CN112529594A - Material quality inspection method and related device - Google Patents
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Abstract
The application provides a material quality inspection method and a material quality inspection device, which are applied to a block chain service platform, wherein the block chain service platform comprises: supplier node, buyer node, quality inspection node and gateway node; the gateway node is used for storing production data and quality inspection data; the method comprises the following steps: acquiring a test report stored on a blockchain service platform by a provider node and a quality inspection report stored on the blockchain service platform by a quality inspection node; calculating the credibility range of the product parameter statistical index on the test report and the credibility range of the product quality inspection statistical index on the quality inspection report; and judging whether the statistical index of the product test value in the production data exceeds the credibility range of the statistical index of the product parameter on the corresponding test report or not, and whether the statistical index of the product quality inspection value in the quality inspection data exceeds the credibility range of the statistical index of the product quality inspection on the corresponding quality inspection report or not. The method and the device can ensure the authenticity of the detection report and the quality inspection report.
Description
Technical Field
The application relates to the field of material quality inspection, in particular to a material quality inspection method and a related device.
Background
The quality inspection management of the materials is to manage the performance certification, quality inspection report and qualification certification of a supplier by an informatization means, realize the full life cycle management and control of the quality of the materials, and generally manage the materials based on an enterprise production management system.
At present, a material quality inspection management system based on ERP generally starts from ERP, and transmits external detection reports to a server for storage and processing through a Web system in material purchasing and internal and external quality inspection links, all detection reports are related to material products, and data is generally stored through a relational database and a centralized file system. The quality control organization generally provides a paper certificate of eligibility, the supplier transmits a scanned certificate of eligibility to the material quality management system, and the user performs quality confirmation by checking the paper or the scanned certificate.
Because of the great benefits related to supplies, there is a certain possibility that the detection report and the quality inspection report are modified. Moreover, paper-based inspection reports and quality control reports cannot prevent the suppliers from making false reports, i.e., the authenticity of the inspection reports and quality control reports cannot be guaranteed.
Disclosure of Invention
The application provides a material quality inspection method and a related device, and aims to solve the problem that the authenticity of a detection report and a quality inspection report cannot be guaranteed.
In order to achieve the above object, the present application provides the following technical solutions:
the application provides a material quality inspection method, which is applied to a block chain service platform, wherein the block chain service platform comprises: supplier node, buyer node, quality inspection node and gateway node; the gateway node is used for storing production data and quality inspection data; the production data is obtained by sampling and collecting product test values of all suppliers by preset internet of things equipment; the product test value of any supplier is a product parameter value measured in the process of testing the product by the supplier; the quality inspection data is obtained by sampling and collecting the product quality inspection value of each quality inspection unit by the Internet of things equipment; the product quality inspection value of any quality inspection unit is a product parameter value measured in the quality inspection process of the product of the supplier by the quality inspection unit; the method comprises the following steps:
acquiring a test report stored on the blockchain service platform by the provider node and a quality inspection report stored on the blockchain service platform by the quality inspection node;
calculating the credibility range of the product parameter statistical index on the test report, and calculating the credibility range of the product quality inspection statistical index on the quality inspection report;
and judging whether the statistical index of the product test value in the production data exceeds the credible range of the statistical index of the product parameter on the corresponding test report or not, and whether the statistical index of the product quality inspection value in the quality inspection data exceeds the credible range of the statistical index of the product quality inspection on the corresponding quality inspection report or not.
Optionally, after the determining whether the statistical indicator of the product test value in the production data exceeds the confidence range of the statistical indicator of the product parameter on the corresponding test report, and whether the statistical indicator of the product quality inspection value in the quality inspection data exceeds the confidence range of the product quality inspection statistical indicator on the corresponding quality inspection report, the method further includes:
when the statistical index of the product test value in the production data exceeds the credible range of the product parameter statistical index on the corresponding test report, carrying out preset processing operation on the supplier node corresponding to the corresponding test report; the processing operation includes: at least one of right reduction, blacklist addition and notification;
and under the condition that the statistical index of the product quality inspection value in the quality inspection data exceeds the credible range of the product quality inspection statistical index on the corresponding quality inspection report, performing the processing operation on the quality inspection node corresponding to the corresponding quality inspection report.
Optionally, the method further includes:
acquiring historical data respectively stored by the supplier node, the buyer node and the quality inspection node; the historical data includes: the method comprises the following steps of (1) enterprise registration scale parameters, historical transaction numbers and historical credit points;
according to the historical data, calculating credit values corresponding to the supplier node, the buyer node and the quality inspection node respectively;
respectively determining the node roles of the supplier node, the buyer node and the quality inspection node according to a preset corresponding relation between a preset node role and a credit score interval; the preset node roles include: the method comprises the following steps of (1) consensus nodes, trust nodes and common nodes;
and selecting one trust node from all trust nodes as a main node by taking the ratio of the credit score of the credit node to the total credit score of all the credit nodes as a selection probability.
