CN115249135A - Material quality monitoring method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention provides a method and a device for supervising material quality, electronic equipment and a storage medium, which relate to the technical field of big data analysis, and the method comprises the following steps: acquiring material quality data and supplier credit data corresponding to materials to be supervised based on a query request of a first user terminal; and inputting the material quality data and the supplier reputation data into the strategy reasoning model to obtain a quality supervision strategy output by the strategy reasoning model. The monitoring method, the monitoring device, the electronic equipment and the storage medium for the quality of the material, provided by the invention, can be used for analyzing various factors related to supply and sale of the material by a supplier from the aspects of science and profession, providing differentiated quality monitoring management according to the suppliers under different conditions, optimizing the allocation of quality monitoring resources and improving the quality level of purchased material.
Description
Technical Field
The invention relates to the technical field of big data analysis, in particular to a method and a device for supervising material quality, electronic equipment and a storage medium.
Background
Due to rapid development and universal access of the internet, acquisition, aggregation, storage, transmission, processing and the like of a large amount of data become more and more convenient, and the large data gradually becomes the inevitable trend of the internet era. The Internet and the big data supplement each other. On the one hand, the development of the internet provides more information, information and data for the big data era. On the other hand, big data also promotes the development of the internet era, and creates more possibilities for internet business.
At present, the supervision and management of material quality mainly comprises two modes of material sampling inspection and production monitoring. In the aspect of material sampling inspection, the material range for sampling inspection mainly realizes three comprehensive coverage for all bidding batches, all suppliers and all equipment types. In the aspect of production monitoring, R point, W point and H point witnesses are respectively developed for large-scale equipment according to different production process nodes, and online monitoring and monitoring are realized by accessing to a supplier production line.
In the prior art, most basic data depending on quality supervision is material quality data in the business transaction process of supply, the collection capability is insufficient, a more comprehensive theoretical basis cannot be provided for the internal association logic of the data, a data index system is not sound, and a comprehensive and differentiated quality supervision strategy cannot be formulated.
Disclosure of Invention
The invention provides a material quality supervision method, a material quality supervision device, electronic equipment and a storage medium, which are used for overcoming the defect that a differentiated quality supervision strategy cannot be met in the prior art.
The invention provides a method for supervising material quality, which comprises the following steps:
acquiring material quality data and supplier credit data corresponding to materials to be supervised based on a query request of a first user terminal;
inputting the quality data of the materials and the credit data of the suppliers to a strategy reasoning model to obtain a quality supervision strategy output by the strategy reasoning model;
wherein the material quality data is obtained from an internal network of a supply chain and the provider reputation data is obtained from the internet outside the supply chain; the strategy reasoning model is a decision reasoning machine which is self-learned and maintained based on a knowledge base where historical material quality data are located and a rule base corresponding to the knowledge base; the first user terminal is a user terminal used by a purchasing user of the materials to be supervised.
According to the method for supervising the quality of the goods and materials, provided by the invention, the strategy reasoning model comprises a knowledge base, a rule base and a reasoning engine;
the step of inputting the quality data of the goods and materials and the reputation data of the supplier into a strategy reasoning model to obtain a quality supervision strategy output by the strategy reasoning model comprises the following steps:
performing index quantization on the material quality data and the supplier reputation data to update the knowledge base;
determining detection items implemented by detection and processing measures corresponding to the detection items according to a material detection mode to form the rule base;
and performing rule matching on the knowledge base and the rule base through the inference engine to generate the quality supervision strategy.
According to the method for supervising the quality of the material, after the quality supervision strategy output by the strategy inference model is obtained, the method further comprises the following steps:
acquiring a supervision result in the process of controlling the first user terminal to cooperatively execute the quality supervision strategy;
inputting the supervision result into an analysis model to obtain quality defect data output by the analysis model;
wherein the analytical model is trained based on historical operational failure data and confidence values of the historical operational failure data in corresponding indicators.
According to the monitoring method for the quality of the material, provided by the invention, the analysis model comprises a quantitative analysis layer and a defect prediction layer;
the inputting the supervision result into an analysis model to obtain quality defect data output by the analysis model comprises:
inputting the supervision result into the quantitative analysis layer, and acquiring a confidence value corresponding to each production process index output by the quantitative analysis layer;
and inputting the confidence value corresponding to each production process index into the defect prediction layer, and acquiring the quality defect data output by the defect prediction layer when the confidence value corresponding to the production process index meets the preset condition.
According to the method for supervising the quality of the material, after the quality defect data output by the analysis model is obtained, the method further comprises the following steps:
and matching corresponding rectification measure information based on the quality defect data, and carrying out front-end notification on the rectification measure information.
The method for supervising the quality of the materials provided by the invention further comprises the following steps:
generating an analysis report based on the quality defect data, and sending the analysis report to the first user terminal and/or the second user terminal;
and the second user terminal is a user terminal used by a third party checking user of the materials to be supervised.
