CN119338386A - Material early warning method, device, equipment and storage medium - Google Patents
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Abstract
本申请公开了一种物料预警方法、装置、设备及存储介质，涉及数据处理技术领域，包括：获取产品生产时所使用的物料的标识信息，根据标识信息确定物料的批次信息；根据批次信息确定各批次物料对应产品的特性值的数量；在特性值的数量大于预设数量阈值时，根据特性值、特性值的数量确定当前检验统计量；在当前检验统计量大于或等于预设阈值时，进行物料预警；通过上述方式，在利用标识信息确定物料的批次信息后，进一步确定各批次物料对应产品的特性值的数量，然后利用显著性假设验证中的检验统计量与预设阈值的比较结果判断是否存在显著异常，若是，则进行物料预警，从而能够有效提高物料预警的准确性和及时性，以及提高产品生产的合格率。
The present application discloses a material early warning method, device, equipment and storage medium, which relate to the field of data processing technology, including: obtaining identification information of materials used in product production, determining batch information of materials according to the identification information; determining the number of characteristic values of products corresponding to each batch of materials according to the batch information; when the number of characteristic values is greater than a preset quantity threshold, determining the current test statistic according to the characteristic value and the number of characteristic values; when the current test statistic is greater than or equal to the preset threshold, performing a material early warning; through the above-mentioned method, after determining the batch information of the material using the identification information, further determining the number of characteristic values of products corresponding to each batch of materials, and then using the comparison result of the test statistic in the significance hypothesis verification with the preset threshold to judge whether there is a significant abnormality, and if so, performing a material early warning, thereby effectively improving the accuracy and timeliness of material early warning, and improving the qualified rate of product production.
Description
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method, an apparatus, a device, and a storage medium for material early warning.
Background
The assembly line is used for producing materials into products, but in the actual production process, the situation that the performances of the materials are different among batches can occur, and the abnormal performances of the materials can lead to unqualified products, so that timely and accurate early warning of the materials is particularly important, at present, the common mode for early warning of the materials is based on a material management and control system, the materials used by the products with poor performances are grabbed, but due to timeliness, the materials are all put into the assembly line when the materials are grabbed by the material management and control system, on the other hand, after the materials used by the products with poor performances are grabbed, engineers are required to judge the probability of poor performances of the products according to experience, then early warning is carried out according to judging results, but experience judgment is more or less wrong and a lot of time is delayed, and therefore, the early warning of the materials is not accurate and timely.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present application and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The application mainly aims to provide a material early warning method, a device, equipment and a storage medium, and aims to solve the technical problems that the material early warning is not accurate enough and timely in the prior art.
In order to achieve the above purpose, the present application provides a material early warning method, which includes:
Acquiring identification information of materials used in production of products, and determining batch information of the materials according to the identification information;
determining the quantity of characteristic values of products corresponding to each batch of materials according to the batch information;
when the number of the characteristic values is larger than a preset number threshold, determining current test statistics according to the characteristic values and the number of the characteristic values;
and when the current test statistic is greater than or equal to a preset threshold value, carrying out material early warning.
In one embodiment, the step of determining the current test statistic according to the characteristic value and the number of the characteristic values when the number of the characteristic values is greater than a preset number threshold value includes:
when the number of the characteristic values is larger than a preset number threshold, calculating a current characteristic value mean value according to the characteristic values and the number of the characteristic values;
calculating a sample standard deviation according to the current characteristic value mean value and the characteristic value;
and determining current test statistics according to the sample standard deviation and the current characteristic value mean value.
In one embodiment, the step of determining the current test statistic according to the sample standard deviation and the current characteristic value mean comprises:
Acquiring characteristic values of products corresponding to the historical batches of materials;
Calculating a historical characteristic value average value according to the characteristic values of the products corresponding to the historical batches of materials;
And calculating current test statistics according to the historical characteristic value mean value, the current characteristic value mean value and the sample standard deviation.