Optionally, after the determining whether the product test value in the production data exceeds the confidence range of the product parameter value on the corresponding test report, and whether the product quality inspection value in the quality inspection data exceeds the confidence range of the product quality inspection value on the corresponding quality inspection report, the method further includes:
under the condition that the product test value of the production data does not exceed the credible range of the product parameter value on the corresponding test report, storing the corresponding test report in a preset test report library;
and under the condition that the product quality inspection value in the quality inspection data does not exceed the credible range of the product quality inspection value on the corresponding quality inspection report, storing the corresponding quality inspection report in a preset quality inspection report library.
The application also provides a material quality inspection device which is applied to the block chain service platform,
the block chain service platform comprises: supplier node, buyer node, quality inspection node and gateway node; the gateway node is used for storing production data and quality inspection data; the production data is obtained by sampling and collecting product test values of all suppliers by preset internet of things equipment; the product test value of any supplier is a product parameter value measured in the process of testing the product by the supplier; the quality inspection data is obtained by sampling and collecting the product quality inspection value of each quality inspection unit by the Internet of things equipment; the product quality inspection value of any quality inspection unit is a product parameter value measured in the quality inspection process of the product of the supplier by the quality inspection unit; the device comprises:
an obtaining module, configured to obtain a test report that is stored on the blockchain service platform by the provider node, and a quality inspection report that is stored on the blockchain service platform by the quality inspection node;
the calculation module is used for calculating the credibility range of the product parameter statistical index on the test report and the credibility range of the product quality inspection statistical index on the quality inspection report;
and the judging module is used for judging whether the statistical index of the product test value in the production data exceeds the credible range of the statistical index of the product parameter on the corresponding test report or not and whether the statistical index of the product quality inspection value in the quality inspection data exceeds the credible range of the product quality inspection statistical index on the corresponding quality inspection report or not.
Optionally, the method further includes:
the processing module is used for performing preset processing operation on the provider node corresponding to the corresponding test report under the condition that the statistical index of the product test value in the production data exceeds the credible range of the product parameter statistical index on the corresponding test report after the judging module judges whether the statistical index of the product test value in the production data exceeds the credible range of the product parameter statistical index on the corresponding test report and whether the statistical index of the product quality inspection value in the quality inspection data exceeds the credible range of the product quality inspection statistical index on the corresponding quality inspection report; the processing operation includes: at least one of right reduction, blacklist addition and notification;
and under the condition that the statistical index of the product quality inspection value in the quality inspection data exceeds the credible range of the product quality inspection statistical index on the corresponding quality inspection report, performing the processing operation on the quality inspection node corresponding to the corresponding quality inspection report.
Optionally, the method further includes:
the consensus module is used for acquiring historical data respectively stored by the supplier node, the buyer node and the quality inspection node; the historical data includes: the method comprises the following steps of (1) enterprise registration scale parameters, historical transaction numbers and historical credit points; according to the historical data, calculating credit values corresponding to the supplier node, the buyer node and the quality inspection node respectively; respectively determining the node roles of the supplier node, the buyer node and the quality inspection node according to a preset corresponding relation between a preset node role and a credit score interval; the preset node roles include: the method comprises the following steps of (1) consensus nodes, trust nodes and common nodes; and selecting one trust node from all trust nodes as a main node by taking the ratio of the credit score of the credit node to the total credit score of all the credit nodes as a selection probability.
Optionally, the method further includes:
the storage module is used for storing the corresponding test report in a preset test report library under the condition that the product test value of the production data does not exceed the credible range of the product parameter value on the corresponding test report after judging whether the product test value in the production data exceeds the credible range of the product parameter value on the corresponding test report and whether the product quality inspection value in the quality inspection data exceeds the credible range of the product quality inspection value on the corresponding quality inspection report; and under the condition that the product quality inspection value in the quality inspection data does not exceed the credible range of the product quality inspection value on the corresponding quality inspection report, storing the corresponding quality inspection report in a preset quality inspection report library.
The application also provides a storage medium, which comprises a stored program, wherein the program executes any one of the material quality inspection methods.
The application also provides a device, which comprises at least one processor, at least one memory connected with the processor, and a bus; the processor and the memory complete mutual communication through the bus; the processor is used for calling the program instructions in the memory so as to execute any one of the material quality detection methods.
