CN116523466A - Production data tracing system and method based on big data - Google Patents
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Abstract
The invention discloses a production data tracing system and method based on big data, which relate to the technical field of information tracing and comprise a management and control center, a production data database, a production supervision model construction module, a production management module and an abnormality tracing module; according to the invention, a production data information table is built by acquiring relevant information of production data, a production line-product information set is built, information in the production line-product information set is mapped into a plurality of rectangular coordinate systems, the coordinate distance density of the production line-product information set is obtained, a production supervision model is built, the number of the production line and the number of expected products are input into the production supervision model, the number of required production raw materials is obtained, the number of each production raw material in the production process is monitored in real time, whether production abnormality occurs is judged in sequence, and the production line with the occurrence of the production abnormality is traced; the invention monitors whether the production line is abnormal in real time by establishing the production supervision model, and improves the accuracy and efficiency of supervision of the production process to a certain extent.
Description
Technical Field
The invention relates to the technical field of information tracing, in particular to a production data tracing system and method based on big data.
Background
Along with the continuous development of digital economy, the traditional manufacturing industry is faced with new development opportunities, so that workshop operation requirements of manufacturing factories are more and more efficient and accurate, production information is recorded and production flows are tracked by means of past pure manual modes, the efficiency is low, errors are prone to occur, and the development requirements of industrial production and manufacturing industries are not met;
and along with the continuous development of industrial production manufacturing industry, the variety of products produced by the method is more and more, so that corresponding production data information is more and more, once errors occur in the production process, the prior art is difficult to detect and trace the production line with the errors through the production data information, and therefore, the production data tracing system and method based on big data are provided.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a production data tracing system and method based on big data.
In order to achieve the above object, the present invention provides the following technical solutions:
the production data tracing system based on big data comprises a management and control center, wherein the management and control center is in communication connection with a production data database, a production supervision model construction module, a production management module and an abnormal tracing module;
the production data information database is provided with a production data information acquisition unit, a production data information processing unit and an information table establishment unit;
the production data information acquisition unit is used for acquiring basic information of production raw materials, products and production lines so as to obtain initial information of production data;
the production data information processing unit is used for carrying out data cleaning on the initial information of the production data so as to obtain new production data information;
the information table establishing unit is used for establishing a production data information table according to the new production data information;
the production supervision model construction module is used for constructing a production line-product relationship set through a production data information table so as to further establish a production supervision model;
the production management module is used for supervising the production conditions of each production line according to the production supervision model, so as to judge whether production errors occur in the production line and generate corresponding production abnormal records;
the anomaly traceability module is used for carrying out traceability query on the production line and the production element raw materials corresponding to the production anomaly record.
Further, the basic information of the production raw materials, the products and the production lines comprises purchasing records and in-and-out records of the production raw materials, names of the products, names and numbers of the required production raw materials, numbers of the production lines, historical production records and equipment names.
Further, the data cleaning process of the initial information of the production data comprises the following steps:
the production data information processing unit performs data cleaning on the initial information of the production data by setting a target data mapping mechanism and standard result data;
the target data mapping mechanism consists of a plurality of characteristic target points and a preset data arrangement rule, and standard result data are sample data arranged according to the preset data arrangement rule;
and arranging the obtained target data according to a preset arrangement rule to obtain new production data information, comparing the new production data information with the result data, judging that the data cleaning is finished and sending the new production data information to an information table building unit if the comparison result is consistent, and cleaning the production data initial information again through a target data mapping mechanism and standard result data if the comparison result is inconsistent.
Further, the production data information table consists of a production raw material information table and a production information table;
the production raw material information sub-table comprises names of production raw materials, dealer names, production dates, current stock quantity, stock entering quantity, corresponding time, stock leaving quantity and corresponding time;
the production information sub-table comprises the serial numbers of the production lines, the names of the equipment and the corresponding produced product names, the production date, the types and the quantity of the required raw materials.
