CN115293467B - Method, device, equipment and medium for predicting out-of-date risk of product manufacturing - Google Patents
Method, device, equipment and medium for predicting out-of-date risk of product manufacturing Download PDFInfo
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Abstract
The application discloses a method, a device, equipment and a medium for predicting the overdue risk of product manufacturing, which solve the technical problem of low accuracy of the conventional method for predicting the overdue risk of product manufacturing. The method comprises the following steps: obtaining historical manufacturing data of a product, wherein the product is manufactured based on a plurality of sub-products and a plurality of raw materials, the historical manufacturing data comprises raw material data corresponding to the raw materials, and the raw material data comprises the out-of-date probability, the personnel availability and the equipment availability of each raw material; acquiring the overdue probability of the sub-products according to a logistic regression algorithm; and acquiring the expiration probability of the product according to a logistic regression algorithm, the historical manufacturing data and the expiration probability of each sub-product. According to the method and the device, the finally obtained out-of-date probability can be closer to a scene, and the accuracy of the out-of-date probability is improved.
Description
Technical Field
The application relates to the field of airplane digital manufacturing, in particular to a method, a device, equipment and a medium for predicting out-of-date risk in product manufacturing.
Background
The aircraft, as a typical complex product, has the characteristics of complex structure and many parts, and the main manufacturing process method comprises the following steps: sheet metal forming, structural member machining, combined material shaping, part assembly and general assembly, its manufacturing process needs many specialty processing factory cooperations, and the disturbance factor that influences product quality is many, and mutual influence each other, intercoupling. In the prior art, aiming at the overdue risk prediction of a certain product, whether the overdue risk prediction can be completed on time is often judged only according to the planned schedule of the product, and the problem of inaccurate prediction result exists.
The above is only for the purpose of assisting understanding of the technical solutions of the present application, and does not represent an admission that the above is prior art.
Disclosure of Invention
The application mainly aims to provide a method, a device, equipment and a medium for predicting the overdue risk in product manufacturing, and aims to solve the technical problem that the prediction result of the conventional method for predicting the overdue risk in product manufacturing is inaccurate.
To achieve the above object, the present application provides a method for predicting an overdue risk of manufacturing a product, comprising the steps of:
obtaining historical manufacturing data of a product, wherein the product is manufactured based on a plurality of sub-products and a plurality of raw materials, the historical manufacturing data comprises raw material data corresponding to the raw materials, and the raw material data comprises the out-of-date probability, the personnel availability and the equipment availability of each raw material;
obtaining the overdue probability of the sub-products according to a logistic regression algorithm;
and acquiring the expiration probability of the product according to a logistic regression algorithm, the historical manufacturing data and the expiration probability of each sub-product.
As some optional embodiments of the present application, the historical manufacturing data further includes sub-product data corresponding to a number of the sub-products; the obtaining of the expiration probability of the sub-product according to a logistic regression algorithm includes:
and acquiring the overdue probability corresponding to each sub-product according to the sub-product data and a logistic regression algorithm.
As some optional embodiments of the present application, the step of obtaining an expiration probability corresponding to each of the sub-products according to the historical manufacturing data of the sub-products and a logistic regression algorithm includes:
calculating the overdue probability of the sub-product by the following formula;
in the formula, h (x) i ) Is the probability of expiry, x, of the sub-product i As a p-dimensional column vector
,
In order for the device to be available for use,
in order for the availability of the personnel to be available,
is the probability of expiry of each of the raw materials, w T And b is a coefficient obtained by maximum likelihood estimation.
As some optional embodiments of the present application, the obtaining the expiration probability of the product according to the logistic regression algorithm, the historical manufacturing data, and the expiration probability of each of the sub-products comprises:
calculating the probability of overdue of the product by the following formula;
in the formula, h (y) i ) Is the probability of expiry of said product, y i As a p + n dimensional column vector
，
In order for the device to be available for use,
in order for the availability of the personnel to be available,
is the probability of the expiration of each of the raw materials,
is the probability of expiry, w, of each of said sub-products T And b is a coefficient obtained by maximum likelihood estimation.
As some optional embodiments of the present application, after obtaining the expiration probability of the product according to a logistic regression algorithm, the historical manufacturing data, and the expiration probability of each sub-product, the method further includes:
and when the expiration probability of the product exceeds the preset expiration probability, giving an alarm.
