Industrial internet production real-time monitoring system and method based on SaaS level

Abstract

Embodiments of the present application provide a SaaS level-based industrial internet yield real-time monitoring system and method. The real-time yield sent by the acquisition device in a unit time is obtained; the real-time yield is summarized based on a set statistical period to obtain the corresponding total yield in the statistical period; based on the total yield and the number of faults in the quality inspection information, a production parameter for representing production quality is determined; if the production parameter is greater than or equal to a set threshold, the production time required for supplementary production is determined according to the number of faults and a preset yield quota; if the production time is less than a time threshold, a supplementary production instruction is generated and sent to a production control device to instruct the production control device to continue the supplementary production. The technical scheme of the embodiments of the present application improves the efficiency and reliability of production monitoring by monitoring the real-time yield based on actual production data and controlling the quality in combination with the production quality.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to a SaaS-level-based industrial internet yield real-time monitoring system and method, computer readable medium and electronic device. BACKGROUND

[0002] In many production applications, it is necessary to monitor the actual production environment in real time to obtain real-time production data to evaluate the production situation. However, the existing production monitoring often cannot obtain production data in real time, and it is also difficult to monitor production quality while monitoring production data, resulting in the problem of low production monitoring efficiency. SUMMARY

[0003] Embodiments of the present application provide a SaaS-level-based industrial internet yield real-time monitoring system and method, computer readable medium and electronic device, thereby at least to some extent, the efficiency and reliability of yield monitoring can be improved.

[0004] Other characteristics and advantages of the present application will become apparent from the following detailed description, or will be learned by practice of the present application.

[0005] According to an aspect of an embodiment of the present application, a SaaS-level-based industrial internet yield real-time monitoring method is provided, comprising: acquiring real-time yield sent by a collection device in a unit time; based on a set statistical period, the real-time yield is summarized to obtain the corresponding total yield in the statistical period; acquiring product quality inspection information, wherein the quality inspection information includes the fault quantity corresponding to the unqualified fault product; based on the total yield and the fault quantity, a production parameter representing production quality is determined; if the production parameter is greater than or equal to a set threshold, the production time required for supplementary production is determined according to the fault quantity and a preset yield quota; if the production time is less than a time threshold, a supplementary production instruction is generated; and the supplementary production instruction is sent to a production control device.

[0006] In some embodiments of the present application, based on the foregoing scheme, the acquisition of product quality inspection information comprises: based on product identification and a set sampling frequency, the product is sampled to obtain a sample product; the image corresponding to the sample product is acquired; and the image is analyzed by a pre-trained quality inspection model to obtain the quality inspection information corresponding to the product.

[0007] In some embodiments of the present application, based on the foregoing scheme, the image includes at least two images; the image is analyzed by the pre-trained quality inspection model to obtain the quality inspection information corresponding to the product, including: determining the gray scale based on the pixel information of the image; determining the image parameter representing the image definition based on the pixel information and the gray scale; selecting the image with the largest image parameter, and analyzing the image by the pre-trained quality inspection model to obtain the quality inspection information corresponding to the product.

[0008] In some embodiments of the present application, based on the foregoing scheme, the production parameter is determined based on the total production and the number of failures, including: determining a failure parameter based on the ratio between the number of failures and the total production; and determining a normal parameter based on the difference between the number of failures and the total production; determining the production parameter for representing the production quality based on the failure parameter and the normal parameter.

[0009] In some embodiments of the present application, based on the foregoing scheme, the method further comprises: deploying sensor devices and gateway devices in the production environment; and constructing a big data-based production monitoring system among the sensor devices, the gateway devices, and the host computer.

[0010] In some embodiments of the present application, based on the foregoing scheme, after obtaining the quality inspection information of the product, the method further comprises: if the ratio between the number of failures and the total production in the quality inspection information is greater than or equal to a set threshold, determining that the production line is a failure production line; and repairing the failure production line.

[0011] In some embodiments of the present application, based on the foregoing scheme, after the total production corresponding to the statistical period is obtained by aggregating the real-time production based on the set statistical period, the method further comprises: if the difference between the total production and the set production is greater than or equal to a set production threshold, determining that the production line is a high-yield line; and using the high-yield line as a priority production line.

