Data product monitoring method and device, electronic equipment and nonvolatile storage medium

Abstract

The invention discloses a data product monitoring method and device, electronic equipment and a nonvolatile storage medium. The method comprises the steps that evaluation modules are adopted to perform evaluation operation on a target data product, the evaluation modules are deployed on nodes of a block chain, each node is deployed with one evaluation module, and the evaluation operation comprises at least one of data compliance evaluation, data quality evaluation and data value evaluation; sending alarm information under the condition that a data evaluation result obtained by the evaluation operation does not meet a preset product standard; and under the condition that a data evaluation result obtained by the evaluation operation is changed compared with a data evaluation result obtained by the historical evaluation operation, sending an uplink request to other nodes in the block chain, the uplink request being used for synchronizing the change content and the alarm condition of the data evaluation result to other nodes in the block chain. The technical problems of low efficiency and poor safety of evaluating and monitoring data products in related technologies are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data element evaluation, in particular to a data product monitoring method and device, electronic equipment and nonvolatile storage medium. BACKGROUND

[0002] With the development of "data elements" in the digital economic society, many data element platforms have been established in various places. In the process of data product flow, the supervision of data compliance, quality and value evaluation is the key to the stable and continuous operation of data element platforms.

[0003] Data element products are mainly delivered in the form of interfaces, database access, etc., and have obvious timeliness characteristics. Data providers need to update data in a timely manner to ensure data effectiveness. In order to ensure that data demanders obtain data compliance, quality and value evaluation results in a timely manner, data sets need to be evaluated every time data is updated online. In related technologies, data products are evaluated by experts or artificial intelligence models, and the evaluation results are synchronized to data demanders as transaction information. However, this method has technical problems such as long time consumption, high cost, and easy tampering of results.

[0004] At present, there is no effective solution to the above problems. SUMMARY

[0005] The embodiments of the present application provide a data product monitoring method and device, electronic equipment and nonvolatile storage medium, to at least solve the technical problems of low efficiency and poor security of related technologies in evaluating and monitoring data products.

[0006] According to an aspect of an embodiment of the present application, a data product monitoring method is provided, comprising: using an evaluation module to perform an evaluation operation on a target data product, wherein the evaluation module is deployed on a node of a blockchain, one evaluation module is deployed on each node, and the evaluation operation includes at least one of the following: data compliance evaluation, data quality evaluation, and data value evaluation; in the case that the data evaluation result obtained by the evaluation operation does not meet a preset product standard, sending an alarm information, wherein the preset product standard is used to represent the requirements that the data product should meet in each evaluation dimension, and the evaluation dimension includes at least one of the following: data compliance, data quality, and data value; in the case that the data evaluation result obtained by the evaluation operation is changed compared with the data evaluation result obtained by a historical evaluation operation, sending a chain request to other nodes in the blockchain, wherein the chain request is used to synchronize the change content of the data evaluation result and the alarm situation to other nodes in the blockchain.

[0007] Optionally, the evaluation operation on the target data product comprises: determining an application level corresponding to the target data product, wherein the application level comprises a public level, an industry level, an enterprise level, and a personal level; obtaining a target rule corresponding to the application level from a compliance rule library, wherein the compliance rule library comprises a plurality of compliance rules, and the compliance rules are obtained by analyzing and converting relevant laws and regulations and industry standard specifications of data products into executable rules; determining whether the target data product satisfies the target rule by using a rule engine to obtain a compliance evaluation result, wherein the compliance evaluation result is in the form of a triple, and the triple comprises an identifier of the target data product, an identifier of the target rule, and an evaluation result, and the evaluation result in the triple is used to represent whether the target data product satisfies the target rule.

[0008] Optionally, the evaluation operation on the target data product further comprises: determining a quality evaluation index for data quality evaluation on the target data product, wherein the quality evaluation index comprises at least one of a normative index, a completeness index, an accuracy index, a consistency index, a timeliness index, and an accessibility index; obtaining demand information provided by a demand party of the target data product, and determining a weight coefficient corresponding to each quality evaluation index according to the demand information; determining a first number of data elements in the target data product that satisfy a quality requirement corresponding to the quality evaluation index, and determining an index value corresponding to each quality evaluation index of the target data product according to the first number and a total number of data elements in the target data product; and determining a quality evaluation result of the target data product according to the index values and the weight coefficients of the quality evaluation indexes, wherein the quality evaluation result is used to represent a degree to which data characteristics of the target data product satisfy requirements of the demand party.

[0009] Optionally, the evaluation operation on the target data product further comprises: determining a stage period in which the target data product is located, wherein the stage period comprises at least one of a development stage, an online preparation period and an initial stage, an online mature stage, and an offline stage; determining a value evaluation algorithm corresponding to the stage period, and determining a product value of the target data product by using the value evaluation algorithm to obtain a value evaluation result, wherein the value evaluation result is used to represent a market economic value of the target data product, in the development stage and the offline stage, the product value of the target data product is determined according to a replacement cost of the target data product, in the online preparation period and the initial stage, the product value of the target data product is determined by comparing value evaluation results of other data products that have been disclosed on the market, and in the online mature stage, the product value of the target data product is determined by predicting profit earnings of the product.

[0010] Optionally, in the case that the data evaluation result obtained by the evaluation operation does not satisfy the preset product standard, the sending of the alarm information comprises: in the case that the compliance evaluation result represents that the target data product does not satisfy the target rule, sending first alarm information, wherein the first alarm information is used to represent that the target data product does not satisfy the target rule corresponding to the identifier in the compliance evaluation result.

[0011] Optionally, in the case that the data evaluation result obtained by the evaluation operation does not satisfy the preset product standard, the sending of the alarm information further comprises: in the case that the index value of the quality evaluation index corresponding to the target data product is less than the preset index threshold corresponding to the quality evaluation index, sending second alarm information, wherein the second alarm information is used to represent that the quality evaluation index of the target data product is unqualified; in the case that the quality evaluation result corresponding to the target data product is less than the preset quality score threshold, sending third alarm information.

[0012] Optionally, the method further comprises: receiving an evaluation result update request, wherein the evaluation result update request comprises: an on-chain request sent by other nodes in the blockchain except the current node, the on-chain request containing a result change data block, the result change data block containing: a timestamp, a hash digest, a digital signature, and change content; decrypting the digital signature in the evaluation result update request by using a public key, and performing identity authenticity verification by using the decrypted digital signature; in the case that the identity authenticity verification is passed, updating the result change data block to the blockchain of the current node.

[0013] According to another aspect of the embodiments of the present application, a data product monitoring device is further provided, comprising: an evaluation module, configured to perform an evaluation operation on a target data product by using the evaluation module, wherein the evaluation module is deployed on a node of a blockchain, one evaluation module is deployed on each node, and the evaluation operation comprises at least one of the following: data compliance evaluation, data quality evaluation, and data value evaluation; an alarm module, configured to send alarm information in the case that a data evaluation result obtained by the evaluation operation does not satisfy a preset product standard, wherein the preset product standard is used to represent requirements that a data product should satisfy in each evaluation dimension, and the evaluation dimension comprises at least one of the following: data compliance, data quality, and data value; and an on-chain module, configured to send an on-chain request to other nodes in the blockchain in the case that the data evaluation result obtained by the evaluation operation is changed compared with a data evaluation result obtained by a historical evaluation operation, wherein the on-chain request is used to synchronize change content of the data evaluation result and an alarm situation to other nodes in the blockchain.

