Blockchain-based identification data evaluation method, device and electronic device

Abstract

The present application is applicable to the field of blockchain technology, and provides a blockchain-based identification data evaluation method, device, and electronic device, including: obtaining the identification data of each IoT device in the logistics process, each identification data having a corresponding measurement accuracy and measurement range; generating quality data of multiple dimensions based on each identification data and its measurement accuracy and measurement range; determining the current stakeholders, and the stakeholders are set with a data quality threshold for at least one target dimension; if the target quality data corresponding to the target dimension meets the requirements of the data quality threshold, the identification data for generating the target quality data is stored in the blockchain. Through the above method, the data quality of the identification data in the logistics process can meet the needs of various stakeholders, helping stakeholders to determine that the logistics process meets their requirements.

Description

Technical Field

[0001] The embodiments of the present application belong to the field of blockchain technology, and in particular, relate to a blockchain-based identification data evaluation method, device, and electronic device. Background Art

[0002] In the logistics process, the entire delivery process of goods from the shipper to the consignee can involve multiple stakeholders. The identification data in the logistics process is very important for various stakeholders to monitor the entire logistics process.

[0003] However, in the existing Internet of Things, it is impossible to determine the data quality of identification data, but all identification data are directly stored, resulting in that the stored identification data may be wrong. Summary of the invention

[0004] In view of this, the embodiments of the present application provide a blockchain-based identification data evaluation method, device and electronic device, which can evaluate the data quality of identification data in the logistics process according to the needs of various stakeholders, and then evaluate whether the logistics process meets the requirements of the stakeholders.

[0005] A first aspect of an embodiment of the present application provides a blockchain-based identification data evaluation method, comprising:

[0006] Obtain identification data of each IoT device in the logistics process, each of which has a corresponding measurement accuracy and measurement range;

[0007] Generate quality data in multiple dimensions according to each of the above identification data and its measurement accuracy and measurement range;

[0008] Identifying current stakeholders who have set data quality thresholds for at least one target dimension;

[0009] If the target quality data corresponding to the above target dimension meets the requirements of the above data quality threshold, the above identification data for generating the above target quality data will be stored in the blockchain.

[0010] A second aspect of an embodiment of the present application provides an identification data evaluation device based on blockchain, comprising:

[0011] An acquisition module is used to acquire identification data of each IoT device in the logistics process, each of which has a corresponding measurement accuracy and measurement range;

[0012] A quality data generation module is used to generate quality data of multiple dimensions according to each of the above identification data and its measurement accuracy and measurement range;

[0013] A determination module, used to determine a current stakeholder, wherein the stakeholder is set with a data quality threshold for at least one target dimension;

[0014] The storage module is used to store the above-mentioned identification data for generating the above-mentioned target quality data in the blockchain if the target quality data corresponding to the above-mentioned target dimension meets the requirements of the above-mentioned data quality threshold.

[0015] A third aspect of an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method in the first aspect when executing the computer program.

[0016] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method in the first aspect is implemented.

[0017] Compared with the prior art, the embodiments of the present application have the following advantages:

[0018] The embodiment of the present application provides a blockchain-based identification data evaluation method, which can be used to evaluate the identification data of each IoT device collected during the logistics process. Since each IoT device has a certain measurement accuracy and measurement range, the identification data collected by each IoT device also has a certain measurement accuracy and measurement range. The embodiment of the present application can generate quality data of multiple dimensions based on each identification data and its measurement accuracy and measurement range. For different stakeholders, it can set a corresponding data quality threshold for the quality data of each dimension. By judging whether the target quality data corresponding to the target dimension meets the requirements of the data quality threshold set by the stakeholders, the identification data generating the target quality data can be stored in the blockchain when the requirements of the data quality threshold are met. The embodiment of the present application can determine whether the identification data meets the requirements of each stakeholder by evaluating the data quality of the identification data in the logistics process, thereby helping to ensure the accuracy of the identification data and avoid storing erroneous data. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or prior art descriptions. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1It is a schematic diagram of a blockchain-based identification data evaluation method provided in an embodiment of the present application;