The invention also provides a device for supervising the quality of the materials, which comprises:
the data integration module is used for acquiring material quality data and supplier credit data corresponding to materials to be supervised based on the query request of the first user terminal;
the strategic reasoning module is used for inputting the material quality data and the supplier reputation data into a strategic reasoning model to obtain a quality supervision strategy output by the strategic reasoning model;
wherein the material quality data is obtained from an internal network of a supply chain and the provider reputation data is obtained from an internet outside the supply chain; the strategy reasoning model is a decision reasoning machine which is self-learned and maintained based on a knowledge base where historical material quality data are located and a rule base corresponding to the knowledge base; the first user terminal is a user terminal used by a purchasing user of the materials to be supervised.
According to the supervision device of material quality that the invention provides, the said device also includes:
the plan monitoring module is used for acquiring a monitoring result in the process of controlling the first user terminal to cooperatively execute the quality monitoring strategy;
the defect prediction module is used for inputting the supervision result into an analysis model to obtain quality defect data output by the analysis model;
the report sharing module is used for generating an analysis report based on the quality defect data and sending the analysis report to the first user terminal and/or the second user terminal;
wherein the analytical model is trained based on historical operational failure data and confidence values of the historical operational failure data in corresponding indicators. And the second user terminal is a user terminal used by a third party checking user of the materials to be monitored.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the method for supervising the quality of the goods and materials.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of supervising the quality of a material as described in any of the above.
The material quality supervision method, the device, the electronic equipment and the storage medium provided by the invention respond to the query request, collect and integrate material quality data and supplier credit data from multi-source data of internal and external networks, take the material quality data and the supplier credit data as the input of a strategy reasoning model, and output results as quality supervision strategies corresponding to all suppliers. The method realizes evaluation and analysis of various factors related to supply and sale of the suppliers from the aspects of science and profession, can provide differentiated quality supervision management according to the suppliers under different conditions, optimizes quality supervision resource allocation, and improves the quality level of purchased materials.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for monitoring quality of materials according to the present invention;
FIG. 2 is a schematic diagram of the structure of big data processing provided by the present invention;
FIG. 3 is a schematic diagram of quality supervision policy decision making provided by the present invention;
FIG. 4 is a schematic diagram illustrating the principle of the analysis and prediction of the quality risk of the device according to the present invention;
FIG. 5 is a schematic structural diagram of a monitoring device for monitoring quality of materials provided by the present invention;
FIG. 6 is a second schematic flow chart of the method for monitoring quality of materials according to the present invention;
fig. 7 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. 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 invention.
Fig. 1 is a schematic flow chart of a method for supervising the quality of materials provided by the present invention. As shown in fig. 1, the method for supervising the quality of materials provided by the embodiment of the present invention includes: step 101, acquiring quality data of goods and materials to be supervised and credit data of a supplier based on a query request of a first user terminal.
Wherein the quality data of the material is obtained from an internal network of the supply chain, and the credit data of the supplier is obtained from an internet outside the supply chain. The first user terminal is a user terminal used by a purchasing user of the materials to be supervised.
It should be noted that the execution subject of the method for supervising the quality of materials provided by the embodiment of the present invention is a device for supervising the quality of materials.
The application scenario of the monitoring method for material quality provided by the embodiment of the invention is that in the purchasing process of enterprise materials, the conditions of each supplier are collected from the internal network and the external network, deep data mining is carried out, and adaptive quality monitoring strategies are provided for different suppliers.
The method for supervising the quality of the materials is suitable for making a quality supervision strategy by a user of a purchasing party or a first user terminal operated by an inspector through the device for supervising the requirements of any supplier.
The first user terminal described above may be implemented in various forms of electronic devices. For example, the electronic devices described in the embodiments of the present application may include mobile terminals such as a mobile phone, a smart phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a navigation device, a smart band, a smart watch, and the like, and fixed terminals such as a digital TV, a desktop computer, and the like. In the following, it is assumed that the electronic device is a mobile terminal. However, it will be understood by those skilled in the art that the configuration according to the embodiment of the present application can be applied to a fixed type terminal in addition to elements particularly used for moving purposes.
It should be noted that, before step 101, the user of the purchasing party or the inspector inputs unique identification information (for example, supplier name) of one or more suppliers corresponding to the material to be monitored in the display interface of the first user terminal to send a query request to the monitoring device of the material quality.
Specifically, in step 101, the monitoring device for material quality receives and responds to an inquiry request sent by the first user terminal on the front-end page, and according to the unique identification information of the provider carried in the inquiry request, respectively extracts material quality data corresponding to the material to be monitored from the internal network related to the supply chain, and extracts provider reputation data capable of selling the material to be monitored from the external internet.
The embodiment of the invention provides a supervising device for quality of goods and materials, and a specific implementation mode for integrating and collecting multi-source data based on a big data processing mode is not particularly limited.