In an embodiment, the step of performing material early warning when the current test statistic is greater than or equal to a preset threshold value includes:
generating a threshold comparison object according to the current test statistic, the batch information of the materials and the number of characteristic values;
determining significance level data according to a target hypothesis verification principle;
determining a preset threshold according to the significance level data;
When the threshold comparison object is larger than or equal to a preset threshold, generating material early warning information;
and carrying out material early warning according to the material early warning information.
In an embodiment, the step of determining the number of characteristic values of the corresponding products of each batch of materials according to the batch information includes:
Determining whether each batch of materials is put into an assembly line according to the batch information;
If yes, grabbing a target batch data table;
writing characteristic values of products corresponding to the materials in each batch into the target batch data table;
and counting the number of characteristic values in the target batch data table.
In an embodiment, after the step of determining whether the corresponding batch material is put into the batch processing device according to the batch information, the method further includes:
if not, a batch data table is newly built;
designating the newly-built batch data table as the latest batch data table;
writing characteristic values of products corresponding to the materials in each batch into the latest batch data table;
and counting the number of characteristic values in the latest batch data table.
In an embodiment, after the step of performing material early warning when the current test statistic is greater than or equal to a preset threshold, the method further includes:
When receiving a verification request of a target object, performing performance test on the residual materials in the current batch to obtain a current performance test result;
And when the current performance test result does not meet the preset performance requirement, determining the residual materials as abnormal performance materials.
In addition, in order to achieve the above purpose, the present application further provides a material early warning device, which includes:
the acquisition module is used for acquiring identification information of materials used in production of products and determining batch information of the materials according to the identification information;
the determining module is used for determining the quantity of the characteristic values of the products corresponding to the materials in each batch according to the batch information;
the determining module is further configured to determine a current test statistic according to the characteristic value and the number of the characteristic values when the number of the characteristic values is greater than a preset number threshold;
And the early warning module is used for carrying out material early warning when the current test statistic is greater than or equal to a preset threshold value.
In addition, in order to achieve the aim, the application also provides a material early warning device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the computer program is configured to realize the steps of the material early warning method.
In addition, in order to achieve the above object, the present application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the steps of the material early warning method as described above.
The technical scheme provided by the application has at least the following technical effects that the method comprises the steps of determining batch information of materials according to identification information of the materials used during production of the products, determining the quantity of characteristic values of the materials corresponding to the products of each batch according to the batch information, determining current test statistics according to the characteristic values and the quantity of the characteristic values when the quantity of the characteristic values is larger than a preset quantity threshold, carrying out material early warning when the current test statistics are larger than or equal to the preset threshold, further determining the quantity of the characteristic values of the materials corresponding to the products of each batch after the batch information of the materials is determined by utilizing the identification information, and then carrying out material early warning if the comparison result of the test statistics in significance hypothesis verification and the preset threshold is utilized to judge whether the significant abnormality exists or not, so that the accuracy and the timeliness of the material early warning can be effectively improved, the qualification rate of the product production can be improved, and the product cost can be reduced.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a schematic flow chart of a material early warning method according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a second embodiment of a material early warning method according to the present application;
Fig. 3 is a schematic flow chart of a material early warning method according to a second embodiment of the present application;
FIG. 4 is a schematic diagram of a module structure of a material early warning device according to an embodiment of the present application;
Fig. 5 is a schematic diagram of a device structure of a hardware operating environment related to a material early warning method in an embodiment of the present application.
The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be noted that, the execution body of the embodiment may be a computing service device with functions of data processing, network communication and program running, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device, a material early warning device, etc. capable of implementing the above functions. The present embodiment and the following embodiments will be described below using a material early warning device as an example.
Based on this, the embodiment of the application provides a material early warning method, referring to fig. 1, fig. 1 is a schematic flow chart of a first embodiment of the material early warning method of the application.
In this embodiment, the material early warning method includes steps S10 to S40:
step S10, acquiring identification information of materials used in production of products, and determining batch information of the materials according to the identification information.
It should be noted that the identification information refers to information of materials used when the assembly line performs product production, the identification information may be a Serial Number (SN), the product may be a 1Pcs product, in addition, the materials may be a single material, or may be multiple materials, processing components, etc., the batch information refers to batch information of materials when the assembly line performs product production, batches of materials used for producing different products are different, after the identification information is determined, the batch information of the materials needs to be grasped by using a coding rule of the materials, and a representation mode of the batch information may be yyyymmdd.