The material quality inspection method and the related device are applied to a block chain service platform, wherein the block chain service platform comprises: supplier node, buyer node, quality inspection node and gateway node; the gateway node is used for storing production data and quality inspection data; the method comprises the following steps: and acquiring a test report stored on the blockchain service platform by the provider node and a quality inspection report stored on the blockchain service platform by the quality inspection node. The production data is obtained by sampling and collecting product parameter values measured in the test process of each supplier by the preset internet of things equipment; the quality inspection data is obtained by sampling and collecting the product quality inspection value measured in the quality inspection process of each quality inspection unit by the Internet of things equipment, namely, the production data and the quality inspection data are actual measurement data in the testing process and the quality inspection process and are real data.
On the one hand, since the blockchain has non-tamper-proof properties, the production data, the quality inspection data, the test reports and the quality inspection reports stored on the blockchain are all non-tamper-proof. On the other hand, the block chain can determine the authenticity of the test report and the quality inspection report by judging whether the production data exceeds the credible range of the product parameter values on the test report of the corresponding supplier and whether the quality inspection data exceeds the credible range of the product quality inspection values on the quality inspection report of the corresponding quality inspection unit, so that the test report and the quality inspection report which pass the quality inspection of the application can be ensured to be authentic.
In addition, in the application, the gateway is used as a node in the blockchain service platform, so that nodes for storing production data and quality inspection data sampled and collected by the internet of things equipment do not need to be arranged in the blockchain service platform, and the pressure of the blockchain service platform can be reduced.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a block chain service platform disclosed in an embodiment of the present application;
fig. 2 is a flowchart of a method for determining a node role disclosed in an embodiment of the present application;
FIG. 3 is a flowchart of a material quality inspection method according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a material quality inspection apparatus according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an apparatus disclosed in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Fig. 1 is a block chain service platform provided in an embodiment of the present application, including: the system comprises a cloud infrastructure layer, a basic function layer, a block management layer, a business domain management layer, a cross-chain management layer, a block chain exhibition board layer, a network management layer and a monitoring management layer. The cloud infrastructure layer has a function of providing basic resources required by operation for the block chain service platform, and specifically may include cloud service, network, storage, cluster management and automation operation and maintenance. The function of the basic function layer is to implement basic services of the blockchain service platform, and specifically may include: consistency verification, trust verification, privacy services, intelligent contracts, block construction, block synchronization, cryptographic algorithms, and consensus algorithms. The function of the block management layer is to manage the full life cycle of the block, and may specifically include: block initialization strategy, block generation and release, block state management and the like. The function of the service domain management layer is to perform configuration management on a service domain, and may specifically include: the method comprises the following steps of service domain registration management, service domain authority configuration management and service domain data privacy management. The function of the cross-chain management layer is to implement cross-chain interaction between the blockchain service platform and other blockchain platforms, and may specifically include: cross-chain service registration management, cross-chain interaction request management and cross-chain interaction log management. The function of the blockchain display board layer is to realize the visual display function of the blockchain service platform, and specifically, the visual display function of data, user management, transaction management and terminal access management can be included. The network management layer is used for managing the state of the consensus node and the node communication, and specifically includes: node configuration management, node operation management, node exception management and data transceiving management. The monitoring management layer is used for monitoring the operation process of the blockchain service platform, and specifically includes: network node monitoring, block chain running state, service call statistics, running log management and data interface management.
Specifically, the block chain service platform provided by the embodiment of the present application may be developed by using a JAVA technology, a relational database is used as a bottom layer for storing the hash value of the original data and the previous data block, and a consensus algorithm based on the reputation is used for performing algorithm consensus on the nodes in the chain, so as to ensure that the data in the whole chain cannot be tampered with.
In the embodiment of the present application, blockchain service nodes are allocated in the blockchain service platform for each supplier, each purchasing unit, and each quality inspection unit, and for convenience of description, the blockchain service nodes are referred to as supplier nodes, purchasing node, and quality inspection node. And each node stores the full data and ensures the safety and credibility of the data together. And the control right of each node is in the affiliated unit, so that other people can not change the control right freely.
In the embodiment of the present application, the gateway access blockchain service platform is also referred to as a gateway node. The gateway serves as a lightweight consensus node, the gateway node does not store the full data, only stores the data provided by the subordinate Internet of things equipment of the gateway, and stores the hash values of other nodes, so that the nodes for storing the data sampled and collected by the Internet of things equipment do not need to be redistributed on the block chain service platform, and the pressure of the block chain platform is reduced. The gateway node stores production data and quality inspection data acquired by subordinate internet of things equipment. The production data is obtained by sampling and collecting product parameter values measured in the test process of each supplier by the Internet of things equipment; the quality inspection data is obtained by sampling and collecting product quality inspection values measured in the quality inspection process of each quality inspection unit by the Internet of things equipment.