As some alternative embodiments of the present application, the human availability is obtained from the human actual availability time and the human time required by the product manufacturing theory.
As some optional embodiments of the present application, the equipment availability is obtained from the actual equipment availability time and the theoretical equipment time required for the manufacture of the product.
In addition, to achieve the above object, the present application further provides a product manufacturing overdue risk prediction apparatus, comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring historical manufacturing data of a product, the product is manufactured based on a plurality of sub-products and a plurality of raw materials, the historical manufacturing data comprises raw material data corresponding to the raw materials, and the raw material data comprises the overdue probability, the personnel availability and the equipment availability of each raw material;
the second acquisition module is used for acquiring the overdue probability of the sub-product according to a logistic regression algorithm;
and the excess time probability obtaining module is used for obtaining the excess time probability of the product according to a logistic regression algorithm, the historical manufacturing data and the excess time probability of each sub-product.
In order to solve the above technical problem, the present application further provides an electronic device, including: at least one processor, at least one memory, and computer program instructions stored in the memory that, when executed by the processor, implement the method as described above.
In order to solve the above technical problem, the present application further proposes a storage medium having computer program instructions stored thereon, which when executed by a processor implement the above method.
In conclusion, the beneficial effects of the invention are as follows:
according to the method, the device, the equipment and the medium for predicting the overdue risk in the product manufacturing, historical manufacturing data of the product are obtained, wherein the product is manufactured based on a plurality of sub-products and a plurality of raw materials, the historical manufacturing data comprise raw material data corresponding to the raw materials, and the raw material data comprise the overdue probability, the personnel availability and the equipment availability of each raw material; the method comprises the steps that according to a logistic regression algorithm, the overdue probability of the sub-products is obtained, the products are made of a plurality of sub-products and a plurality of raw materials, the manufacture of the sub-products inevitably affects the manufacture of the products, the implementation of logistic regression is simple and highly efficient, the logistic regression can be used in a big data scene, and the overdue probability of the sub-products can be obtained through the logistic regression; and finally, acquiring the overdue probability of the product according to a logistic regression algorithm, the historical manufacturing data and the overdue probability of each sub-product, and considering downstream products, raw materials, equipment and artificial influence of the product, so that the finally obtained overdue probability is closer to a scene, and the accuracy of the overdue probability is improved.
Drawings
Fig. 1 is a schematic flowchart illustrating a method for predicting an overdue risk in manufacturing a product according to an embodiment of the present disclosure.
Fig. 2 is a schematic diagram of an overdue risk prediction apparatus for manufacturing products according to an embodiment of the present application.
Fig. 3 is a schematic diagram of an electronic device according to another embodiment of the present application.
Detailed Description
Features and exemplary embodiments of various aspects of the present invention will be described in detail below, and in order to make objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. It will be apparent to one skilled in the art that the present invention may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present invention by illustrating examples of the present invention.
It should be noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
It should be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad application.
In the prior art, the aviation equipment has extremely high complexity, such as the number and the variety of airplane parts are numerous, the manufacturing process is complex, the development period is long, the cost is high, the risk is large, and the main manufacturing process method comprises the following steps: the sheet metal shaping, structure piece machining, combined material shaping, part assembly and general assembly, its manufacturing process needs many specialty processing factory cooperations, the disturbance factor that influences product quality is many, and mutual influence each other, intercoupling leads to manufacturing cycle unstability, be difficult to the early warning, to the early warning of certain product manufacturing cycle, often only judge whether it can accomplish on time according to the planned scheduling of self, and neglected the influence of its downstream product, raw and other materials and equipment personnel factor, thereby lead to the inaccurate early warning of product manufacturing cycle.
To solve the above problem, as shown in fig. 1, the present application provides a product manufacturing overdue risk prediction method, which includes the following steps:
s1, obtaining historical manufacturing data of a product, wherein the product is manufactured on the basis of a plurality of sub-products and a plurality of raw materials, the historical manufacturing data comprises raw material data corresponding to the raw materials, and the raw material data comprises the overdue probability, the personnel availability and the equipment availability of each raw material;
specifically, historical manufacturing data of a product is firstly obtained, wherein the product can be an airplane, the airplane is a typical complex product and mainly comprises sub-products such as a fuselage, wings, an empennage, an undercarriage, a power device and airborne equipment, the manufacturing cycle of each sub-product necessarily affects the manufacturing cycle of the airplane, for example, if the fuselage is manufactured to exceed the period, the manufacturing cycle of the airplane is necessarily caused, and based on the same rule, if the raw material exceeds the period, the manufacturing of the airplane is also necessarily affected, and equipment and personnel factors also affect the manufacturing cycle of the airplane.