Step S20, determining the quantity of characteristic values of products corresponding to each batch of materials according to the batch information.
It is understood that the number refers to the total number of the characteristic values of the products corresponding to each batch of materials, the characteristic values of the products may be represented by y i, and after the batch information is determined, the number of the characteristic values of the products corresponding to each batch of materials is counted.
Further, step S20 includes determining whether each batch of materials is put into an assembly line according to the batch information, grabbing a target batch data table if yes, writing characteristic values of products corresponding to each batch of materials into the target batch data table, and counting the number of the characteristic values in the target batch data table.
It should be understood that the target batch data table refers to a data table for recording characteristic values of products, the target batch data table may be denoted as sheet1, after determining batch information of materials, whether corresponding batch materials are put into an assembly line, that is, whether the corresponding batch materials are produced as products is determined according to the batch information, if yes, the target batch data table is grabbed at this time, characteristic values of the products corresponding to each batch of materials are written into the target batch data table, and at this time, characteristic values of a plurality of products are recorded in the target batch data table.
Further, after the step of determining whether the corresponding batch materials are put into according to the batch information, the method further comprises the steps of creating a batch data table if not, naming the created batch data table as a latest batch data table, writing characteristic values of products corresponding to the batch materials into the latest batch data table, and counting the number of the characteristic values in the latest batch data table.
It will be understood that when it is determined that each batch of material is not put into the assembly line, it is indicated that the material is not produced as a product, at this time, a new data table for recording the characteristic values of the product needs to be created, the new batch data table is named as the latest batch data table, and then the characteristic values of the corresponding products of each batch of material are written into the latest batch data table in the same manner, and at this time, the characteristic values of a plurality of products are also recorded in the latest batch data table.
And step S30, when the number of the characteristic values is larger than a preset number threshold, determining the current test statistic according to the characteristic values and the number of the characteristic values.
It should be appreciated that to ensure the validity of calculating the current test statistic, the number of characteristic values that need to be accumulated is greater than a preset number threshold, which may be set to 100, the current test statistic being a number that is used in the hypothesis test to decide whether to reject the original hypothesis.
And S40, carrying out material early warning when the current test statistic is greater than or equal to a preset threshold value.
It can be understood that the preset threshold refers to a threshold determined by using a target hypothesis verification principle, after determining the current test statistic, whether the current test statistic is greater than or equal to the preset threshold is determined, if yes, it is indicated that there may be a difference in performance between the material lot, and at this time, material early warning is required to be performed to prompt that the performance of the material needs to be tested.
Further, step S40 comprises the steps of generating a threshold comparison object according to the current test statistic, batch information of materials and the number of characteristic values, determining significance level data according to a target hypothesis verification principle, determining a preset threshold according to the significance level data, generating material early warning information when the threshold comparison object is larger than or equal to the preset threshold, and carrying out material early warning according to the material early warning information.
It should be understood that the threshold comparison object refers to an object compared with a preset threshold, and after determining the current test statistic, the threshold comparison object is generated in combination with the lot information of the material and the number of characteristic values, for example, the current test statistic is denoted as Z, the lot information of the material is denoted as yyyymmdd, the number of characteristic values is denoted as n, and the threshold comparison object is denoted as Z yyyymmdd_n.
It will be appreciated that the target hypothesis verification principle may be a significance hypothesis verification principle, whose original hypothesis H 0: Alternative hypothesis H 1: selecting a significance level alpha=0.05, wherein the rejection domain is |Z| not less than Z 1-α/2, namely the preset threshold value can be 1.96, judging whether the threshold value comparison object is greater than or equal to the preset threshold value after generating the threshold value comparison object and determining the preset threshold value, if so, generating material early warning information, and immediately sending the material early warning information to a terminal of a professional engineer, wherein the professional engineer determines whether to verify the material.