Exemplarily, fig. 2 is a schematic structural diagram of big data processing provided by the present invention. As shown in fig. 2, the material quality monitoring device can collect structured, semi-structured, and unstructured massive data from various sources, process the data in a Hadoop platform, and store, process, associate, and summarize service data using a distributed database (MPP), thereby providing low-latency and high-concurrency processing capability for deep data analysis and mining applications.
On one hand, the material quality supervision device retrieves material quality data from production monitoring systems, e-commerce platforms, enterprise Resource Planning (ERP) systems and the like in the supply chain.
The quality data of the material includes, but is not limited to, problem data of a production process link of a provider, qualification information of the provider, bad behavior information of the provider, equipment detection result information, purchase orders, supply data, a supply plan, material main data, provider main data, bidding documents, technical specifications, design drawings, operation stage defect information, defect lists, equipment installation and debugging records, and the like.
On the other hand, the supervision device of the quality of the goods and materials collects the credit data of the suppliers from the internet outside the supply chain.
The provider reputation data includes, but is not limited to, credit investigation information, social penalty, and the like of the provider.
And 102, inputting the quality data of the goods and materials and the credit data of the suppliers into a strategy reasoning model to obtain a quality supervision strategy output by the strategy reasoning model.
The strategy reasoning model is a decision reasoning machine which is self-learned and maintained based on a knowledge base where historical material quality data are located and a rule base corresponding to the knowledge base.
It should be noted that the strategic reasoning model can be a self-learning reasoning machine established by using artificial intelligence technology, the reasoning process of the strategic reasoning model is similar to that of experts, so the reasoning strategy comprises three types of forward reasoning, reverse reasoning and mixed reasoning.
For example, the policy inference model may be an inference engine for forward inference, which infers a quality supervision policy for a corresponding provider from material quality data and provider reputation data according to a bottom-up inference policy by using knowledge in a knowledge base.
Specifically, in step 102, the quality monitoring device determines the qualification level of the provider according to the quality data of the material and the reputation data of the provider in combination with the knowledge base through a forward reasoning mechanism of a reasoning machine of the policy reasoning model, and compares and matches the qualification level with the rules in the rule base to obtain a quality monitoring policy corresponding to the quality.
The quality supervision strategy refers to material detection contents and means fed back to a user or an inspector of a purchasing party by a first user terminal on which the supervision device of the material quality is operated.
It can be understood that, as the business data is gradually accumulated, new knowledge is formed according to the change of new material quality data and provider reputation data (such as new provider purchase data, provider quality problem data, provider credit investigation data, etc.), so that the knowledge base is continuously updated and has self-improvement capability.
The embodiment of the invention responds to the query request, collects and integrates the material quality data and the supplier reputation data from the multi-source data of the internal and external networks, takes the material quality data and the supplier reputation data as the input of the strategy reasoning model, and outputs the result as the quality supervision strategy corresponding to each supplier. The method and the system realize evaluation and analysis of various factors related to supply and sale materials of the suppliers from the aspects of science and profession, can provide differentiated quality supervision management according to the suppliers under different conditions, optimize quality supervision resource allocation, and improve the quality level of purchased materials.
On the basis of any one of the above embodiments, the policy inference model comprises a knowledge base, a rule base and an inference engine.
Inputting the material quality data and the supplier reputation data into a policy reasoning model to obtain a quality supervision policy output by the policy reasoning model, wherein the quality supervision policy comprises the following steps:
and performing index quantification on the material quality data and the supplier reputation data to update the knowledge base.
And determining the detection items to be detected and the processing measures corresponding to the detection items according to the detection mode of the material to form a rule base.
And carrying out rule matching on the knowledge base and the rule base through an inference engine to generate a quality supervision strategy.
Specifically, in step 102, the policy inference model built in the monitoring device for material quality includes a knowledge base, a rule base and an inference engine.
Illustratively, fig. 3 is a schematic diagram of a quality supervision policy decision provided by the present invention. As shown in fig. 3, the policy inference model provided by the embodiment of the present invention is an artificial intelligence algorithm that can simulate the thinking way of human experts to make decisions, and the core of the algorithm is a knowledge base, a rule base, and an inference engine. The expert system typically includes 7 parts: human-computer interaction interface, knowledge base, rule base, comprehensive database, inference engine, interpreter and machine learning, wherein:
(1) A human-computer interaction interface: the human-computer interface is a way to enable communication between the user (querying for problem solutions) and the expert system.
(2) And the knowledge base is used for preprocessing the material quality data to form a quantized purchase data index, a performance evaluation data index and a credit investigation data index, and constructing a complete supplier material quality knowledge base so as to match the IF (conditions) in the rule base.
(3) The rule base comprises related detection items of the materials under different detection modes and corresponding processing measures in the detection items under different conditions. May be expressed in a set of rules. It has an IF (condition) THEN (action) structure, which triggers a rule when its condition is satisfied, and THEN executes an action.