[0021] Figure 2 It is a schematic diagram of an implementation method of S102 in a blockchain-based identification data evaluation method provided in an embodiment of the present application;

[0022] Figure 3 It is a schematic diagram of another blockchain-based identification data evaluation method provided in an embodiment of the present application;

[0023] Figure 4 It is a schematic diagram of an identification data evaluation device based on blockchain provided in an embodiment of the present application;

[0024] Figure 5 It is a schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] In the following description, specific details such as specific system structures, technologies, etc. are proposed for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from hindering the description of the present application.

[0026] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0027] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0028] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0029] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0030] References to "one embodiment" or "some embodiments" etc. described in the specification of the present application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways. The technical solution of the present application is illustrated below by means of specific embodiments.

[0031] Figure 1 is a schematic diagram of a blockchain-based identification data evaluation method provided in an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0032] S101. Obtain identification data of each IoT device in the logistics process.

[0033] The method in this embodiment can be applied to blockchain, and the method can be used to evaluate identification data in the logistics process.

[0034] In the logistics process, various IoT devices can obtain measurement data of transported goods. IoT devices can be temperature measuring instruments, global positioning systems, weight measuring instruments, etc. The measurement data obtained can be data that belongs to the characteristics of the transported goods, such as the temperature, geographical location, and weight of the transported goods.

[0035] Blockchain can generate corresponding identification data based on the measurement data of each IoT device.

[0036] The identification data may be a data that represents the measurement data with numbers or symbols. For example, the measurement data may be 25° C., and the corresponding identification data may be “25.”.

[0037] Each IoT device has a measurement accuracy and a measurement range. The measurement accuracy and measurement range of each IoT device are determined by the manufacturer of the IoT device.

[0038] Since each IoT device has measurement accuracy and measurement range, the identification data generated by the measurement data obtained by each IoT device also has corresponding measurement accuracy and measurement range.

[0039] By applying this method, it is possible to obtain identification data generated by measurement data acquired by various IoT devices during the logistics process as well as its measurement accuracy and measurement range.

[0040] S102: Generate quality data of multiple dimensions according to each identification data and its measurement accuracy and measurement range.

[0041] Quality data in multiple dimensions can be generated based on the acquired identification data and their measurement accuracy and measurement range.

[0042] Quality data in multiple dimensions can evaluate the data quality of each identification data from different dimensions.

[0043] Exemplarily, the quality data of multiple dimensions may be: IoT device measurement accuracy data, IoT measurement precision accuracy data, IoT data event accuracy data, IoT data source accuracy data, and logistics overall accuracy data, which is not limited in the embodiments of the present application.

[0044] Specifically, according to each identification data and its measurement accuracy and measurement range, multiple dimensions of quality data are generated. When the quality data is the measurement accuracy data of the IoT device, the following steps S1021-S1023 may be included:

[0045] S1021. Calculate the data sum and data difference of the identification data and the measurement accuracy.

[0046] The data sum and data difference of the identification data and the measurement accuracy can be calculated based on the obtained identification data and the measurement accuracy.

[0047] Exemplarily, if a is identification data and b is measurement accuracy, the sum of the identification data and the measurement accuracy can be expressed as a+b, and the difference between the identification data and the measurement accuracy can be expressed as ab.

[0048] S1022. Determine the relationship between the identification data, the data sum, the data difference and the measurement range.

[0049] The size relationship among the identification data, the data difference between the identification data and the measurement accuracy, the data sum of the identification data and the measurement accuracy, the minimum value of the measurement range, and the maximum value of the measurement range can be determined.

[0050] S1023. Determine measurement accuracy data of the IoT device based on the size relationship.