[0014] According to still another aspect of the embodiments of the present application, an electronic device is further provided, comprising: a memory and a processor, the processor being configured to run a program stored in the memory, wherein the program performs a data product monitoring method when running.

[0015] According to another aspect of the embodiments of the present application, a non-volatile storage medium is also provided, which comprises a stored computer program, wherein a device in which the non-volatile storage medium is located executes a data product monitoring method by running the computer program.

[0016] According to another aspect of the embodiments of the present application, a computer program product is also provided, which comprises a computer program, and the computer program is executed by a processor to implement the steps of the data product monitoring method.

[0017] In the embodiments of the present application, an evaluation module is used to perform an evaluation operation on the target data product, wherein the evaluation module is deployed on a node of a blockchain, one evaluation module is deployed on each node, and the evaluation operation comprises at least one of the following: data compliance evaluation, data quality evaluation, and data value evaluation; in the case that a data evaluation result obtained by the evaluation operation does not satisfy a preset product standard, an alarm information is sent, wherein the preset product standard is used to represent requirements that should be satisfied by the data product in each evaluation dimension, and the evaluation dimension comprises at least one of the following: data compliance, data quality, and data value; in the case that the data evaluation result obtained by the evaluation operation is changed compared with a data evaluation result obtained by a historical evaluation operation, an on-chain request is sent to other nodes in the blockchain, wherein the on-chain request is used to synchronize the change content of the data evaluation result and the alarm situation to other nodes in the blockchain in a manner, through the deployment of the evaluation module on each node of the blockchain, the data product is dynamically evaluated in real time, and meanwhile, three key dimensions of data compliance, data quality, and data value are covered, the purposes of improving the efficiency and accuracy of data evaluation and ensuring the transparency and non-tamperability of the evaluation result are achieved, and thus the technical problems of low efficiency and poor security of related technologies in the evaluation and monitoring of data products are solved. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings, which are included to provide a further understanding of the present application, form a part of the present application and illustrate the illustrative embodiments of the present application and the explanation of the present application, and do not constitute improper limitations on the present application. In the drawings:

[0019] Figure 1 is a hardware structure block diagram of a computer terminal (or electronic device) for implementing a data product monitoring method according to an embodiment of the present application;

[0020] Figure 2 is a schematic diagram of a data product monitoring method flow according to an embodiment of the present application;

[0021] Figure 3 is a schematic diagram of a functional module architecture of a data element product automatic evaluation monitoring and alarm according to an embodiment of the present application;

[0022] Figure 4 is a schematic diagram of a business process of data element product automatic evaluation monitoring and alarm according to an embodiment of the present application;

[0023] Figure 5 is a schematic diagram of data element product phased value evaluation according to an embodiment of the present application;

[0024] Figure 6 is a structural schematic diagram of a data product monitoring device according to an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the personnel in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0026] It should be noted that the terms "first", "second" and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0027] In order to facilitate those skilled in the art to better understand the embodiments of the present application, some technical terms or nouns involved in the embodiments of the present application will be explained as follows:

[0028] Metadata: data about data or data elements (may include data description thereof), and data about data ownership, access path, access right and data volatility.

[0029] Data quality: the degree to which the characteristics of data meet explicit and implicit requirements when used under specified conditions.

[0030] In the related art, the main challenge faced by the data element platform is the lack of full-life-cycle automatic evaluation monitoring and alarm methods for data element products. The data element evaluation system in the related art mainly performs one-time evaluation before data product transaction, which has technical problems such as time-consuming, high cost, and easy tampering of results.

[0031] To solve the above problems, the related solutions are provided in the embodiments of the present application, which are described in detail below.

[0032] According to the embodiments of the present application, a method embodiment of data product monitoring is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0033] The method embodiment provided by the embodiments of the present application can be executed in a mobile terminal, a computer terminal, or a similar computing device. Figure 1 A hardware structure block diagram of a computer terminal (or electronic device) for implementing the data product monitoring method is shown. As shown in Figure 1 The computer terminal 10 (or electronic device) can include one or more processors 102 (the processor 102 can include but is not limited to a microprocessor MCU or a programmable logic device FPGA processing device), a memory 104 for storing data, and a transmission device 106 for communication function. In addition, it can also include a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which can be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. Those skilled in the art can understand that Figure 1 The structure shown is only schematic, which does not limit the structure of the above-mentioned electronic device. For example, the computer terminal 10 can include more or fewer components than those shown in Figure 1 or have a different configuration than that shown in Figure 1 .

[0034] It should be noted that the one or more processors 102 and / or other data processing circuits described above can be referred to as "data processing circuits" herein. The data processing circuit can be embodied in whole or in part as software, hardware, firmware or any combination thereof. In addition, the data processing circuit can be a single independent processing module, or all or part of any one of the other elements combined into the computer terminal 10 (or electronic device). As referred to in the embodiments of the present application, the data processing circuit serves as a processor control (for example, selection of a variable resistance terminal path connected to an interface).

[0035] The memory 104 can be used to store software programs of application software and modules, such as program instructions / data storage devices corresponding to the data product monitoring method in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implements the above-mentioned data product monitoring method. The memory 104 can include a high-speed random access memory, and can also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 104 can further include a memory remotely arranged with respect to the processor 102, which can be connected to the computer terminal 10 through a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0036] The transmission device 106 is used to receive or send data via a network. Specific examples of the above-mentioned network can include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to be able to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, RF) module, which is used to communicate with the Internet in a wireless manner.

[0037] The display can be, for example, a touch screen type liquid crystal display (LCD), which can enable a user to interact with the user interface of the computer terminal 10 (or electronic device).

[0038] Under the above-mentioned running environment, the embodiments of the present application provide a data product monitoring method, Figure 2 is a schematic diagram of a method flow of a data product monitoring method according to the embodiments of the present application, as Figure 2 shown, the method comprises the following steps:

[0039] In step S202, an evaluation module is used to perform an evaluation operation on the target data product, wherein the evaluation module is deployed on a node of a block chain, one evaluation module is deployed on each node, and the evaluation operation includes at least one of the following: data compliance evaluation, data quality evaluation, and data value evaluation;

[0040] In step S204, in the case that the data evaluation result obtained by the evaluation operation does not satisfy a preset product standard, an alarm information is sent, wherein the preset product standard is used to represent the requirements that the data product should satisfy in each evaluation dimension, and the evaluation dimension includes at least one of the following: data compliance, data quality, and data value;

[0041] Step S206: When the data evaluation result obtained by the evaluation operation changes compared with the data evaluation result obtained by the historical evaluation operation, a chain request is sent to other nodes in the blockchain, wherein the chain request is used to synchronize the changed content and alarm status of the data evaluation result to other nodes in the blockchain.

[0042] Through the above steps, by deploying the evaluation module at each node of the blockchain, data products are evaluated dynamically in real time, and the three key dimensions of data compliance, data quality and data value are covered at the same time, thereby achieving the purpose of improving the efficiency and accuracy of data evaluation and ensuring the transparency and non-tamperability of the evaluation results, thereby solving the technical problems of low efficiency and poor security in the evaluation and monitoring of data products by related technologies.