Further, after step S40, performance testing is carried out on the residual materials in the current batch to obtain a current performance test result when a verification request of the target object is received, and the residual materials are determined to be abnormal performance materials when the current performance test result does not meet the preset performance requirement.
It can be understood that the target object may be a professional engineer of the assembly line, when receiving a verification request of the target object, it indicates that the material needs to be verified, that is, the performance of the remaining material in the current batch is tested, then it is determined whether the test result meets the preset performance requirement, if not, it indicates that there is a difference in performance between the batches of the material, and then it further determines that the material has abnormal performance, so that the material in the current batch is returned.
According to the method, the device and the system, the identification information of materials used in production of the products is obtained, the batch information of the materials is determined according to the identification information, the number of characteristic values of the materials corresponding to the products in batches is determined according to the batch information, when the number of the characteristic values is larger than a preset number threshold, the current test statistics are determined according to the characteristic values and the number of the characteristic values, when the current test statistics are larger than or equal to the preset threshold, material early warning is conducted, after the batch information of the materials is determined according to the identification information, the number of the characteristic values of the materials corresponding to the products in batches is further determined, then whether the materials are obviously abnormal is judged according to the comparison result of the test statistics in significance hypothesis verification and the preset threshold, if yes, material early warning is conducted, and therefore accuracy and timeliness of material early warning can be effectively improved, the qualification rate of product production is improved, and the cost of the products is reduced.
In the second embodiment of the present application, the same or similar content as in the first embodiment of the present application may be referred to the above description, and will not be repeated. On this basis, referring to fig. 2, step S30 includes steps S301 to S303:
step S301, when the number of the characteristic values is greater than a preset number threshold, calculating a current characteristic value average value according to the characteristic values and the number of the characteristic values.
It should be noted that, the current characteristic value mean value refers to a mean value of characteristic values of products corresponding to each batch of materials, and when the number of the characteristic values is determined to be greater than a preset number threshold, the current characteristic value mean value is calculated, specifically:
Wherein, The average value of the current characteristic values is represented, n represents the number of the characteristic values, and y i represents the characteristic value of the corresponding product of each batch of materials.
Step S302, calculating a sample standard deviation according to the current characteristic value mean and the characteristic value.
It can be understood that after calculating the mean value of the current characteristic value, the standard deviation of the sample is calculated by combining the characteristic value, specifically:
wherein s represents the standard deviation of the sample, The average value of the current characteristic values is represented, n represents the number of the characteristic values, and y i represents the characteristic value of the corresponding product of each batch of materials.
Step S303, determining the current test statistic according to the sample standard deviation and the current characteristic value mean.
Further, step S303 includes obtaining characteristic values of products corresponding to each historical batch of materials, calculating a historical characteristic value mean value according to the characteristic values of the products corresponding to each historical batch of materials, and calculating current test statistics according to the historical characteristic value mean value, the current characteristic value mean value and the sample standard deviation.
It can be understood that, in this embodiment, since the current test statistic in the significance hypothesis verification principle is used to test whether the batch of materials causes significant abnormality between the product characteristics and the historical average, it is also necessary to calculate the historical characteristic value average by using the characteristic values of the products corresponding to the historical batch materials, and at this time, the current test statistic is calculated by combining the current characteristic value average and the sample standard deviation, specifically:
where Z represents the current test statistic, Represents the current characteristic value mean, mu 0 represents the historical characteristic value mean, and s represents the sample standard deviation.
According to the embodiment, when the number of the characteristic values is larger than a preset number threshold, a current characteristic value average value is calculated according to the characteristic values and the number of the characteristic values, a sample standard deviation is calculated according to the current characteristic value average value and the characteristic values, a current test statistic is determined according to the sample standard deviation and the current characteristic value average value, when the number of the characteristic values is larger than the preset number threshold, the current characteristic value average value is calculated by combining the characteristic values, then the sample standard deviation is calculated by combining the characteristic values, and then the current test statistic is determined by combining the current characteristic value average value, so that the accuracy of determining the current test statistic can be effectively improved.
