performs well in processing sequential data and can be used for time series prediction tasks. Then:

[0137] ht=σ(Wxhxt+Whhht-1+bh),

[0138] yt=σ(Whyht+by),

[0139] where ht is the hidden state at time step t, xt is the input at time step t, yt is the output at time step t, Wxh, Whh and Why are weight matrices, bh and by are bias terms, and σ is an activation function (such as the Hyperbolic Tangent Function (Tanh) or the Rectified Linear Unit (ReLU)). At each time step, the RNN accepts an input vector of the current time step (which can be an element in the sequence data) and a hidden state of the previous time step as input. Using these two inputs, the RNN calculates the hidden state and the output vector of the current time step. The calculation of the hidden state takes into account the hidden state of the previous time step and the input vector of the current time step, thereby capturing the temporal dependencies in the sequence. The hidden state and the output vector are then passed to the next time step as one of the inputs to the next time step, thereby distributing the storage node space of the data according to the time sequence characteristics, and determining whether the data corresponding to different time periods is cold storage or hot storage.

[0140] In step S2023, the second cold and hot storage classification, the compression type and the time period are determined according to the hidden state and the output vector of the current time step.

[0141] It can be understood that the sample data is as shown in the following table:

[0142]

[0143] The positive samples are shown in the following table.

[0144] File name Time period Cold hot storage dw_nb_mobile_weather_dm 20241020 Hot dw_nb_mobile_weather_dm 20241019 Hot dw_nb_mobile_weather_dm 20241018 Hot dw_nb_mobile_weather_dm 20241017 Hot ... dw_nb_mobile_weather_dm 20240831 Cold dw_nb_mobile_weather_dm 20240830 Cold dw_nb_mobile_weather_dm 20240829 Cold ...

[0145] It can be understood that a simple recurrent network can be established, and the structural diagram can refer to Figure 8 . Define the loss function E to represent the error of the output y^ and the real label y, and derive the partial derivative of E with respect to the network weight from top to bottom by chain rule. Update the value of the weight in the opposite direction of the gradient until E converges. The update process can refer to Figure 9 . According to the sample data, the time series training is performed, and the output results of the second cold and hot storage classification, compression type and time period are shown in the following table.

[0146]

[0147] In step S203, the first cold and hot storage classification and the second cold and hot storage classification are encrypted and fused according to the preset weight, and the cold and hot storage classification is obtained.

[0148] It can be understood that the key attribute "cold and hot storage classification" therein is related to time and space, and the two models output respective calculation results, and the fusion algorithm determines the final result according to the weight. Regarding the key field cold and hot storage classification output by the time model and the space model, it is considered that the space model lacks the time dimension, and all data in the data middle platform is stored according to the time period, so the weight rule of time 0.8 or 0.9 and space 0.2 or 0.1 can be set. The reason why it is not completely determined according to the time model is determined by the business characteristics. In the data middle platform storage process, time discontinuity is a common phenomenon. The case of discontinuity of a data period often occurs. If there is a time discontinuity, the running result of the time model will cause the model output to be incomplete. When the time model output is missing, the result of the space model will be used as a model output supplement. For example, refer to Figure 10 , the time model is empty because of data discontinuity, the storage path supplements the result of the space model, and is recorded as cold storage.

[0149] In step S204, the resource pool, the resource pool area, the cold and hot storage classification and the compression type are encrypted according to the multi-level linear congruential algorithm, a plurality of encrypted ciphertexts are obtained, and a storage path is generated according to the plurality of encrypted ciphertexts, the data directory, the time period and the file name.