Further, the process for establishing the production supervision model comprises the following steps:
according to the corresponding relation between the production lines and the products in the production information sub-table, a plurality of production line-product relation sets S are established;
wherein the line-product information set is denoted as S (t n,m ，H n )＝{(x m ，Num)，(y c N，b ，num)}；
Wherein y is c N，b Indicated at production number x m The number of the required b-th production raw material, wherein N is the production number x m The number of kinds of production raw materials required for the production of (a) and N is greater than or equal to b, num represents the production line H n At date t n,m The number of products produced, num represents the number of uses corresponding to the raw materials produced, t n,m Representing a production line H n Production of product x m Is the date of (2);
establishing a plurality of rectangular coordinate systems, mapping data in a production line-product information set into each rectangular coordinate system according to the number of products, and calculating the coordinate distance density d of each rectangular coordinate system;
setting a coordinate distance density threshold, removing a rectangular coordinate system with the coordinate distance density being larger than the coordinate distance density threshold, and reserving a rectangular coordinate system with the coordinate distance density being smaller than or equal to the coordinate distance density threshold;
mapping each coordinate unit set in the reserved rectangular coordinate system into one rectangular coordinate system, further constructing a plurality of linear regression equations, and combining all the linear regression equations to obtain a production supervision model;
the linear regression equation may be F n (x m ，Num)＝a 1 num 1 +a 2 num 2 +……+a N num n Wherein a is 1 、a 2 、……、a N Is a constant coefficient.
Further, the calculation formula of the coordinate distance density d is as follows:
wherein d is Num And c represents the index number of the corresponding number of the production raw materials.
Further, the process of monitoring the production conditions of each production line by the production management module comprises the following steps:
according to the serial numbers of the production lines, searching all the linear regression equations of the corresponding production lines from the production supervision model, and inputting the types of the products to be produced and the corresponding expected generation quantity into the corresponding linear regression equations so as to obtain the types of the required production raw materials and the corresponding required quantity;
each time a product is completed by the production line, the production management module detects the residual quantity of each production data and substitutes the residual quantity into a linear regression equation to further obtain the expected producible quantity of the residual quantity of production raw materials;
the expected actual production quantity of the expected producible product quantity and the produced product quantity are added, and the expected production quantity of the product is compared, and whether the production line is abnormal in production or not is judged according to the comparison result;
when one production line is judged to be abnormal in production, the production supervision module stops the corresponding production line in real time, generates a production abnormal record and sends the production abnormal record to the abnormal tracing module;
wherein the production anomaly record includes production video data of each device of the production line, the number of the production line, the name of the product produced, and the production anomaly judged.
Further, the process of tracing and inquiring by the anomaly tracing module according to the production anomaly record comprises the following steps:
the abnormal tracing module acquires production video data of each device in the production line with normal production from the production supervision module, establishes a normal production model of each device according to the production video data of the devices with normal production, substitutes the normal production model into the production video data of each device in the production line in the production abnormal record, and further judges whether the production of each device is abnormal;
if the production abnormality of the equipment is judged according to the normal production model, the number of the equipment is recorded, and the number of the equipment and the corresponding production abnormality video data are sent to related management personnel so as to check and maintain the equipment;
if the production of the equipment is judged to be normal according to the normal production model, the abnormality of the production raw materials is deduced, the names of the production raw materials required by the product are obtained according to the product information, and each production raw material is traced according to the production data information table and the production raw material names.
Further, a production data tracing method of a production data tracing system based on big data comprises the following steps:
step one, collecting relevant information of production data, and carrying out data cleaning on the relevant information of the production data so as to establish a production data information table;
step two, a production line-product information set is established according to a production data information table, a plurality of coordinate systems are established, and the information set in the production line-product information set is mapped into a rectangular coordinate system;
calculating the coordinate distance density of each rectangular coordinate system, setting a coordinate distance density threshold value, removing rectangular coordinate systems with the coordinate distance density larger than the coordinate distance density threshold value, and reserving rectangular coordinate systems with the coordinate distance density smaller than or equal to the coordinate distance density threshold value;
establishing a corresponding linear regression equation according to the coordinate points in the reserved rectangular coordinate system;
integrating all the linear regression equations to obtain a production supervision model;
step six, inputting the production line number and the expected product yield into a production supervision model, and searching a corresponding linear regression equation to further obtain the number of corresponding production raw materials;
step seven, setting a plurality of cameras to monitor the production state of the production line in real time, calculating the expected residual quantity of each production raw material according to the current production quantity of the product, comparing the estimated residual quantity with the residual quantity of each production raw material, and judging whether the production line has abnormal production or not according to the comparison result;
and step eight, if the production line is abnormal, generating a production abnormal record, tracing the production line according to the production abnormal record, and sending tracing information to related management staff.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, the production data information is subjected to data cleaning by setting the target data mapping mechanism and the standard result data, and then the production data information subjected to data cleaning is arranged by presetting the data arrangement rule, so that a production data information table is established, and whether the production data information subjected to data cleaning is in error or not is checked by setting the target data mapping mechanism and the standard result data, so that the accuracy of the production data information subjected to data cleaning is ensured to a certain extent;
2. the method comprises the steps of establishing a production line-product information set through production data information, mapping information in the production line-product information set to a plurality of rectangular coordinate systems, further calculating the coordinate distance density of each rectangular coordinate system, removing rectangular coordinate systems with the coordinate distance density larger than the coordinate distance density through setting a coordinate distance density threshold value, further establishing a production supervision model, and further filtering error data by mapping data in the production line-product information set to the rectangular coordinate systems and calculating the coordinate distance density of each rectangular coordinate system, so that the accuracy of the production process of a subsequent supervision production line is guaranteed.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
Fig. 1 is a schematic diagram of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, based on the examples herein, which are within the scope of the invention as defined by the claims, will be within the scope of the invention as defined by the claims.