In the embodiment of the application, the blockchain respectively determines the node roles of the supplier node, the buyer node and the quality inspection node. The specific determination process shown in fig. 2 may include the following steps:
s201, acquiring historical data respectively stored by a supplier node, a buyer node and a quality inspection node.
In this step, the history data may include: enterprise registration size parameters, historical deal figures, and historical credit points.
And S202, calculating credit values corresponding to the supplier node, the buyer node and the quality inspection node respectively according to the historical data.
In this step, the specific implementation process of calculating the credit score of each node is the prior art, and is not described herein again.
And S203, respectively determining the node roles of the supplier node, the buyer node and the quality inspection node according to the preset corresponding relation between the preset node role and the credit score interval.
In this embodiment, the preset node roles include: consensus nodes, trust nodes and common nodes.
In this step, the correspondence between the node role and the credit score interval includes: a node role corresponds to a credit score interval. For example, 80 points or more are used as trust nodes, 60 points or less are used as consensus nodes, and other intervals are used as normal nodes.
And S204, selecting a trust node from all trust nodes as a main node by taking the ratio of the credit score of the credit node to the total credit score of all the credit nodes as a selection probability.
In the present embodiment, among the supplier node, the buyer node, the quality inspection node and the gateway node, a plurality of nodes may be credit nodes, that is, the number of the credit nodes may be multiple.
In this step, one credit node is selected from all credit nodes as a master node, wherein the specific selection mode includes: and selecting by taking the ratio of the credit score of the credit node to the total credit score of all the credit nodes as the selection probability.
In this step, the ratio of the credit score of the credit node to the total credit score (sum of credit scores) of all the nodes is referred to as the proportion of the credit node.
In this step, the proportion of any trust node in all trust nodes is the probability of selecting the trust node as the master node. In the embodiment of the application, the higher the occupation ratio of any trust node is, the higher the probability that the trust node is selected as the master node is, but the trust node with the highest occupation ratio is not always the master node, so that the problem that the traditional consensus algorithm needs a large number of nodes to participate to cause low efficiency is solved, and the problem that the master node of the traditional Byzantine algorithm fails is solved.
According to the method for performing material quality inspection on the intelligent contracts in the block chains, the inspection rules are transparent and can not be tampered, the inspection rules can be upgraded only by agreement of a plurality of common identification nodes, and a specific material quality inspection process is shown in fig. 3 and can comprise the following steps:
s301, obtaining a test report stored on the blockchain service platform by the provider node, and a quality inspection report stored on the blockchain service platform by the quality inspection node.
In this embodiment, a supplier produces a product and tests the product to obtain a test report, where each product corresponds to a test report. And the quality testing unit performs quality testing on the products produced by the suppliers to obtain quality testing reports. The test report of the provider node is stored on the blockchain, and the quality inspection report of the quality inspection node is also stored on the blockchain.
In this step, a test report of the provider node and a quality inspection report of the quality inspection node are obtained to perform quality inspection.
In this embodiment, the statistical index of the product parameter value is recorded in the test report, and the statistical index of the product quality inspection value is recorded in the quality inspection report. In this embodiment, the product parameters and the product quality inspection parameters need to be determined according to an actual scenario, for example, for a cable, the product parameters may be an inner diameter parameter and an outer diameter parameter. The product quality inspection parameters can be inner diameter quality inspection parameters and outer diameter quality inspection parameters.
The statistical index may include a maximum value, a minimum value, an average value, and the like, and certainly, in practice, the statistical index may also include other indexes, and the specific meaning of the statistical index is not limited in this embodiment.
In this embodiment, taking the cable as an example, the maximum value, the minimum value and the average value of the inner diameter, and the maximum value, the minimum value and the average value of the outer diameter can be recorded on the test report. The maximum value, the minimum value and the average value of the inner diameter quality inspection can be recorded on the quality inspection report, and the maximum value, the minimum value and the average value of the outer diameter quality inspection can be recorded.
S302, calculating the credibility range of the product parameter statistical index on the test report and the credibility range of the product quality inspection statistical index on the quality inspection report.
The present embodiment performs the reliability check according to the statistical idea. In this step, the confidence ranges of the product parameter statistical indexes on each test report are respectively calculated, and the confidence ranges of the product quality inspection statistical indexes on each quality inspection report are respectively calculated. For example, the confidence ranges corresponding to the maximum value, the minimum value, the average value, the maximum value, the minimum value and the average value of the inner diameter on the test report are calculated respectively. And calculating the credible ranges respectively corresponding to the inner diameter quality inspection maximum value, the minimum value, the average value, the outer diameter quality inspection maximum value, the minimum value and the average value on the quality inspection report. The calculation method of the trusted range is the prior art, and is not described herein again.
