JP2009205388A - Analysis inspection support device, program and analysis inspection support method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a technology for determining the priority order of components to be analyzed or the necessity of analyzing components by quantitatively evaluating the certification failure risk of chemical substances included in components. <P>SOLUTION: This analysis inspection support device calculates abnormality occurrence probability being the occurrence probability of the number of lots configured of configuring raw materials containing chemical substances having content with abnormal change and an abnormality causing risk mean value being the mean value of the abnormality causing risk of the configuring raw elements containing chemical substances having content with abnormal change, and multiples the abnormality occurrence probability by the abnormality causing risk mean value to calculate the first certification failure probability of each lot. Also, this analysis inspection support device calculates management value excess probability being probability exceeding prescribed content from the content of the chemical substances contained in the configuring raw elements configuring components for each component and aggregates the calculated probability to calculate the second certification failure probability of each lot. Then, this analysis inspection support device aggregates the first certification failure probability and the second certification failure probability to calculate the certification failure probability of each lot. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a technique for supporting analysis and inspection of substances contained in parts.

  Currently restricting the use of certain chemicals such as the European Union's RoHS directive (Restriction of the use of Hazardous Substances) and the ELV directive (End of Life Vehicles Directive) Laws and regulations are being enacted and enforced in each country. These recent trends in strengthening environmental regulations include the systematization of chemical substance regulations found in the REACH (Registration, Evaluation, Authorization of Chemicals) regulations, and regulations other than chemical substances (such as the obligation to disclose environmental impact information on products). This suggests the possibility of spreading to the addition of.

  Therefore, the assembly manufacturer extracts a part of the parts delivered by the supplier as a sample and analyzes it using a fluorescent X-ray analyzer, an inductively coupled plasma analyzer, etc., so that the chemical substances contained in the parts can be analyzed. The content is checked. At this time, in order to realize more efficient parts analysis, the assembly manufacturer specifies and specifies the constituent materials that are likely to contain the regulated substances in advance using reference information such as Non-Patent Document 1. By extracting the parts in which the component material is used as a sample and checking the content of chemical substances, inspection omissions are prevented.

Fujitsu Analytical Laboratories, “Technical Survey Report Possibility of inclusion of prohibited substances related to RoHS regulation for each material”, p. 7, February 2005

  However, even if the components are the same, the amount of chemical substances contained in the parts may vary from lot to lot. Therefore, in order to perform a reliable inspection in the prior art, samples must be extracted from all lots. It must be analyzed, and the analysis load is very heavy.

  Therefore, the present invention provides a technology that can determine the priority of components to be analyzed and the necessity of component analysis by quantitatively evaluating the risk of failure of certification of chemical substances contained in the components. Objective.

  In order to solve the above-mentioned problems, the present invention is based on the probability of violating the certification standard regarding the content of a substance from an abnormal change in the content of the substance and the number of lots in a specific period. The probability of certification failure is calculated as the certification failure risk.

  For example, the present invention is an analytical inspection support apparatus including a storage unit and a control unit, and the storage unit includes, for each part, a part ID of the part, a part classification of the part, and a lot of the part. , Component attribute information having information specifying the component material, the chemical substance contained in the component material, the content rate of the chemical material, and the date on which the content rate was detected, and for each delivered component , Manufacturing information having information for identifying the part ID of the part, the lot of the part, the delivery date of the part, the part classification, the constituent material of the part belonging to the part classification, the constituent material for each part classification Is stored as risk information having information for identifying an abnormality-causing risk that is a probability of failing the certification standard of the chemical substance due to an abnormal change in the content of the chemical substance contained in Department From the component attribute information, for each component, a determination process for acquiring the content rate of the chemical substance contained in the constituent material constituting the component and determining whether there is an abnormal change in the acquired content rate The abnormal lot counting process for counting the number of abnormal lots, which is the number of lots made of a constituent material containing a chemical substance having a content rate determined to have an abnormal change by the determination process, for each part; By dividing the number of abnormal lots for each part by the total number of lots for each part, an abnormality occurrence probability calculation process for calculating an abnormality occurrence probability that is an occurrence probability of an abnormality, and the determination process, there is an abnormal change The abnormality-causing risk of the constituent material including the chemical substance having the determined content rate is acquired from the above-mentioned risk information, and the abnormality-causing risk that is an average value in the abnormal lot from the acquired abnormality-causing risk By calculating the abnormality-caused risk average value calculating process for calculating the average value, the abnormality occurrence probability, and the abnormality-causing risk average value, a first certification failure probability for each lot is calculated. From the authorization failure risk calculation process and the part attribute information, the content rate specifying process for specifying the content rate of the chemical substance contained in the constituent material constituting the part for each part, and the content rate specifying process Included in the lot, the management value excess probability calculation process for calculating the management value excess probability, which is the probability of exceeding the predetermined content rate, by calculating the frequency of the specified content rate with a predetermined probability density function A second certification failure risk calculation process for calculating a second certification failure probability for each lot by aggregating the probability exceeding the control value calculated from the content rate of the chemical substances contained in the constituent materials , And a certification failure risk calculation process for calculating a certification failure probability for each lot by counting the first certification failure probability and the second certification failure probability. .

  As described above, according to the present invention, it is possible to determine the priority order of the components to be analyzed and the necessity of analysis of the components by quantitatively evaluating the certification failure risk of the chemical substances contained in the components.

  FIG. 1 is a schematic diagram of an analytical test support apparatus 100 according to the first embodiment of the present invention.

  As shown in the figure, the analytical test support apparatus 100 includes a storage unit 110, a control unit 120, an input unit 130, and an output unit 140.

  The storage unit 110 includes a part attribute information storage area 111, a manufacturing information storage area 112, an analysis know-how information storage area 113, a cause risk information storage area 114, a management value excess information storage area 115, and an evaluation result storage area 116. And comprising.

  In the component attribute information storage area 111, information for specifying the attribute of a component extracted as a sample is stored.

  For example, in the present embodiment, a component attribute table 111a as shown in FIG. 2 (schematic diagram of the component attribute table 111a) is stored.

  The component attribute table 111a includes a component management information field 111b, a component configuration information field 111c, and an abnormal field 111d.

  The component management information field 111b stores information for managing the distribution route of the component. For example, the part management information field 111b includes a part ID field 111e, a part classification field 111f, a supplier field 111g, a manufacturer field 111h, a lot field 111i, a manufacturing place field 111j, and a registration date field 111k. .

  The component configuration information field 111c stores information for specifying a chemical substance constituting the component. For example, the component configuration information field 111c includes a component material field 111l, a part field 111m, a chemical substance field 111n, and a content rate field 111o.

  The component ID field 111e stores information for identifying a component ID, which is identification information for uniquely identifying each component.

  The component classification field 111f stores information for identifying the component classification for classifying the component identified by the component ID field 111e.

  The supplier field 111g stores information for specifying a supplier name, which is identification information of a supplier (supplier) who has delivered the part specified in the part ID field 111e.

  The manufacturer field 111h stores information for specifying a manufacturer name, which is identification information of a manufacturer (manufacturer) that manufactured the part specified in the part ID field 111e.

  The lot field 111i stores information for specifying a lot number, which is identification information for identifying the lot of the part specified in the part ID field 111e.

  The manufacturing location field 111j stores information for specifying the manufacturing location of the component specified by the component ID field 111e and the lot field 111i.

  The registration date field 111k stores information specifying the date when the part management information of the part specified in the part ID field 111e and the lot field 111i is registered in the part management information field 111b.