[0043] The data product monitoring method in steps S202 to S206 of the embodiment of the present application is further introduced below.

[0044] In this embodiment, an evaluation agent (assessment module) is deployed on each blockchain node. This agent can regularly evaluate the compliance, quality, and value of data products, and upload the evaluation results to the blockchain. This evaluation result is shared across all blockchain nodes, ensuring that each node in the blockchain has a timely and consistent perception of the compliance, quality, and value of data products on other nodes. Other nodes in the blockchain can purchase new data product services or suspend purchased data product services based on the evaluation results.

[0045] like Figure 3 As shown in the figure, the main functional modules of the evaluation agent include: S1 data compliance evaluation module, S2 data quality evaluation module, S3 data value evaluation module, S4 monitoring and alarm module and S5 change chain and update module, among which the data compliance evaluation module can evaluate whether the compliance rules are passed according to the different classifications of data products; the data quality evaluation module can evaluate the quality of data products according to the dimensions of standardization, completeness, accuracy, consistency, timeliness, accessibility and other dimensions in the relevant data quality evaluation indicators; the data value evaluation module can select different calculation methods according to the different stages of the data element products in the company to evaluate the value of data products; the monitoring and alarm module is used to set and issue evaluation timer tasks, as well as analyze the evaluation results of other nodes and generate alarms; in the multi-node data element blockchain, the change chain and update module is used to initiate a chain request when a change in the node data element evaluation result is detected, and to obtain the target data element evaluation result when an update request for the data element evaluation result of other nodes in the blockchain is received.

[0046] By automatically executing assessment tasks through these modules, a method for full-life cycle compliance, quality, and value assessment is provided for interface-type and indicator-type data products that are more widely available in the real data element trading market. At the same time, the assessment results are uploaded to the chain, and the results are immutable, so that other nodes in the multi-node data element blockchain can obtain reliable assessment results in a timely manner.

[0047] Figure 4 This is a schematic diagram of a business process for automated evaluation, monitoring and alarming of data element products provided in accordance with an embodiment of the present application. Figure 4 The specific process of data product evaluation and monitoring is further explained.

[0048] When evaluating the target data product, the embodiment of this application mainly includes three types of evaluation methods: data compliance evaluation, data quality evaluation, and data value evaluation. The following is a detailed introduction to each type of evaluation method.

[0049] First, you can use the data compliance assessment module to evaluate whether the target data product passes the compliance rules based on its different classifications. The specific steps are as follows.

[0050] In some embodiments of the present application, the evaluation operation on the target data product includes the following steps: determining the application level corresponding to the target data product, wherein the application level includes: public level, industry level, enterprise level, and personal level; obtaining the target rule corresponding to the application level in the compliance rule library, wherein the compliance rule library contains multiple compliance rules, and the compliance rules are obtained by parsing the relevant laws, regulations and industry standards and specifications of the data product and converting them into executable rules; using a rule engine to determine whether the target data product meets the target rules and obtain a compliance assessment result, wherein the compliance assessment result is in the form of a triple, and the triple includes: an identifier of the target data product, an identifier of the target rule, and an evaluation result, and the evaluation result in the triple is used to characterize whether the target data product meets the target rule.

[0051] Specifically, in this embodiment, domestic laws and regulations, industry standards and specifications, international common standards and other documents related to data products can be collected first, and then the regulatory policies in these documents can be converted into compliance rules and stored in the compliance rules library.

[0052] For example, if Section 2 of a law stipulates that "sensitive personal information is personal information that, once leaked or illegally used, is likely to cause infringement of the personal dignity of a natural person or endanger personal or property safety, including medical health, financial accounts, whereabouts, and other information", it can be converted into compliance rule R001: "Determine whether the field includes medical insurance accounts, financial accounts, IP addresses, and address-related plain text information."

[0053] Further, the formed compliance rule library also needs to be classified and processed, for example, it can be divided into four application levels of public, industry, enterprise and individual, and the above distance rule R001 belongs to the individual level.

[0054] In the compliance assessment of the target data product, the corresponding target rule in the compliance rule library is determined according to the application level of the target data product, and then the compliance assessment result is output through the rule engine. In this embodiment, the compliance assessment result can be in the form of a triple: (data product number, rule number, assessment result), wherein the data product number is the unique identifier of the target data product in the multi-node data element block chain, the rule number is the unique identifier of the target rule in the multi-node data element block chain, and the assessment result is a value of 0 or 1, 0 representing that the data product does not pass the rule, and 1 representing that the data product can pass the rule.

[0055] For example, the data product number P001 mobile phone home address query interface is of the individual level and needs to meet the rule R001 of the same individual level: judging whether the field includes a medical insurance account, a financial account, an IP address, and address type plaintext information. If the rule engine determines that the rule is met, the output result is (P001, R001, 1).

[0056] The specific steps of using the data quality evaluation module to perform quality evaluation on the target data product are described below.

[0057] In some embodiments of the present application, the evaluation operation on the target data product further includes the following steps: determining a quality evaluation index for data quality evaluation on the target data product, wherein the quality evaluation index includes at least one of the following: a specification index, a completeness index, an accuracy index, a consistency index, a timeliness index, and an accessibility index; obtaining demand information provided by a demand side of the target data product, and determining a weight coefficient corresponding to each quality evaluation index according to the demand information; determining a first number of data elements in the target data product that meet the quality requirements corresponding to the quality evaluation index, and determining an index value corresponding to each quality evaluation index of the target data product according to the first number and a total number of data elements in the target data product; and determining a quality evaluation result of the target data product according to the index values and the weight coefficients of the quality evaluation indexes, wherein the quality evaluation result is used to represent the degree to which the data characteristics of the target data product meet the requirements of the demand side.

[0058] Specifically, in this embodiment, the quality evaluation indexes of the six dimensions of specification, completeness, accuracy, consistency, timeliness and accessibility can be set according to relevant quality evaluation criteria.

[0059] In one aspect, it is necessary to determine the weight coefficients corresponding to each quality evaluation index. In the embodiment, the weight coefficients can be determined according to the demand information provided by the demand side of the target data product. For example, the data product demand side can be asked to fill in a comparison ranking questionnaire, and the six-dimensional self-defined weight coefficients can be calculated by using the analytic hierarchy process. The comparison ranking questionnaire includes the following questions: A. Dimension X is completely important, B. Dimension X is very important, C. Dimension X is relatively important, D. Dimension X is slightly important, E. Dimension X and Dimension Y are equally important, F. Dimension Y is slightly important, G. Dimension Y is relatively important, H. Dimension Y is very important, and I. Dimension Y is completely important.

[0060] In a specific embodiment, there are six dimensions of data quality evaluation indexes, and the data demand side needs to complete 15 multiple-choice questions, such as: Compared with data specification and completeness, A. Specification is completely important, B. Specification is very important, C. Specification is relatively important, D. Specification is slightly important, E. Specification and completeness are equally important, F. Completeness is slightly important, G. Completeness is relatively important, H. Completeness is very important, and I. Completeness is completely important.