[0051] The measurement accuracy data of the IoT device can be determined based on the size relationship between the identification data, the data difference between the identification data and the measurement accuracy, the data sum of the identification data and the measurement accuracy, the minimum value of the measurement range, and the maximum value of the measurement range.

[0052] Specifically, if the data difference between the identification data and the measurement accuracy is greater than or equal to the minimum value of the measurement range, and the data sum of the identification data and the measurement accuracy is less than or equal to the maximum value of the measurement range, it can be determined that the measurement accuracy data of the Internet of Things device is 1.

[0053] If the data difference between the identification data and the measurement accuracy is less than the minimum value of the measurement range, and the identification data is greater than or equal to the minimum value of the measurement range, it can be determined that the measurement accuracy data of the IoT device is equal to the ratio of the data difference to the measurement accuracy.

[0054] If the sum of the identification data and the measurement accuracy data is greater than the maximum value of the measurement range, and the identification data is less than or equal to the maximum value of the measurement range, it can be determined that the measurement accuracy data of the IoT device is equal to the ratio of the data difference to the measurement accuracy.

[0055] Otherwise, it can be determined that the IoT device measurement accuracy data is 0.

[0056] Specifically, the following formula can be used to generate IoT device measurement accuracy data.

[0057]

[0058] Among them, b is the measurement accuracy, a is the identification data, FTh min is the minimum value of the measurement range, FTh max is the maximum value of the measuring range.

[0059] The measurement accuracy data of IoT devices is quality data based on the identification data of each IoT device.

[0060] According to each identification data and its measurement accuracy and measurement range, quality data of multiple dimensions are generated. When the quality data is the measurement accuracy data of the IoT device, the relevant device can be first determined from multiple IoT devices.

[0061] The related devices may be used to measure the same type of identification data in each node of the logistics process. In other words, the related devices may be a plurality of IoT devices used to measure the same type of identification data in each node of the logistics process.

[0062] The various nodes of the logistics process can be stakeholders who play various roles in the logistics process, such as the shipper, the consignee, and various transport units.

[0063] Correspondingly, the node measuring the logistics process can be the consignor, and the relevant equipment can be used to measure the identification data of the temperature of the consignor in the logistics process. The node measuring the logistics process can be the consignee, and the relevant equipment can also be used to measure the identification data of the geographical location of the consignee in the logistics process.

[0064] After determining the relevant devices from multiple IoT devices, IoT measurement accuracy data can be generated based on the individual IoT measurement accuracy data and the number of relevant devices.

[0065] The number of related devices may be the number of IoT devices used to measure the same type of identification data in each node of the logistics process.

[0066] For example, if the shipper that measures the logistics process has three temperature measuring instruments, then the number of IoT devices that measure the temperature identification data is three.

[0067] The IoT measurement accuracy data may be an average value of IoT measurement accuracy data corresponding to a plurality of related devices.

[0068] Specifically, the following formula can be used to generate IoT measurement accuracy data.

[0069]

[0070] Where m is the number of related devices, Acc MsrVali Measuring accuracy data for the Internet of Things.

[0071] The IoT measurement accuracy data is quality data based on various types of identification data.

[0072] Based on the various identification data and their measurement accuracy and measurement range, quality data of multiple dimensions are generated. When the quality data is the accuracy data of IoT data events, the number of related IoT involved in the logistics process can be determined first.

[0073] The relevant Internet of Things can be used to measure different types of identification data in various nodes of the logistics process, that is, the relevant Internet of Things can be multiple Internet of Things devices used to measure different types of identification data in various nodes of the logistics process.

[0074] For example, the node for measuring the logistics process may be the consignor, and the relevant Internet of Things may be used to measure the temperature identification data, geographic location identification data, weight identification data, etc. of the consignor in the logistics process.

[0075] After determining the number of relevant IoTs involved in the logistics process, IoT data event accuracy data can be generated based on the measurement accuracy data of each IoT and the number of relevant IoTs.

[0076] The number of related Internet of Things can be the number of types of identification data measured in each node of the logistics process.