The inspection mode for quality supervision comprises material sampling inspection and production supervision, corresponding quality supervision strategy rule bases are respectively constructed, each strategy comprises different processing measures, and the generated rule bases are shown in a table 1.
TABLE 1 quality supervision policies and enforcement rules Table
Therefore, the quality supervision strength is improved for materials and suppliers with more historical quality problems, serious quality problems, large scalar in purchasing, high money amount, low price of winning bid, new network access, new technology, new materials, new parts, new processes and the like, and otherwise, the quality supervision strength is reduced.
(4) The comprehensive database can provide the fact information in the material detection field.
(5) And the inference engine links the facts in the knowledge base and the rules in the rule base, and performs inference decision through intelligently matching the knowledge base and the rule base, so that the expert system finds the corresponding quality supervision strategy.
(6) An interpreter, for a user to use the interpreter to see how the expert system derived the solution.
(7) Machine learning, wherein a self-learning mechanism is utilized, new supplier purchase data, supplier quality problem data and supplier credit investigation data change along with gradual accumulation of business data to form new knowledge, and a knowledge base is continuously updated and has self-perfection capability.
The embodiment of the invention is based on a reasoning machine mechanism, potential values of material quality data and supplier reputation data are mined, a self-learning expert system is established, and a differentiated quality supervision strategy is generated. The method can establish a complete data analysis index system according to the quantitative data indexes of suppliers with different dimensions, construct a supplier evaluation knowledge base, a quality supervision strategy rule base and a quality supervision strategy decision inference machine through a big data analysis and artificial intelligence fusion technology, intelligently recommend and output the quality supervision strategy, and realize the difference suitability of the quality supervision of the suppliers.
On the basis of any of the above embodiments, after obtaining the quality supervision policy output by the policy inference model, the method further includes: and acquiring a supervision result in the process of controlling the first user terminal to cooperatively execute the quality supervision strategy.
Specifically, after step 102, the material quality supervision apparatus transfers the inferred quality supervision policy to the first user terminals, drives the first user terminals to perform online collaborative cooperation across business departments, compiles a quality supervision plan for the quality supervision policy, participates in the plan with corresponding role identities, and integrates the operation states in the process into a supervision result.
It can be understood that, in the process of executing the plan, the supervision device for material quality can visually track and control the whole process of implementing the quality supervision plan, and warn and prompt abnormal progress and activity risk.
And inputting the supervision result into the analysis model to obtain the quality defect data output by the analysis model.
The analysis model is trained based on historical operation fault data and confidence values of the historical operation fault data in corresponding indexes.
It should be noted that the analysis model may be a neural network model, and the structure and parameters of the neural network include, but are not limited to, the input layer, the number of hidden layers and output layers of the neural network, and the weight parameter of each layer. The type and structure of the neural network are not particularly limited in the embodiments of the present invention.
For example, the analytical model may be a neural network model consisting of an input layer, a hidden layer and an output layer, wherein:
the input layer receives the supervision results directly at the most front part of the whole network.
The hidden layer can have one or more layers, and the operation is performed by the neuron of the hidden layer in a weighted summation mode on the input vector.
And the output layer is the last layer and is used for decoding the vector obtained after weighted summation, mapping the vector to corresponding quality defect data and outputting the data.
It should be noted that the sample data includes historical operation fault data, and the historical operation fault data is on indexes such as sampling inspection detection indexes and monitoring process indexes, and in combination with the condition that the stability of the index is judged to be in a degradation trend by using a critical value, the correlation between the historical operation fault and the index data is mined, a rule learning mechanism is established according to factors such as suppliers, equipment types and the frequency of occurrence of degradation index faults, and a fault rule base is established.
And the higher the frequency is, the higher the corresponding confidence value is, and the number and the confidence value of the rules are continuously mined and updated along with the sampling inspection, the development of monitoring work and the operation of equipment.
Specifically, the material quality monitoring device initializes the weight coefficients between the layers of the constructed analysis model, inputs the labeled contents of a group of sample question data and sample answer data in the training set into the neural network under the current weight coefficient, and sequentially calculates the output of each node of the input layer, the hidden layer and the output layer. And correcting the weight coefficient between each node of the input layer and the hidden layer according to a gradient descent method by the accumulated error between the final output result of the output layer and the actual connection position state type of the output layer. According to the above process, the weight coefficients of the input layer and the hidden layer can be obtained until all samples in the training set are traversed.
The material quality monitoring device restores the analysis model according to the weight coefficients of the neural network input layer and the hidden layer, inputs a new group of monitoring results in the test set into the trained analysis model, and can analyze the corresponding quality defect data of the monitoring results.
The embodiment of the invention formulates and executes a corresponding plan for each user in a supply chain based on a quality supervision strategy to obtain a supervision result, the supervision result is used as the input of an analysis model, and the output result is quality defect data corresponding to the supervision result. Through the study of data deposit and historical fault, carry out hidden danger analysis and study and judge for the material production equipment that the supplier brings in carries out the full life cycle of corresponding supervision plan, and the influence of profile equipment production process index degradation to operating stability realizes discovering the quality problem in the manufacturing process, carries out evasion in advance and risk precontrol.