[0012] In some embodiments of the present application, based on the foregoing scheme, if the production parameter is greater than or equal to a set threshold, the production time required for supplementary production is determined according to the number of failures and a preset production quota, and then the method further comprises: if the production time is greater than or equal to a time threshold, determining not to perform supplementary production.

[0013] In some embodiments of the present application, based on the foregoing scheme, the method further comprises: performing quality inspection on the product of supplementary production to generate supplementary quality inspection information; and merging and analyzing the supplementary quality inspection information and the quality inspection information to obtain final quality inspection information.

[0014] According to an aspect of some embodiments of the present application, a SaaS-level-based industrial internet production real-time monitoring system is provided, comprising:

[0015] an acquisition unit configured to acquire real-time production sent by the collection device in a unit time;

[0016] a summary unit configured to summarize the real-time production based on a set statistical period to obtain corresponding total production in the statistical period;

[0017] a quality inspection unit configured to acquire quality inspection information of the product, wherein the quality inspection information comprises a fault number of fault products that are unqualified in quality inspection;

[0018] a parameter unit configured to determine a production parameter for indicating production quality based on the total production and the fault number;

[0019] a time length unit configured to, if the production parameter is greater than or equal to a set threshold, determine a production time length required for supplementary production according to the fault number and a preset production quota;

[0020] an instruction unit configured to, if the production time length is less than a time length threshold, generate a supplementary production instruction;

[0021] a sending unit configured to send the supplementary production instruction to a production control device.

[0022] In some embodiments of the present application, based on the foregoing scheme, the quality inspection information of the product comprises: sampling the product based on a product identifier and a set sampling frequency to obtain a sample product; acquiring an image corresponding to the sample product; and performing image analysis on the image through a pre-trained quality inspection model to obtain the quality inspection information corresponding to the product.

[0023] In some embodiments of the present application, based on the foregoing scheme, the image comprises at least two images; and the image analysis on the image through the pre-trained quality inspection model to obtain the quality inspection information corresponding to the product comprises: determining a gray level based on pixel information of the image; determining an image parameter for indicating image definition based on the pixel information and the gray level; selecting an image with the largest image parameter; and performing image analysis on the image through the pre-trained quality inspection model to obtain the quality inspection information corresponding to the product.

[0024] In some embodiments of the present application, based on the foregoing scheme, the determination of the production parameter based on the total production and the fault number comprises: determining a fault parameter based on a ratio between the fault number and the total production; determining a normal parameter based on a difference between the fault number and the total production; and determining the production parameter for indicating production quality based on the fault parameter and the normal parameter.

[0025] In some embodiments of the present application, based on the foregoing scheme, the method further comprises: deploying the sensor device and the gateway device in a production environment; and constructing a big data-based production monitoring system among the sensor device, the gateway device, and the host computer.

[0026] In some embodiments of the present application, based on the foregoing scheme, after obtaining the quality inspection information of the product, the method further comprises: determining that the production line is a fault production line if a ratio between the number of faults in the quality inspection information and the total output is greater than or equal to a set threshold; and performing maintenance on the fault production line.

[0027] In some embodiments of the present application, based on the foregoing scheme, after aggregating the real-time output based on the set statistical period to obtain the corresponding total output in the statistical period, the method further comprises: determining that the production line is a high-output line if a difference between the total output and a set output is greater than or equal to a set output threshold; and using the high-output line as a priority production line.

[0028] In some embodiments of the present application, based on the foregoing scheme, after determining the production time required for supplementary production according to the number of faults and a preset output quota if the production parameter is greater than or equal to a set threshold, the method further comprises: determining not to perform supplementary production if the production time is greater than or equal to a time threshold.

[0029] In some embodiments of the present application, based on the foregoing scheme, the method further comprises: performing quality inspection on the product of the supplementary production to generate supplementary quality inspection information; and merging and analyzing the supplementary quality inspection information and the quality inspection information to obtain final quality inspection information.

[0030] According to an aspect of an embodiment of the present application, there is provided a computer readable medium having stored thereon a computer program, the computer program being executed by a processor to implement the SaaS-level industrial internet output real-time monitoring method as described in the above embodiments.

[0031] According to an aspect of an embodiment of the present application, there is provided an electronic device, comprising: one or more processors; and a storage device configured to store one or more programs, the one or more programs, when executed by the one or more processors, causing the one or more processors to implement the SaaS-level industrial internet output real-time monitoring method as described in the above embodiments.