S303, judging whether the statistical index of the product test value in the production data exceeds the credible range of the product parameter statistical index on the corresponding test report, and whether the statistical index of the product quality inspection value in the quality inspection data exceeds the credible range of the product quality inspection statistical index on the corresponding quality inspection report, if so, executing S304, and if not, executing S305.
The production data is obtained by sampling and collecting the product test values of each supplier by the external equipment, wherein the product test value of any supplier is the product parameter value measured in the process of testing the product by the supplier. In this embodiment, since the product test value in the production data is a parameter test value of a certain product, the product corresponds to a test report, and therefore, the product test value in the production data and the test report provided by the supplier have a corresponding relationship.
Taking the product as a cable, and the product parameters as the inner diameter parameter and the outer diameter parameter as examples, the production data may include: and respectively sampling and collecting the obtained inner diameter test value and the outer diameter test value in the test process of each supplier.
The quality inspection data is obtained by sampling and collecting the product quality inspection value of each quality inspection unit by the Internet of things equipment; the product quality inspection value of any quality inspection unit is a product parameter value measured in the quality inspection process of the product of the supplier by the quality inspection unit. Taking the product quality inspection parameters as the inner diameter quality inspection parameter and the outer diameter quality inspection parameter as an example, the quality inspection data may include inner diameter quality inspection values and outer diameter quality inspection values corresponding to products of various suppliers, respectively.
In this embodiment, since the product quality inspection value in the quality inspection data is a parameter quality inspection value of a certain product, and the product corresponds to a quality inspection report, there is a corresponding relationship between the product quality inspection value in the quality inspection data and the quality inspection report provided by the quality inspection unit.
In this step, the product test value of each product in the production data is determined separately, wherein the manner of determining each product is the same, and any product is taken as an example and described herein. Specifically, whether the statistical indexes of the product test values of the product belong to the product test statistical indexes in the corresponding test reports respectively is judged, if all the statistical indexes belong to the product test statistical indexes, the test reports are determined to be credible, and if any one of the statistical indexes does not belong to the product test statistical indexes, the test reports are determined to be incredible.
For example, whether the statistical indexes of the inner diameter test value of the product belong to the credible ranges of the maximum value, the minimum value and the average value of the inner diameter in the corresponding test report respectively is judged, if all the statistical indexes belong to the credible ranges, the test report is determined to be credible, and if any one of the statistical indexes does not belong to the credible ranges, the test report is determined to be incredible. And judging whether the statistical indexes of the outer diameter test values of the product belong to the credible ranges of the maximum value, the minimum value and the average value of the outer diameter in the corresponding test report respectively, if all the statistical indexes belong to the credible ranges, determining that the maximum value, the minimum value and the average value of the outer diameter recorded in the test report are credible, and if any one of the statistical indexes does not belong to the credible ranges, determining that the test report is not true.
In this step, the product quality inspection value of each product in the quality inspection data is determined, wherein the manner of determining the product quality inspection value of each product is the same, and any product is taken as an example and described herein. Specifically, whether the statistical index of the product quality inspection value of the product in the quality inspection data is within the credible range of the statistical index of the product quality inspection in the corresponding quality inspection report is judged.
For example, it is determined whether the maximum value, the minimum value, and the average value of the inner diameter quality inspection values of the product in the quality inspection data respectively correspond to the confidence ranges of the inner diameter maximum value, the inner diameter minimum value, and the inner diameter average value in the corresponding quality inspection report, and if all of the inner diameter maximum value, the inner diameter minimum value, and the inner diameter average value belong to the confidence ranges, it is determined that the quality inspection report is authentic, and if any one of the inner diameter maximum value, the inner diameter minimum value, and the. And judging whether the maximum value, the minimum value and the average value in the outer diameter quality inspection values of the product in the quality inspection data respectively belong to the credible ranges of the maximum value, the minimum value and the average value of the outer diameter in the corresponding quality inspection reports, if all the values belong to the credible ranges, determining that the quality inspection reports are credible, and if any one of the values does not belong to the credible ranges, determining that the quality inspection reports are incredible.
And executing S304 under the condition that the statistical index of the product test value in the production data exceeds the credible range of the product parameter statistical index on the corresponding test report, and executing S304 under the condition that the statistical index of the product quality inspection value in the quality inspection data exceeds the credible range of the product quality inspection statistical index on the corresponding quality inspection report.