  In the constituent material field 111l, information for specifying the constituent material constituting the part field 111m described later is stored.

  It should be noted that the constituent material can also be handled as “Homogeneous Material”. Homogeneous material is defined, for example, as “material that cannot be mechanically separated separately” in the RoHS directive. In accordance with this RoHS directive, it is standard for an assembly product manufacturer or the like to request registration of “homogeneous materials” in the child hierarchy of products and the parent hierarchy of chemical substances.

  The part field 111m stores information for specifying a part which is a part constituting the part specified by the part ID field 111e and the lot field 111i. Here, it is assumed that at least one part is included in one part, and a plurality of parts may be included in one part.

  The chemical substance field 111n stores information for specifying a chemical substance included in the part specified by the part field 111m. Here, at least one or more chemical substances are included in one part, and a plurality of chemical substances may be included in one part.

  The chemical substance field 111n stores actual measurement information analyzed using, for example, a fluorescent X-ray analyzer or an inductively coupled plasma analyzer.

  In the content rate field 111o, information for specifying a ratio in which the chemical substance specified in the chemical substance field 111n is contained in the part specified in the part field 111m is stored. Here, in this embodiment, “ppm (parts per million)” is used as the unit of this ratio, but it is not limited to such a mode.

  The abnormal field 111d stores information for specifying that there has been an abnormal change in the content rate specified in the content rate field 111o of the chemical substance specified in the chemical substance field 111n. Here, in the present embodiment, when the character string “NG” is stored in the abnormal field 111d, it indicates that there is an abnormal change in the content rate of the chemical substance.

  In the component attribute table 111a, the information stored in the component management information field 111b is basically information supplied from a supplier or a procurement created by an assembly manufacturer who is a user of the analysis / inspection support apparatus 100. Information. On the other hand, the information stored in the part configuration information field 111c is obtained by using a fluorescent X-ray analysis apparatus, an inductively coupled plasma analysis apparatus, or the like in a quality assurance department of the assembly maker or the supplier who supplies the parts to the assembly maker. This is part measurement data. The information stored in the abnormal field 111d is determined by the risk quantification unit 121 described later.

  Therefore, in this embodiment, the information stored in the part management information field 111b, the part configuration information field 111c, and the abnormality field 111d is managed by one table, but the information stored in each field is It is also possible to manage with another table. In such a case, each table may be linked (linked) with a part ID, a lot number, a constituent material, a chemical substance, and the like.

  Returning to FIG. 1, the manufacturing information storage area 112 stores information for specifying delivered parts and lots.

  For example, in the present embodiment, a manufacturing table 112a as shown in FIG. 3 (schematic diagram of the manufacturing table 112a) is stored in the manufacturing information storage area 112.

  The manufacturing table 112a has a delivery ID field 112b, a part ID field 112c, a lot field 112d, a supplier field 112e, a manufacturer field 112f, and a delivery date field 112g.

  The delivery ID field 112b stores a delivery ID, which is identification information for uniquely identifying each delivery when delivering a part.

  The part ID field 112c stores information for identifying a part ID, which is identification information for uniquely identifying each delivered part in the delivery specified in the delivery ID field 112b.

  The lot field 112d stores information for specifying a lot number, which is identification information for identifying the lot of the part specified in the part ID field 112c.

  The supplier field 112e stores information for specifying a supplier name, which is identification information for identifying a supplier (supplier) who has delivered the parts specified in the part ID field 112c and the lot field 112d.

  The manufacturer field 112f stores information for specifying a manufacturer name, which is identification information for identifying a manufacturer (manufacturer) that manufactured the part specified by the part ID field 112c and the lot field 112d.

  The delivery date field 112g stores information for specifying the date of delivery specified in the delivery ID field 112b.

  Returning to FIG. 1, the analysis know-how information storage area 113 stores information that serves as a reference when analyzing the parts constituting the part.

  For example, in the present embodiment, an analysis know-how table 113a as shown in FIG. 4 (schematic diagram of the analysis know-how table 113a) is stored in the analysis know-how information storage area 113.

  The analysis know-how table 113a includes a part classification field 113b, a part field 113c, a remarks field 113d, and a photo field 113e.

  The part classification field 113b stores information for identifying the part classification indicating the classification of the part to be analyzed.

  The part field 113c stores information for specifying the part of the part to be analyzed.

  In the remarks field 113d, information for identifying information to be used as a reference when analyzing a part specified in the part field 113c in a part belonging to the classification specified in the part classification field 113b is stored.

  The photo field 113e stores information for identifying a photograph to be used as a reference when analyzing a part specified by the part field 113c in a part belonging to the classification specified by the part classification field 113b.

  Returning to FIG. 1, the cause risk information storage area 114 includes information for specifying the probability of failing the certification regarding the content of chemical substances for each combination of part classification, supplier name, manufacturer name, manufacturing place, and constituent material. Stored.

  For example, in the present embodiment, the certification failure table 114a as shown in FIG. 5 (schematic diagram of the certification failure table 114a) is stored in the cause risk information storage area 114.

  The certification failure table 114a includes a parts classification field 114b, a supplier field 114c, a manufacturer field 114d, a manufacturing place field 114e, a component material field 114f, and an abnormality cause risk field 114g.

  The part classification field 114b stores information for identifying a part classification for classifying the part.

  The supplier field 114c stores information for identifying a supplier name, which is identification information of a supplier (supplier) that delivers the parts.

  In the manufacturer field 114d, information for specifying the manufacturer name, which is identification information of a manufacturer (manufacturer) that manufactured the part, is stored.

  Information for specifying the manufacturing location of the part is stored in the manufacturing location field 114e.

  The component material field 114f stores information for specifying the component component material.

  The anomaly-caused risk field 114g includes a component classification, a supplier name, a manufacturer name, a manufacturing location, and a constituent material, which are respectively specified by a parts classification field 114b, a supplier field 114c, a manufacturer field 114d, a manufacturing location field 114e, and a constituent material field 114f In this combination, information for identifying an abnormality-causing risk that is a probability of failing the certification standard regarding the chemical substance content rate due to an abnormality in the chemical substance content is stored.

  Here, the abnormality-caused risk is determined in advance from past statistics.

  Returning to FIG. 1, the management value excess information storage area 115 stores information for specifying the probability that a chemical substance contained in a part extracted as a sample exceeds a predetermined regulation value (management value).

  For example, in the present embodiment, a management value excess table 115a as shown in FIG. 6 (schematic diagram of the management value excess table 115a) is stored.

  The management value excess table 115a includes a component management information field 115b, a component configuration information field 115c, and a management value excess probability field 115d.

  The component management information field 115b includes a component ID field 115e, a component classification field 115f, a supplier field 115g, a manufacturer field 115h, and a manufacturing location field 115i. Information stored in the component ID field 111e, the component classification field 111f, the supplier field 111g, the manufacturer field 111h, and the manufacturing location field 111j in the component management information field 111b of the component attribute table 111a shown in FIG.

  Further, the part configuration information field 115c includes a constituent material field 115j, a part field 115k, and a chemical substance field 115l. Each of these fields excluding the chemical substance field 115l includes a part attribute table shown in FIG. Information stored in the component material field 111l and the part field 111m in the component configuration information field 111c of 111a is stored.

  In the chemical substance field 115l, information for specifying a chemical substance for which the management value excess probability stored in the management value excess probability field 115d described later is calculated is stored for each part ID.