[0032] According to an aspect of some embodiments of the present application, a computer program product or computer program is provided, which includes computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the SaaS-level-based industrial internet yield real-time monitoring method provided in the various optional implementation manners described above.

[0033] In the technical solution provided in some embodiments of the present application, the real-time yield of the collection device in a unit time is acquired; the real-time yield is summarized based on a set statistical period to obtain a corresponding total yield in the statistical period; a production parameter representing production quality is determined based on the total yield and a failure number in quality inspection information; if the production parameter is greater than or equal to a set threshold, a production time required for supplementary production is determined according to the failure number and a preset yield quota; if the production time is less than a time threshold, a supplementary production instruction is generated and sent to a production control device to instruct the production control device to continue the supplementary production. The technical solution of the embodiments of the present application improves the efficiency and reliability of production monitoring by performing real-time yield monitoring based on actual production data and combining quality control with production quality.

[0034] It should be understood that the foregoing general description and the following detailed description are only exemplary and explanatory, and are not limiting to the present application. BRIEF DESCRIPTION OF DRAWINGS

[0035] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and serve to explain the principles of the present application. It is readily apparent to one of ordinary skill in the art that the accompanying drawings are merely some embodiments of the present application, and other drawings can be obtained by those of ordinary skill in the art without any creative effort, based on the accompanying drawings.

[0036] Figure 1 A flowchart of a SaaS-level-based industrial internet yield real-time monitoring method according to an embodiment of the present application is schematically shown.

[0037] Figure 2 A flowchart of acquiring quality inspection information of a product according to an embodiment of the present application is schematically shown.

[0038] Figure 3 A schematic diagram of a SaaS-level-based industrial internet yield real-time monitoring system according to an embodiment of the present application is schematically shown.

[0039] Figure 4A structural diagram of a computer system of an electronic device suitable for implementing the embodiments of the present application is shown. DETAILED DESCRIPTION

[0040] Example implementations are now described in greater detail in conjunction with the figures. It should be understood, however, that the example implementations can be practiced with modification and alteration, and are not limited to the examples described herein. Rather, the example implementations are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the example implementations to those skilled in the art.

[0041] Moreover, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the

[0042] The block diagrams in the drawings show only the functional entities and not necessarily the physical separation of the functional entities. That is, the functional entities can be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0043] The flow diagrams shown in the drawings are merely examples and not necessarily to be construed as having all content and operations / steps, nor necessarily to be executed in the order described. For example, some operations / steps can be further divided, and some operations / steps can be combined or partially combined, so that the actual execution order can be changed according to actual conditions.

[0044] In one embodiment of the present application, sensor devices and gateway devices are deployed in a production environment; the sensor devices can be temperature, humidity, and weight sensors, etc., and then a big data-based production monitoring system is constructed among the sensor devices, the gateway devices, and an upper computer, for real-time production monitoring. Meanwhile, a monitoring platform is built in the upper computer or monitoring software is installed, for production monitoring and control instruction decentralization through the monitoring platform. This way, without purchasing software and hardware or building a computer room, the information system can be used to manage through the Internet.

[0045] The implementation details of the technical solutions of the embodiments of the present application are described in detail as follows:

[0046] Figure 1A flow chart of a SaaS-level-based industrial internet yield real-time monitoring method according to an embodiment of the present application is shown. Referring to Figure 1 As shown, the SaaS-level-based industrial internet yield real-time monitoring method includes at least steps S110 to S170, which are described in detail as follows:

[0047] In step S110, real-time yield of a collection device sent in a unit time is acquired.

[0048] In an embodiment of the present application, during operation of the device, real-time yield of the collection device in a unit time is acquired. The real-time yield in this embodiment is counted in a unit time.

[0049] The collection device in this embodiment can be a gravity sensor. The unit time can be one hour, three hours, etc.

[0050] In step S120, the real-time yield is summarized based on a set statistical period to obtain corresponding total yield in the statistical period.

[0051] In an embodiment of the present application, the statistical period can be one day or one week, etc. When the statistical period is reached, the real-time yield is summarized to obtain the total yield in the statistical period.