As shown in FIG. 1, the production data tracing system based on big data comprises a management and control center, wherein the management and control center is in communication connection with a production data database, a production supervision model construction module, a production management module and an abnormality tracing module;
the production data information database is provided with a production data information acquisition unit, a production data information processing unit and an information table establishment unit;
the production data information acquisition unit is used for acquiring basic information of production raw materials, products and production lines so as to obtain initial information of production data;
the production data information processing unit is used for carrying out data cleaning on the initial information of the production data so as to obtain new production data information;
the information table establishing unit is used for establishing a production data information table according to the new production data information;
the following is a specific description by way of examples:
the production data information acquisition unit is provided with a plurality of cameras, so that image data of production raw materials, products and production lines are obtained through the cameras, and basic information data of the production raw materials, the products and the production lines are input to the production data information acquisition unit by related staff;
the basic information includes purchasing records and warehouse-in records of production raw materials, names of products, names and quantity of the required production raw materials, numbers of production lines, historical production records and equipment names;
integrating the production raw materials, the products, the production line image data, the video data and the related basic information to obtain production data initial information, and sending the production data initial information to a production data information processing unit;
the production data information processing unit performs data cleaning on initial information of production data by setting a target data mapping mechanism and standard result data, wherein the target data mapping mechanism consists of a plurality of characteristic target points and preset data arrangement rules, the standard result data is sample data arranged according to the preset data arrangement rules, for example, the names, dealer names, production dates, warehouse-in and warehouse-out time and quantity of the production data are collected from purchase records of production raw materials according to characteristic target points and are arranged according to the preset data arrangement rules, and then standard data formats and arrangement sequences are set;
extracting target data from the initial information of the production data through the characteristic targets;
wherein the target data includes:
the name of the production raw material, the name of the dealer, the date of production, and the quantity; the name of the product, the name of the raw materials to be produced, and the number; the number of each production line, the name of the produced product, the name and the number of the used production raw materials and the corresponding production date;
the obtained target data are arranged according to a preset arrangement rule to obtain new production data information, the new production data information is compared with the result data, if the comparison result is consistent, the data cleaning is judged to be completed, the new production data information is sent to the information table building unit, and if the comparison result is inconsistent, the data cleaning is carried out on the production data initial information again through a target data mapping mechanism and standard result data;
the information table building unit builds a production data information table according to the new production data information;
the production data information table consists of a production raw material information sub-table and a production information sub-table;
the production raw material information sub-table comprises names of production raw materials, dealer names, production dates, current stock quantity, stock entering quantity, corresponding time, stock leaving quantity and corresponding time;
the production information sub-table comprises the number of the production line, the equipment name, the corresponding produced product name, the production date, the type and the quantity of the required raw materials;
the production supervision model construction module is used for constructing a production line-product relationship set through a production data information table so as to further establish a production supervision model, and specifically comprises the following steps:
the production supervision model construction module obtains the production data information table from the production data database, and further sets numbers for each data in the production data information table, such as setting number H for the production line 1 、H 2 、……、H i The product is provided with a number x 1 、x 2 、……、x j The production raw materials are provided with a number y 1 、y 2 、……、y k And establishing a production supervision model, wherein i, j and k are natural numbers larger than 0;
further, the process of establishing the production supervision model comprises the following steps:
according to the corresponding relation between the production lines and the products in the production information sub-table, a plurality of production line-product relation sets S are established;
wherein the line-product information set is denoted as S (t n,m ，H n )＝{(x m ，Num)，(y c N，b ，num)}；
Wherein y is c N，b Indicated at production number x m The number of the required b-th production raw material, wherein N is the production number x m The number of kinds of production raw materials required for the production of (a) and N is greater than or equal to b, num represents the production line H n At date t n,m The number of products produced, num represents the number of uses corresponding to the raw materials produced, t n,m Representing a production line H n Production of product x m Is the date of (2);
establishing a plurality of rectangular coordinate systems, mapping data in a production line-product information set into each rectangular coordinate system according to the number of products, and calculating the coordinate distance density of each rectangular coordinate system;
the calculation formula of the coordinate distance density d of each rectangular coordinate system is as follows:
wherein d is Num Representing the coordinate distance density corresponding to the number of products for producing Num, c represents the subscript number of the corresponding number of the production raw materials;