As some alternative embodiments of the present application, the human availability is obtained from the actual human availability time and the human time required by the product manufacturing theory.
Specifically, if the human availability is p1, the actual available time of the human is t1, and the human time required by the product manufacturing theory is t2, the human availability is calculated by the following formula:
p1=t1/t2
the personnel availability ratio is calculated through the actual personnel availability time and the personnel time required by the product manufacturing theory, and in the subsequent calculation, the personnel availability ratio is used as input, so that the personnel availability ratio directly influences the manufacturing of the product, the influence of personnel factors on the product expiration probability is considered, and the accuracy of the product expiration probability is further improved.
As some optional embodiments of the present application, the equipment availability is obtained from the actual equipment availability time and the theoretical equipment time required for the manufacture of the product.
Specifically, if the available rate of the equipment is p2, the actual available time of the equipment is t3, and the equipment time required by the product manufacturing theory is t4, the available rate of the equipment is calculated by the following formula:
P2=t3/t4
the equipment availability ratio is calculated through the actual available time of the equipment and the equipment time needed by the product manufacturing theory, and in the subsequent calculation, the equipment availability ratio is used as input, the equipment availability ratio directly influences the manufacturing of the product, the influence of personnel factors on the product expiration probability is considered, and the accuracy of the product expiration probability is further improved.
S2, acquiring the overdue probability of the sub-products according to a logistic regression algorithm;
specifically, the product is made based on a plurality of raw materials and a plurality of sub-products, the logistic regression algorithm of the expiration probabilities of the sub-products to the product estimates the probabilities by using the relation between a dependent variable and one or more independent variables, the dependent variable is usually a label or content to be predicted, the independent variable is usually a feature, the expiration probabilities of the sub-products are obtained through the logistic regression algorithm, and the expiration probabilities of the sub-products corresponding to the product are taken as input when the expiration probabilities of the product are subsequently calculated, so that the accuracy of the expiration probabilities of the product is improved.
As some optional embodiments of the present application, the historical manufacturing data further includes sub-product data corresponding to a number of the sub-products; the step of obtaining the expiration probability of the sub-product according to a logistic regression algorithm comprises the following steps:
s21, obtaining the corresponding expiration probability of each sub-product according to the historical manufacturing data of the sub-products and a logistic regression algorithm.
In this embodiment, the sub-products are manufactured based on only a plurality of raw materials, and the manufacturing process, the process rules, the raw materials, and the products of the sub-products are different, so the historical manufacturing data further includes sub-product data corresponding to the plurality of sub-products, the sub-product data includes the overrun probabilities of the plurality of raw materials, the corresponding availability ratios of personnel, and the corresponding availability ratios of equipment, and the overrun probability corresponding to each sub-product can be obtained according to the historical manufacturing data of the sub-products and a logistic regression algorithm.
In another embodiment, the sub-product is manufactured based on a plurality of parts and a plurality of raw materials, and based on the same idea as the method, historical manufacturing data of the parts are firstly obtained, the expiration probabilities corresponding to the parts are obtained according to the historical manufacturing data of the parts and a logistic regression algorithm, and finally the expiration probabilities of the sub-product can be obtained according to the historical manufacturing data of the sub-product, the expiration probabilities corresponding to the parts and the logistic regression algorithm; for example, a first product is manufactured based on a first sub-product, a second sub-product and a first raw material, the first sub-product is composed of a first part and a second raw material, the second sub-product is manufactured based on a third raw material, the first part is manufactured based on a fourth raw material, and when the expiration probability of the first product is obtained, the expiration probability of the first part is firstly obtained according to historical manufacturing data of the first part and a logistic regression algorithm; then, according to the expiration probability of the first part and the historical manufacturing data of the first sub-product, the expiration probability of the first sub-product is obtained as the input of a logistic regression algorithm; acquiring the overdue probability of the second sub-product according to the historical manufacturing data of the second sub-product and a logistic regression algorithm; and finally, taking the expiration probabilities of the first sub-product and the second sub-product and the historical manufacturing data of the first product as the input of a logistic regression algorithm, and calculating the expiration probability of the first product.