[0150] It can be understood that after the calculation result fusion is completed, the full path is output in the model. At this time, the storage location encryption needs to be performed according to the multi-level linear congruential algorithm. The storage location encryption of the present application mainly considers the following three points: the data storage location avoids clear text from appearing, and the encrypted storage further improves the data security; the path label is set in the storage location encryption process, the calculated location is marked by encrypted storage in sections, which is convenient for subsequent scheduling analysis and call execution; the scheduling call analysis performance is improved, and the analysis performance of the encrypted array format is higher than that of the pure text path. According to the time-space algorithm, the storage path of the fusion calculation model is shown in the following table:

[0151] File name Time period Full path dw_nb_mobile_weather_dm 20241020 / huchi / pod10 / ssd / 20241020 / dat / dw_nb_mobile_weather_dm 20241019 / huchi / pod10 / ssd / 20241019 / dat / dw_nb_mobile_weather_dm 20241018 / huchi / pod10 / ssd / 20241018 / dat / dw_nb_mobile_weather_dm 20241017 / huchi / pod10 / ssd / 20241017 / dat / ... dw_nb_mobile_weather_dm 20240831 / huchi / pod10 / ssd / 20240831 / gz / dw_nb_mobile_weather_dm 20240830 / huchi / pod10 / ssd / 20240830 / gz / dw_nb_mobile_weather_dm 20240829 / huchi / pod10 / ssd / 20240829 / gz /

[0152] It is worth noting that the core storage path is: / resource pool / resource pool area / cold and hot storage classification / compression type / data directory / time period / file name. The full path of one period is as follows:

[0153] / huchi / pod10 / ssd / dat / dw / newbusi / 20241020 / dw_nb_mobile_weather_dm.dat

[0154] Resource pool: huchi, resource pool area: pod10, cold and hot storage classification: ssd, compression type: dat, data directory: 'dw / newbusi', time period: 20241020, file name: dw_nb_mobile_weather_dm.dat. The attributes that need to be encrypted are: resource pool, resource pool area, cold and hot storage classification, compression type; the attributes that do not need to be encrypted are: data directory, time period, file name. Because the character length of the data directory and the file name will be relatively long and has no meaning for global resource scheduling, it belongs to static constant, and the time period is a digital display, which also belongs to high-performance computing constant in global resource scheduling and has lower security attributes.

[0155] It can be understood that after the attributes that need to be encrypted are encrypted one by one, the encrypted resource pool, the encrypted resource pool area, the encrypted cold and hot storage classification, and the encrypted compression type are obtained. Then, the attributes that do not need to be encrypted are spliced, that is, / data directory / time period / file name / , to obtain the storage path after encryption.

[0156] In one possible implementation, step S204 can include steps S2041-S2042:

[0157] Step S2041, respectively converting the resource pool, the resource pool area, the cold and hot storage classification, and the compression type into a digital sequence.

[0158] It can be understood that a simple conversion mode is first assumed: a-z is converted into 0-25 respectively. Therefore, taking the first path node that needs to be encrypted, the resource pool "hachi" as an example, "hachi" is converted into a digital sequence [7, 0, 2, 7, 8] (h = 7, a = 0, c = 2, i = 8).

[0159] In step S2042, the encrypted resource pool, the encrypted resource pool area, the encrypted hot and cold storage classification, and the encrypted compression type are determined according to the digital sequence, the preset constant, and the size of the encrypted character set.

[0160] It can be understood that only key position elements are stored and encrypted, and the encryption process can be encrypted according to a linear congruence equation as follows:

[0161] C = (a * m + b) mod n,

[0162] Where C is the encrypted ciphertext, m is the plaintext to be encrypted (usually converted into a corresponding digital or numerical representation), a, b, and n are known constants (a and n are usually coprime), and n is usually the size of the character set used by the encryption system (for example, n = 26 for the English character set).

[0163] For example, taking the first path node that needs to be encrypted, the resource pool "hachi" as an example, each character needs to be converted into a number and encrypted by applying a linear congruence equation. Next, a set of encryption parameters a, b, n is selected. Assuming a = 5, b = 3, n = 26 (the size of the English character set). Then apply the linear congruence equation to each number for encryption:

[0164] C1 = (5 * 7 + 3) mod 26 = (35 + 3) mod 26 = 38 mod 26 = 12,

[0165] C2 = (5 * 0 + 3) mod 26 = (0 + 3) mod 26 = 3,

[0166] C3 = (5 * 2 + 3) mod 26 = (10 + 3) mod 26 = 13,

[0167] C4 = (5 * 7 + 3) mod 26 = 38 mod 26 = 12 (same as C1, which is a property of the linear congruence equation),

[0168] C5 = (5 * 8 + 3) mod 26 = (40 + 3) mod 26 = 43 mod 26 = 17,

[0169] Therefore, the encrypted ciphertext of 'hachi' is [12, 3, 13, 12, 17].