[0061] Then, a judgment matrix can be constructed according to the selection of the comparison ranking questionnaire of the data product demand side. Specifically, if A is selected for comparison between Dimension X and Dimension Y, 9 is filled in the row of “Dimension X” and the column of “Dimension Y”, and 1 / 9 is filled in the row of “Dimension Y” and the column of “Dimension X”; if B is selected, 7 is filled in the row of “Dimension X” and the column of “Dimension Y”, and 1 / 7 is filled in the row of “Dimension Y” and the column of “Dimension X”; if C is selected, 5 is filled in the row of “Dimension X” and the column of “Dimension Y”, and 1 / 5 is filled in the row of “Dimension Y” and the column of “Dimension X”; if D is selected, 3 is filled in the row of “Dimension X” and the column of “Dimension Y”, and 1 / 3 is filled in the row of “Dimension Y” and the column of “Dimension X”; if E is selected, 1 is filled in the row of “Dimension X” and the column of “Dimension Y”, and 1 is filled in the row of “Dimension Y” and the column of “Dimension X”; if F is selected, 1 / 3 is filled in the row of “Dimension X” and the column of “Dimension Y”, and 3 is filled in the row of “Dimension Y” and the column of “Dimension X”; if G is selected, 1 / 5 is filled in the row of “Dimension X” and the column of “Dimension Y”, and 5 is filled in the row of “Dimension Y” and the column of “Dimension X”; if H is selected, 1 / 7 is filled in the row of “Dimension X” and the column of “Dimension Y”, and 7 is filled in the row of “Dimension Y” and the column of “Dimension X”; and if I is selected, 1 / 9 is filled in the row of “Dimension X” and the column of “Dimension Y”, and 9 is filled in the row of “Dimension Y” and the column of “Dimension X”.

[0062] For example, the constructed judgment matrix is shown in the following table.

[0063] Normative Completeness Accuracy Consistency Timeliness Accessibility Normative 1 3 5 7 9 9 Completeness 1 / 3 1 3 5 7 9 Accuracy 1 / 5 1 / 3 1 3 5 7 Consistency 1 / 7 1 / 5 1 / 3 1 3 5 Timeliness 1 / 9 1 / 7 1 / 5 1 / 3 1 3 Accessibility 1 / 9 1 / 9 1 / 7 1 / 5 1 / 3 1

[0064] Further, the weight of each quality evaluation index can be calculated according to the judgment matrix. For example, the weight can be calculated by using the arithmetic average method (sum product method), as follows.

[0065] First, the above judgment matrix is summed by column, as shown in the following table.

[0066]

[0067] Then, the judgment matrix is normalized by column (i.e. by column to account for the proportion), as shown in the following table.

[0068] Normative Completeness Accuracy Consistency Timeliness Accessibility Normative 0.5268 0.6267 0.5167 0.4234 0.3553 0.2647 Completeness 0.1756 0.2089 0.3100 0.3024 0.2763 0.2647 Accuracy 0.1054 0.0696 0.1033 0.1815 0.1974 0.2059 Consistency 0.0753 0.0418 0.0344 0.0605 0.1184 0.1471 Timeliness 0.0585 0.0298 0.0207 0.0202 0.0395 0.0882 Accessibility 0.0585 0.0232 0.0148 0.0121 0.0132 0.0294

[0069] Finally, the normalized matrix is averaged by row, i.e. the weight coefficient W is obtained, as shown in the following table.

[0070]

[0071]

[0072] After that, consistency check can also be performed. First, the maximum eigenvalue is solved, and the maximum eigenvalue of the original judgment matrix is calculated through the weight W obtained above, as shown in the following formula:

[0073]

[0074] Wherein, n is the order of the matrix, here n = 6. W is the weight coefficient obtained in the previous step, and AW is the product of the original judgment matrix and the weight W.

[0075] For example, as shown in the following table.

[0076]

[0077] Substituting the formula, finally λ max is 6.4718; secondly, the consistency index CI is calculated, as shown in the following formula:

[0078]

[0079] Wherein, λ max is the maximum eigenvalue obtained in the previous step, and n is the order of the matrix, here n = 6. In this embodiment, CI = 0.0944 is obtained.

[0080] Further, the random consistency index RI value is obtained by looking up the table. In this embodiment, RI = 1.26.

[0081]

[0082] Finally, the According to the comparison of the obtained CR with the threshold value, if it is less than 0.1, the consistency check is passed, and in this embodiment, CR = 0.07488 < 0.1, i.e. the consistency check is passed.

[0083] On the other hand, it is also necessary to determine the index value corresponding to each quality evaluation index, as follows.

[0084] In the present embodiment, the index value can be output in the form of a triple: (data product number, index dimension number, quality evaluation result (i.e. index value)), wherein the data product number is the unique identifier of the data product in the multi-node data element blockchain; the index dimension number is the number of the six dimensions of normativity, integrity, accuracy, consistency, timeliness, and accessibility, which is unique in the multi-node data element blockchain; and the quality evaluation result is the score of the data product in this dimension (i.e. the above-mentioned index value), the specific calculation method can be as follows: A / B, A = the number of elements in the data set that meet the data quality requirements (i.e. the first number mentioned above), B = the number of elements in the data set being evaluated (i.e. the total number of data elements in the target data product), resulting in a value between 0 and 1, the closer to 1, the higher the data quality, and vice versa, the closer to 0, the lower the data quality.

[0085] For example, the satisfaction standard of normativity (Q001) is: metadata complete, and the normativity score calculation method is: X1 = A1 / B1, where: A1 = the number of elements in the data set that meet the metadata complete requirement, B1 = the number of elements in the data set being evaluated.

[0086] The satisfaction standard of integrity (Q002) is: element not missing, and the integrity score calculation method is: X2 = A2 / B2, where: A2 = the number of elements in the data set that meet the element not missing requirement, B2 = the number of elements in the data set being evaluated.

[0087] The satisfaction standard of accuracy (Q003) is: 1) indexed by unique primary key, judge whether there are duplicate records, with metadata as reference, 2) judge whether the data format (including data type, value range, data length, precision) meets the metadata description; 3) judge whether the data is within the threshold described in the metadata, and the accuracy score calculation method is: X3 = A3 / B3, where: A3 = the number of elements in the data set that meet requirements 1), 2), and 3) at the same time, B3 = the number of elements in the data set being evaluated.

[0088] The satisfaction standard of consistency (Q004) is: with data bloodline in metadata as reference, meet the requirement of keeping consistent in two-level bloodline tracing upwards, and the consistency score calculation method is: X4 = A4 / B4, where: A4 = the number of elements in the data set that meet the requirement of keeping consistent in two-level bloodline tracing upwards, B4 = the number of elements in the data set being evaluated.

[0089] The satisfaction standard of timeliness (Q005) is: 1) the number of records or frequency distribution based on the date range meets the extent of business requirements; 2) the number of records or frequency distribution based on the timestamp or delay time meets the extent of business requirements, and the timeliness score is calculated in the following manner: X5=A5 / B5, wherein: A5=the number of elements in the data set that meet the corresponding business requirements (only records containing date ranges need to meet condition 1), only records containing timestamps need to meet condition 2), and records containing both date ranges and timestamps meet conditions 1) and 2) at the same time), B5=the number of elements in the data set being evaluated.

[0090] The satisfaction standard of accessibility (Q006) is that the relevant data interface can return a message within a specified time, and the accessibility score is calculated in the following manner: X6=A6 / B6, wherein: A6=the number of elements in the data set that meet the requirement that the relevant data interface can return a message within a specified time, B6=the number of elements in the data set being evaluated.