[0077] For example, if the shipper that measures the logistics process has a temperature measuring instrument, a global positioning system, and a weight measuring instrument, then the number of related IoTs is 3.

[0078] The IoT data event accuracy data is the average value of the IoT measurement accuracy data corresponding to multiple related IoTs.

[0079] Specifically, the IoT data event accuracy data can be generated using the following formula.

[0080]

[0081] Where n is the number of related IoT, Acc Msr Measuring precision accuracy data for the Internet of Things.

[0082] The accuracy data of IoT data events is quality data with multiple different types of identification data as dimensions. Stakeholders can determine the types and quantity of identification data according to their needs.

[0083] For example, the types of identification data may include: temperature, humidity, geographic location, and weight, and the quality data of the identification data of humidity, geographic location, and weight may be used as the accuracy data of the IoT data event. The quality data of the identification data of humidity and geographic location may also be used as the accuracy data of the IoT data event.

[0084] Based on each identification data and its measurement accuracy and measurement range, quality data of multiple dimensions are generated. When the quality data is the accuracy data of the IoT data source, the number of IoT data events in the logistics process can be determined first.

[0085] IoT data events can be composed of different types of identification data at various nodes in the logistics process.

[0086] After determining the number of IoT data events in the logistics process, the IoT data source accuracy data can be generated based on the accuracy data of each IoT data event and the number of IoT data events.

[0087] The IoT data source accuracy data may be an average value of IoT data event accuracy data corresponding to multiple IoT data events.

[0088] Specifically, the following formula can be used to generate the IoT data source accuracy data.

[0089]

[0090] Where p is the number of IoT data events, Acc Evt Provides accuracy data for IoT data events.

[0091] The accuracy data of the IoT data source is quality data with the identification data of each node in the logistics process as the dimension.

[0092] For example, the quality data of the identification data of the consignor can be used as the accuracy data of the IoT data source, and the quality data of the identification data of the consignee can also be used as the accuracy data of the IoT data source.

[0093] Based on the various identification data and their measurement accuracy and measurement range, quality data of multiple dimensions are generated. When the quality data is the overall accuracy data of logistics, the number of IoT data sources in the logistics process can be determined first.

[0094] The IoT data source can consist of all IoT data events at important nodes in the logistics process.

[0095] After determining the number of IoT data sources in the logistics process, the overall logistics accuracy data can be generated based on the various identification data and their measurement accuracy and measurement range.

[0096] The overall logistics accuracy data may be an average value of the IoT data source accuracy data corresponding to multiple IoT data sources.

[0097] Specifically, the following formula can be used to generate overall logistics accuracy data.

[0098]

[0099]

[0100] In the first formula, p EvtAssign is the number of IoT data events in each IoT data source, Acc Evt is the accuracy data of IoT data events. In the second formula, q is the number of IoT data sources, Acc Assign Provide accuracy data for IoT data sources.

[0101] The overall logistics accuracy data is the quality data with the identification data of all nodes in the logistics process as the dimension. In other words, the overall logistics accuracy data is the quality data with all the identification data of the entire logistics process as the dimension.

[0102] S103. Identify current stakeholders.

[0103] Stakeholders can be parties that play various roles in the logistics process, such as shippers, consignees, and various transport units.

[0104] Stakeholders can set a data quality threshold for at least one target dimension. That is, stakeholders can set a threshold for at least one of IoT device measurement accuracy data, IoT measurement precision accuracy data, IoT data event accuracy data, IoT data source accuracy data, and logistics overall accuracy data.

[0105] Stakeholders can set thresholds based on their own needs. If stakeholders value the data quality of a certain dimension, a threshold for that dimension can be set, thereby providing stakeholders with identification data of a quality that meets their needs.

[0106] For example, if a stakeholder values ​​the temperature of each shipment, a threshold can be set for the accuracy of the data measured by the IoT device.