It can be seen that when an upstream product is manufactured based on a plurality of downstream products with a hierarchical relationship, the overdue probability of the lowest downstream product is firstly obtained according to a logistic regression algorithm and historical manufacturing data, the overdue probability of the downstream product is sequentially calculated through the logistic regression algorithm from bottom to top according to the hierarchical relationship, the overdue probability of the downstream product is finally taken as one of the inputs of the logistic regression algorithm to calculate the overdue probability of the upstream product, the overdue probability of the downstream product is sequentially calculated from bottom to top, the overdue probability of the downstream product is taken as one of the inputs of the logistic regression algorithm, the influence of all downstream products, raw materials and equipment personnel factors of the upstream product is considered, the early warning accuracy of the manufacturing period of the product is remarkably improved, and compared with the traditional method of judging whether the upstream product is manufactured in an overdue mode according to a planned scheduling time, the accuracy is improved from 10% to about 80%.
As some optional embodiments of the present application, the step of obtaining the expiration probability of the product according to a logistic regression algorithm, the historical manufacturing data and the expiration probability of each sub-product comprises:
s221, calculating the overdue probability of the sub-product through the following formula;
in the formula, h (x) i ) Is the probability of expiry, x, of the sub-product i As a p-dimensional column vector
,
In order for the device to be available for use,
in order for the availability of the personnel to be available,
is the probability of expiry of each of the raw materials, w T And b is a coefficient obtained by maximum likelihood estimation.
Specifically, the function is a sigmoid function, according to the characteristics of the sigmoid function in the logistic regression algorithm, the prediction result of the overdue probability is between 0 and 1, and the larger the value is, the larger the probability of the overdue of the product manufacturing is represented; the maximum likelihood estimation is an important method for parameter estimation in mathematical statistics, and the key of the maximum likelihood estimation is to utilize known sample result information to reversely deduce a model parameter value of a maximum probability resulting in occurrence of the sample results, that is, an event occurs, so that the probability of the event is the maximum, that is, firstly, the event is assumed to have a certain probability distribution, but the parameter of the event is unknown, then, the parameter of the probability distribution is estimated based on a training sample, and the maximum likelihood estimation belongs to the prior art, and is not described herein again.
And S3, acquiring the expiration probability of the product according to a logistic regression algorithm, the historical manufacturing data and the expiration probability of each sub-product.
As some optional embodiments of the present application, the step of obtaining the expiration probability of the product according to a logistic regression algorithm, the historical manufacturing data, and the expiration probability of each of the sub-products includes:
s31, calculating the expiration probability of the product through the following formula;
in the formula, h (y) i ) Is the probability of expiry of said product, y i As a p + n dimensional column vector
，
In order for the device to be available for use,
in order for the availability of the personnel to be available,
is the probability of the expiration of each of the raw materials,
is the probability of expiry, w, of each of said sub-products T And b is a coefficient obtained by maximum likelihood estimation.
The formula of this step is substantially the same as that of step S221, except that the input of the formula of this step is a p + n-dimensional column vector,
，
in order for the device to be available for use,
in order for the availability of the personnel to be available,
is the probability of the expiration of each of the raw materials,
is the probability of expiry, w, of each of said sub-products T And b is a coefficient obtained through maximum likelihood estimation, and the established logistic regression model can be closer to the actual situation on site by taking the overdue probability of the sub-product as input, so that the prediction accuracy is higher.
As some optional embodiments of the present application, after the step of obtaining the expiration probability of the product according to the logistic regression algorithm, the historical manufacturing data, and the expiration probability of each sub-product, the method further includes:
and S5, when the expiration probability of the product exceeds the preset expiration probability, giving an alarm.
Specifically, the preset expiration probability may be set by a user according to an actual situation, and is not specifically limited herein, the alarm may be sent in a form of a message, or an alarm is sent in a manner of an audible and visual signal, when the expiration probability of the product exceeds the preset expiration probability, the alarm is sent so that a field person can obtain current manufacturing information of the product in time, in order to enable the field person to quickly confirm a reason that the expiration probability of the product is high, in a specific embodiment, the alarm is sent through an alarm message, and the alarm message includes expiration probabilities of raw materials corresponding to the product, expiration probabilities of sub-products, a staff availability rate, and a device availability rate, so that the field person locates an expiration anomaly, and the operation efficiency of field manufacturing is improved.