setting a coordinate distance density threshold, removing a rectangular coordinate system with the coordinate distance density being larger than the coordinate distance density threshold, and reserving a rectangular coordinate system with the coordinate distance density being smaller than or equal to the coordinate distance density threshold;
mapping each coordinate unit set in the reserved rectangular coordinate system into one rectangular coordinate system, further constructing a plurality of linear regression equations, and combining all the linear regression equations to obtain a production supervision model;
the linear regression equation may be F n (x m ，Num)＝a 1 num 1 +a 2 num 2 +……+a N num n Wherein a is 1 、a 2 、……、a N Is a constant coefficient;
for each production line, the number of the products to be produced can be input into a corresponding linear regression equation, so that the corresponding expected required production raw material number can be obtained, or the expected production product number can be obtained by inputting the number of the production raw materials;
further, an error allowance zone is set according to the obtained expected required production raw material quantity or expected production product quantity, if the actual required production raw material quantity is higher or lower than 5% of the expected required production raw material quantity, the abnormal production is judged, and if the actual production product quantity is lower or higher than 5% of the expected production product quantity, the abnormal production is judged.
The production management module is used for supervising the production conditions of each production line according to the production supervision model, and the specific process comprises the following steps:
before production, the production management module acquires a production data information table from a production data database and acquires a production supervision model from the production supervision model construction module;
according to the serial numbers of the production lines, searching all the linear regression equations of the corresponding production lines from the production supervision model, and inputting the types of the products to be produced and the corresponding expected generation quantity into the corresponding linear regression equations so as to obtain the types of the required production raw materials and the corresponding required quantity;
the production line is provided with a plurality of cameras which are used for shooting video data in the production process of each device in the production process, searching the corresponding names of each device in the production line according to the production data information table, and marking the video data of each device with the device name after the generation is finished;
further, each time a product is completed by the production line, the production management module detects the remaining quantity of each production data and substitutes the remaining quantity into a linear regression equation to obtain the expected producible quantity of the remaining quantity of production raw materials;
the method comprises the steps of adding the expected actual production quantity of the expected producible product quantity and the produced product quantity, comparing the expected production quantity with the expected production quantity of the product, judging that the production is normal if the difference between the expected actual production quantity and the expected production quantity of the product is less than 5%, and judging that the production is abnormal if the difference between the expected actual production quantity and the expected production quantity of the product is more than or equal to 5%;
when one production line is judged to be abnormal in production, the production supervision module stops the corresponding production line in real time, generates a production abnormal record and sends the production abnormal record to the abnormal tracing module;
wherein the production anomaly record includes production video data of each device of the production line, the number of the production line, the name of the product produced, and the production anomaly judged.
The anomaly traceability module is used for carrying out traceability query according to a production line and a production element raw material corresponding to production anomaly records, and specifically comprises the following steps:
the abnormal tracing module acquires a production data information table from the production data database;
after the abnormal tracing module receives the production abnormal record from the production supervision module, the production line information and the product information are searched from the production data information table according to the production line number and the produced product name in the production abnormal record;
according to the production video data of each device of the production line in the production anomaly record, further analyzing and judging whether each device is abnormal in the production process;
the process for judging whether the equipment is abnormal in the production process comprises the following steps:
the abnormal tracing module acquires production video data of each device in the production line with normal production from the production supervision module, establishes a normal production model of each device according to the production video data of the devices with normal production, substitutes the normal production model into the production video data of each device in the production line in the production abnormal record, and further judges whether the production of each device is abnormal;
if the production abnormality of the equipment is judged according to the normal production model, the number of the equipment is recorded, corresponding production abnormality video data are obtained from the production abnormality record, and the number of the equipment and the corresponding production abnormality video data are sent to related management personnel to check and maintain the equipment;
if the equipment is judged to be normally produced according to the normal production model, deducing that the production raw materials are abnormal, obtaining the names of the production raw materials required by the product according to the product information, and tracing each production raw material according to the production data information table and the names of the production raw materials;
the traceability information comprises the names of production raw materials, dealer names, production dates, current stock quantity, stock entering quantity, corresponding time, stock leaving quantity and corresponding time;
and sending the traceability information to related managers, and detecting by the related managers according to the traceability information.