[0091] The final output quality evaluation triple is, for example: (P001, Q001, X1).

[0092] Further, after obtaining the index values and weight coefficients of the quality evaluation indexes, the data quality comprehensive score (the quality evaluation result of the target data product) can also be calculated, and the calculation manner is shown in the following formula:

[0093]

[0094] wherein, W i is a reference weight coefficient, which can be a weight obtained from the weight coefficient determined according to the demand information or a system-provided expert weight; X i is the obtained index value of the six-dimensional quality evaluation index.

[0095] In the data quality evaluation process, the application embodiment compares and sorts questionnaires, calculates the self-defined weight by using the analytic hierarchy process, meets the needs of the data demand end to self-define the dimension weight according to the real business requirements, and improves the requirements of differentiated services of each node.

[0096] The process of evaluating the value of the data product by selecting different calculation algorithms through the data value evaluation module according to different stages of the target data product is received, and the specific steps are as follows.

[0097] In some embodiments of the present application, the evaluation operation on the target data product further comprises: determining a stage period in which the target data product is located, wherein the stage period comprises at least one of the following: a development stage, an online preparation period and an early stage, an online mature stage, and an offline stage; determining a value evaluation algorithm corresponding to the stage period, and using the value evaluation algorithm to determine the product value of the target data product, to obtain a value evaluation result, wherein the value evaluation result is used to represent the market economic value of the target data product, in the development stage and the offline stage, the product value of the target data product is determined according to the replacement cost of the target data product, in the online preparation period and the early stage, the product value of the target data product is determined by comparing the value evaluation results of other data products publicly disclosed in the market, and in the online mature stage, the product value of the target data product is determined by predicting the profit of the product.

[0098] In the embodiments of the present application, different value evaluation algorithms can be set for different target data products in different stage periods of the company. Specifically, as shown in Figure 5 The stage period can include a development stage, an online preparation period and an early stage, an online mature stage, and an offline stage, wherein the development stage refers to a stage before the data product is put online, in which data governance, classification and grading, data cataloging, data analysis and other productization are carried out, and a large amount of research and development cost is generated; the online preparation period and the early stage refer to the data product online preparation stage and the early stage of online, in which the transaction volume is small or zero, the market penetration stage, the income of this stage is small, but the future income can be predicted; the online mature stage refers to the stable period of the data product online, in which the transaction volume is large and the market activity is high, the income of this stage is high and stable; the offline stage refers to the later stage of the data product online or after the data product is offline, in which the transaction volume gradually decreases, and due to the timeliness of data or technology, the data product is gradually eliminated in the market, the income of this stage is small, and the maintenance cost increases.

[0099] After determining the stage period in which the target data product is located, the value evaluation algorithm corresponding to the stage period is used to perform value evaluation, and the data product value evaluation result key-value pair (data product number, value evaluation result) is output, wherein the data product number is the unique identifier of the data product in the multi-node data element block chain, and the value evaluation result is a specific amount of money.

[0100] Specifically, as shown in Figure 5 When the data product is in the development stage, the cost method can be used for calculation, and the calculation method is as shown in the following formula:

[0101] P=C x δ

[0102] Wherein, P is the value of the data asset being evaluated, C is the replacement cost of the data asset, mainly including the front-end cost, direct cost, indirect cost, opportunity cost and related taxes and fees, etc. The front-end cost includes the front-end planning cost, the direct cost includes the cost of continuous investment in the process of data from collection to processing to form assets, the indirect cost includes the hardware and software procurement, infrastructure cost and public management cost directly related to the data asset or can be reasonably allocated; δ is the value adjustment coefficient. The value adjustment coefficient is the coefficient for adjusting the difference between the expected condition corresponding to all inputs of the data asset and the actual condition of the data asset on the evaluation base date, for example: the coefficient for adjusting the difference between the expected quality and the actual quality of the data asset.

[0103] When the data product is in the online preparation period and the initial stage, the market method can be used for calculation, and the calculation method is shown in the following formula:

[0104]

[0105] Wherein, P is the value of the data asset being evaluated, n is the number of data sets decomposed from the data asset being evaluated, i is the serial number of the data set decomposed from the data asset being evaluated, Q i is the value of the reference data set, X i1 is the quality adjustment coefficient, X i2 is the supply and demand adjustment coefficient, X i3 is the period adjustment coefficient, X i4 is the capacity adjustment coefficient, X i5 is other adjustment coefficients.

[0106] When the data product is in the online mature stage, the direct income method can be used for calculation, and the calculation method is shown in the following formula:

[0107] F t = R t

[0108] Wherein, F t is the predicted income of the data asset in the t period, R t is the predicted pre-tax profit of the data asset in the t period. When the data product is in the offline stage, the cost method can be used for calculation, and the calculation method is as follows:

[0109] P = C × δ

[0110] Wherein, P is the evaluated data asset value, C is the replacement cost of the data asset, mainly including the front-end cost, direct cost, indirect cost, opportunity cost and related taxes and fees, etc. The front-end cost includes the front-end planning cost, the direct cost includes the cost of continuous investment in the process of data from collection to processing to form an asset, the indirect cost includes the software and hardware procurement, infrastructure cost and public management cost directly related to the data asset or can be reasonably allocated, and δ is the value adjustment coefficient. The value adjustment coefficient is a coefficient for adjusting the difference between the expected condition corresponding to all inputs of the data asset and the actual condition of the data asset on the evaluation benchmark day, for example: a coefficient for adjusting the difference between the expected quality and the actual quality of the data asset.

[0111] For example, assuming that the data product number P001 is currently in the development state and has no historical record of online sales, the evaluated data asset value P of P001 is equal to 200,000 yuan by accumulating the calculation, storage, network cost, human cost generated by the development of P001, and the third-party data cost introduced in the development process of P001. In the case where the value adjustment coefficient is equal to 1, the output P001 value evaluation result key-value pair is (P001, 200,000). Assuming that the data product number P002 is currently in the initial online stage, by comparing other publicly valued data products on the market, it is found that P002 can be decomposed into P003 and P004, the sum of the values of P003 and P004 is 200,000 million, and in the case where the adjustment coefficient is equal to 1, the output P002 value evaluation result key-value pair is (P002, 200,000). Assuming that the data product number P005 is currently in the mature online stage, by calculating the predicted current data asset EBITDA, the output P002 current value evaluation result key-value pair is (P005, 200,000).

[0112] The parameters involved in the above calculation method can be calculated and obtained from the data element platform transaction history record, or input by the data provider. In addition, in the embodiments of the present application, whether the value evaluation result calculated can be chained can be set according to actual needs.

[0113] In the data value evaluation process of the embodiments of the present application, different calculation methods are used according to different stages of the data product in the company, which realizes differentiated evaluation of data value and better protects the interests of the company.

[0114] In addition, in the embodiments of the present application, the evaluation timing task can be set and issued by the monitoring alarm module, and after obtaining the data evaluation result, it is judged whether it meets the preset product standard. If not, an alarm information is generated, as follows.

[0115] In some embodiments of the present application, in the case that the data evaluation result obtained by the evaluation operation does not meet the preset product standard, sending the alarm information comprises: in the case that the compliance evaluation result represents that the target data product does not meet the target rule, sending first alarm information, wherein the first alarm information is used to represent that the target data product does not meet the target rule corresponding to the identifier in the compliance evaluation result.