For an example, in order to facilitate understanding of the implementation flow of the material early warning method obtained by combining the first embodiment with the second embodiment, please refer to fig. 3, fig. 3 provides a schematic flow diagram of a material early warning method, specifically:
Firstly, inputting identification information of materials and characteristic values of the products used in production of the products, then grabbing batch information of the materials by adopting a coding rule of the materials, judging whether the materials of the batch information are put into, if not, creating a new annotation data table, naming the new annotation data table as a latest annotation data table sheet2, if not, grabbing a target batch data table, naming the target batch data table as sheet1, counting the number of characteristic values in the target batch data table sheet1, calculating current test statistics according to a historical characteristic value mean value, the current characteristic value mean value and a sample standard deviation when the number of the characteristic values is determined to be greater than a preset number threshold, and then judging whether the current test statistics are greater than or equal to the preset threshold, if so, carrying out material early warning.
It should be noted that the foregoing examples are only for understanding the present application, and are not meant to limit the material early warning method of the present application, and many simple changes based on this technical concept are all within the scope of the present application.
The application also provides a material early warning device, please refer to fig. 4, the material early warning device includes:
the obtaining module 10 is configured to obtain identification information of a material used in production of a product, and determine batch information of the material according to the identification information.
The determining module 20 is configured to determine the number of characteristic values of the corresponding products of each batch of materials according to the batch information.
The determining module 20 is further configured to determine a current test statistic according to the characteristic value and the number of characteristic values when the number of characteristic values is greater than a preset number threshold.
And the early warning module 30 is used for carrying out material early warning when the current test statistic is greater than or equal to a preset threshold value.
According to the method, the device and the system, the identification information of materials used in production of the products is obtained, the batch information of the materials is determined according to the identification information, the number of characteristic values of the materials corresponding to the products in batches is determined according to the batch information, when the number of the characteristic values is larger than a preset number threshold, the current test statistics are determined according to the characteristic values and the number of the characteristic values, when the current test statistics are larger than or equal to the preset threshold, material early warning is conducted, after the batch information of the materials is determined according to the identification information, the number of the characteristic values of the materials corresponding to the products in batches is further determined, then whether the materials are obviously abnormal is judged according to the comparison result of the test statistics in significance hypothesis verification and the preset threshold, if yes, material early warning is conducted, and therefore accuracy and timeliness of material early warning can be effectively improved, the qualification rate of product production is improved, and the cost of the products is reduced.
The material early warning device provided by the application adopts the material early warning method in the embodiment, and can solve the technical problems of inaccurate and timely material early warning in the prior art. Compared with the prior art, the material early warning device has the same beneficial effects as the material early warning method provided by the embodiment, and other technical features in the material early warning device are the same as the features disclosed by the method of the embodiment, and are not repeated herein.
In an embodiment, the determining module 20 is further configured to determine whether each batch of materials is put into the assembly line according to the batch information, if yes, grasp a target batch data table, write the characteristic value of the product corresponding to each batch of materials into the target batch data table, and count the number of the characteristic values in the target batch data table.
In an embodiment, the determining module 20 is further configured to create a batch data table if not, name the created batch data table as a latest batch data table, write the characteristic values of the products corresponding to each batch of materials into the latest batch data table, and count the number of the characteristic values in the latest batch data table.
In an embodiment, the determining module 20 is further configured to calculate a current characteristic value average value according to the characteristic value and the number of characteristic values when the number of characteristic values is greater than a preset number threshold, calculate a sample standard deviation according to the current characteristic value average value and the characteristic value, and determine a current test statistic according to the sample standard deviation and the current characteristic value average value.
In an embodiment, the determining module 20 is further configured to obtain a characteristic value of a product corresponding to each historical batch of material, calculate a historical characteristic value average according to the characteristic value of the product corresponding to each historical batch of material, and calculate the current test statistic according to the historical characteristic value average, the current characteristic value average and the sample standard deviation.
In an embodiment, the early warning module 30 is further configured to generate a threshold comparison object according to the current test statistic, the lot information of the material, and the number of characteristic values, determine significance level data according to a target hypothesis verification principle, determine a preset threshold according to the significance level data, generate material early warning information when the threshold comparison object is greater than or equal to the preset threshold, and perform material early warning according to the material early warning information.