To sum up, the product manufacturing overdue risk prediction method provided by the application obtains historical manufacturing data of a product, wherein the product is manufactured based on a plurality of sub-products and a plurality of raw materials, the historical manufacturing data comprises raw material data corresponding to the plurality of raw materials, and the raw material data comprises the overdue probability, the personnel availability and the equipment availability of each raw material; the method comprises the steps that according to a logistic regression algorithm, the overdue probability of the sub-products is obtained, the products are made of a plurality of sub-products and a plurality of raw materials, the manufacture of the sub-products inevitably affects the manufacture of the products, the implementation of logistic regression is simple and highly efficient, the logistic regression can be used in a big data scene, and the overdue probability of the sub-products can be obtained through the logistic regression; and finally, acquiring the overdue probability of the product according to a logistic regression algorithm, the historical manufacturing data and the overdue probability of each sub-product, and considering the downstream products, raw materials, equipment and artificial influence of the product, so that the finally obtained overdue probability is closer to the scene, and the accuracy of the overdue probability is improved.
In addition, to achieve the above object, the present application also provides a product manufacturing overdue risk prediction apparatus, including:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring historical manufacturing data of a product, the product is manufactured based on a plurality of sub-products and a plurality of raw materials, the historical manufacturing data comprises raw material data corresponding to the raw materials, and the raw material data comprises the overdue probability, the personnel availability and the equipment availability of each raw material;
the second acquisition module is used for acquiring the overdue probability of the sub-product according to a logistic regression algorithm;
and the excess time probability obtaining module is used for obtaining the excess time probability of the product according to a logistic regression algorithm, the historical manufacturing data and the excess time probability of each sub-product.
It should be noted that, each module in the device for predicting the overdue risk of product manufacturing of the present embodiment corresponds to each step in the method for predicting the overdue risk of product manufacturing of the previous embodiment one by one, and therefore, the specific implementation manner and the achieved technical effect of the present embodiment can refer to the implementation manner of the method for predicting the overdue risk of product manufacturing, which is not described herein again.
In addition, the method for predicting the overdue risk in manufacturing the product according to the embodiment of the present invention described in conjunction with fig. 1 may be implemented by an electronic device. Fig. 3 shows a hardware structure diagram of an electronic device according to an embodiment of the present invention.
The electronic device may comprise at least one processor 301, at least one memory 302 and computer program instructions stored in the memory 302 as shown, which when executed by the processor 301, implement the method of the above described embodiments.
In particular, the processor 301 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured as one or more Integrated circuits implementing embodiments of the present invention.
The processor 301 reads and executes the computer program instructions stored in the memory 302 to implement any one of the product manufacturing overdue risk prediction methods in the above embodiments.
In one example, the electronic device may also include a communication interface and a bus. As shown in fig. 3, the processor 301, the memory 302, and the communication interface 303 are connected via a bus 310 to complete communication therebetween. The communication interface is mainly used for realizing communication among modules, devices, units and/or equipment in the embodiment of the invention.
A bus comprises hardware, software, or both that couple the components of the electronic device to each other. By way of example, and not limitation, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hypertransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an infiniband interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable bus or a combination of two or more of these. A bus may include one or more buses, where appropriate. Although specific buses have been described and shown in the embodiments of the invention, any suitable buses or interconnects are contemplated by the invention.
In addition, in combination with the product manufacturing overdue risk prediction method in the foregoing embodiment, the embodiment of the present invention may be implemented by providing a computer-readable storage medium. The computer readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any one of the methods of predicting product manufacturing overdue risk in the above embodiments.
It is to be understood that the invention is not limited to the specific arrangements and instrumentality described above and shown in the drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present invention are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps, after comprehending the spirit of the present invention.
The functional blocks shown in the above-described structural block diagrams may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), suitable firmware, plug-in, function card, or the like. When implemented in software, the elements of the invention are the programs or code segments used to perform the required tasks. The program or code segments can be stored in a machine-readable medium or transmitted by a data signal carried in a carrier wave over a transmission medium or a communication link. A "machine-readable medium" may include any medium that can store or transfer information. Examples of a machine-readable medium include an electronic circuit, a semiconductor memory device, a ROM, a flash memory, an Erasable ROM (EROM), a floppy disk, a CD-ROM, an optical disk, a hard disk, an optical fiber medium, a Radio Frequency (RF) link, and so forth. The code segments may be downloaded via computer networks such as the internet, intranet, etc.