  In the management value excess probability field 115d, the risk quantification unit 121, which will be described later, calculates as information for specifying the probability that a chemical substance contained in a part extracted as a sample exceeds a predetermined regulation value (management value). The management value exceeded probability is stored.

  Returning to FIG. 1, the evaluation result storage area 116 stores information for specifying an evaluation value evaluated by the control unit 120 described later.

  For example, in the present embodiment, an evaluation result table 116a as shown in FIG. 7 (schematic diagram of the evaluation result table 116a) is stored in the evaluation result storage area 116.

  The evaluation result table 116a includes a part ID field 116b, a component material field 116c, a certification failure risk field 116d, a risk field 116e per unit time, and a recommended number of inspections field 116f.

  The component ID field 116b stores a component ID that is identification information for identifying the evaluated component.

  The component material field 116c stores information for identifying the component material that has been evaluated.

  In the authorization failure risk field 116d, information for identifying the authorization failure risk calculated by the risk quantification unit 121 described later for the component material specified in the component material field 116c of the component specified in the component ID field 116b. Is stored.

  The risk per unit time field 116e specifies the risk per unit time calculated by the risk quantification unit 121 described later for the component material specified in the component material field 116c of the component specified in the component ID field 116b. Information is stored.

  In the recommended number-of-inspections field 116f, information for specifying the recommended number of inspections calculated by the analysis priority determining unit 122, which will be described later, for the component material specified in the component material field 116c of the component specified in the component ID field 116b. Stored.

  Returning to FIG. 1, the control unit 120 includes a risk quantification unit 121, an analysis priority order determination unit 122, and an analysis necessity determination unit 123.

  The risk quantification unit 121 performs, for each constituent material of each part, a process of calculating a certification failure risk that fails the certification criteria for the chemical substance content with respect to the chemical substance content. This process will be described in detail below.

  The analysis priority order determination unit 122 calculates the order of constituent materials to be preferentially analyzed from the certification failure risk calculated by the risk quantification unit 121 and the amount of parts delivered.

  Further, the analysis priority order determination unit 122 calculates the recommended number of times to perform analysis in a predetermined unit period from the order of the constituent materials to be preferentially analyzed.

  The analysis necessity determination unit 123 performs a process of determining whether analysis is necessary by comparing the past number of analyzes with the recommended number calculated by the analysis priority order determination unit 122.

  The input unit 130 receives input of information.

  The output unit 140 outputs information.

  The analytical test support apparatus 100 described above includes, for example, a CPU (Central Processing Unit) 501, a memory 502, and an external storage such as an HDD (Hard Disk Drive) as shown in FIG. 8 (schematic diagram of the computer 500). A device 503, a reading device 505 for reading information from a portable storage medium 504 such as a CD-ROM (Compact Disk Read Only Memory) and a DVD-ROM (Digital Versatile Disk Read Only Memory), and an input such as a keyboard and a mouse This can be realized by a general computer 500 that includes a device 506, an output device 507 such as a display, and a communication device 508 such as a NIC (Network Interface Card) for connecting to a communication network.

  For example, the storage unit 110 can be realized by the CPU 501 using the memory 502 or the external storage device 503, and the control unit 120 loads a predetermined program stored in the external storage device 503 into the memory 502. The input unit 130 can be realized by using the input device 506 by the CPU 501, and the output unit 140 can be realized by using the output device 507 by the CPU 501. .

  The predetermined program is downloaded from the storage medium 504 via the reading device 505 or from the network via the communication device 508 to the external storage device 503, and then loaded onto the memory 502 and executed by the CPU 501. You may do it. Alternatively, the program may be directly loaded on the memory 502 from the storage medium 504 via the reading device 505 or from the network via the communication device 508 and executed by the CPU 501.

  FIG. 9 is a flowchart showing the processing in the analysis / inspection support apparatus 100.

  First, the risk quantification unit 121 of the analysis inspection support apparatus 100 performs a risk quantification process for calculating a certification failure risk for each component material stored in the part attribute table 111a (S10). This process will be described in detail with reference to FIG.

  Next, the analysis priority order determination unit 122 aggregates the certification failure risk from the amount of parts delivered per unit time so that the analysis is performed in order from the constituent material having the highest certification failure risk. In addition, the order of the constituent materials is determined, and an analysis prioritizing process for calculating the recommended number of inspections for each part is performed (S11). This process will be described in detail with reference to FIG.

  Next, the analysis necessity determination unit 123 determines whether the analysis is necessary by comparing the number of analyzes of the identified part with the recommended number of inspections of the part calculated in step S11. A determination process is performed (S12). This process will be described in detail with reference to FIG.

  FIG. 10 is a flowchart showing the certification failure risk calculation process in FIG.

  First, the risk quantifying unit 121 extracts one part ID that has not been processed in step S23 from the part ID field 111e of the part attribute table 111a, and records corresponding to the part ID extracted in the part attribute table 111a. Is identified (S20).

  Next, the risk quantifying unit 121 extracts one constituent material included in the part identified by the part ID extracted in step S20, which has not been subjected to the processing in step S23, from the constituent material field 111l. Then, a record storing the same constituent material as the constituent material extracted in the part attribute table 111a is specified (S21).

  Next, the risk quantifying unit 121 extracts one chemical substance contained in the constituent material extracted in step S20, which has not been subjected to the process in step S23, from the chemical substance field 111n, and the component attribute table. A record in which the same chemical substance as the chemical substance extracted in 111a is stored is identified (S22).

  Next, the risk quantifying unit 121 specifies the content rate of the chemical substance in the record specified in step S22 from the content rate field 111o, and specifies an abnormal change in the content rate (S23).

  Regarding the abnormal change of the content rate, two forms of “cliff” and “peak” of the content rate are assumed here, and the risk quantifying unit 121 extracts “cliff” and “peak” of the content rate.

  First, the “cliff” extraction method by the risk quantification unit 121 will be described.

  FIG. 11 is a time series graph of the chemical substance content rate in the record specified in step S22, using the date stored in the registration date field 111k.

  As shown in FIG. 11, “cliffs” are observed at the “change” portion across the average content rate. Such a behavior can be detected by, for example, a method of determining the presence or absence of series correlation using “Durbin-Watson ratio”.

  Specifically, the risk quantification unit 121 assigns a number L (L = 1,...) That is a natural number sequentially from 1 to each lot stored in the lot field 111i of the record specified in step S22. , N (N is the total number of lots stored in the record specified in step S22)).

The risk quantifying section 121, a content C _L of chemicals lot corresponds to the number L of the record specified in step S22, the content of chemicals in all lots of the identified record in step S22 The difference between the average value C _mean and the average value C _mean is calculated as c _L (= C _L −C _mean ).

Next, the risk quantification unit 121 determines the difference c _L between the chemical substance content rate C _L-1 of the lot corresponding to the number L-1 immediately before the number L and the average value C _mean of the content rate. _-1 is specified, and the Durbin-Watson ratio DW is calculated by the following equation (1).

  That is, the content ratio of the chemical substance of the lot corresponding to the number L, the content ratio of the chemical substance of the lot corresponding to the preceding number L-1, and the average value of the chemical substance contents of all the lots And squared the difference between the sum of squares of the differences obtained by subtracting and the difference between the chemical content of the lot corresponding to the number L and the average value of the chemical content of all the lots. The ratio of the added value to the added value is the Durbin-Watson ratio DW.

  Then, the risk quantifying unit 121 determines that there is a positive series correlation when the DW calculated in this way is equal to or less than a threshold value corresponding to a predetermined reliability, that is, a time-series change in content rate. It is determined that there is a high possibility that there was an abnormal behavior.