[0107] Each stakeholder can set a data quality threshold for at least one target dimension, so that stakeholders can reach a consensus on the requirements for the data quality of the identification data.

[0108] S104. If the target quality data corresponding to the target dimension meets the requirements of the data quality threshold, the identification data for generating the target quality data is stored in the blockchain.

[0109] If the target quality data corresponding to the target dimension meets the data quality threshold requirements set by the stakeholders, the data quality of the identification data meets the needs of the stakeholders, and the identification data that generates the target quality data can be stored in the blockchain, which can help stakeholders determine that the logistics process meets their requirements.

[0110] If the target quality data corresponding to the target dimension does not meet the requirements of the data quality threshold set by the stakeholders, the identification data does not meet the needs of the stakeholders, and an alarm can be issued to facilitate the detection of the source of the fault in the logistics process, so that the source of the fault can be repaired or replaced in time.

[0111] In this embodiment, quality data of multiple dimensions are generated according to each identification data in the logistics process and its measurement accuracy and measurement range, and it is determined whether the target quality data corresponding to the target dimension meets the data quality threshold set by the stakeholders. If it meets the requirements, the identification data for generating the target quality data is stored in the blockchain, so that the data quality of the identification data can meet the needs of stakeholders and help stakeholders determine that the logistics process meets their requirements. If it does not meet the requirements, an alarm can be issued to facilitate the detection of the source of the fault in the logistics process, and the source of the fault can be repaired or replaced in time. Each stakeholder can set a data quality threshold for at least one target dimension, so that stakeholders can reach a consensus on the requirements for the data quality of the identification data.

[0112] To facilitate understanding, the following is an introduction to a blockchain-based identification data evaluation method provided in an embodiment of the present application, with reference to a specific example.

[0113] See also Figure 3 When evaluating identification data, the identification data of each IoT device in the logistics process is first obtained, and multiple dimensions of quality data are generated based on each identification data and its measurement accuracy and measurement range. The multiple dimensions of quality data can be the IoT device measurement accuracy data, IoT measurement accuracy data, IoT data event accuracy data, IoT data source accuracy data, and logistics overall accuracy data introduced in the above embodiments.

[0114] Next, current stakeholders are determined, and the stakeholders may be set with a data quality threshold for at least one target dimension.

[0115] Then, the target quality data corresponding to the target dimension can be compared with the data quality threshold set by the stakeholders to determine whether the target quality data corresponding to the target dimension meets the requirements of the data quality threshold set by the stakeholders. If so, the identification data for generating the target quality data is stored in the blockchain, otherwise, an alarm is issued. The target quality data corresponding to the target dimension can be calculated using the calculation process of the IoT device measurement accuracy data, IoT measurement precision accuracy data, IoT data event accuracy data, IoT data source accuracy data, and logistics overall accuracy data introduced in the above embodiments.

[0116] Reference Figure 4 , shows a schematic diagram of a blockchain-based identification data evaluation device provided in an embodiment of the present application, the device 400 may include an acquisition module 401, a quality data generation module 402, a determination module 403 and a storage module 404, wherein:

[0117] The acquisition module 401 is used to acquire the identification data of each IoT device in the logistics process, and each identification data has a corresponding measurement accuracy and measurement range;

[0118] A quality data generating module 402 is used to generate quality data of multiple dimensions according to each identification data and its measurement accuracy and measurement range;

[0119] A determination module 403 is used to determine a current stakeholder, where the stakeholder is set with a data quality threshold for at least one target dimension;

[0120] The storage module 404 is used to store the identification data of the generated target quality data in the blockchain if the target quality data corresponding to the target dimension meets the requirements of the data quality threshold.

[0121] In an embodiment of the present application, the quality data of multiple dimensions include measurement accuracy data of the Internet of Things device. The above-mentioned module 402 for generating quality data can also be used to: calculate the data sum and data difference of the identification data and the measurement accuracy; determine the size relationship between the identification data, the data sum and the data difference and the measurement range; and generate the measurement accuracy data of the Internet of Things device based on the size relationship.