It should also be noted that the exemplary embodiments mentioned in this patent describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.
As described above, only the specific embodiments of the present invention are provided, and it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the module and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. It should be understood that the scope of the present invention is not limited thereto, and any equivalent modifications or substitutions can be easily made by those skilled in the art within the technical scope of the present invention.
Claims (7)
1. A method of predicting risk of overdue manufacturing of a product, the method comprising the steps of:
obtaining historical manufacturing data of a product, wherein the product is manufactured based on a plurality of sub-products and a plurality of raw materials, the historical manufacturing data comprises raw material data corresponding to the plurality of raw materials, the raw material data comprises the overdue probability, the personnel availability and the equipment availability of each raw material, and the historical manufacturing data further comprises sub-product data corresponding to the plurality of sub-products;
obtaining the overdue probability of each sub-product according to the historical manufacturing data of the sub-products and a logistic regression algorithm; calculating the expiration probability of the sub-product by the following formula:
in the formula, h (x) i ) Is the probability of expiry, x, of the sub-product i As a p-dimensional column vector
,
In order for the device to be available for use,
in order for the availability of the personnel to be available,
is the probability of expiry of each of the raw materials, w T And b is a coefficient obtained by maximum likelihood estimation;
obtaining the expiration probability of the product according to a logistic regression algorithm, the historical manufacturing data and the expiration probability of each sub-product; calculating the probability of expiration of the product by the following formula:
in the formula, h (y) i ) Is the probability of expiry of said product, y i As a p + n dimensional column vector
，
In order for the device to be available for use,
in order for the availability of the personnel to be available,
is the probability of the expiration of each of the raw materials,
is the probability of expiry, w, of each of said sub-products T And b is a coefficient obtained by maximum likelihood estimation.
2. The method of predicting the risk of overdue in manufacturing a product according to claim 1, wherein after said obtaining the probability of overdue of said product based on a logistic regression algorithm, said historical manufacturing data and the probability of overdue of each of said sub-products, further comprising:
and when the expiration probability of the product exceeds the preset expiration probability, giving an alarm.
3. The method of predicting overdue risk in manufacturing products according to claim 1, wherein said availability of persons is obtained by actual availability time of persons and theoretical required time of persons for manufacturing products.
4. The method of predicting risk of overdue manufacturing of a product according to claim 1, wherein the availability of equipment is obtained from an actual available time of equipment and an equipment time theoretically required for manufacturing the product.
5. An apparatus for predicting risk of overdue manufacturing of a product, the apparatus comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring historical manufacturing data of a product, the product is manufactured based on a plurality of sub-products and a plurality of raw materials, the historical manufacturing data comprises raw material data corresponding to the plurality of raw materials, the raw material data comprises the overdue probability, the personnel availability and the equipment availability of each raw material, and the historical manufacturing data further comprises sub-product data corresponding to the plurality of sub-products;
the second acquisition module is used for acquiring the overdue probability of each sub-product according to the historical manufacturing data of the sub-products and a logistic regression algorithm; calculating the expiration probability of the sub-product by the following formula:
in the formula, h (x) i ) Is the probability of expiry, x, of the sub-product i As a p-dimensional column vector
,
In order for the device to be available for use,
in order for the availability of the personnel to be available,
is the probability of expiry of each of the raw materials, w T And b is a coefficient obtained by maximum likelihood estimation;
the excess period probability obtaining module is used for obtaining the excess period probability of the product according to a logistic regression algorithm, the historical manufacturing data and the excess period probability of each sub-product; calculating the probability of expiration of said product by the following formula:
in the formula, h (y) i ) Is the probability of expiry of said product, y i As a p + n dimensional column vector
，
In order for the device to be available for use,
in order for the availability of the personnel to be available,
is the probability of the expiration of each of the raw materials,
is the probability of expiry, w, of each of said sub-products T And b is a coefficient obtained by maximum likelihood estimation.
6. An electronic device, comprising: at least one processor, at least one memory, and computer program instructions stored in the memory that, when executed by the processor, implement the method of any of claims 1-4.
7. A storage medium having a computer-readable program stored thereon, the computer-readable program implementing the method of any one of claims 1-4 when executed by a processor.
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