[0116] In some embodiments of the present application, in the case that the data evaluation result obtained by the evaluation operation does not meet the preset product standard, sending the alarm information further comprises: in the case that the index value of the quality evaluation index corresponding to the target data product is less than the preset index threshold corresponding to the quality evaluation index, sending second alarm information, wherein the second alarm information is used to represent that the quality evaluation index of the target data product is unqualified; in the case that the quality evaluation result corresponding to the target data product is less than the preset quality score threshold, sending third alarm information.

[0117] Specifically, the automatic evaluation period can be set by the monitoring alarm module, and the evaluation task can be issued regularly to run the data compliance evaluation module, the data quality evaluation module, and the data value evaluation module to perform the evaluation operation on the target data product. The quality evaluation six-dimension unqualified score threshold and the comprehensive score unqualified score threshold can be set to analyze the evaluation results of other nodes. After obtaining the data evaluation result of the evaluation operation, an alarm can be generated for the data product that does not meet the preset product standard by analyzing the evaluation results of itself and other nodes.

[0118] For the data compliance evaluation module, the compliance evaluation result triple (data product number, rule number, compliance evaluation result) is received. For a certain data product, if any rule does not pass (the compliance evaluation result is 0), the overall compliance result of the data product is determined as unqualified. The triple with the compliance evaluation result of 0 is filtered out to generate an alarm record (first alarm information).

[0119] For the data quality evaluation module, the quality evaluation result triple (data product number, index dimension number, quality evaluation result) is received. For a certain data product, when the quality evaluation result is less than the preset index threshold set in advance, the quality evaluation result of the data product in that dimension is unqualified, and an alarm record (second alarm information) is generated. For the data quality evaluation module, the comprehensive score of the data quality evaluation of the data product (quality evaluation result) is calculated, and the comprehensive score unqualified score threshold (i.e., the preset quality score threshold) is set in advance. When the comprehensive score is less than the preset quality score threshold, the quality evaluation result of the data product is unqualified, and an alarm record (third alarm information) is generated.

[0120] For the data value evaluation module, the data product value evaluation result key-value pair (data product number, value evaluation result) is received: the value evaluation result is a specific amount of value, the higher the value, the higher the evaluated data value, and the module does not generate an alarm record.

[0121] For example, assume that the evaluation result of the on-chain attention node data product received is: {“isLegal”:[(P001,R001,1),(P001,R002,0),(P002,R002,1)],“quality”:[(P001,Q001,0.96),(P001,Q002,0.80),(P001,Q003,0.86),(P001,Q004,0.99),(P001,Q005,0.91),(P001,Q006,0.78),(P002,Q001,0.72),(P002,Q002,0.88),(P002,Q003,0.96),(P002,Q004,0.91),(P002,Q005,0.77),(P002,Q006,0.78)],“value”:[(P001,200000)]};

[0122] Combined with the pre-set quality evaluation six-dimension not-pass thresholds: Q001<0.8, Q002<0.8, Q003<0.8, Q004<0.8, Q005<0.8, Q006<0.8, and S2.2 custom weight is [0.2, 0.3, 0.1, 0.1, 0.2, 0.1], the pre-set comprehensive score not-pass threshold is 0.85, so the P001 comprehensive score is 0.96x0.2+0.80x0.3+0.86x0.1+0.99x0.1+0.91x0.2+0.78x0.1=0.877, and the P002 comprehensive score is 0.72x0.2+0.88x0.3+0.96x0.1+0.91x0.1+0.77x0.2+0.78x0.1=0.827.

[0123] The final generated alarm information is: {“alert”:{“isLegal”:[(P001,R002,0)],“quality”:[(P001,Q006,0.78),(P002,Q001,0.72),(P002,Q005,0.77),(P002,Q006,0.78)],“totalQuanlity”:[(P002,0.827)]}。

[0124] In addition, the monitoring alarm module also detects whether there is an evaluation result change (i.e., whether the data evaluation result obtained by the evaluation operation changes compared with the data evaluation result obtained by the historical evaluation operation), and if so, generates a data element evaluation result change notification and specific change content to the change chaining and updating module.

[0125] The following describes the specific flow steps of initiating a chaining request when detecting a change in the node data element evaluation result by using the change chaining and updating module, and obtaining the target data element evaluation result when receiving an evaluation result update request.

[0126] When detecting a change in the node data element evaluation result, i.e., in the case of a change in the data evaluation result obtained by the evaluation operation compared with the data evaluation result obtained by the historical evaluation operation, a chaining request is initiated. Specifically, the data supply end node where the data evaluation result changes generates a data element evaluation result change data block containing a timestamp, a hash digest, a digital signature, data element evaluation result change content, and a data element evaluation result index, and generates a chaining request containing the data element evaluation result change data block and sends it to the multi-node data element block chain.

[0127] For example, when the data evaluation result of the data product changes, the data supply end node where the evaluation result changes generates a data element evaluation result change data block containing a timestamp Timestamp, a hash digest Hash(Block), a digital signature Sign(key private ,key public ), data element evaluation result change content Data, and data element evaluation result index Index, and generates a chaining request containing the data element evaluation result change data block Block and sends it to the multi-node data element block chain.

[0128] The timestamp Timestamp is used to record the data element evaluation result change time; the hash digest Hash(Block) can be a feature code obtained by performing a hash operation on the entire data element evaluation result change data block using SHA-2, SHA-256, or MD5 (Message-Digest Algorithm), which is used to ensure that the data element evaluation result change data block has not been tampered with; the digital signature Sign(key private ,key public) can be used to uniquely identify the node using the node private key key private The encrypted ciphertext identifier can be decrypted by other nodes on the chain using the public key key public After decryption, the identity of the node that generates the data element evaluation result change data block (i.e., the node that has the data element evaluation result change) is verified for authenticity; the data element evaluation result change content Data is used to record all the contents of the data element evaluation result change; and the data element evaluation result index Index is the keyword of the data element evaluation result change content. The form of the data element evaluation result change data block can be as follows:

[0129] Block = <Timestamp, Index, Data, Hash(Block), Sign(key private ,key public )>

[0130] The multi-node data element block chain receives the on-chain request and responds to the data element evaluation result change initiation node, and after successful verification, the data element evaluation result change data block is added and stored in the multi-node data element block chain.

[0131] When receiving a data product evaluation result update request from other nodes of the block chain, the target evaluation result is obtained, as follows.

[0132] In some embodiments of the present application, the method further comprises receiving an evaluation result update request, wherein the evaluation result update request includes an on-chain request sent by a node other than the current node in the block chain, and the on-chain request contains a result change data block, and the result change data block contains a timestamp, a hash digest, a digital signature, and change content; the digital signature in the evaluation result update request is decrypted using a public key, and the decrypted digital signature is used to verify the identity authenticity; and if the identity authenticity verification is passed, the result change data block is updated to the block chain of the current node.

[0133] Specifically, when receiving an evaluation result update request from other nodes in the multi-node data element block chain, the public key key publicDecryption, get the digital signature of the node initiating the update request, compare and verify the identity of the update request node, if the verification is passed, parse the data element evaluation result index from the data element evaluation result change data block, obtain the data element evaluation result index matched with the index information from the query index table, access the physical address of the data element evaluation result change data block according to the matched data element evaluation result index, obtain the target data element evaluation result data, and complete the update.