[0170] Similarly, according to the above algorithm, the following path elements are encrypted to obtain the corresponding ciphertext as:

[0171] Resource pool area: pod10, ciphertext: [63, 106, 251, 12, 7],

[0172] Cold and hot storage classification: ssd, ciphertext: [96, 96, 51],

[0173] Compression type: dat, ciphertext: [10, 73, 11].

[0174] Therefore, the storage path can be stored in segments as follows:

[0175] Encrypted part: / resource pool / resource pool area / cold and hot storage classification / compression type / ;

[0176] Encrypted as four arrays for storage:

[0177] [12, 3, 13, 12, 17][63, 106, 251, 12, 7][96, 96, 51][10, 73, 11],

[0178] Plaintext part: / data directory / time period / filename / : / dw / newbusi / 20241020 / dw_nb_mobile_weather_dm.dat.

[0179] It can be understood that the basic model trained in combination with time is used to analyze and label the encrypted storage data and schedule the strategy, and the advantages of cold and hot storage are fully utilized for data processing. This innovation has important significance for saving resource overhead. According to the current model statistics, 80% of the storage in the data center can be stored in cold storage, such as historical data, monthly, quarterly, and annual data processing. The timeliness requirement is relatively low. At the same time, the migration and traceability of historical data are not very time-sensitive operations. Some daily and weekly small data volume timeliness requirements can also meet the requirements on cold storage. The data volume proportion of high timeliness data in the entire data center is relatively less than 10%. If this model strategy is implemented, 85% of the storage investment of the data warehouse can be gradually replaced by cold storage, and 15% of the storage can be replaced by hot storage with faster access. Not only will the data warehouse speed up in terms of hardware resources, but also will greatly reduce resource investment.

[0180] In the embodiment, the resource pool, the resource pool area and the first cold and hot storage classification are determined according to a preset space algorithm; the second cold and hot storage classification, the compression type and the time period are determined according to a preset time algorithm; the first cold and hot storage classification and the second cold and hot storage classification are encrypted and fused according to a preset weight, and then the resource pool, the resource pool area, the cold and hot storage classification and the compression type are encrypted according to a multi-level linear congruential algorithm, so that the storage path can be generated more accurately according to the plurality of encrypted ciphertexts, the data directory, the time period and the file name.

[0181] It should be noted that the above examples are only used for understanding the present application and do not constitute a limitation on the data development method of the present application. More forms of simple transformation based on this technical concept are within the protection scope of the present application.

[0182] The present application also provides a data development device, please refer to Figure 11 , the data development device comprises:

[0183] The storage space division module 10 is used for dividing the storage space into a plurality of node spaces according to a cold and hot storage algorithm, and the node spaces include virtual spaces;

[0184] The storage path determination module 20 is used for determining the storage path of the to-be-stored data according to a preset time algorithm and a preset space algorithm, and determining the correspondence between the to-be-stored data and the node spaces according to the storage path, wherein the attribute in the storage path includes a cold and hot storage classification;

[0185] The resource scheduling module 30 is used for performing resource scheduling according to the correspondence and the priority parameter corresponding to each storage path, and storing the to-be-stored data into the node spaces;

[0186] The statement value determination module 40 is used for determining the current virtual space statement value according to the received data development statement and the virtual mapping relationship between the virtual spaces and the node spaces;

[0187] The data development module 50 is used for determining the decision data of the data development statement according to the current virtual space statement value, and performing data development according to the decision data.

[0188] The data development device provided by the present application adopts the data development method in the above embodiment, and can solve the technical problem. Compared with the prior art, the data development device provided by the present application has the same beneficial effects as the data development method provided by the above embodiment, and other technical features in the data development device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0189] The application provides a data development device, comprising: at least one processor; and a memory connected with the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the data development method in the above embodiment one.