Next, the risk quantification unit 121 corresponds to the number L as shown in FIG. 11 in the content ratio of the chemical substance determined that there is a high possibility that the time series change of the content ratio has an abnormal behavior. content and C _L lot of chemicals, between the content of C _L-1 chemicals lot corresponding to the previous number L-1, so that the average value C _mean the content is present case, i.e., the change between the content of C _L of chemicals lot corresponding to the numbers L, the content of C _L-1 chemicals lot corresponding to the previous number L-1 When there is a change that crosses the average value C _mean of the content rate, such a change in content rate is specified as an abnormal change in content rate.

Then, the risk quantification unit 121 records either one before or after the abnormal change (in this embodiment, the content ratio C _L after the abnormal change) in order to identify the abnormal content change. The “NG” flag is attached to the abnormal field 111d corresponding to

Here, was calculated "cliff" of content using the Durbin-Watson ratio DW, not limited to such embodiments, for example, the content C _L of chemicals lot corresponds to the number L , if the content of C _L-1 chemicals lot corresponding to the previous number L-1, the difference value between greater than a predetermined value, it is determined that "cliff" May be.

  Next, the “peak” extraction method by the risk quantification unit 121 will be described.

  FIG. 12 shows the chemical substance content rate in the record specified in step S22 as a time series graph using the date stored in the registration date field 111k, and a distribution graph (histogram) for each content rate. ).

  As shown in FIG. 12, when a content rate histogram is created from the content rate time-series graph, a lot with a content rate outside a specific confidence interval has an abnormal change as a “peak” in the histogram. it is conceivable that.

  Specifically, the risk quantification unit 121 calculates a probability density function expressed by equation (2). Here, it is assumed that the probability density function of the content rate estimated from the histogram as shown in FIG. 12 follows a beta distribution having an average and a variance obtained from a sample. Here, it is considered that the probability density function of the content rate follows other distributions such as a normal distribution, but since the content rate is always 0 ppm or more and 100 wt% or less, it is reasonable that the probability density function also has a finite range. It is desirable to apply the beta distribution in view of the fact that the risk can be quantified relatively easily by using the beta distribution.

Note that the coefficients α and β of the beta distribution B (α, β) at this time are expressed as in equation (3), where C _{mean is} the average content rate obtained from the sample and C _var is the variance of the content rate. Is done.

  In this way, the risk quantifying unit 121 can determine the upper limit and the lower limit of the content rate distribution by specifying the beta distribution and setting a specific confidence interval (for example, 99%), and outside the confidence interval. A certain content rate can be specified as having an abnormal change.

  Then, the risk quantifying unit 121 attaches an “NG” flag to the abnormal field 111d of the record corresponding to the content rate having the abnormal change specified in this way.

  In addition, although the peak was specified using the probability density function here, it is not limited to such an aspect, for example, a specific content rate is larger than the predetermined value rather than the average value of a content rate. Or when it is small, you may determine with a peak.

  As described above, the risk quantifying unit 121 performs the abnormality specifying process shown in step S23 (S23).

  Next, the risk quantifying unit 121 determines whether or not the chemical substance identified in step S22 is a predetermined substance (S24). If the chemical substance is a predetermined substance (in S24). Yes) Proceed to step S25, and if it is not a predetermined substance (No in S24), proceed to step S26.

  In step S24, for example, are the chemical substances regulated by the RoHS Directive 6 types (lead, mercury, cadmium 3 elements, hexavalent chromium, polybromobiphenyl (PBB), polybromodiphenyl ether (PBDE))? Determine whether or not. In particular, when the information stored in the component configuration information field 111c of the component attribute table 111a is the output result of the fluorescent X-ray analyzer, only the content rate in element units is output, so three elements of lead, mercury, and cadmium are included. It is normal to be subject to evaluation.

  In step S25, the risk quantification unit 121 performs a management value excess probability calculation process.

  Here, the risk quantification part 121 calculates | requires the probability density function of a content rate using (2) Formula and (3) Formula similarly to step S24. From the distribution B (α, β) at this time, a management value excess probability, which is a probability that the content rate exceeds the management value defined by the RoHS command, is obtained.

  For example, when the chemical substance to be processed in S25 is lead, the management value of lead is 1000 ppm according to the RoHS directive, and therefore the frequency of content exceeding 1000 ppm in the distribution B (α, β) is increased. Then, by dividing by all the frequencies of the distribution B (α, β), the probability of exceeding the management value is calculated.

  Then, the risk quantifying unit 121 stores the management value excess probability thus obtained in the step S management value excess probability field 115d of the management value excess table 115a.

  The risk quantifying unit 121 deletes the lot field 111i, the registration date field 111k, the chemical substance field 111n, the content rate field 111o, and the abnormal field 111d from the manufacturing table 111a shown in FIG. In addition, the management value excess table 115 a is generated in advance by adding the management value excess probability field 115 d and stored in the management value excess information storage area 115.

  In step S26, the processes from step S22 to S25 are repeated for all chemical substances contained in the constituent material specified in step S21.

  Next, the risk quantification unit 121 measures the abnormal behavior occurrence frequency (S27).

  For example, the abnormal change in the content rate specified in step S23 includes a behavior (hereinafter referred to as “irrelevant behavior”) that is not related to the change in the content rate of the component itself, such as poor adjustment of the measuring instrument. It seems to be. Therefore, among the records in which the character string “NG” is stored in the abnormal field 111d of the part attribute table 111a, the records having the same date stored in the registration date field 111k are the third of the whole. If there is one or more, a correction is made to delete the character string “NG” stored in the abnormal field 111d of the record of the date. The record to be corrected at this time is not limited to the record of the component ID and component material that is currently evaluated, but is applied to all data stored in the component attribute table 111a.

  Next, the risk quantification unit 121 quantifies the certification failure risk of the constituent material specified in step S21 (S28).

  First, the risk quantification unit 121 calculates a certification failure risk due to an abnormal change in content rate.

  Specifically, the risk quantifying unit 121 identifies a record in which the character string “NG” is stored in the abnormality field 111d from the component attribute information table 111a, and identifies the component classification field 111f and the supplier field 111g of the identified record. The information that matches the information stored in the manufacturer field 111h, the manufacturing location field 111j, and the component material field 111l is caused by an abnormality from the abnormality cause risk field 114g of the record stored in the corresponding field of the certification failure table 114a. Get risk.

And since the certification failure risk (probability) of the constituent material is considered to be the product of the probability that an abnormal change will occur and the probability of reaching certification failure at that time, the risk quantification unit 121 uses the equation (4). Then, a certification failure risk R ′ _{component material} caused by an abnormal change in the content rate for each _{component material} is calculated.

  Here, i is an identification number in which a serial number is assigned sequentially from 1 for each lot stored in the lot field 111i of the record in which the character string “NG” is stored in the abnormality field 111d from the component attribute information table 111a. (That is, the same part ID, the same part classification, the same supplier, and the same lot of the same manufacturer are assigned one identification number).

  N is the total number of lots stored in the lot field 111i of the part attribute information table 111a, and n is the lot of the record in which the character string “NG” is stored in the abnormal field 111d of the part attribute information table 111a. This is the total number of lots stored in the field 111i.

R _i ′ is information that matches the information stored in the parts classification field 111f, supplier field 111g, manufacturer field 111h, manufacturing place field 111j, and component material field 111l of the record corresponding to the lot with the identification number i. The anomaly-caused risk acquired from the anomaly-caused risk field 114g of the record stored in the corresponding field of the causal risk table 114a.