[0122] In an embodiment of the present application, the above-mentioned quality data generation module 402 can also be used for: if the data difference between the identification data and the measurement accuracy is greater than or equal to the minimum value of the measurement range, and the data sum of the identification data and the measurement accuracy is less than or equal to the maximum value of the measurement range, then the measurement accuracy data of the Internet of Things device is determined to be 1; if the data difference between the identification data and the measurement accuracy is less than the minimum value of the measurement range, and the identification data is greater than or equal to the minimum value of the measurement range, then the measurement accuracy data of the Internet of Things device is determined to be equal to the ratio of the data difference to the measurement accuracy; if the data sum of the identification data and the measurement accuracy is greater than the maximum value of the measurement range, and the identification data is less than or equal to the maximum value of the measurement range, then the measurement accuracy data of the Internet of Things device is determined to be equal to the ratio of the data difference to the measurement accuracy; otherwise, the measurement accuracy data of the Internet of Things device is determined to be 0.

[0123] In an embodiment of the present application, the quality data of multiple dimensions also include IoT measurement precision accuracy data. The above-mentioned quality data generation module 402 can also be used to: determine related devices from multiple IoT devices, and the related devices are used to measure the same type of identification data in each node of the logistics process; generate IoT measurement precision accuracy data based on each IoT measurement accuracy data and the number of related devices, and the IoT measurement precision accuracy data is the average value of the IoT measurement accuracy data corresponding to multiple related devices.

[0124] In an embodiment of the present application, the quality data of multiple dimensions also includes IoT data event accuracy data. The above-mentioned quality data generation module 402 can also be used to: determine the number of related IoTs involved in the logistics process, and the related IoTs are used to measure different types of identification data in each node of the logistics process; generate IoT data event accuracy data based on the measurement precision accuracy data of each IoT and the number of related IoTs, and the IoT data event accuracy data is the average value of the IoT measurement precision accuracy data corresponding to multiple related IoTs.

[0125] In an embodiment of the present application, the quality data of multiple dimensions also includes the accuracy data of the Internet of Things data source. The above-mentioned module 402 for generating quality data can also be used to: determine the number of Internet of Things data events in the logistics process, where the Internet of Things data events are composed of different types of identification data in each node of the logistics process; generate the accuracy data of the Internet of Things data source according to the accuracy data of each Internet of Things data event and the number of Internet of Things data events, where the accuracy data of the Internet of Things data source is the average value of the accuracy data of the Internet of Things data events corresponding to multiple Internet of Things data events.

[0126] In an embodiment of the present application, the quality data of multiple dimensions also includes overall logistics accuracy data. The above-mentioned quality data generation module 402 can also be used to: determine the number of IoT data sources in the logistics process, and the IoT data source is composed of all IoT data events of important nodes in the logistics process; generate overall logistics accuracy data according to the accuracy data of each IoT data source and the number of IoT data sources, and the overall logistics accuracy data is the average value of the IoT data source accuracy data corresponding to multiple IoT data sources.

[0127] Reference Figure 5 , shows a schematic diagram of an electronic device provided by an embodiment of the present application. Figure 5 As shown, the electronic device 500 in the embodiment of the present application includes: a processor 510, a memory 520, and a computer program 521 stored in the memory 520 and executable on the processor 510. When the processor 510 executes the computer program 521, the steps in each embodiment of the blockchain-based identification data evaluation method are implemented, such as Figure 1 Steps S101 to S104 are shown.