On the basis of any of the above embodiments, the analysis model includes a quantitative analysis layer and a defect prediction layer.
Inputting the supervision result into the analysis model to obtain the quality defect data output by the analysis model, wherein the quality defect data comprises the following steps:
and inputting the supervision result into the quantitative analysis layer, and acquiring a confidence value corresponding to each production process index output by the quantitative analysis layer.
And inputting the confidence value corresponding to each production process index into the defect prediction layer, and acquiring quality defect data output by the defect prediction layer when the confidence value corresponding to the production process index meets the preset condition.
Specifically, the hidden layers of the analysis model include at least a quantitative analysis layer and a defect prediction layer.
And the quantitative analysis layer is used for extracting the running state data corresponding to various processes in the production process from the supervision result, and calculating the confidence value corresponding to the production process index by utilizing the existing metering algorithm and combining the frequency of the faults under the index.
And the confidence value can reflect the degradation trend of the index stability.
And the defect prediction layer is used for comparing the confidence value corresponding to the production process index with the critical value corresponding to the index, and if the confidence value is greater than or equal to the critical value, the supervision result can be matched with one or more familial defects recorded in the fault rule base, the familial defects of the supplier equipment are identified, and final quality defect data is formed.
The embodiment of the invention decides equipment with the confidence value at the critical value edge based on the confidence value of the supervision result under each production process index, identifies the familial defects of the supplier equipment by deeply mining the association between the historical operation fault and the production process index data, predicts the quality risk point and eliminates the critical hidden danger of the quality index. The method and the device can remind a supplier of paying attention to the defective process nodes in time, eliminate the risk hidden danger in advance, and effectively avoid the cost waste and the resource waste caused by repeated production of equipment due to unqualified tests.
On the basis of any of the above embodiments, after obtaining the quality defect data output by the analysis model, the method further includes: and matching corresponding rectification measure information based on the quality defect data, and performing front-end notification on the rectification measure information.
Specifically, the material quality monitoring device predicts the quality defect data in real time according to the monitoring result, and then matches the data through a preset risk evasion measure database to obtain the rectification measure corresponding to the corresponding quality defect.
The embodiments of the present invention are not particularly limited to the specific embodiments.
Illustratively, taking a cable device as an example, fig. 4 is a schematic diagram of a principle of analyzing and predicting the quality risk of the device provided by the present invention. As shown in fig. 4, according to different quality supervision means (i.e. inspection methods), corresponding production process indexes are set, if a confidence value corresponding to the thinnest thickness is calculated according to the index of the insulation thickness, when the confidence value is close to the lower limit of the critical value interval, the fault risk corresponding to the value is most likely to be cable insulation breakdown, and then operation and maintenance personnel are prompted to take active and differentiated preventive measures.
The embodiment of the invention matches corresponding rectification measure information based on the familial defect corresponding to the quality defect data. The method and the device can guide operation and maintenance personnel to correspondingly modify the defective process nodes in time, eliminate the risk potential in advance and effectively avoid cost waste and resource waste.
On the basis of any one of the above embodiments, the method further comprises the following steps: an analysis report is generated based on the quality defect data.
Specifically, the quality supervision device for the materials firstly compiles a quality supervision management plan, then implements execution according to the plan, tracks and controls the execution process, constructs an open shared resource platform in the last loop in the closed loop processing process of the quality problem, and integrates the result data related to each stage into an analysis report.
Sending the analysis report to the first user terminal and/or the second user terminal;
and the second user terminal is a user terminal used by a third party checking user for materials to be monitored.
Specifically, the monitoring device for material quality sends the analysis report to a first user terminal held by a capital construction and operation and inspection professional outside a material management professional, wherein the first user terminal is shared internally, and the second user terminal is pushed to a second user terminal held by each supplier externally.
It is understood that analysis reports with different emphasis points may also be generated according to different user roles.
If the user role is the material management professional, the analysis report focuses on implementing differentiated quality supervision management according to the conditions of various suppliers, optimizing quality supervision resource allocation and reducing enterprise operation cost.
If the user role is the capital construction and operation and inspection professional, the analysis report focuses on the risk pre-control of various production equipment. And (3) providing evasive measures for the critical quality hidden danger in the production process of the equipment and the faults possibly occurring in the future, and adopting active operation and maintenance to improve the running stability of the equipment.
If the user role is the supplier, the analysis report is focused on intelligently predicting common familial defects of the equipment quality by utilizing big data analysis, the supplier is reminded to focus on process nodes with defects in time, the risk potential is eliminated in advance, and cost waste and resource waste caused by repeated production of the equipment due to unqualified tests are effectively avoided.