A production data tracing method based on big data comprises the following steps:
step one, collecting relevant information of production data, and carrying out data cleaning on the relevant information of the production data so as to establish a production data information table;
step two, a production line-product information set is established according to a production data information table, a plurality of coordinate systems are established, and the information set in the production line-product information set is mapped into a rectangular coordinate system;
calculating the coordinate distance density of each rectangular coordinate system, setting a coordinate distance density threshold value, removing rectangular coordinate systems with the coordinate distance density larger than the coordinate distance density threshold value, and reserving rectangular coordinate systems with the coordinate distance density smaller than or equal to the coordinate distance density threshold value;
establishing a corresponding linear regression equation according to the coordinate points in the reserved rectangular coordinate system;
integrating all the linear regression equations to obtain a production supervision model;
step six, inputting the production line number and the expected product yield into a production supervision model, and searching a corresponding linear regression equation to further obtain the number of corresponding production raw materials;
step seven, setting a plurality of cameras to monitor the production state of the production line in real time, calculating the expected residual quantity of each production raw material according to the current production quantity of the product, comparing the estimated residual quantity with the residual quantity of each production raw material, and judging whether the production line has abnormal production or not according to the comparison result;
and step eight, if the production line is abnormal, generating a production abnormal record, tracing the production line according to the production abnormal record, and sending tracing information to related management staff.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (9)
1. The production data traceability system based on the big data comprises a management and control center, and is characterized in that the management and control center is in communication connection with a production data database, a production supervision model construction module, a production management module and an abnormal traceability module;
the production data information database is provided with a production data information acquisition unit, a production data information processing unit and an information table establishment unit;
the production data information acquisition unit is used for acquiring basic information of production raw materials, products and production lines so as to obtain initial information of production data;
the production data information processing unit is used for carrying out data cleaning on the initial information of the production data so as to obtain new production data information;
the information table establishing unit is used for establishing a production data information table according to the new production data information;
the production supervision model construction module is used for constructing a production line-product relationship set through a production data information table so as to further establish a production supervision model;
the production management module is used for supervising the production conditions of each production line according to the production supervision model, so as to judge whether production errors occur in the production line and generate corresponding production abnormal records;
the anomaly traceability module is used for carrying out traceability query on the production line and the production element raw materials corresponding to the production anomaly record.
2. The big data based production profile traceability system according to claim 1, wherein the basic information of the production raw materials, products and production lines comprises the procurement records and the in-out records of the production raw materials, the names of the products and the names and the number of the required production raw materials, the serial numbers of the production lines, the historical production records and the equipment names.
3. The big data based production profile traceability system of claim 1, wherein the data cleansing process of the production profile initial information comprises:
the production data information processing unit performs data cleaning on the initial information of the production data by setting a target data mapping mechanism and standard result data;
the target data mapping mechanism consists of a plurality of characteristic target points and a preset data arrangement rule, and standard result data are sample data arranged according to the preset data arrangement rule;
and arranging the obtained target data according to a preset arrangement rule to obtain new production data information, comparing the new production data information with the result data, judging that the data cleaning is finished and sending the new production data information to an information table building unit if the comparison result is consistent, and cleaning the production data initial information again through a target data mapping mechanism and standard result data if the comparison result is inconsistent.
4. The big data-based production data tracing system of claim 1, wherein said production data information table is composed of a production raw material information sub-table and a production information sub-table;
the production raw material information sub-table comprises names of production raw materials, dealer names, production dates, current stock quantity, stock entering quantity, corresponding time, stock leaving quantity and corresponding time;
the production information sub-table comprises the serial numbers of the production lines, the names of the equipment and the corresponding produced product names, the production date, the types and the quantity of the required raw materials.