S304, carrying out preset processing operation on the provider node corresponding to the corresponding test report, and carrying out processing operation on the quality inspection node corresponding to the corresponding quality inspection report.
In this embodiment, the processing operation may include: at least one of right drop, blacklist addition and announcement.
S305, storing the test report in a preset test report library, and storing the quality inspection report in a preset quality inspection report library.
This step is performed when the statistical indicator of the product test value in the production data does not exceed the confidence range of the product parameter statistical indicator on the corresponding test report, and is performed when the statistical indicator of the product quality inspection value in the quality inspection data does not exceed the confidence range of the product quality inspection statistical indicator on the corresponding quality inspection report.
In this step, the specific implementation manner of saving is the prior art, and is not described herein again.
In this embodiment, the buyer node in the blockchain service platform can verify whether the product provided by the supplier passes the detection through this embodiment, thereby avoiding the phenomenon of falsification and ensuring the quality of material supply.
Fig. 4 is a material quality inspection apparatus provided in an embodiment of the present application, where the block chain service platform includes: supplier node, buyer node, quality inspection node and gateway node; the gateway node is used for storing production data and quality inspection data; the production data is obtained by sampling and collecting product test values of all suppliers by preset internet of things equipment; the product test value of any supplier is a product parameter value measured in the process of testing the product by the supplier; the quality inspection data is obtained by sampling and collecting the product quality inspection value of each quality inspection unit by the Internet of things equipment; the product quality inspection value of any quality inspection unit is a product parameter value measured in the quality inspection process of the product of the supplier by the quality inspection unit;
the apparatus may include: an acquisition module 401, a calculation module 402 and a decision module 403, wherein,
an obtaining module 401, configured to obtain a test report that is stored by the provider node on the blockchain service platform, and a quality inspection report that is stored by the quality inspection node on the blockchain service platform;
a calculating module 402, configured to calculate a confidence range of the product parameter statistical indicator on the test report, and calculate a confidence range of the product quality inspection statistical indicator on the quality inspection report;
the determining module 403 is configured to determine whether the statistical index of the product test value in the production data exceeds the confidence range of the statistical index of the product parameter in the corresponding test report, and whether the statistical index of the product quality inspection value in the quality inspection data exceeds the confidence range of the product quality inspection statistical index in the corresponding quality inspection report.
Optionally, the apparatus may further include:
the processing module is used for performing preset processing operation on the provider node corresponding to the corresponding test report under the condition that the statistical index of the product test value in the production data exceeds the credible range of the product parameter statistical index on the corresponding test report after the judging module judges whether the statistical index of the product test value in the production data exceeds the credible range of the product parameter statistical index on the corresponding test report and whether the statistical index of the product quality inspection value in the quality inspection data exceeds the credible range of the product quality inspection statistical index on the corresponding quality inspection report; the processing operation includes: at least one of right reduction, blacklist addition and notification;
and under the condition that the statistical index of the product quality inspection value in the quality inspection data exceeds the credible range of the product quality inspection statistical index on the corresponding quality inspection report, performing the processing operation on the quality inspection node corresponding to the corresponding quality inspection report.
Optionally, the apparatus may further include:
the consensus module is used for acquiring historical data respectively stored by the supplier node, the buyer node and the quality inspection node; the historical data includes: the method comprises the following steps of (1) enterprise registration scale parameters, historical transaction numbers and historical credit points; according to the historical data, calculating credit values corresponding to the supplier node, the buyer node and the quality inspection node respectively; respectively determining the node roles of the supplier node, the buyer node and the quality inspection node according to a preset corresponding relation between a preset node role and a credit score interval; the preset node roles include: the method comprises the following steps of (1) consensus nodes, trust nodes and common nodes; and selecting one trust node from all trust nodes as a main node by taking the ratio of the credit score of the credit node to the total credit score of all the credit nodes as a selection probability.
Optionally, the apparatus may further include:
the storage module is used for storing the corresponding test report in a preset test report library under the condition that the product test value of the production data does not exceed the credible range of the product parameter value on the corresponding test report after judging whether the product test value in the production data exceeds the credible range of the product parameter value on the corresponding test report and whether the product quality inspection value in the quality inspection data exceeds the credible range of the product quality inspection value on the corresponding quality inspection report; and under the condition that the product quality inspection value in the quality inspection data does not exceed the credible range of the product quality inspection value on the corresponding quality inspection report, storing the corresponding quality inspection report in a preset quality inspection report library.
The material quality inspection device comprises a processor and a memory, wherein the acquisition module 401, the calculation module 402, the judgment module 403 and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can be set to be one or more than one, and the problem that the authenticity of the detection report and the quality inspection report cannot be ensured is solved by adjusting the kernel parameters.