[0134] The scheme of the present application aims at the more widely existing data set products in the real data element transaction market. An evaluation Agent is deployed on each blockchain node to evaluate the compliance, quality and value of data products regularly, and the evaluation result change data block is uploaded to the chain, filling the gap in the whole data life cycle monitoring and management technology in the process of continuous multiple transactions. Not only does it improve the efficiency and accuracy of data evaluation, but also ensures the transparency and tamper resistance of the evaluation results, enhancing the trust of the data element market. Specifically, through automated evaluation and monitoring, changes in data products in terms of compliance, quality and value can be discovered in a timely manner, effectively preventing potential risks in the transaction process and protecting the rights and interests of data demanders and providers. In addition, the uploading mechanism of the evaluation results promotes the rapid sharing of data evaluation results in the blockchain network, helping to build a healthier and more transparent data transaction ecosystem. The scheme of the present application significantly improves the operational efficiency of the data element platform, reduces compliance costs, enhances the security and reliability of data transactions, and provides strong technical support for the healthy development of the data element market.

[0135] According to the embodiments of the present application, an embodiment of a data product monitoring device is also provided. Figure 6 is a structural schematic diagram of a data product monitoring device according to an embodiment of the present application. As shown in Figure 6 , the device comprises:

[0136] The evaluation module 60 is configured to perform an evaluation operation on the target data product by using an evaluation module, wherein the evaluation module is deployed on a node of a blockchain, and one evaluation module is deployed on each node, and the evaluation operation comprises at least one of the following: data compliance evaluation, data quality evaluation, and data value evaluation.

[0137] The alarm module 62 is configured to send an alarm information in a case that the data evaluation result obtained by the evaluation operation does not satisfy a preset product standard, wherein the preset product standard is used to represent the requirements that the data product should satisfy in each evaluation dimension, and the evaluation dimension comprises at least one of the following: data compliance, data quality, and data value.

[0138] The upper chain module 64 is configured to, in a case where the data evaluation result obtained by the evaluation operation is changed compared with the data evaluation result obtained by the historical evaluation operation, send an upper chain request to other nodes in the block chain, wherein the upper chain request is used to synchronize the change content of the data evaluation result and the alarm condition to other nodes in the block chain.

[0139] Optionally, the evaluation operation on the target data product comprises: determining an application level corresponding to the target data product, wherein the application level comprises a public level, an industry level, an enterprise level, and a personal level; obtaining a target rule corresponding to the application level from a compliance rule library, wherein the compliance rule library comprises a plurality of compliance rules, and the compliance rules are obtained by analyzing and converting relevant laws and regulations and industry standard specifications of data products into executable rules; determining whether the target data product meets the target rule by using a rule engine to obtain a compliance evaluation result, wherein the compliance evaluation result is in the form of a triple, and the triple comprises an identifier of the target data product, an identifier of the target rule, and an evaluation result, and the evaluation result in the triple is used to represent whether the target data product meets the target rule.

[0140] Optionally, the evaluation operation on the target data product further comprises: determining a quality evaluation index for data quality evaluation on the target data product, wherein the quality evaluation index comprises at least one of the following: a specification index, a completeness index, an accuracy index, a consistency index, a timeliness index, and an accessibility index; obtaining demand information provided by a demand side of the target data product, and determining a weight coefficient corresponding to each quality evaluation index according to the demand information; determining a first number of data elements in the target data product that meet a quality requirement corresponding to the quality evaluation index, and determining an index value corresponding to each quality evaluation index of the target data product according to the first number and a total number of data elements in the target data product; and determining a quality evaluation result of the target data product according to the index value and the weight coefficient of each quality evaluation index, wherein the quality evaluation result is used to represent a degree to which a data characteristic of the target data product meets a requirement of the demand side.

[0141] Optionally, the evaluation operation on the target data product further comprises: determining a stage period in which the target data product is located, wherein the stage period comprises at least one of the following: a development stage, an online preparation period and an early stage, an online mature stage, and an offline stage; determining a value evaluation algorithm corresponding to the stage period, and determining the product value of the target data product by using the value evaluation algorithm to obtain a value evaluation result, wherein the value evaluation result is used to represent the market economic value of the target data product, in the development stage and the offline stage, the product value of the target data product is determined according to the replacement cost of the target data product, in the online preparation period and the early stage, the product value of the target data product is determined by comparing the value evaluation results of other data products publicly disclosed on the market, and in the online mature stage, the product value of the target data product is determined by predicting the profit of the product.

[0142] Optionally, in the case that the data evaluation result obtained by the evaluation operation does not meet the preset product standard, the sending of the alarm information comprises: in the case that the compliance evaluation result represents that the target data product does not meet the target rule, sending first alarm information, wherein the first alarm information is used to represent that the target data product does not meet the target rule corresponding to the identifier in the compliance evaluation result.

[0143] Optionally, in the case that the data evaluation result obtained by the evaluation operation does not meet the preset product standard, the sending of the alarm information further comprises: in the case that the index value of the quality evaluation index corresponding to the target data product is less than the preset index threshold value corresponding to the quality evaluation index, sending second alarm information, wherein the second alarm information is used to represent that the quality evaluation index of the target data product is unqualified; and in the case that the quality evaluation result corresponding to the target data product is less than the preset quality score threshold value, sending third alarm information.

[0144] Optionally, the data product monitoring device is further configured to: receive an evaluation result update request, wherein the evaluation result update request comprises a chain request sent by other nodes in the blockchain except the current node, the chain request comprises a result change data block, and the result change data block comprises a timestamp, a hash digest, a digital signature, and change content; decrypt the digital signature in the evaluation result update request by using a public key, and perform identity authenticity verification by using the decrypted digital signature; and in the case that the identity authenticity verification is passed, update the result change data block to the blockchain of the current node.

[0145] It should be noted that each module in the above data product monitoring device can be a program module (for example, a program instruction set for implementing a certain specific function) or a hardware module. For the latter, it can be in the following form, but is not limited to this: the form of each module is a processor, or the functions of each module are implemented by a processor.

[0146] It should be noted that the data product monitoring device provided in this embodiment can be used to perform Figure 2 The data product monitoring method shown, therefore, the relevant explanations and descriptions of the above-mentioned data product monitoring method are also applicable to the embodiments of this application and will not be repeated here.

[0147] An embodiment of the present application also provides a non-volatile storage medium, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the following data product monitoring method by running the computer program: using an evaluation module to perform an evaluation operation on the target data product, wherein the evaluation module is deployed on the node of the blockchain, and each node deploys an evaluation module, and the evaluation operation includes at least one of the following: data compliance evaluation, data quality evaluation, and data value evaluation; when the data evaluation result obtained by the evaluation operation does not meet the preset product standard, an alarm information is sent, wherein the preset product standard is used to characterize the requirements that the data product should meet in each evaluation dimension, and the evaluation dimension includes at least one of the following: data compliance, data quality, and data value; when the data evaluation result obtained by the evaluation operation changes compared with the data evaluation result obtained by the historical evaluation operation, a chain request is sent to other nodes in the blockchain, wherein the chain request is used to synchronize the changed content and alarm status of the data evaluation result to other nodes in the blockchain.