[0128] Exemplarily, the computer program 521 may be divided into one or more modules / units, which are stored in the memory 520 and executed by the processor 510 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which may be used to describe the execution process of the computer program 521 in the electronic device 500. For example, the computer program 521 may be divided into an acquisition module, a quality data generation module, a determination module, and a storage module, and the specific functions of each module are as follows:

[0129] An acquisition module is used to acquire identification data of each IoT device in the logistics process, and each identification data has a corresponding measurement accuracy and measurement range;

[0130] A quality data generation module is used to generate quality data of multiple dimensions according to various identification data and their measurement accuracy and measurement range;

[0131] A determination module, for determining a current stakeholder, wherein the stakeholder is set with a data quality threshold for at least one target dimension;

[0132] The storage module is used to store the identification data of the generated target quality data in the blockchain if the target quality data corresponding to the target dimension meets the requirements of the data quality threshold.

[0133] The electronic device 500 may be an electronic device for implementing the identification data evaluation method based on blockchain in the above-mentioned embodiments. The electronic device 500 may be a computing device such as a desktop computer or a cloud server. The electronic device 500 may include, but is not limited to, a processor 510 and a memory 520. Those skilled in the art may understand that Figure 5 It is only an example of the electronic device 500 and does not constitute a limitation of the electronic device 500. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 500 may also include input and output devices, network access devices, buses, etc.

[0134] The processor 510 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0135] The memory 520 may be an internal storage unit of the electronic device 500, such as a hard disk or memory of the electronic device 800. The memory 520 may also be an external storage device of the electronic device 500, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 500. Further, the memory 520 may also include both an internal storage unit of the electronic device 500 and an external storage device. The memory 520 is used to store the computer program 521 and other programs and data required by the electronic device 500. The memory 520 may also be used to temporarily store data that has been output or is to be output.

[0136] The embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0137] The embodiment of the present application also provides a computer program product. When the computer program product is run on a computer, the computer is enabled to execute the steps in the above-mentioned method embodiments.

[0138] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application is described in detail with reference to the above-mentioned embodiments, a person skilled in the art should understand that the technical solutions described in the above-mentioned embodiments can still be modified, or some of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A blockchain-based identification data evaluation method, characterized in that: include: Obtain identification data of each IoT device in the logistics process, each of which has a corresponding measurement accuracy and measurement range; Generating quality data of multiple dimensions according to each of the identification data and its measurement accuracy and measurement range; determining a current stakeholder having a data quality threshold set for at least one target dimension; If the target quality data corresponding to the target dimension meets the requirement of the data quality threshold, the identification data for generating the target quality data is stored in the blockchain; Wherein, the quality data of multiple dimensions include measurement accuracy data of the Internet of Things device, and the generation of the quality data of multiple dimensions according to each of the identification data and its measurement accuracy and measurement range includes: calculating the data sum and data difference of the identification data and the measurement accuracy; determining the size relationship between the identification data, the data sum and the data difference and the measurement range; and determining the measurement accuracy data of the Internet of Things device according to the size relationship; Determining the measurement accuracy data of the Internet of Things device according to the size relationship includes: if the data difference between the identification data and the measurement accuracy is greater than or equal to the minimum value of the measurement range, and the data sum of the identification data and the measurement accuracy is less than or equal to the maximum value of the measurement range, then determining that the measurement accuracy data of the Internet of Things device is 1; if the data difference between the identification data and the measurement accuracy is less than the minimum value of the measurement range, and the identification data is greater than or equal to the minimum value of the measurement range, then determining that the measurement accuracy data of the Internet of Things device is equal to the ratio of the data difference to the measurement accuracy; if the data sum of the identification data and the measurement accuracy is greater than the maximum value of the measurement range, and the identification data is less than or equal to the maximum value of the measurement range, then determining that the measurement accuracy data of the Internet of Things device is equal to the ratio of the data difference to the measurement accuracy; otherwise, determining that the measurement accuracy data of the Internet of Things device is 0.

2. The method according to claim 1, characterized in that The quality data of multiple dimensions also includes IoT measurement accuracy data. The quality data of multiple dimensions is generated according to each of the identification data and its measurement accuracy and measurement range, and also includes: Determining a related device from the plurality of IoT devices, wherein the related device is used to measure identification data of the same type in each node of the logistics process; The Internet of Things measurement accuracy data is generated according to each of the Internet of Things measurement accuracy data and the number of the related devices, and the Internet of Things measurement accuracy data is an average value of the Internet of Things measurement accuracy data corresponding to the multiple related devices.