An embodiment of the present invention provides a storage medium, on which a program is stored, and the program implements the material quality inspection method when executed by a processor.
The embodiment of the invention provides a processor, which is used for running a program, wherein the material quality inspection method is executed when the program runs.
An embodiment of the present invention provides an apparatus, as shown in fig. 5, the apparatus includes at least one processor, and at least one memory and a bus connected to the processor; the processor and the memory complete mutual communication through a bus; the processor is used for calling the program instructions in the memory so as to execute the material quality detection method. The device herein may be a server, a PC, a PAD, a mobile phone, etc.
The present application further provides a computer program product adapted to perform a program for initializing the following method steps when executed on a data processing device:
acquiring a test report stored on the blockchain service platform by the provider node and a quality inspection report stored on the blockchain service platform by the quality inspection node;
calculating the credibility range of the product parameter statistical index on the test report, and calculating the credibility range of the product quality inspection statistical index on the quality inspection report;
and judging whether the statistical index of the product test value in the production data exceeds the credible range of the statistical index of the product parameter on the corresponding test report or not, and whether the statistical index of the product quality inspection value in the quality inspection data exceeds the credible range of the statistical index of the product quality inspection on the corresponding quality inspection report or not.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a device includes one or more processors (CPUs), memory, and a bus. The device may also include input/output interfaces, network interfaces, and the like.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip. The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
The functions described in the method of the embodiment of the present application, if implemented in the form of software functional units and sold or used as independent products, may be stored in a storage medium readable by a computing device. Based on such understanding, part of the contribution to the prior art of the embodiments of the present application or part of the technical solution may be embodied in the form of a software product stored in a storage medium and including several instructions for causing a computing device (which may be a personal computer, a server, a mobile computing device or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Features described in the embodiments of the present specification may be replaced with or combined with each other, each embodiment is described with a focus on differences from other embodiments, and the same or similar portions among the embodiments may be referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A material quality inspection method is applied to a block chain service platform, and the block chain service platform comprises: supplier node, buyer node, quality inspection node and gateway node; the gateway node is used for storing production data and quality inspection data; the production data is obtained by sampling and collecting product test values of all suppliers by preset internet of things equipment; the product test value of any supplier is a product parameter value measured in the process of testing the product by the supplier; the quality inspection data is obtained by sampling and collecting the product quality inspection value of each quality inspection unit by the Internet of things equipment; the product quality inspection value of any quality inspection unit is a product parameter value measured in the quality inspection process of the product of the supplier by the quality inspection unit; the method comprises the following steps:
acquiring a test report stored on the blockchain service platform by the provider node and a quality inspection report stored on the blockchain service platform by the quality inspection node;
calculating the credibility range of the product parameter statistical index on the test report, and calculating the credibility range of the product quality inspection statistical index on the quality inspection report;
and judging whether the statistical index of the product test value in the production data exceeds the credible range of the statistical index of the product parameter on the corresponding test report or not, and whether the statistical index of the product quality inspection value in the quality inspection data exceeds the credible range of the statistical index of the product quality inspection on the corresponding quality inspection report or not.
2. The method of claim 1, wherein after determining whether the statistical indicator of the product test value in the production data exceeds the confidence range of the statistical indicator of the product parameter on the corresponding test report and whether the statistical indicator of the product quality inspection value in the quality inspection data exceeds the confidence range of the statistical indicator of the product quality inspection on the corresponding quality inspection report, the method further comprises:
when the statistical index of the product test value in the production data exceeds the credible range of the product parameter statistical index on the corresponding test report, carrying out preset processing operation on the supplier node corresponding to the corresponding test report; the processing operation includes: at least one of right reduction, blacklist addition and notification;
and under the condition that the statistical index of the product quality inspection value in the quality inspection data exceeds the credible range of the product quality inspection statistical index on the corresponding quality inspection report, performing the processing operation on the quality inspection node corresponding to the corresponding quality inspection report.
3. The method of claim 1, further comprising:
acquiring historical data respectively stored by the supplier node, the buyer node and the quality inspection node; the historical data includes: the method comprises the following steps of (1) enterprise registration scale parameters, historical transaction numbers and historical credit points;
according to the historical data, calculating credit values corresponding to the supplier node, the buyer node and the quality inspection node respectively;
respectively determining the node roles of the supplier node, the buyer node and the quality inspection node according to a preset corresponding relation between a preset node role and a credit score interval; the preset node roles include: the method comprises the following steps of (1) consensus nodes, trust nodes and common nodes;
and selecting one trust node from all trust nodes as a main node by taking the ratio of the credit score of the credit node to the total credit score of all the credit nodes as a selection probability.