In an embodiment, the early warning module 30 is further configured to perform a performance test on the remaining materials of the current batch to obtain a current performance test result when receiving the verification request of the target object, and determine that the remaining materials are abnormal performance materials when the current performance test result does not meet the preset performance requirement.
The application provides a material early warning device which comprises at least one processor and a memory in communication connection with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the material early warning method in the first embodiment.
Referring now to FIG. 5, a schematic diagram of a material early warning device suitable for use in implementing embodiments of the present application is shown. The material early warning device in the embodiment of the present application may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (Personal DIGITAL ASSISTANT: personal digital assistant), a PAD (Portable Application Description: tablet computer), a PMP (Portable MEDIA PLAYER: portable multimedia player), a vehicle-mounted terminal (e.g., a vehicle-mounted navigation terminal), etc., and a fixed terminal such as a digital TV, a desktop computer, etc. The material early warning device shown in fig. 5 is only an example, and should not be construed as limiting the function and scope of use of the embodiment of the present application.
As shown in fig. 5, the material early warning apparatus may include a processing device 1001 (e.g., a central processor, a graphic processor, etc.) that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access Memory (RAM: random Access Memory) 1004. In the RAM1004, various programs and data required for the operation of the material early-warning apparatus are also stored. The processing device 1001, the ROM1002, and the RAM1004 are connected to each other by a bus 1005. An input/output (I/O) interface 1006 is also connected to the bus. In general, a system including an input device 1007 such as a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc., an output device 1008 including a Liquid crystal display (LCD: liquid CRYSTAL DISPLAY), a speaker, a vibrator, etc., a storage device 1003 including a magnetic tape, a hard disk, etc., and a communication device 1009 may be connected to the I/O interface 1006. The communication means 1009 may allow the material alert device to communicate wirelessly or by wire with other devices to exchange data. While materials pre-warning devices with various systems are shown in the figures, it should be understood that not all of the illustrated systems are required to be implemented or provided. More or fewer systems may alternatively be implemented or provided.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. The computer program contains program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. The above-described functions defined in the method of the disclosed embodiment of the application are performed when the computer program is executed by the processing device 1001.
The material early warning device provided by the application adopts the material early warning method in the embodiment, and can solve the technical problems of inaccurate and timely material early warning in the prior art. Compared with the prior art, the material early-warning device has the same beneficial effects as the material early-warning method provided by the embodiment, and other technical features in the material early-warning device are the same as those disclosed by the method of the previous embodiment, and are not repeated herein.
It is to be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the description of the above embodiments, particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
The present application provides a computer readable storage medium having computer readable program instructions (i.e., a computer program) stored thereon for performing the material early warning method of the above embodiments.
The computer readable storage medium provided by the present application may be, for example, a U disk, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples of a computer-readable storage medium may include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access Memory (RAM: random Access Memory), a Read-Only Memory (ROM), an erasable programmable Read-Only Memory (EPROM: erasable Progra prior art material early warning is not accurate and is timely able Read Only Memory or flash), an optical fiber, a portable compact disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this embodiment, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to electrical wiring, fiber optic cable, RF (Radio Frequency) and the like, or any suitable combination of the foregoing.
The computer readable storage medium may be included in the material early warning device or may exist alone without being assembled into the material early warning device.
Computer program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of remote computers, the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN: local Area Network) or a wide area network (WAN: wide Area Network), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules involved in the embodiments of the present application may be implemented in software or in hardware. Wherein the name of the module does not constitute a limitation of the unit itself in some cases.
The readable storage medium provided by the application is a computer readable storage medium, and the computer readable storage medium stores computer readable program instructions (namely computer programs) for executing the material early warning method, so that the technical problems of inaccurate and timely material early warning in the prior art can be solved. Compared with the prior art, the beneficial effects of the computer readable storage medium provided by the application are the same as those of the material early warning method provided by the embodiment, and are not repeated here.
The foregoing description is only a partial embodiment of the present application, and is not intended to limit the scope of the present application, and all the equivalent structural changes made by the description and the accompanying drawings under the technical concept of the present application, or the direct/indirect application in other related technical fields are included in the scope of the present application.