[0052] In this embodiment, the real-time yield is summarized based on the statistical period to obtain the total yield in the statistical period for targeted analysis.

[0053] In an embodiment of the present application, after the real-time yield is summarized based on the set statistical period to obtain corresponding total yield in the statistical period, the method further includes:

[0054] If the difference between the total yield and the set yield is greater than or equal to the set yield threshold, i.e., the total yield exceeds the set yield greatly, it is determined that the production line is a high-yield line, and then the high-yield line is used as a priority production line. In this way, production efficiency and utilization rate of the production line are improved.

[0055] In step S130, quality inspection information of a product is acquired, wherein the quality inspection information includes a fault quantity of a fault product that fails quality inspection.

[0056] In an embodiment of the present application, the product in the statistical period is subjected to quality inspection to acquire the quality inspection information, specifically including product identification of a fault product that fails quality inspection or is unqualified, and a fault quantity of the fault product.

[0057] In an embodiment of the present application, as Figure 2As shown, the quality inspection information of the product is obtained in step S130, including:

[0058] S210, sampling the product based on the product identifier and the preset sampling frequency to obtain a sampled product;

[0059] S220, obtaining an image corresponding to the sampled product;

[0060] S230, performing image analysis on the image through the pre-trained quality inspection model to obtain quality inspection information corresponding to the product.

[0061] Specifically, we first sample the product based on the product identifier of the product and the preset sampling frequency to obtain a sampled product. For example, if the preset sampling frequency is 10, sample every 10 products. Then obtain the image corresponding to the sampled product to perform image analysis through the quality inspection model to obtain the quality inspection information corresponding to the product.

[0062] In this embodiment, the quality inspection model is constructed based on a convolutional neural network, and then the quality inspection model is trained through image samples and their corresponding labels, so that the loss function of the quality inspection model converges to the minimum to obtain an accurate quality inspection model.

[0063] In an embodiment of the present application, the image includes at least two images; in step S230, the image is analyzed through the pre-trained quality inspection model to obtain the quality inspection information corresponding to the product, including:

[0064] Based on the pixel information of the image, determine the number of gray levels;

[0065] Based on the pixel information and the number of gray levels, determine an image parameter representing image sharpness;

[0066] Select the image with the largest image parameter, and perform image analysis through the pre-trained quality inspection model to obtain the quality inspection information corresponding to the product.

[0067] In an embodiment of the present application, a gray level histogram of the image is generated based on the pixel information of the image, and then the number of gray levels f in the gray level histogram and the number of pixels Pix_i corresponding to each gray level are determined. Based on the above information, the pixel mean value Pix_tal / f corresponding to each gray level is determined. In this embodiment, the pixel mean value is used to measure the deviation between the pixel values of other pixel points and the pixel mean value; the total number of pixels of the image Pix_tal is obtained, and then based on the above information, the image parameter is determined as:

[0068]

[0069] Wherein, a represents a preset image factor, i represents a natural number less than or equal to f. The smaller the image parameter obtained by the above calculation, the smaller the difference between the image pixels, and the less clear the image. The higher the image parameter, the higher the difference between the pixels, that is, the clearer the image.

[0070] For multiple images corresponding to the same product, we select the image with the largest image parameter, perform image analysis on the product through the pre-trained quality inspection model, and obtain the quality inspection information of the product, that is, whether the product is a qualified product.

[0071] In an embodiment of the present application, after obtaining the quality inspection information of the product, if the ratio between the number of faults and the total output, that is, the failure rate, is greater than or equal to a set threshold, it indicates that there is a larger fault problem in the production line, and the production line is determined as a fault production line. Then, the fault production line is repaired.

[0072] In step S140, a production parameter for indicating production quality is determined based on the total output and the number of faults.

[0073] In an embodiment of the present application, after the products in the statistical period are sampled and inspected, the number of fault products that do not meet the quality inspection is determined, and the production parameter for indicating the production quality is determined based on the total output and the number of faults. In this embodiment, the production parameter is used to measure the production situation in the statistical period.

[0074] In an embodiment of the present application, the production parameter is determined based on the total output and the number of faults, including:

[0075] A fault parameter is determined based on the ratio between the number of faults and the total output;

[0076] A normal parameter is determined based on the difference between the number of faults and the total output;

[0077] The production parameter for indicating the production quality is determined based on the fault parameter and the normal parameter.