3. The method according to claim 2, characterized in that The quality data of multiple dimensions also includes the accuracy data of IoT data events. The quality data of multiple dimensions is generated according to each of the identification data and its measurement accuracy and measurement range, and also includes: Determine the number of related Internet of Things involved in the logistics process, where the related Internet of Things is used to measure different types of identification data in various nodes of the logistics process; The Internet of Things data event accuracy data is generated according to the measurement precision accuracy data of each of the Internet of Things and the number of the related Internet of Things. The Internet of Things data event accuracy data is the average value of the Internet of Things measurement precision accuracy data corresponding to multiple related Internet of Things.

4. The method according to claim 3, characterized in that The quality data of multiple dimensions also includes the Internet of Things data source accuracy data. The quality data of multiple dimensions is generated according to each of the identification data and its measurement accuracy and measurement range, and also includes: Determine the number of IoT data events in the logistics process, where the IoT data events are composed of different types of identification data in various nodes of the logistics process; The Internet of Things data source accuracy data is generated according to the accuracy data of each of the Internet of Things data events and the number of the Internet of Things data events, and the Internet of Things data source accuracy data is the average value of the Internet of Things data event accuracy data corresponding to multiple Internet of Things data events.

5. The method according to claim 4, characterized in that The quality data of multiple dimensions also includes overall logistics accuracy data. The quality data of multiple dimensions is generated according to each of the identification data and its measurement accuracy and measurement range, and also includes: Determine the number of IoT data sources in the logistics process, where the IoT data sources consist of all IoT data events at important nodes of the logistics process; The overall logistics accuracy data is generated according to the accuracy data of each of the IoT data sources and the number of the IoT data sources. The overall logistics accuracy data is the average value of the IoT data source accuracy data corresponding to the multiple IoT data sources.

6. A blockchain-based identification data evaluation device, characterized in that: include: An acquisition module is used to acquire identification data of each IoT device in the logistics process, each of which has a corresponding measurement accuracy and measurement range; A quality data generation module, used to generate quality data of multiple dimensions according to each of the identification data and its measurement accuracy and measurement range; A determination module, configured to determine a current stakeholder, wherein the stakeholder is set with a data quality threshold for at least one target dimension; A storage module, configured to store the identification data for generating the target quality data in a blockchain if the target quality data corresponding to the target dimension meets the requirement of the data quality threshold; Wherein, the quality data of multiple dimensions include measurement accuracy data of the Internet of Things device, and the module for generating quality data is specifically further used to: calculate the data sum and data difference of the identification data and the measurement accuracy; determine the size relationship between the identification data, the data sum and the data difference and the measurement range; determine the measurement accuracy data of the Internet of Things device according to the size relationship; The quality data generation module determines the measurement accuracy data of the Internet of Things device according to the size relationship, and is specifically used for: if the data difference between the identification data and the measurement accuracy is greater than or equal to the minimum value of the measurement range, and the data sum of the identification data and the measurement accuracy is less than or equal to the maximum value of the measurement range, then determining that the measurement accuracy data of the Internet of Things device is 1; if the data difference between the identification data and the measurement accuracy is less than the minimum value of the measurement range, and the identification data is greater than or equal to the minimum value of the measurement range, then determining that the measurement accuracy data of the Internet of Things device is equal to the ratio of the data difference to the measurement accuracy; If the sum of the identification data and the measurement accuracy data is greater than the maximum value of the measurement range, and the identification data is less than or equal to the maximum value of the measurement range, then the measurement accuracy data of the Internet of Things device is determined to be equal to the ratio of the data difference to the measurement accuracy; otherwise, the measurement accuracy data of the Internet of Things device is determined to be 0.

7. An electronic device, characterized in that: The electronic device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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