  Next, the risk quantification unit 121 calculates a certification failure risk due to the management value excess probability.

First, the risk quantifying unit 121 identifies the record in which the constituent material identified in step S21 is stored in the constituent material field 115j of the management value excess table 115a, and stores it in the chemical substance field of the identified record. Each chemical substance is assigned an identification number j as a natural number in order from 1, and a management value excess probability R _j ′ corresponding to the identification number j is obtained.

Then, the risk quantification unit 121 calculates a certification failure risk R ″ _{constituent material} due to the probability of exceeding the management value for each constituent material by the equation (5).

  Note that m is the total number of chemical substances stored in the chemical substance field 115l of the management value excess table 115a corresponding to the constituent material specified in step S21.

  And the risk quantification part 121 calculates the certification failure risk for every lot by (6) Formula from the two types of certification failure risk computed as mentioned above.

  Then, the risk quantification unit 121 creates a new record in the evaluation result table 116a, the component ID identified in step S20 in the component ID field 116b, the component material identified in step S21 in the component material field 116c, ( 6) The certification failure risk for each lot calculated by the equation (6) is stored in the certification failure risk field 116d.

  Next, the risk quantification unit 121 repeats the processes from Steps S21 to S28 for all the constituent materials included in the part ID identified in Step S20 (S29).

  Then, the risk quantification unit 121 repeatedly performs the processing from step S20 to S29 for the parts corresponding to all the part IDs stored in the part ID field 111e of the part attribute table 111a (S30).

  FIG. 13 is a flowchart of the analysis prioritizing process in step S11 in FIG.

  First, the analysis priority determination unit 122 receives an input of a component ID list having a plurality of component IDs for specifying components to be analyzed and an analysis condition via the input unit 130 (S40).

  For example, the analysis priority order determination unit 122 outputs an input screen 160 as shown in FIG. 14 (schematic diagram of the input screen 160) to the output unit 140 and receives input of necessary information via the input unit 130.

  The input screen 160 includes a part ID list input area 160a, a reference instruction input area 160b, an analysis capability input area 160c, a minimum inspection frequency unit input area 160d, a minimum inspection frequency input area 160e, and an evaluation start instruction input area 160f. And a cancel instruction input area 160g.

  The component ID list input area 160a accepts input of information specifying a component ID list that specifies component IDs for ranking analysis.

  In this embodiment, a component ID list file in which a list of component IDs is registered in a predetermined format is stored in the storage unit 110 in advance, and an execution instruction specifying the reference instruction input area 160b via the input unit 130 is performed. By specifying the component ID list file stored in the storage unit 110, a list of component IDs is input.

  It is also possible to directly input information for specifying a component ID in the component ID list input area 160a.

  The analysis capability input area 160c receives input of information for specifying the analysis capability of a device that performs analysis, for example, an X-ray fluorescence analyzer or an inductively coupled plasma analyzer. Here, in the present embodiment, input of information specifying an analysis possible portion (number of times) per unit time (here, one day) of the analyzer is accepted.

  The minimum inspection frequency unit input area 160d accepts input of information specifying the unit of the number of inspections input to the later-described minimum inspection frequency input area 160e for the analysis target portion (component material).

  Here, in this embodiment, the period (here, one month) or the number of lots (here, 10 lots) can be selected.

  The minimum inspection frequency input area 160e stores information for specifying the minimum number of times that inspection must be performed in the unit specified in the minimum inspection frequency unit input area 160d.

  When an input of necessary information is received in the input area described above and an execution instruction specifying the evaluation start instruction input area 160f is input via the input unit 130, the analysis priority order determination unit 122 will be described later. The process of step S41 is performed.

  On the other hand, when an execution instruction specifying the cancel instruction input area 160g is input via the input unit 130, the information input to the input area described above is canceled (deleted).

  In step S41, the analysis priority order determination unit 122 extracts one component ID that has not been processed in step S41 and thereafter from the component ID list input in step S40.

  Next, the analysis priority order determination unit 122 identifies the record in which the component ID extracted in step S41 is stored in the component ID field 116b from the evaluation result table 116, and enters the certification failure risk field 116d of the identified record. The stored certification failure risk is acquired (S42).

  Next, the analysis priority determination part 122 calculates the certification failure risk per unit period (S43).

  Specifically, the analysis priority determining unit 122 identifies the record in which the component ID extracted in step S41 is stored in the component ID field 116b from the manufacturing table 112a, and refers to the delivery date field 112g of the identified record. By doing this, the number of lots (number of records) in a predetermined unit period (for example, the period from the processing date in which step S43 is processed in the analysis priority determining unit 122 to one month ago) is determined for each constituent material. The certification failure risk for each component material in the unit period is calculated by multiplying the calculated lot number for each component material by the certification failure risk acquired in step S42.

  Note that the analysis priority order determination unit 122 corresponds to the certification failure risk for each constituent material in the unit period calculated as described above to the part ID extracted in step S41 of the evaluation result table 116a and the calculated constituent material. Stored in the risk field 116e per unit time of the record.

  Then, the analysis priority order determination unit 122 repeats the processing of steps S41 to S43 for all the component IDs included in the component ID list received in step S40 (S44).

  Next, the analysis priority order determination unit 122 identifies the record corresponding to the component ID included in the component ID list received in step S40 from the evaluation result table 116a, and the risk per unit time of the identified record. The certification failure risk in the unit period stored in the field 116e is extracted, and the combination of the component ID and the constituent material is ranked so that the risk is ranked in descending order (from the highest value) ( S45).

  Next, the analysis priority order determination unit 122 assigns the analysis frequency so that the certification failure risk for all parts is minimized (S46).

  Specifically, the analysis priority determining unit 122 calculates the analysis capability (analysable location) in the unit period for which the certification failure risk is calculated in step 43 from the value specified in the analysis capability input area 160c (here, , Input value x number of days in unit period).

  Then, the analysis priority order determination unit 122 sequentially satisfies the minimum inspection frequency per place received as an analysis condition in step S40 in descending order of the combination of the component ID and the constituent material in step S45. Allocate analytical capabilities in unit periods.

  Specifically, when the analysis capability in the unit period is “600”, first, the analysis capability (number of times) satisfying the minimum inspection frequency is assigned to the combination of the part ID and the constituent material that is ranked highest in step S45. . For example, when “one month” is input in the minimum inspection frequency unit input area 160d and “20” is input in the minimum inspection frequency input area 160e, the unit period in step S43 is also “one month”. Since all of the minimum inspection frequency “20” can be allocated from the analysis capability “600”, all of these are allocated, and the remaining analysis capability is “600−20 = 580”.

  Then, the remaining analysis ability is applied to the combination of the part ID and the constituent material of the next order, and the process of performing the allocation is repeated until the analysis ability is exhausted or the combination of the part ID and the constituent material to be assigned is eliminated. Do.

  When the period input in the minimum inspection frequency unit input area 160d is different from the unit period in step S43, the unit period in step S43 is divided by the period input in the minimum inspection frequency unit input area 160d. A value obtained by multiplying the value input in the minimum inspection frequency input area 160e is assigned to the combination of the component ID and the constituent material as the minimum inspection frequency.

  Further, when the number of lots is input in the minimum inspection frequency unit input area 160d, a value obtained by multiplying the number of lots for each component specified in step S43 by the number of lots specified in the minimum inspection frequency unit input area 160d is obtained. A value obtained by multiplying the value input in the minimum inspection frequency input area 160e is assigned to the combination of the component ID and the constituent material as the minimum inspection frequency.