[0190] Reference will now be made to the drawings Figure 12 , which show structural schematic diagrams of a data development device suitable for implementing embodiments of the application. The data development device in embodiments of the application can include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (for example, vehicle-mounted navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. Figure 12 The data development device shown is merely an example and should not impose any limitation on the functions and use range of embodiments of the application.

[0191] As shown in Figure 12 , the data development device can include a processing apparatus 1001 (for example, a central processor, a graphics processor, or the like) that can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 1002 or loaded from a storage apparatus 1003 into a random access memory (RAM) 1004. Various programs and data required for operation of the data development device are also stored in the RAM 1004. The processing apparatus 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. In general, the following systems can be connected to the I / O interface 1006: input apparatuses 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, and the like; output apparatuses 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, and the like; the storage apparatus 1003 including, for example, a magnetic tape, a hard disk, and the like; and a communication apparatus 1009. The communication apparatus 1009 can allow the data development device to communicate with other devices wirelessly or by wire to exchange data. Although a data development device having various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be alternatively implemented or provided.

[0192] In particular, according to the embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, the embodiments of the present application include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program code for executing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network by a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the methods of the embodiments of the present application are executed.

[0193] The data development device provided by the present application adopts the data development method in the above-mentioned embodiments, and can solve the technical problem of data development. Compared with the prior art, the data development device provided by the present application has the same beneficial effects as the data development method provided by the above-mentioned embodiments, and other technical features in the data development device are the same as the features disclosed in the above-mentioned embodiments, and thus will not be described here.

[0194] It should be understood that various parts of the present application can be realized by hardware, software, firmware or a combination thereof. In the description of the above-mentioned embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0195] The above describes only the specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0196] The present application provides a computer readable storage medium having stored thereon computer readable program instructions (i.e. computer program) for executing the data development method in the above-mentioned embodiments.

[0197] The computer readable storage medium provided by the present application may, for example, be a U disk, but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, system, or device, or any combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more conductive wires, a portable computer disk, a hard disk, a random access memory (RAM), a read only memory (ROM), an erasable programmable read only memory (EPROM or flash memory), an optical fiber, a portable compact disk read only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present embodiment, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer readable storage medium can be transmitted in any suitable medium, including but not limited to electrical wires, optical cables, RF (Radio Frequency), and the like, or any suitable combination of the above.

[0198] The above computer readable storage medium can be contained in the data development device, or can exist separately without being assembled into the data development device.

[0199] Computer program code for carrying out operations of the present application can be written in one or more programming languages or combinations of languages including object oriented programming languages such as Java, Smalltalk, C++ or conventional procedural programming languages such as "C" or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, through an Internet service provider to connect through the Internet).

[0200] The flowcharts and block diagrams in the drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowcharts or block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations thereof, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or combinations of hardware and software.

[0201] The modules involved in the embodiments of the present application can be implemented in software or in hardware. In some cases, the names of the modules do not limit the modules themselves.

[0202] The readable storage medium provided by the present application is a computer readable storage medium, which stores computer readable program instructions (i.e. computer programs) for executing the above-mentioned data development method, and can solve the technical problems. Compared with the prior art, the computer readable storage medium provided by the present application has the same beneficial effects as the data development method provided by the above-mentioned embodiments, which will not be repeated here.

[0203] The present application also provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the steps of the above-mentioned data development method.

[0204] The computer program product provided by the present application can solve the technical problems. Compared with the prior art, the computer program product provided by the present application has the same beneficial effects as the data development method provided by the above-mentioned embodiments, which will not be repeated here.

[0205] The above only describes some embodiments of the present application, and does not limit the patent scope of the present application. Any equivalent structural transformation made by using the content of the present application specification and drawings, or direct / indirect application in other related technical fields is included in the patent protection scope of the present application.