[0078] In an embodiment of the present application, the fault parameter Par_fau is determined based on the ratio between the number of faults Num_fau and the total output Num_tal as follows:

[0079]

[0080] The normal parameter Par_nor is determined based on the difference between the number of faults Num_fau and the total output Num_tal as follows:

[0081]

[0082] Based on the failure parameter Par_fau and the normal parameter Par_nor, a production parameter Par_pro used to represent production quality is determined as:

[0083] Par_pro = θ·Par_fau + ω·Par_nor

[0084] Wherein, θ, ω represent preset production factors. The above manner determines the production parameter based on the total production and the number of failures, which is used to measure the production situation in the statistical period. The higher the production parameter is, the higher the failure rate is. Whether to perform supplementary production needs to be determined according to actual conditions.

[0085] In step S150, if the production parameter is greater than or equal to a set threshold, a production duration required for supplementary production is determined according to the number of failures and a preset production quota.

[0086] In an embodiment of the present application, if the production parameter is greater than or equal to a set threshold, a production duration required for supplementary production is determined according to the number of failures and a preset production quota. The production quota is the number of qualified products completed in a unit of time under the condition that the technical conditions are normal, the production tools are reasonably used, and the labor organization is correct.

[0087] Specifically, in the embodiment, the quotient between the number of failures and the production quota can be used as the production duration required for supplementary production.

[0088] In step S160, if the production duration is less than a duration threshold, a supplementary production instruction is generated.

[0089] In an embodiment of the present application, if the production duration is less than a duration threshold, it indicates that the supplementary production will not affect the normal production plan, and thus the supplementary production instruction is generated.

[0090] In an embodiment of the present application, the duration threshold can be the difference between the planned production duration and the used production duration.

[0091] The supplementary production instruction in the embodiment can include production objects, production quantities, and the like.

[0092] In an embodiment of the present application, if the production duration is greater than or equal to the duration threshold, it indicates that the time required for supplementary production is too long, and thus it is determined not to perform supplementary production. In this case, a production order is regenerated for re-production.

[0093] In step S170, the supplementary production instruction is sent to a production control device.

[0094] In an embodiment of the present application, after the supplementary production instruction is generated, the supplementary production instruction is sent to the production control device to instruct the production control device to continue production.

[0095] In an embodiment of the present application, the method further comprises: performing quality inspection on the supplementary production product to generate supplementary quality inspection information; and then merging and analyzing the supplementary quality inspection information and the quality inspection information to obtain final quality inspection information, that is, taking the sum of the normal product data in the quality inspection information and the normal product data in the supplementary quality inspection information as the normal product data in the final quality inspection information, and taking the fault product data in the supplementary quality inspection information as the fault product data in the final quality inspection information.

[0096] In an embodiment of the present application, the real-time yield sent by the collection device in a unit time is obtained; the real-time yield is summarized based on a set statistical period to obtain corresponding total yield in the statistical period; a production parameter for indicating production quality is determined based on the total yield and the fault quantity in the quality inspection information; if the production parameter is greater than or equal to a set threshold, the production time required for supplementary production is determined according to the fault quantity and a preset yield quota; if the production time is less than a time threshold, a supplementary production instruction is generated and sent to the production control device to instruct the production control device to continue supplementary production. The technical scheme of the embodiment of the present application improves the efficiency and reliability of production monitoring by monitoring real-time yield based on actual production data and controlling quality in combination with production quality.

[0097] The device embodiment of the present application is introduced below, which can be used to execute the SaaS-level industrial internet yield real-time monitoring method in the above embodiments of the present application. It can be understood that the device can be a computer program (including program code) running in a computer device, for example, the device is an application software; the device can be used to execute the corresponding steps in the method provided by the embodiments of the present application. For details not disclosed in the device embodiment of the present application, please refer to the above embodiments of the SaaS-level industrial internet yield real-time monitoring method.

[0098] Figure 3 A block diagram of a SaaS-level industrial internet yield real-time monitoring system according to an embodiment of the present application is shown.