Claims (10)
1. A material early warning method, the method comprising:
Acquiring identification information of materials used in production of products, and determining batch information of the materials according to the identification information;
determining the quantity of characteristic values of products corresponding to each batch of materials according to the batch information;
when the number of the characteristic values is larger than a preset number threshold, determining current test statistics according to the characteristic values and the number of the characteristic values;
and when the current test statistic is greater than or equal to a preset threshold value, carrying out material early warning.
2. The method of claim 1, wherein the step of determining the current test statistic based on the characteristic value, the number of characteristic values, when the number of characteristic values is greater than a preset number threshold, comprises:
when the number of the characteristic values is larger than a preset number threshold, calculating a current characteristic value mean value according to the characteristic values and the number of the characteristic values;
calculating a sample standard deviation according to the current characteristic value mean value and the characteristic value;
and determining current test statistics according to the sample standard deviation and the current characteristic value mean value.
3. The method of claim 2, wherein the step of determining a current test statistic from the sample standard deviation and the current characteristic value mean comprises:
Acquiring characteristic values of products corresponding to the historical batches of materials;
Calculating a historical characteristic value average value according to the characteristic values of the products corresponding to the historical batches of materials;
And calculating current test statistics according to the historical characteristic value mean value, the current characteristic value mean value and the sample standard deviation.
4. The method of claim 1, wherein the step of performing material pre-warning when the current test statistic is greater than or equal to a preset threshold value comprises:
generating a threshold comparison object according to the current test statistic, the batch information of the materials and the number of characteristic values;
determining significance level data according to a target hypothesis verification principle;
determining a preset threshold according to the significance level data;
When the threshold comparison object is larger than or equal to a preset threshold, generating material early warning information;
and carrying out material early warning according to the material early warning information.
5. The method of claim 1, wherein the step of determining the number of characteristic values of the respective products for each batch of material based on the batch information comprises:
Determining whether each batch of materials is put into an assembly line according to the batch information;
If yes, grabbing a target batch data table;
writing characteristic values of products corresponding to the materials in each batch into the target batch data table;
and counting the number of characteristic values in the target batch data table.
6. The method of claim 5, further comprising, after the step of determining whether the corresponding lot is put in based on the lot information:
if not, a batch data table is newly built;
designating the newly-built batch data table as the latest batch data table;
writing characteristic values of products corresponding to the materials in each batch into the latest batch data table;
and counting the number of characteristic values in the latest batch data table.
7. The method of any one of claims 1 to 6, wherein after the step of performing material pre-warning when the current test statistic is greater than or equal to a preset threshold, further comprising:
When receiving a verification request of a target object, performing performance test on the residual materials in the current batch to obtain a current performance test result;
And when the current performance test result does not meet the preset performance requirement, determining the residual materials as abnormal performance materials.
8. A material early warning device, the device comprising:
the acquisition module is used for acquiring identification information of materials used in production of products and determining batch information of the materials according to the identification information;
the determining module is used for determining the quantity of the characteristic values of the products corresponding to the materials in each batch according to the batch information;
the determining module is further configured to determine a current test statistic according to the characteristic value and the number of the characteristic values when the number of the characteristic values is greater than a preset number threshold;
And the early warning module is used for carrying out material early warning when the current test statistic is greater than or equal to a preset threshold value.
9. A material early warning apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the material early warning method according to any one of claims 1 to 7.
10. A storage medium, characterized in that the storage medium is a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the material early warning method according to any one of claims 1 to 7.
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| CN202411291224.2A CN119338386A (en) | 2024-09-13 | 2024-09-13 | Material early warning method, device, equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN121007624A (en) * | 2025-10-28 | 2025-11-25 | 苏州艾隆科技股份有限公司 | Methods, devices, electronic equipment and storage media for weight testing of traditional Chinese medicine products |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN121007624A (en) * | 2025-10-28 | 2025-11-25 | 苏州艾隆科技股份有限公司 | Methods, devices, electronic equipment and storage media for weight testing of traditional Chinese medicine products |
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