On the basis of a material quality supervision service process, theoretical research and data capacity research, the embodiment of the invention collects relevant data of suppliers in the external Internet on the basis of historical quality data of the suppliers accumulated inside enterprises and the like, analyzes the internal association of the data by applying technologies such as big data, artificial intelligence and the like, and establishes a quality supervision management mode which can effectively find the quality problems of the suppliers, pre-control the quality risk hidden danger of equipment in advance and share the quality data for the material purchasing process of the enterprises. The quality of purchasing equipment of enterprises is improved, the suppliers are helped to improve the quality level of materials, and the upstream and downstream enterprises in the supply chain are driven to share high-quality development.
Fig. 5 is a schematic structural diagram of a material quality monitoring device provided by the invention. On the basis of any of the above embodiments, as shown in fig. 5, the apparatus includes: a data integration module 510 and a policy inference module 520, wherein:
and the data integration module 510 is configured to obtain, based on the query request of the first user terminal, material quality data and provider reputation data corresponding to the material to be supervised.
And the policy reasoning module 520 is used for inputting the quality data of the materials and the reputation data of the suppliers into the policy reasoning model and obtaining the quality supervision policy output by the policy reasoning model.
Wherein the quality data of the material is obtained from an internal network of the supply chain, and the credit data of the supplier is obtained from an internet outside the supply chain. The strategy reasoning model is a decision-making reasoning machine which is self-learned and maintained based on a knowledge base where the historical material quality data are located and a rule base corresponding to the knowledge base. The first user terminal is a user terminal used by a purchasing user of materials to be supervised.
Specifically, the data integration module 510 and the policy inference module 520 are electrically connected in sequence.
The data integration module 510 receives and responds to an inquiry request sent by the first user terminal on the front-end page, and according to the unique identification information of the provider carried in the inquiry request, respectively extracts the quality data of the material corresponding to the material to be supervised from the internal network related to the supply chain, and extracts the provider reputation data capable of selling the material to be supervised from the external internet.
The policy inference module 520 determines the qualification level of the provider according to the quality data of the material and the reputation data of the provider in combination with the knowledge base through a forward inference mechanism of an inference engine of a policy inference model, and compares and matches the qualification level with rules in the rule base to obtain a quality supervision policy corresponding to the quality.
Optionally, the policy inference model comprises a knowledge base, a rule base and an inference engine.
The policy inference module 520 includes a knowledge base maintenance unit, a rule base maintenance unit, and an inference engine application unit, where:
and the knowledge base maintenance unit is used for carrying out index quantization on the material quality data and the supplier reputation data so as to update the knowledge base.
And the rule base maintenance unit is used for determining detection items implemented by the detection and processing measures corresponding to the detection items according to the detection mode of the materials to form a rule base.
And the inference engine application unit is used for carrying out rule matching on the knowledge base and the rule base through the inference engine so as to generate the quality supervision strategy.
Optionally, the apparatus further comprises a plan monitoring module and a defect prediction module, wherein:
and the plan monitoring module is used for acquiring a monitoring result in the process of controlling the first user terminal to cooperatively execute the quality monitoring strategy.
And the defect prediction module is used for inputting the supervision result into the analysis model to obtain the quality defect data output by the analysis model.
The analysis model is trained based on historical operation fault data and confidence values of the historical operation fault data in corresponding indexes.
Optionally, the analytical model includes a quantitative analysis layer and a defect prediction layer.
The defect prediction module includes a quantization analysis unit and a defect prediction unit, wherein:
and the quantitative analysis unit is used for inputting the supervision result into the quantitative analysis layer and acquiring a confidence value corresponding to each production process index output by the quantitative analysis layer.
And the defect prediction unit is used for inputting the confidence value corresponding to each production process index into the defect prediction layer and acquiring quality defect data output by the defect prediction layer when the confidence value corresponding to the production process index meets the preset condition.
Optionally, the defect prediction module further comprises a rectification measure matching unit, wherein:
and the rectification measure matching unit is used for matching corresponding rectification measure information based on the quality defect data and carrying out front-end notification on the rectification measure information.
Optionally, the apparatus further comprises a report sharing module, wherein:
and the report sharing module is used for generating an analysis report based on the quality defect data and sending the analysis report to the first user terminal and/or the second user terminal.
And the second user terminal is a user terminal used by a third party checking user for materials to be monitored.
The embodiment of the invention responds to the query request, integrates the quality data of the goods and the supplier credit data from the multi-source data of the internal and external networks, takes the quality data of the goods and the supplier credit data as the input of the strategy reasoning model, and outputs the result as the quality supervision strategy corresponding to each supplier. The method and the system realize evaluation and analysis of various factors related to supply and sale materials of the suppliers from the aspects of science and profession, can provide differentiated quality supervision management according to the suppliers under different conditions, optimize quality supervision resource allocation, and improve the quality level of purchased materials.