[0099] Referring to Figure 3 The SaaS-level industrial internet yield real-time monitoring system 300 according to an embodiment of the present application includes:

[0100] The acquisition unit 310 is configured to obtain real-time yield sent by the collection device in a unit time;

[0101] The aggregation unit 320 is configured to aggregate the real-time production based on a set statistical period to obtain a total production corresponding to the statistical period;

[0102] The quality inspection unit 330 is configured to obtain quality inspection information of the product, wherein the quality inspection information comprises a fault quantity of a fault product that is unqualified in quality inspection;

[0103] The parameter unit 340 is configured to determine a production parameter for indicating production quality based on the total production and the fault quantity.

[0104] The time length unit 350 is configured to determine a production time length required for supplementary production according to the fault quantity and a preset production quota if the production parameter is greater than or equal to a set threshold.

[0105] The instruction unit 360 is configured to generate a supplementary production instruction if the production time length is less than a time length threshold.

[0106] The sending unit 370 is configured to send the supplementary production instruction to a production control device.

[0107] In some embodiments of the present application, based on the foregoing scheme, the quality inspection information of the product comprises: sampling the product based on a product identifier and a set sampling frequency to obtain a sample product; obtaining an image corresponding to the sample product; and performing image analysis on the image by using a pre-trained quality inspection model to obtain the quality inspection information corresponding to the product.

[0108] In some embodiments of the present application, based on the foregoing scheme, the image comprises at least two images; and the image analysis on the image by using the pre-trained quality inspection model to obtain the quality inspection information corresponding to the product comprises: determining a gray scale based on pixel information of the image; determining an image parameter for indicating image definition based on the pixel information and the gray scale; selecting an image with the largest image parameter; and performing image analysis on the image by using the pre-trained quality inspection model to obtain the quality inspection information corresponding to the product.

[0109] In some embodiments of the present application, based on the foregoing scheme, the determination of the production parameter based on the total production and the fault quantity comprises: determining a fault parameter based on a ratio between the fault quantity and the total production; determining a normal parameter based on a difference between the fault quantity and the total production; and determining the production parameter for indicating production quality based on the fault parameter and the normal parameter.

[0110] In some embodiments of the present application, based on the foregoing scheme, the method further comprises: deploying a sensor device and a gateway device in a production environment; and constructing a production monitoring system based on big data among the sensor device, the gateway device, and an upper computer.

[0111] In some embodiments of the present application, based on the foregoing scheme, after obtaining the quality inspection information of the products, if the ratio between the number of failures in the quality inspection information and the total output is greater than or equal to a set threshold, the production line is determined as a failure production line; and the failure production line is repaired.

[0112] In some embodiments of the present application, based on the foregoing scheme, after aggregating the real-time output based on the set statistical period to obtain the corresponding total output in the statistical period, if the difference between the total output and the set output is greater than or equal to a set output threshold, the production line is determined as a high-output line; and the high-output line is used as a priority production line.

[0113] In some embodiments of the present application, based on the foregoing scheme, after determining the production duration required for supplementary production according to the number of failures and a preset output quota if the production parameter is greater than or equal to a set threshold, if the production duration is greater than or equal to a duration threshold, it is determined that no supplementary production is performed.

[0114] In some embodiments of the present application, based on the foregoing scheme, the method further comprises: performing quality inspection on the products of the supplementary production to generate supplementary quality inspection information; and merging and analyzing the supplementary quality inspection information and the quality inspection information to obtain final quality inspection information.

[0115] In an embodiment of the present application, the real-time output sent by the collection device in a unit time is obtained; the real-time output is aggregated based on a set statistical period to obtain the corresponding total output in the statistical period; a production parameter for indicating production quality is determined based on the total output and the number of failures in the quality inspection information; if the production parameter is greater than or equal to a set threshold, a production duration required for supplementary production is determined according to the number of failures and a preset output quota; and if the production duration is less than a duration threshold, a supplementary production instruction is generated and sent to a production control device to instruct the production control device to continue the supplementary production. The technical scheme of the embodiment of the present application improves the efficiency and reliability of production monitoring by monitoring the real-time output based on actual production data and controlling the quality in combination with the production quality.

[0116] Figure 4 A structural schematic diagram of a computer system of an electronic device suitable for implementing the embodiments of the present application is shown.

[0117] It should be noted that, Figure 4 The computer system 400 of the electronic device shown is only an example and should not impose any limitation on the functions and use range of the embodiments of the present application.