4. The method of claim 1, wherein after determining whether the product test values in the production data are outside the confidence range of the product parameter values on the corresponding test report and the product quality inspection values in the quality inspection data are outside the confidence range of the product quality inspection values on the corresponding quality inspection report, the method further comprises:
under the condition that the product test value of the production data does not exceed the credible range of the product parameter value on the corresponding test report, storing the corresponding test report in a preset test report library;
and under the condition that the product quality inspection value in the quality inspection data does not exceed the credible range of the product quality inspection value on the corresponding quality inspection report, storing the corresponding quality inspection report in a preset quality inspection report library.
5. A material quality inspection device is characterized in that the device is applied to a block chain service platform,
the block chain service platform comprises: supplier node, buyer node, quality inspection node and gateway node; the gateway node is used for storing production data and quality inspection data; the production data is obtained by sampling and collecting product test values of all suppliers by preset internet of things equipment; the product test value of any supplier is a product parameter value measured in the process of testing the product by the supplier; the quality inspection data is obtained by sampling and collecting the product quality inspection value of each quality inspection unit by the Internet of things equipment; the product quality inspection value of any quality inspection unit is a product parameter value measured in the quality inspection process of the product of the supplier by the quality inspection unit; the device comprises:
an obtaining module, configured to obtain a test report that is stored on the blockchain service platform by the provider node, and a quality inspection report that is stored on the blockchain service platform by the quality inspection node;
the calculation module is used for calculating the credibility range of the product parameter statistical index on the test report and the credibility range of the product quality inspection statistical index on the quality inspection report;
and the judging module is used for judging whether the statistical index of the product test value in the production data exceeds the credible range of the statistical index of the product parameter on the corresponding test report or not and whether the statistical index of the product quality inspection value in the quality inspection data exceeds the credible range of the product quality inspection statistical index on the corresponding quality inspection report or not.
6. The apparatus of claim 5, further comprising:
the processing module is used for performing preset processing operation on the provider node corresponding to the corresponding test report under the condition that the statistical index of the product test value in the production data exceeds the credible range of the product parameter statistical index on the corresponding test report after the judging module judges whether the statistical index of the product test value in the production data exceeds the credible range of the product parameter statistical index on the corresponding test report and whether the statistical index of the product quality inspection value in the quality inspection data exceeds the credible range of the product quality inspection statistical index on the corresponding quality inspection report; the processing operation includes: at least one of right reduction, blacklist addition and notification;
and under the condition that the statistical index of the product quality inspection value in the quality inspection data exceeds the credible range of the product quality inspection statistical index on the corresponding quality inspection report, performing the processing operation on the quality inspection node corresponding to the corresponding quality inspection report.
7. The apparatus of claim 5, further comprising:
the consensus module is used for acquiring historical data respectively stored by the supplier node, the buyer node and the quality inspection node; the historical data includes: the method comprises the following steps of (1) enterprise registration scale parameters, historical transaction numbers and historical credit points; according to the historical data, calculating credit values corresponding to the supplier node, the buyer node and the quality inspection node respectively; respectively determining the node roles of the supplier node, the buyer node and the quality inspection node according to a preset corresponding relation between a preset node role and a credit score interval; the preset node roles include: the method comprises the following steps of (1) consensus nodes, trust nodes and common nodes; and selecting one trust node from all trust nodes as a main node by taking the ratio of the credit score of the credit node to the total credit score of all the credit nodes as a selection probability.
8. The apparatus of claim 5, further comprising:
the storage module is used for storing the corresponding test report in a preset test report library under the condition that the product test value of the production data does not exceed the credible range of the product parameter value on the corresponding test report after judging whether the product test value in the production data exceeds the credible range of the product parameter value on the corresponding test report and whether the product quality inspection value in the quality inspection data exceeds the credible range of the product quality inspection value on the corresponding quality inspection report; and under the condition that the product quality inspection value in the quality inspection data does not exceed the credible range of the product quality inspection value on the corresponding quality inspection report, storing the corresponding quality inspection report in a preset quality inspection report library.
9. A storage medium comprising a stored program, wherein the program executes the material quality inspection method according to any one of claims 1 to 4.
10. An apparatus comprising at least one processor, and at least one memory, bus connected to the processor; the processor and the memory complete mutual communication through the bus; the processor is used for calling the program instructions in the memory to execute the material quality detection method according to any one of claims 1-4.
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| CN202011347393.5A CN112529594B (en) | 2020-11-26 | Material quality inspection method and related device |
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| CN202011347393.5A CN112529594B (en) | 2020-11-26 | Material quality inspection method and related device |
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