On the basis of any one of the above embodiments, the monitoring device for material quality further includes a plan monitoring module, a defect prediction module and a report sharing module, wherein:
and the plan monitoring module is used for acquiring a monitoring result in the process of controlling the first user terminal to cooperatively execute the quality monitoring strategy.
And the defect prediction module is used for inputting the supervision result into the analysis model to obtain the quality defect data output by the analysis model.
And the report sharing module is used for generating an analysis report based on the quality defect data and sending the analysis report to the first user terminal and/or the second user terminal.
The analysis model is trained based on historical operation fault data and confidence values of the historical operation fault data in corresponding indexes. The second user terminal is a user terminal used by a third party checking user for materials to be supervised.
Specifically, the monitoring device for material quality comprises a data integration module, a strategy reasoning module, a plan monitoring module, a defect prediction module and a report sharing module, wherein the five modules form the whole closed-loop monitoring process.
Exemplarily, fig. 6 is a second flow chart of the monitoring method for quality of materials provided by the present invention. As shown in fig. 6, the specific implementation is as follows:
and the data integration module is used for comprehensively collecting the supplier data, and has the main function of extracting, converting and loading internal and external multi-source data based on an ETL data technology.
The strategy reasoning module is used for managing and optimizing the quality supervision strategy, and mainly has the functions of deeply mining the potential value of data, establishing a self-learning strategy reasoning model, establishing a quality supervision strategy decision expert system and automatically recommending a differential quality supervision strategy through big data analysis and artificial intelligence technology.
And the plan monitoring module is used for intelligently compiling the quality supervision plan and implementing tracking management control, and has the main function of intelligently compiling the quality supervision plan according to an optimal quality supervision strategy. And (4) getting through the data barriers and realizing the on-line collaboration of the cross-service departments. And (4) visually tracking, managing and controlling the quality supervision and implementing the whole process, and early warning and prompting abnormal progress and activity risk.
And the defect prediction module is used for digging data analysis and intelligent study and judgment of quality supervision results, and mainly has the functions of deeply digging and analyzing production process indexes of equipment and historical mass fault data, constructing a quality hidden danger analysis rule model base and early warning and reminding critical hidden danger risk points.
The report sharing module is used for sharing and sharing the quality data of the goods and materials of the suppliers, and mainly has the functions of constructing an open shared resource platform, sending the quality evaluation report of the whole life cycle of the equipment to capital construction and operation and inspection specialties outside a goods and materials management professional for internal sharing, and externally pushing the report to each supplier.
On the basis of a material quality supervision service process, theoretical research and data capacity research, the embodiment of the invention collects relevant data of suppliers in the external Internet on the basis of historical quality data of the suppliers accumulated inside enterprises and the like, analyzes the internal association of the data by applying technologies such as big data, artificial intelligence and the like, and establishes a quality supervision management mode which can effectively find the quality problems of the suppliers, pre-control the quality risk hidden danger of equipment in advance and share the quality data for the material purchasing process of the enterprises. The quality of purchasing equipment of enterprises is improved, the suppliers are helped to improve the quality level of materials, and the upstream and downstream enterprises in the supply chain are driven to share high-quality development.
Fig. 7 illustrates a physical structure diagram of an electronic device, and as shown in fig. 7, the electronic device may include: a processor (processor) 710, a communication Interface (Communications Interface) 720, a memory (memory) 730, and a communication bus 740, wherein the processor 710, the communication Interface 720, and the memory 730 communicate with each other via the communication bus 740. Processor 710 may invoke logic instructions in memory 730 to perform a method of monitoring quality of a material, the method comprising: acquiring material quality data and supplier credit data corresponding to materials to be supervised based on a query request of a first user terminal; inputting the material quality data and the supplier reputation data into a strategy reasoning model to obtain a quality supervision strategy output by the strategy reasoning model; wherein the quality data of the materials is obtained from an internal network of the supply chain, and the credit data of the suppliers is obtained from the Internet outside the supply chain; the strategy reasoning model is a decision-making reasoning machine which is self-learned and maintained based on a knowledge base where the historical material quality data are located and a rule base corresponding to the knowledge base; the first user terminal is a user terminal used by a purchasing user of materials to be supervised.
In addition, the logic instructions in the memory 730 can be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. 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 various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, the computer program product including a computer program, the computer program being stored on a non-transitory computer readable storage medium, wherein when the computer program is executed by a processor, the computer is capable of executing the method for supervising the quality of goods provided by the above methods, the method including: acquiring material quality data and supplier credit data corresponding to materials to be supervised based on a query request of a first user terminal; inputting the material quality data and the supplier reputation data into a strategy reasoning model to obtain a quality supervision strategy output by the strategy reasoning model; wherein the quality data of the materials is obtained from an internal network of the supply chain, and the credit data of the suppliers is obtained from the Internet outside the supply chain; the strategy reasoning model is a decision-making reasoning machine which is self-learned and maintained based on a knowledge base where the historical material quality data are located and a rule base corresponding to the knowledge base; the first user terminal is a user terminal used by a purchasing user of the materials to be supervised.