  As described above, even if the analysis ability is assigned to all combinations of the part ID and the constituent material, if there is still a remaining part, the step is performed in descending order of the combination of the part ID and the constituent material in step S45. The process of allocating a value obtained by subtracting the minimum inspection frequency allocated to the combination of the part ID and the constituent material from the number of lots specified for each part in S43, from the analysis ability, until the analysis ability in the unit period disappears, or Repeat until there are no more combinations of component IDs and constituent materials to be allocated.

  Then, as described above, the analysis priority order determination unit 122 stores the analysis capability allocated to the combination of the part ID and the constituent material in the corresponding recommended inspection number field 116f of the evaluation result table 116a.

  Next, the analysis priority order determination unit 122 sets the priority given in step S45 and the analysis capability assigned to the combination of the part ID and the constituent material performed in step S46 to a predetermined output screen 161, and outputs the output unit The process of outputting to 140 is performed (S47).

  FIG. 15 is a schematic diagram of the output screen 161.

  As shown in the figure, the output screen 161 has a ranking field 161a, a component ID field 161b, a constituent material field 161c, a risk field 161d, and a recommended number of inspections field 161e.

  The rank field 161a stores information for specifying the analysis priority calculated in step S45. Here, in this embodiment, it arrange | positions so that it may be located in the upper direction of the output screen 161 in an order from a high analysis priority, However, It is not necessarily limited to such an aspect.

  The component ID field 161b stores information for identifying a component ID corresponding to the rank specified in the rank field 161a.

  The component material field 161c stores information for identifying the component material corresponding to the rank specified in the rank field 161a.

  The risk field 161d stores information stored in the risk field 116e per unit period of the evaluation result table 116a corresponding to the component ID specified in the component ID field 161b and the component material specified in the component material field 161c. Is done.

  In the recommended inspection frequency field 161e, information stored in the recommended inspection frequency field 116f of the evaluation result table 116a corresponding to the component ID specified in the component ID field 161b and the component material specified in the component material field 161c is stored. Stored.

  FIG. 16 is a flowchart showing the analysis necessity determination process in step S12 of FIG.

  First, the analysis necessity determination unit 123 receives an input of a component ID via the input unit 130 (S50).

  Next, the analysis necessity determination unit 123 acquires the recommended number of inspections for the component ID received in step S50 from the recommended number of inspections field 116f of the evaluation result table 116a (S51). In addition, when there are a plurality of constituent materials in the component ID, the recommended number of inspections is acquired.

  Next, the analysis necessity determination unit 123 determines the lot number (number of records) in the unit period (the past one month in this embodiment) of the component ID received in step S50 as the lot in the component attribute table 111a. It is calculated from the field 111i (S52).

  That is, since the record stored in the component attribute table 111a has been analyzed by, for example, a fluorescent X-ray analyzer or an inductively coupled plasma analyzer, the processing in step S52 is performed by the component ID in the unit period. This indicates the number of analysis (inspection) of the specified part.

  Then, the analysis necessity determination unit 123 determines whether it is necessary to analyze the component specified by the component ID input in step S50 (S53).

  Specifically, the analysis necessity determination unit 123 compares the recommended number of inspections acquired in step S51 with the number of analysis lots calculated in step S52, and the recommended number of inspections is larger than the number of analysis lots. If it is determined that inspection is necessary, the process proceeds to step S54. On the other hand, if the recommended number of inspections is equal to or smaller than the number of analysis lots, it is determined that inspection is not necessary, and the process proceeds to step S55. When the recommended number of inspections for each of the plurality of constituent materials is acquired in step S51, if there is at least one recommended number of inspections compared to the number of analysis lots, it is determined that inspection is necessary. Proceed to S54.

  In step S54, the analysis necessity determination unit 123 acquires the component classification corresponding to the component ID acquired in step S50 from the component classification field 111f of the component attribute table 111a, and corresponds to the component classification acquired from the analysis know-how table 113a. The record to be identified is specified, and the part, remark, and photograph stored in the part field 113c, the remarks field 113d, and the photograph field 113e of the specified record are acquired (S54).

  Next, the analysis necessity determination unit 123 generates an output screen 162 and outputs it to the output unit 140 (S55).

  FIG. 17 is a schematic diagram of the output screen 162.

  As illustrated, the output screen 162 includes a part ID display area 162a, an analysis recommended site presence / absence display area 162b, an analysis-required constituent material display area 162c, an analysis-unnecessary constituent material display area 162d, and an analysis know-how information display area 162e. And comprising.

  In the component ID display area 162a, information for identifying the component ID received in step S50 is displayed.

  In the analysis recommended part presence / absence display area 162b, if it is determined in step S53 that the analysis is necessary, a message indicating that there is an analysis recommended part is displayed, for example, a character string “with analysis recommended part” is displayed. If it is determined in S53 that analysis is not necessary, a message indicating that there is no analysis recommended site, for example, a character string “no analysis recommended site” is displayed.

  In the analysis-required constituent material display area 162c, information for specifying the constituent material determined to be analyzed in step S53 is displayed. If there is no constituent material determined to be analyzed in step S53, this display area is not displayed.

  In the analysis unnecessary constituent material display area 162d, information for specifying the constituent material determined to be unnecessary in step S53 is displayed. Note that this display area is not displayed when there is no constituent material determined to be unnecessary in step S53.

  In the analysis know-how information display area 162e, information specifying the part, remarks, and photograph acquired in step S54 is displayed. If there is no such information, this display area is not displayed.

  In the embodiment described above, the part attribute table 111a, the manufacturing table 112a, and the analysis know-how table 113a have been described on the premise that they are stored in the storage unit 110 of the analysis inspection support apparatus 100. For example, as in the communication system shown in FIG. 18 (schematic diagram of the communication system), a storage apparatus 170 capable of transmitting and receiving information via the network 171 and the analysis test support apparatus 100 is provided. In addition, information stored in the component attribute table 111a, the manufacturing table 112a, and the analysis know-how table 113a is accumulated in the storage device 170 in a timely manner, and is necessary when performing abnormally explained processing in the analysis inspection support device 100. Information is acquired from the storage device 170 and the component attribute table 11a, it may be performed and the processing is stored in the prepared tables 112a and analysis knowhow table 113a.

  In the embodiment described above, the process of calculating the certification failure risk for the combination of the component ID and the constituent material has been described in the flowchart shown in FIG. It is also possible to calculate the certification failure risk for each component specified by the component ID.

In this case, the processing of step S27 and step S28 may be performed after step S29 of FIG. At this time, the total number of lots of the component ID for calculating the certification failure risk is N, the number of abnormal parts determined is n, and each abnormality-caused risk is represented by R _i ′ (i = 1,..., N). Then, the certification failure risk per lot due to the lot determined to be abnormal in the component ID is obtained by the equation (7).

Further, the probability of exceeding the control value in the constituent material k (k = 1,..., L) and the chemical substance j (j = 1,..., L) included in the component ID is R ″ _{k, j} . At this time, the probability of exceeding the management value of the component specified by the component ID is obtained by the equation (8).

  Then, regarding the part specified by this part ID, the certification failure risk of the next lot is obtained by equation (9).