[0118] AsFigure 4 As shown, the computer system 400 includes a central processing unit (CPU) 401 which can perform various appropriate actions and processes in accordance with a program stored in a read-only memory (ROM) 402 or a program loaded from the storage section 408 into a random access memory (RAM) 403, such as performing the methods described in the above embodiments. In the RAM 403, various programs and data required for the operation of the system are also stored. The CPU 401, the ROM 402, and the RAM 403 are connected to each other through a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0119] Connected to the I / O interface 405 are an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as necessary. A removable recording medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive 410 as necessary, so that a computer program read therefrom is installed into the storage section 408 as necessary.

[0120] In particular, in accordance with the embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, the embodiments of the present application include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing a computer program for executing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section 409, and / or installed from the removable recording medium 411. When the computer program is executed by the central processing unit (CPU) 401, various functions defined in the system of the present application are performed.

[0121] It should be noted that the computer-readable medium in the embodiments of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination thereof. More specific examples of the computer-readable storage medium can 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), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (Compact Disc Read-Only Memory, CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or apparatus. In the present application, the computer-readable signal medium can include a data signal carrying computer-readable computer programs in a baseband or as a part of a carrier wave. Such a propagated data signal can take various forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, which can send, propagate or transmit programs for use by or in conjunction with an instruction execution system, device or apparatus. The computer programs contained in the computer-readable medium can be transmitted by any suitable medium, including but not limited to wireless, wired, or the like, or any suitable combination thereof.

[0122] The flowcharts and block diagrams in the drawings illustrate the possible implementation architectures, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In the flowcharts or block diagrams, each block can represent a module, a program segment or a part of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in different orders than that shown in the drawings. For example, two blocks represented in succession can actually be executed substantially in parallel, and sometimes in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams or flowcharts, and the combination of blocks in the block diagrams or flowcharts, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0123] The units described in the embodiments of the present application can be implemented by software, or by hardware, or by a combination of software and hardware. The units described can also be located in a single processor. In some cases, the names of the units do not limit the units themselves.

[0124] According to an aspect of the present application, a computer program product or computer program is provided, which includes computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device performs the method provided in the various optional implementation manners described above.

[0125] As another aspect, the present application also provides a computer readable medium, which can be included in the electronic device described in the embodiments above, or can exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to implement the method described in the embodiments above.

[0126] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, the division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into several modules or units.

[0127] From the above description of the embodiments, those skilled in the art will readily appreciate that the example embodiments described herein can be implemented by software and / or by hardware. Accordingly, the technical solutions of the embodiments of the present application can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, or the like) or on a network, and includes a number of instructions for causing a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to perform the methods according to the embodiments of the present application.

[0128] Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice in the art to which the application pertains.

[0129] It is to be understood that the application is not limited to the precise construction herein described and as shown in the drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope thereof. The scope of the application is limited only by the claims appended hereto.

Claims

1. A SaaS-based industrial internet-based real-time output monitoring method, characterized in that, include: Acquire the real-time output transmitted by the acquisition device per unit time; Based on the set statistical period, the real-time output is summarized to obtain the total output corresponding to the statistical period. Obtain product quality inspection information, wherein the quality inspection information includes the number of faulty products that fail quality inspection; Based on the ratio and difference between the total output and the number of failures, production parameters for representing production quality are determined; If the production parameters are greater than or equal to the set threshold, the production time required for supplementary production is determined based on the number of faults and the preset production quota. If the production time is less than the time threshold, a supplementary production instruction is generated; The supplementary production instruction is sent to the production control device; The production parameters are determined based on the total output and the number of failures, including: The fault parameter Par_fau is determined based on the ratio between the number of faults Num_fau and the total output Num_tal: Based on the difference between the number of faults Num_fau and the total output Num_tal, the normal parameter Par_nor is determined as follows: Based on the fault parameter Par_fau and the normal parameter Par_nor, the production parameter Par_pro used to represent production quality is determined as follows: Par_pro=θ·Par_fau+ω·Par_nor Where θ and ω represent preset production factors; Obtaining product quality inspection information includes: Based on the product identifier and the set sampling frequency, the product is sampled to obtain the sampled product; Acquire images corresponding to the sampled products; the images include at least two images; The image is analyzed using a pre-trained quality inspection model to obtain the quality inspection information corresponding to the product. Specifically, the image is analyzed using a pre-trained quality inspection model to obtain the quality inspection information corresponding to the product, including: A grayscale histogram of the image is generated based on the pixel information of the image. The grayscale level f in the grayscale histogram and the number of pixels Pix_i corresponding to each grayscale level are determined. Then, the mean value of the pixels corresponding to each grayscale level is determined as Pix_tal / f based on the total number of pixels Pix_tal. Based on the number of pixels and the number of gray levels, the image parameters representing image sharpness are determined as follows: Where α represents a preset image factor, and i represents a natural number less than or equal to f; The image with the largest image parameters is selected, and image analysis is performed using a pre-trained quality inspection model to obtain the quality inspection information corresponding to the product.