In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method for supervising the quality of goods provided by the above methods, the method comprising: acquiring material quality data and supplier credit data corresponding to materials to be supervised based on a query request of a first user terminal; inputting the quality data of the materials and the credit data of the suppliers into a strategy reasoning model to obtain a quality supervision strategy output by the strategy reasoning model; wherein the quality data of the materials is obtained from an internal network of the supply chain, and the credit data of the suppliers is obtained from the Internet outside the supply chain; the strategy reasoning model is a decision reasoning machine which is self-learned and maintained based on a knowledge base where historical material quality data are located and a rule base corresponding to the knowledge base; the first user terminal is a user terminal used by a purchasing user of materials to be supervised.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Claims (10)
1. A method for supervising material quality is characterized by comprising the following steps:
acquiring material quality data and supplier credit data corresponding to materials to be supervised based on a query request of a first user terminal;
inputting the quality data of the materials and the credit data of the suppliers to a strategy reasoning model to obtain a quality supervision strategy output by the strategy reasoning model;
wherein the material quality data is obtained from an internal network of a supply chain and the provider reputation data is obtained from an internet outside the supply chain; the strategy reasoning model is a decision reasoning machine which is self-learned and maintained based on a knowledge base where historical material quality data are located and a rule base corresponding to the knowledge base; the first user terminal is a user terminal used by a purchasing user of the materials to be supervised.
2. The material quality supervision method according to claim 1, wherein the policy inference model comprises a knowledge base, a rule base and an inference engine;
the step of inputting the quality data of the goods and materials and the reputation data of the suppliers into a strategy reasoning model to obtain a quality supervision strategy output by the strategy reasoning model comprises the following steps:
performing index quantization on the material quality data and the supplier reputation data to update the knowledge base;
determining detection items implemented by detection and processing measures corresponding to the detection items according to a material detection mode to form the rule base;
and performing rule matching on the knowledge base and the rule base through the inference engine to generate the quality supervision strategy.
3. The method for supervising material quality according to claim 1, wherein after obtaining the quality supervision policy output by the policy inference model, the method further comprises:
acquiring a supervision result in the process of controlling the first user terminal to cooperatively execute the quality supervision strategy;
inputting the supervision result into an analysis model to obtain quality defect data output by the analysis model;
wherein the analytical model is trained based on historical operational failure data and confidence values of the historical operational failure data in corresponding indicators.
4. The method of claim 3, wherein the analytical model includes a quantitative analysis layer and a defect prediction layer;
the inputting the supervision result into an analysis model to obtain quality defect data output by the analysis model comprises:
inputting the supervision result into the quantitative analysis layer, and acquiring confidence values corresponding to all production process indexes output by the quantitative analysis layer;
and inputting the confidence value corresponding to each production process index into the defect prediction layer, and acquiring the quality defect data output by the defect prediction layer when the confidence value corresponding to the production process index meets the preset condition.
5. The method for supervising material quality according to claim 4, wherein after obtaining the quality defect data output by the analysis model, the method further comprises:
and matching corresponding rectification measure information based on the quality defect data, and carrying out front-end notification on the rectification measure information.
6. The method of claim 3, further comprising:
generating an analysis report based on the quality defect data, and sending the analysis report to the first user terminal and/or the second user terminal;
and the second user terminal is a user terminal used by a third party checking user of the materials to be monitored.
7. A supervision device for material quality is characterized by comprising:
the data integration module is used for acquiring material quality data and supplier credit data corresponding to materials to be supervised based on the query request of the first user terminal;
the strategy reasoning module is used for inputting the quality data of the materials and the supplier reputation data into a strategy reasoning model and obtaining a quality supervision strategy output by the strategy reasoning model;
wherein the material quality data is obtained from an internal network of a supply chain and the provider reputation data is obtained from an internet outside the supply chain; the strategy reasoning model is a decision reasoning machine which is self-learned and maintained based on a knowledge base where historical material quality data are located and a rule base corresponding to the knowledge base; the first user terminal is a user terminal used by a purchasing user of the materials to be supervised.
8. The material quality supervision apparatus according to claim 7, wherein the apparatus further comprises:
the plan monitoring module is used for acquiring a monitoring result in the process of controlling the first user terminal to cooperatively execute the quality monitoring strategy;
the defect prediction module is used for inputting the supervision result into an analysis model to obtain quality defect data output by the analysis model;
the report sharing module is used for generating an analysis report based on the quality defect data and sending the analysis report to the first user terminal and/or the second user terminal;
wherein the analysis model is trained based on historical operating fault data and confidence values of the historical operating fault data in corresponding indicators; and the second user terminal is a user terminal used by a third party checking user of the materials to be monitored.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of supervising the quality of materials according to any of claims 1 to 6 when executing the program.
10. A non-transitory computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements a method for the supervision of quality of materials according to any one of claims 1 to 6.
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