  However, the risk of failing certification is usually significantly different depending on the constituent materials. In addition, since analysis devices used for normal environmental quality assurance operations such as fluorescent X-ray analyzers can perform non-destructive analysis, high-risk component materials and low-risk component materials are identified even for parts identified by the same part ID. It is possible to operate by classifying the former and analyzing only the former. Therefore, the present invention recommends a method of calculating the certification failure risk for each constituent material in order to realize an efficient quality assurance operation.

  Moreover, in embodiment described above, although the risk regarding components was quantified, it is not limited to such an aspect, For example, this invention is also applicable to foodstuffs.

  In this case, instead of the part attribute table 111a, the food having the parts management information field as a food management information field storing food management information and the parts configuration information field as a food composition information field storing food configuration information is used. An attribute table may be used.

  Further, in food products, the information to be inspected may be information other than the concentration of chemical substances such as temperature and humidity. However, there is no difference between the evaluation method and the first embodiment.

  Furthermore, in food products, not only the upper limit value but also the lower limit value may be set as the inspection object information, such as allergen concentration and nutrient concentration, as in the case of heavy metals. Therefore, the risk quantification unit 121 described above mainly calculates the risk exceeding the upper limit value, but here, the risk deviating from a certain section is calculated. However, the evaluation method can be calculated with the same logic as in the above-described embodiment, both in the case of risk calculation exceeding the upper limit value and in the case of risk calculation exceeding the lower limit value.

1 is a schematic diagram of an analysis inspection support apparatus 100. Schematic of the component attribute table 111a. Schematic of the production table 112a. Schematic of the analysis know-how table 113a. Schematic of the certification failure table 114a. Schematic of the management value excess table 115a. Schematic of the evaluation result table 116a. 1 is a schematic diagram of a computer 500. FIG. 5 is a flowchart showing processing in the analysis inspection support apparatus 100. The flowchart which shows a certification failure risk calculation process. Time series graph of chemical content. Schematic which shows the time series graph of the content rate of a chemical substance, and the distribution graph for every content rate. The flowchart of an analysis prioritization process. Schematic of the input screen 160. FIG. Schematic of the output screen 161. FIG. The flowchart which shows an analysis necessity determination process. Schematic of the output screen 162. FIG. 1 is a schematic diagram of a communication system.

Explanation of symbols

DESCRIPTION OF SYMBOLS 100 Analysis test | inspection assistance apparatus 110 Storage part 111 Parts attribute information storage area 112 Manufacturing information storage area 113 Analysis know-how information storage area 114 Cause risk information storage area 115 Management value excess information storage area 116 Evaluation result storage area 120 Control part 121 Risk quantification Unit 122 analysis priority determination unit 123 analysis necessity determination unit

Claims (13)