Claims

1. A data development method, characterized in that, The method includes: The storage space is divided into multiple node spaces according to the cold and hot storage algorithm, and the node space includes virtual space; The storage path of the data to be stored is determined according to a preset time algorithm and a preset space algorithm, and the correspondence between the data to be stored and the node space is determined according to the storage path. The attributes in the storage path include cold and hot storage classification. Resource scheduling is performed based on the correspondence and the priority parameters corresponding to each storage path, and the data to be stored is stored in the node space; The current virtual space statement value is determined based on the received data development statement and the virtual mapping relationship between the virtual space and the node space; The decision data for the data development statement is determined based on the current virtual space statement value, and data development is performed based on the decision data.

2. The method as described in claim 1, characterized in that, The attributes in the storage path include resource pool, resource pool region, cold and hot storage category, compression type, data directory, time period, and file name; The step of determining the storage path of the data to be stored based on a preset time algorithm and a preset space algorithm includes: The resource pool, resource pool area, and first cold and hot storage category are determined according to the preset spatial algorithm; The second cold and hot storage category, compression type, and time period are determined according to a preset time algorithm; The first cold and hot storage category and the second cold and hot storage category are encrypted and fused according to preset weights to obtain a cold and hot storage category. The resource pool, the resource pool region, the cold and hot storage categories, and the compression type are encrypted using a multi-level linear congruential algorithm to obtain multiple encrypted ciphertexts. A storage path is then generated based on the multiple encrypted ciphertexts, the data directory, the time period, and the filename.

3. The method as described in claim 2, characterized in that, The step of determining the resource pool, resource pool area, and first cold and hot storage category according to the preset spatial algorithm includes: Collect the spatial feature attributes of the data to be stored; Determine the information entropy of each spatial feature attribute, and obtain the uncertainty measure corresponding to each spatial feature attribute; The information gain value of each spatial feature attribute is determined based on the information entropy; Determine the optimal spatial feature attribute and the model parameters corresponding to the decision tree algorithm based on each of the spatial feature attributes and the information gain value; The resource pool, resource pool region, and first cold and hot storage classification are determined based on the decision tree algorithm, the model parameters, and the optimal spatial feature attributes.

4. The method as described in claim 2, characterized in that, The step of determining the second cold and hot storage category, compression type, and time period according to a preset time algorithm includes: Collect the temporal characteristic attributes of the data to be stored; Based on the recurrent neural network algorithm, time feature attributes, the input vector of the current time step, and the hidden state of the previous time step, determine the hidden state and output vector of the current time step corresponding to the sequence of each data storage unit in the storage space. The second cold and hot storage category, compression type, and time period are determined based on the hidden state and output vector of the current time step.

5. The method as described in claim 2, characterized in that, The step of encrypting the resource pool, the resource pool region, the cold and hot storage classification, and the compression type according to a multi-level linear congruence algorithm to obtain multiple encrypted ciphertexts includes: The resource pool, the resource pool region, the cold and hot storage category, and the compression type are respectively converted into digital sequences; The encrypted resource pool, encrypted resource pool area, encrypted cold and hot storage classification, and encrypted compression type are determined based on the numerical sequence, preset constant, and encrypted character set size.

6. The method as described in claim 1, characterized in that, After determining the decision data for the data development statement based on the current virtual space statement value, and performing the data development steps based on the decision data, the method further includes: Add resource extraction markers to the decision data; Remove the node resources containing the resource extraction marker from the storage space.

7. A data development apparatus, characterized in that, The data development device includes: A storage space partitioning module is used to partition the storage space into multiple node spaces according to a cold and hot storage algorithm, wherein the node spaces include virtual spaces; The storage path determination module is used to determine the storage path of the data to be stored according to a preset time algorithm and a preset space algorithm, and to determine the correspondence between the data to be stored and the node space according to the storage path. The attributes in the storage path include cold and hot storage classification. The resource scheduling module is used to perform resource scheduling according to the correspondence and the priority parameters corresponding to each storage path, and to store the data to be stored in the node space; The statement value determination module is used to determine the current virtual space statement value based on the received data development statement and the virtual mapping relationship between the virtual space and the node space. The data development module is used to determine the decision data of the data development statement based on the current virtual space statement value, and to perform data development based on the decision data.

8. A data development device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the data development method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the data development method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the data development method as described in any one of claims 1 to 6.

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