[0148] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the data product monitoring method described in each embodiment of the present application: using an evaluation module to perform an evaluation operation on the target data product, wherein the evaluation module is deployed on the node of the blockchain, and each node deploys an evaluation module, and the evaluation operation includes at least one of the following: data compliance evaluation, data quality evaluation, and data value evaluation; when the data evaluation result obtained by the evaluation operation does not meet the preset product standard, an alarm message is sent, wherein the preset product standard is used to characterize the requirements that the data product should meet in each evaluation dimension, and the evaluation dimension includes at least one of the following: data compliance, data quality, and data value; when the data evaluation result obtained by the evaluation operation changes compared to the data evaluation result obtained by the historical evaluation operation, a chain request is sent to other nodes in the blockchain, wherein the chain request is used to synchronize the changed content and alarm status of the data evaluation result to other nodes in the blockchain.

[0149] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0150] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0151] In several embodiments provided in the present application, it should be understood that the disclosed technology can be implemented by other ways. Among them, the above-described device embodiments are only illustrative, for example, the division of the units can be a logical function division, and actual implementation can have another division way, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or modules shown or discussed can be indirect coupling or communication connection through some interfaces, units or modules, which can be electrical or other forms.

[0152] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place or distributed to multiple units. Part or all of the units can be selected to achieve the purpose of the embodiment scheme according to actual needs.

[0153] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0154] When the integrated unit is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a plurality of instructions to make a computer device (which can be a personal computer, a server or a network device, etc.) execute all or part of the steps of the method described in each embodiment of the present application. The foregoing storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and various program codes that can be stored in the medium.

[0155] The above is only the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which should be considered as the protection scope of the present application.

Claims

1. A data product monitoring method, characterized in that: include: An evaluation module is used to evaluate the target data product, wherein the evaluation module is deployed on the nodes of the blockchain, with one evaluation module deployed on each node, and the evaluation operation includes at least one of the following: data compliance evaluation, data quality evaluation, and data value evaluation; If the data evaluation result obtained by the evaluation operation does not meet the preset product standards, sending an alarm message, wherein the preset product standards are used to characterize the requirements that the data product should meet in various evaluation dimensions, and the evaluation dimensions include at least one of the following: data compliance, data quality, and data value; When the data evaluation result obtained by the evaluation operation changes compared with the data evaluation result obtained by the historical evaluation operation, a chain request is sent to other nodes in the blockchain, wherein the chain request is used to synchronize the changed content and alarm status of the data evaluation result to other nodes in the blockchain.

2. The data product monitoring method according to claim 1, characterized in that: The evaluation operations for the target data product include: Determine the application level corresponding to the target data product, wherein the application level includes: public level, industry level, enterprise level, and personal level; Obtaining a target rule corresponding to the application level from a compliance rule library, wherein the compliance rule library includes multiple compliance rules, each of which is obtained by parsing relevant laws, regulations, and industry standards and specifications of the data product and converting them into executable rules; A rule engine is used to determine whether the target data product meets the target rules and obtain a compliance assessment result, wherein the compliance assessment result is in the form of a triple, and the triple includes: an identifier of the target data product, an identifier of the target rule, and an assessment result. The assessment result in the triple is used to characterize whether the target data product meets the target rule.

3. The data product monitoring method according to claim 1, characterized in that: The evaluation of the target data product also includes: Determining quality assessment indicators for performing the data quality assessment on the target data product, wherein the quality assessment indicators include at least one of the following: a standardization indicator, a completeness indicator, an accuracy indicator, a consistency indicator, a timeliness indicator, and an accessibility indicator; Obtaining demand information provided by a demander of the target data product, and determining a weight coefficient corresponding to each of the quality assessment indicators based on the demand information; Determining a first number of data elements in the target data product that meet the quality requirements corresponding to the quality assessment indicators, and determining an indicator value corresponding to each of the quality assessment indicators of the target data product based on the first number and the total number of data elements in the target data product; The quality assessment result of the target data product is determined based on the indicator value and the weight coefficient of each quality assessment indicator, wherein the quality assessment result is used to characterize the extent to which the data characteristics of the target data product meet the requirements of the demander.

4. The data product monitoring method according to claim 1, characterized in that: The evaluation of the target data product also includes: Determining the stage of the target data product, wherein the stage includes at least one of the following: development stage, online preparation stage and initial stage, online mature stage, and offline stage; Determine a value assessment algorithm corresponding to the stage period, and use the value assessment algorithm to determine the product value of the target data product to obtain a value assessment result, wherein the value assessment result is used to characterize the market economic value of the target data product. In the development stage and the offline stage, the product value of the target data product is determined based on the replacement cost of the target data product. In the online preparation period and the initial stage, the product value of the target data product is determined by comparing the publicly available value assessment results of other data products on the market. In the online maturity stage, the product value of the target data product is determined by predicting the profit and income of the product.

5. The data product monitoring method according to claim 2, characterized in that: When the data evaluation result obtained by the evaluation operation does not meet the preset product standard, sending the alarm information includes: When the compliance evaluation result indicates that the target data product does not meet the target rule, a first alarm message is sent, wherein the first alarm message is used to indicate that the target data product does not meet the target rule corresponding to the identifier in the compliance evaluation result.

6. The data product monitoring method according to claim 3, characterized in that: When the data evaluation result obtained by the evaluation operation does not meet the preset product standard, sending the alarm information further includes: When the indicator value of the quality assessment indicator corresponding to the target data product is less than the preset indicator threshold corresponding to the quality assessment indicator, sending a second alarm message, wherein the second alarm message is used to indicate that the quality assessment indicator of the target data product is unqualified; When the quality assessment result corresponding to the target data product is less than a preset quality score threshold, third warning information is sent.

7. The data product monitoring method according to claim 1, characterized in that: The method further comprises: Receive an evaluation result update request, wherein the evaluation result update request includes: the on-chain request sent by a node other than the current node in the blockchain, the on-chain request includes a result change data block, and the result change data block includes: a timestamp, a hash digest, a digital signature, and the change content; Decrypting the digital signature in the evaluation result update request using the public key, and verifying the authenticity of the identity using the decrypted digital signature; If the identity authenticity verification is passed, the result change data block is updated to the blockchain of the current node.

8. A data product monitoring device, characterized in that: include: An evaluation module, configured to use the evaluation module to perform an evaluation operation on the target data product, wherein the evaluation module is deployed on the nodes of the blockchain, with one evaluation module deployed on each node, and the evaluation operation includes at least one of the following: data compliance evaluation, data quality evaluation, and data value evaluation; an alarm module, configured to send an alarm message if the data evaluation result obtained by the evaluation operation does not meet a preset product standard, wherein the preset product standard is used to characterize the requirements that the data product should meet in various evaluation dimensions, wherein the evaluation dimensions include at least one of the following: data compliance, data quality, and data value; The on-chain module is used to send an on-chain request to other nodes in the blockchain when the data evaluation result obtained by the evaluation operation changes compared with the data evaluation result obtained by the historical evaluation operation, wherein the on-chain request is used to synchronize the changed content and alarm status of the data evaluation result to other nodes in the blockchain.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the processor is used to run a program stored in the memory, wherein the data product monitoring method according to any one of claims 1 to 7 is executed when the program is run.

10. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the data product monitoring method according to any one of claims 1 to 7 by running the computer program.

11. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the data product monitoring method according to any one of claims 1 to 7 are implemented.

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