An analytical test support device comprising a storage unit and a control unit,
In the storage unit,
For each part, the part ID of the part, the part classification of the part, the lot of the part, the constituent material of the part, the chemical substance contained in the constituent material, the content rate of the chemical substance, and the content rate are detected. Component attribute information having information for specifying the date,
For each delivered part, manufacturing information having information identifying the part ID of the part, the lot of the part, and the delivery date of the part;
For each part classification, an abnormality that is the probability of failing the certification criteria for the chemical substance due to an abnormal change in the part classification, the component material belonging to the part classification, and the content of the chemical substance contained in the component material Cause risk information having information for identifying the cause risk is stored,
The controller is
From the component attribute information, for each component, a determination process for acquiring the content rate of the chemical substance contained in the constituent material constituting the component and determining whether there is an abnormal change in the acquired content rate; ,
By the determination process, an abnormal lot totaling process for totalizing the number of abnormal lots, which is the number of lots composed of a constituent material containing a chemical substance having a content rate determined to have an abnormal change, for each part;
By dividing the number of abnormal lots for each part by the total number of lots for each part, an abnormality occurrence probability calculation process for calculating an abnormality occurrence probability that is an occurrence probability of an abnormality,
According to the determination process, the abnormality cause risk of the constituent material including the chemical substance having the content rate determined to have an abnormal change is acquired from the cause risk information, and the average value in the abnormal lot from the acquired abnormality cause risk Anomaly-caused risk average value calculating process for calculating anomaly-caused risk average value,
A first certification failure risk calculation process for calculating a first certification failure probability for each lot by multiplying the abnormality occurrence probability and the abnormality-caused risk average value,
From the part attribute information, for each part, a content rate specifying process for specifying the content rate of a chemical substance contained in the constituent material constituting the part,
By calculating the frequency of the content rate specified in the content rate specifying process with a predetermined probability density function, a management value excess probability calculation is performed to calculate a management value excess probability that is a probability of exceeding the predetermined content rate. Processing,
A second certification failure risk that calculates the second certification failure probability for each lot by aggregating the probability of exceeding the control value calculated from the content of chemical substances contained in the constituent materials contained in the lot. Calculation process,
Performing a certification failure risk calculation process for calculating a certification failure probability for each lot by counting the first certification failure probability and the second certification failure probability;
Analytical inspection support device characterized by The analysis inspection support device according to claim 1,
The controller is
In the determination process, from the part attribute information, for each part, the content rate of the chemical substance contained in the constituent material constituting the part and the date on which the content rate is detected are acquired, and the acquired Among the content rates, when the degree of difference between one content rate and the other content rate of the previous date in the time series according to the date of the one content rate exceeds a predetermined level, Performing the process of determining that the one content rate or the other content rate is a content rate having an abnormal change;
Analytical inspection support device characterized by The analysis test support device according to claim 1 or 2,
The controller is
In the determination process, for each part, the content rate of the chemical substance contained in the constituent material constituting the part is acquired from the part attribute information, and the average value of the acquired content rates is calculated. When the degree of difference between the content ratio and the average value exceeds a predetermined degree, a process of determining that the one content ratio is a content ratio having an abnormal change,
Analytical inspection support device characterized by The analysis inspection support device according to any one of claims 1 to 3,
The controller is
A parts list input receiving process for receiving an input of a parts list for identifying a plurality of parts via the input unit;
Lot number calculation processing for calculating the number of lots in a specific period for each constituent material included in each part specified in the part list that has received an input from the part attribute information;
A third certification failure risk calculation process for calculating the certification failure probability for each part and component material in the specific period by multiplying the certification failure probability by the number of lots,
Performing a ranking process for ranking the certification failure probability for each part and component material in the specific period so that the certification failure probability is higher in order from the highest certification failure probability;
Analytical inspection support device characterized by The analysis inspection support device according to claim 4,
The controller is
Condition receiving processing for receiving input of the number of analyzable times in a predetermined period and the minimum number of times of analysis per location in the specific period, via the input unit;
By calculating the number of analyzable times in the specific period, an analysis possible number calculating process for calculating the number of analyzable times in the specific period;
In order from the higher order parts and constituent materials, a first analysis number allocation process for allocating the minimum number of analysis times from the number of analyzable times,
In the first analysis frequency allocation process, when the minimum number of analysis times is allocated to all the constituent materials of all the parts, when there is a surplus in the possible number of analyses, the parts and the constituent materials with the highest order are sequentially ordered. Performing a second analysis number allocation process of allocating the number of lots of the constituent material in a specific period from the number of analyzable times,
Analytical inspection support device characterized by The analysis test support device according to claim 5,
The controller is
Component specifying information input receiving processing for receiving input of component specifying information for specifying one component via the input unit;
A specific part lot number calculation process for calculating the number of lots in a specific period for each constituent material included in the part specified by the part specifying information that received the input, from the part attribute information;
The number of lots in a specific period for each constituent material included in the part, and the first analysis number allocation process and the second analysis number allocation process are allocated to each constituent material included in the part. Output processing that generates a display screen indicating that analysis is necessary and outputs to the output unit if the number of lots is greater than the number of analyzes And do,
Analytical inspection support device characterized by Computer
A program that functions as storage means and control means,
In the storage means,
For each part, the part ID of the part, the part classification of the part, the lot of the part, the constituent material of the part, the chemical substance contained in the constituent material, the content rate of the chemical substance, and the content rate are detected. Component attribute information having information for specifying the date,
For each delivered part, manufacturing information having information identifying the part ID of the part, the lot of the part, and the delivery date of the part;
For each part classification, an abnormality that is the probability of failing the certification criteria for the chemical substance due to an abnormal change in the part classification, the component material belonging to the part classification, and the content of the chemical substance contained in the component material Cause risk information having information for identifying the cause risk, and
In the control means,
From the component attribute information, for each component, a determination process for acquiring the content rate of the chemical substance contained in the constituent material constituting the component and determining whether there is an abnormal change in the acquired content rate; ,
By the determination process, an abnormal lot totaling process for totalizing the number of abnormal lots, which is the number of lots composed of a constituent material containing a chemical substance having a content rate determined to have an abnormal change, for each part;
By dividing the number of abnormal lots for each part by the total number of lots for each part, an abnormality occurrence probability calculation process for calculating an abnormality occurrence probability that is an occurrence probability of an abnormality,
According to the determination process, the abnormality cause risk of the constituent material including the chemical substance having the content rate determined to have an abnormal change is acquired from the cause risk information, and the average value in the abnormal lot from the acquired abnormality cause risk Anomaly-caused risk average value calculating process for calculating anomaly-caused risk average value,
A first certification failure risk calculation process for calculating a first certification failure probability for each lot by multiplying the abnormality occurrence probability and the abnormality-caused risk average value,
From the part attribute information, for each part, a content rate specifying process for specifying the content rate of a chemical substance contained in the constituent material constituting the part,
By calculating the frequency of the content rate specified in the content rate specifying process with a predetermined probability density function, a management value excess probability calculation is performed to calculate a management value excess probability that is a probability of exceeding the predetermined content rate. Processing,
A second certification failure risk that calculates the second certification failure probability for each lot by aggregating the probability of exceeding the control value calculated from the content of chemical substances contained in the constituent materials contained in the lot. Calculation process,
Performing a certification failure risk calculation process for calculating a certification failure probability for each lot by counting the first certification failure probability and the second certification failure probability,
A program characterized by The program according to claim 7,
In the control means,
In the determination process, from the part attribute information, for each part, the content rate of the chemical substance contained in the constituent material constituting the part and the date on which the content rate is detected are acquired, and the acquired Among the content rates, when the degree of difference between one content rate and the other content rate of the previous date in the time series according to the date of the one content rate exceeds a predetermined level, Causing the one content rate or the other content rate to be determined to be a content rate having an abnormal change,
A program characterized by The program according to claim 7 or 8,
In the control means,
In the determination process, for each part, the content rate of the chemical substance contained in the constituent material constituting the part is acquired from the part attribute information, and the average value of the acquired content rates is calculated. If the degree of difference between the content rate and the average value exceeds a predetermined level, the content rate of the one is judged to be a content rate having an abnormal change,
A program characterized by A program according to any one of claims 7 to 9,
In the control means,
A parts list input acceptance process for accepting input of a parts list for identifying a plurality of parts via the input means;
Lot number calculation processing for calculating the number of lots in a specific period for each constituent material included in each part specified in the part list that has received an input from the part attribute information;
A third certification failure risk calculation process for calculating the certification failure probability for each part and component material in the specific period by multiplying the certification failure probability by the number of lots,
Performing a ranking process for ranking the certification failure probability for each part and component material in the specific period so that the certification failure probability is higher in order from the highest certification failure probability;
A program characterized by The program according to claim 10,
In the control means,
Condition receiving process for receiving input of the number of analyzable times in a predetermined period and the minimum number of analysis times per place in the specific period, via the input unit;
By calculating the number of analyzable times in the specific period, an analysis possible number calculating process for calculating the number of analyzable times in the specific period;
In order from the higher order parts and constituent materials, a first analysis number allocation process for allocating the minimum number of analysis times from the number of analyzable times,
In the first analysis frequency allocation process, when the minimum number of analysis times is allocated to all the constituent materials of all the parts, when there is a surplus in the possible number of analyses, the components and the constituent materials in order from the highest order Performing a second analysis number allocation process of allocating the number of lots of the constituent material in a specific period from the number of analyzable times,
A program characterized by The program according to claim 11,
The control means includes
Via the input means, component identification information input acceptance processing for accepting input of component identification information for identifying one component;
A specific part lot number calculation process for calculating the number of lots in a specific period for each constituent material included in the part specified by the part specifying information that received the input, from the part attribute information;
The number of lots in a specific period for each constituent material included in the part, and the first analysis number allocation process and the second analysis number allocation process are allocated to each constituent material included in the part. Output processing that generates a display screen that indicates that analysis is necessary and outputs to the output means if the number of lots is greater than the number of analyzes And letting
A program characterized by For each part, the part ID of the part, the part classification of the part, the lot of the part, the constituent material of the part, the chemical substance contained in the constituent material, the content rate of the chemical substance, and the content rate are detected. Component attribute information having information for specifying the date,
For each delivered part, manufacturing information having information identifying the part ID of the part, the lot of the part, and the delivery date of the part;
For each part classification, an abnormality that is the probability of failing the certification criteria for the chemical substance due to an abnormal change in the part classification, the component material belonging to the part classification, and the content of the chemical substance contained in the component material An analysis test support method performed by an analysis test support device comprising a storage unit storing control information and cause risk information having information for identifying the cause risk,
The control unit obtains, for each part, the content rate of the chemical substance contained in the constituent material constituting the part from the part attribute information, and whether there is an abnormal change in the obtained content rate. A determination process for determining;
An abnormal lot in which the controller counts the number of abnormal lots, which is the number of lots composed of a constituent material containing a chemical substance having a content rate determined to have an abnormal change by the determination process, for each part. Aggregation processing,
The control unit divides the number of abnormal lots for each part by the total number of lots for each part to calculate an abnormality occurrence probability calculation process that calculates an abnormality occurrence probability that is an abnormality occurrence probability;
The control unit obtains an abnormality-causing risk of a constituent material containing a chemical substance having a content rate determined to have an abnormal change by the judgment process from the causal risk information, and from the obtained abnormality-causing risk Anomaly-caused risk average value calculation processing for calculating anomaly-caused risk average value that is an average value in an anomaly lot;
The control unit multiplies the abnormality occurrence probability by the abnormality-caused risk average value, thereby calculating a first certification failure risk calculation process for calculating a first certification failure probability for each lot;
From the part attribute information, the control unit, for each part, a content rate specifying process for specifying the content rate of the chemical substance contained in the constituent material constituting the part,
The control unit calculates a management value excess probability, which is a probability of exceeding a predetermined content rate, by calculating a frequency of the content rate specified by the content rate specifying process by a predetermined probability density function. Management value exceeded probability calculation processing,
The control unit calculates a second certification failure probability for each lot by aggregating the management value exceeded probabilities calculated from the content of chemical substances contained in the constituent materials included in the lot. The process of calculating the risk of failing certification
The controller performs an authorization failure risk calculation process for calculating an authorization failure probability for each lot by counting the first authorization failure probability and the second authorization failure probability. ,
Analytical inspection support method characterized by

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