2. The method according to claim 1, characterized in that, The method further includes: Deploy sensor devices and gateway devices in the production environment; Build a big data-based production monitoring system between sensor devices, gateway devices, and host computers.

3. The method according to claim 1, characterized in that, After obtaining the product's quality inspection information, the following is also included: If the ratio between the number of faults in the quality inspection information and the total output is greater than or equal to a set threshold, the production line is determined to be a faulty production line. The faulty production line was repaired.

4. The method according to claim 1, characterized in that, Based on a set statistical period, after summarizing the real-time output to obtain the total output within the statistical period, the process further includes: If the difference between the total output and the set output is greater than or equal to the set output threshold, the production line is determined to be a high-output line. The high-production line will be the preferred production line.

5. The method according to claim 1, characterized in that, If the production parameters are greater than or equal to a set threshold, then after determining the required production time for supplementary production based on the number of faults and the preset production quota, the process further includes: If the production time is greater than or equal to the time threshold, it is determined that no supplementary production will be carried out.

6. The method according to claim 1, characterized in that, The method further includes: Conduct quality inspections on the supplementary products and generate supplementary quality inspection information. The supplementary quality inspection information and the quality inspection information are combined and analyzed to obtain the final quality inspection information.

7. A SaaS-based industrial internet real-time output monitoring system, characterized in that, include: The acquisition unit is used to acquire the real-time output transmitted by the acquisition device per unit time; The summarization unit is used to summarize the real-time output based on a set statistical period to obtain the total output within the statistical period. The quality inspection unit is used to acquire the quality inspection information of the products, wherein the quality inspection information includes the number of faulty products that fail the quality inspection. A parameter unit is used to determine production parameters representing production quality based on the ratio and difference between the total output and the number of faults. The duration unit is used to determine the production duration required for supplementary production based on the number of faults and the preset production quota if the production parameters are greater than or equal to a set threshold. An instruction unit is used to generate a supplementary production instruction if the production time is less than a time threshold. The sending unit is used to send the supplementary production instruction to the production control device; The production parameters are determined based on the total output and the number of failures, including: The fault parameter Par_fau is determined based on the ratio between the number of faults Num_fau and the total output Num_tal: Based on the difference between the number of faults Num_fau and the total output Num_tal, the normal parameter Par_nor is determined as follows: Based on the fault parameter Par_fau and the normal parameter Par_nor, the production parameter Par_pro used to represent production quality is determined as follows: Par_pro=θ·Par_fau+ω·Par_nor Where θ and ω represent preset production factors; Obtaining product quality inspection information includes: Based on the product identifier and the set sampling frequency, the product is sampled to obtain the sampled product; Acquire images corresponding to the sampled products; the images include at least two images; The image is analyzed using a pre-trained quality inspection model to obtain the quality inspection information corresponding to the product. Specifically, the image is analyzed using a pre-trained quality inspection model to obtain the quality inspection information corresponding to the product, including: A grayscale histogram of the image is generated based on the pixel information of the image. The grayscale level f in the grayscale histogram and the number of pixels Pix_i corresponding to each grayscale level are determined. Then, the mean value of the pixels corresponding to each grayscale level is determined as Pix_tal / f based on the total number of pixels Pix_tal. Based on the number of pixels and the number of gray levels, the image parameters representing image sharpness are determined as follows: Where α represents a preset image factor, and i represents a natural number less than or equal to f; The image with the largest image parameters is selected, and image analysis is performed using a pre-trained quality inspection model to obtain the quality inspection information corresponding to the product.

Images