5. The big data based production profile traceability system of claim 4, wherein the process of creating the production supervision model comprises:
according to the corresponding relation between the production lines and the products in the production information sub-table, a plurality of production line-product relation sets S are established;
wherein the line-product information set is denoted as S (t n,m ，H n )＝{(x m ，Num)，(y c N，b ，num)}；
Wherein y is c N，b Indicated at production number x m The number of the required b-th production raw material, wherein N is the production number x m The number of kinds of production raw materials required for the production of (a) and N is greater than or equal to b, num represents the production line H n At date t n,m The number of products produced, num represents the number of uses corresponding to the raw materials produced, t n,m Representing a production line H n Production of product x m Is the date of (2);
establishing a plurality of rectangular coordinate systems, mapping data in a production line-product information set into each rectangular coordinate system according to the number of products, and calculating the coordinate distance density d of each rectangular coordinate system;
setting a coordinate distance density threshold, removing a rectangular coordinate system with the coordinate distance density being larger than the coordinate distance density threshold, and reserving a rectangular coordinate system with the coordinate distance density being smaller than or equal to the coordinate distance density threshold;
mapping each coordinate unit set in the reserved rectangular coordinate system into one rectangular coordinate system, further constructing a plurality of linear regression equations, and combining all the linear regression equations to obtain a production supervision model;
the linear regression equation may be F n (x m ，Num)＝a 1 num 1 +a 2 num 2 +……+a N num n Wherein a is 1 、a 2 、……、a N Is a constant coefficient.
6. The big data based production data tracing system of claim 5, wherein the calculation formula of the coordinate distance density d is:
wherein d is Num And c represents the index number of the corresponding number of the production raw materials.
7. The big data based production profile traceability system of claim 5, wherein said production management module monitors individual production line production status processes comprising:
according to the serial numbers of the production lines, searching all the linear regression equations of the corresponding production lines from the production supervision model, and inputting the types of the products to be produced and the corresponding expected generation quantity into the corresponding linear regression equations so as to obtain the types of the required production raw materials and the corresponding required quantity;
each time a product is completed by the production line, the production management module detects the residual quantity of each production data and substitutes the residual quantity into a linear regression equation to further obtain the expected producible quantity of the residual quantity of production raw materials;
the expected actual production quantity of the expected producible product quantity and the produced product quantity are added, and the expected production quantity of the product is compared, and whether the production line is abnormal in production or not is judged according to the comparison result;
when one production line is judged to be abnormal in production, the production supervision module stops the corresponding production line in real time, generates a production abnormal record and sends the production abnormal record to the abnormal tracing module;
wherein the production anomaly record includes production video data of each device of the production line, the number of the production line, the name of the product produced, and the production anomaly judged.
8. The big data-based production data tracing system of claim 7, wherein the process of tracing the source query by the anomaly tracing module according to the production anomaly record comprises:
the abnormal tracing module acquires production video data of each device in the production line with normal production from the production supervision module, establishes a normal production model of each device according to the production video data of the devices with normal production, substitutes the normal production model into the production video data of each device in the production line in the production abnormal record, and further judges whether the production of each device is abnormal;
if the production abnormality of the equipment is judged according to the normal production model, the number of the equipment is recorded, and the number of the equipment and the corresponding production abnormality video data are sent to related management personnel so as to check and maintain the equipment;
if the production of the equipment is judged to be normal according to the normal production model, the abnormality of the production raw materials is deduced, the names of the production raw materials required by the product are obtained according to the product information, and each production raw material is traced according to the production data information table and the production raw material names.
9. The method for tracing production data of the production data tracing system based on big data according to any one of claims 1 to 8, comprising the steps of:
step one, collecting relevant information of production data, and carrying out data cleaning on the relevant information of the production data so as to establish a production data information table;
step two, a production line-product information set is established according to a production data information table, a plurality of coordinate systems are established, and the information set in the production line-product information set is mapped into a rectangular coordinate system;
calculating the coordinate distance density of each rectangular coordinate system, setting a coordinate distance density threshold value, removing rectangular coordinate systems with the coordinate distance density larger than the coordinate distance density threshold value, and reserving rectangular coordinate systems with the coordinate distance density smaller than or equal to the coordinate distance density threshold value;
establishing a corresponding linear regression equation according to the coordinate points in the reserved rectangular coordinate system;
integrating all the linear regression equations to obtain a production supervision model;
step six, inputting the production line number and the expected product yield into a production supervision model, and searching a corresponding linear regression equation to further obtain the number of corresponding production raw materials;
step seven, setting a plurality of cameras to monitor the production state of the production line in real time, calculating the expected residual quantity of each production raw material according to the current production quantity of the product, comparing the estimated residual quantity with the residual quantity of each production raw material, and judging whether the production line has abnormal production or not according to the comparison result;
and step eight, if the production line is abnormal, generating a production abnormal record, tracing the production line according to the production abnormal record, and sending tracing information to related management staff.
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