Digital factory data management method and data center

Abstract

The present application is applicable to the field of data management technology, and in particular to a digital factory data management method and a data middle platform. The digital factory data management method includes: obtaining equipment production data of all production equipment in the factory; obtaining equipment scores of all production equipment in the factory based on the equipment production data of all production equipment in the factory; obtaining a list of production equipment for the target product; obtaining a product score of the target product based on the equipment scores of all production equipment in the production equipment list; and generating a first identifier based on the equipment score and the product score. The method improves production efficiency by managing and using data to complete a preliminary evaluation of the factory's production equipment and products.

Description

Technical Field

[0001] The present application belongs to the field of data management technology, and in particular, relates to a digital factory data management method and a data middle platform. Background Art

[0002] A digital factory refers to a new production organization method that uses relevant data from the entire product life cycle as a basis to simulate, evaluate and optimize the entire production process in a computer virtual environment, and further expands it to the entire product life cycle.

[0003] In the existing data management methods of digital factories, the focus is usually on data communication between different devices. There is no detailed method for how to manage and use data. The existing factory production process evaluation method often requires quality inspection of each product and each production equipment, resulting in low evaluation efficiency. Therefore, a method is needed to manage and use data to complete the evaluation of the entire production process of the digital factory. Summary of the invention

[0004] The embodiments of the present application provide a digital factory data management method and a data middle platform, which can solve the problem that the existing production process evaluation method often performs random quality inspections on products and production equipment, resulting in low evaluation efficiency.

[0005] In a first aspect, an embodiment of the present application provides a digital factory data management method, which is applied to a data middle platform, and the method includes: Obtaining equipment production data of all production equipment in the factory; wherein the equipment production data refers to production-related data uploaded by the production equipment during the production process within a preset time period; Obtaining equipment scores for all production equipment in the factory according to the equipment production data of all production equipment in the factory; wherein the equipment scores are used to reflect the production status of the production equipment; Obtaining a list of production equipment for the target product; wherein the list of production equipment refers to a list of all production equipment that have processed the target product; Obtaining a product score of the target product according to the equipment scores of all the production equipment in the production equipment list; wherein the product score is used to reflect the quality of the target product; A first identifier is generated according to the equipment score and the product score; wherein the first identifier is used to allow users or business personnel to query the production process and quality of the product.

[0006] The above technical solutions in the embodiments of the present application have at least the following technical effects: The present application provides a digital factory data management method, first, the equipment production data of all production equipment in the factory is obtained, and the data output by the production equipment is obtained in this step, so that the production status of the production equipment can be analyzed according to the equipment production data. Secondly, the equipment scores of all production equipment in the factory are obtained according to the equipment production data of all production equipment in the factory. In this step, the data output by the production equipment is analyzed, and the production equipment is scored, and the evaluation of the production status of the production equipment is completed through data analysis. Then, the list of production equipment of the target product is obtained, and all production equipment in the production process of an arbitrary product is obtained in this step, which is conducive to completing the evaluation of product quality through data analysis. Then, according to the equipment scores of all production equipment in the list of production equipment of the target product, the product score of the target product is obtained. In this step, the equipment scores of the production equipment in the product path are analyzed, and the product score of the product is estimated, and the preliminary evaluation of product quality is completed through data analysis. Finally, a first identifier is generated according to the equipment score and the product score. In this step, the first identifier is set on the product, and the user or business personnel can query the quality and production process of the product through the first identifier. In this method, the equipment score of the production equipment in the factory is first analyzed, and then the product score of any product is analyzed through the equipment score, and a first mark is generated and set on the product so that users and business personnel can query the physical condition of the product and the physical condition of the production equipment during the production process. By managing and using data, the evaluation of the factory's production equipment and products is completed, thereby improving production efficiency.

[0007] In a second aspect, an embodiment of the present application provides a digital factory data management system, which is applied to a data middle platform, and the system includes: A data acquisition unit, used to obtain equipment production data of all production equipment in the factory; wherein the equipment production data refers to production-related data uploaded by the production equipment during the production process within a preset time period; A first scoring unit, configured to obtain equipment scores of all production equipment in the factory according to the equipment production data of all production equipment in the factory; wherein the equipment scores are used to reflect the production status of the production equipment; The data collection unit is also used to obtain a list of production equipment for the target product; wherein the list of production equipment refers to a list of all production equipment that have processed the target product; A second scoring unit is used to obtain a product score of the target product according to the equipment scores of all the production equipment in the production equipment list; wherein the product score is used to reflect the quality of the target product; A marking unit is used to generate a first identifier according to the device score and the product score; wherein the first identifier is used to allow users or business personnel to query the production process and quality of the product.

[0008] In a third aspect, an embodiment of the present application provides a data middle platform, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any one of the first aspects when executing the computer program.

[0009] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of the first aspects above is implemented.

[0010] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on a digital factory data management device, the digital factory data management device executes the digital factory data management method described in any one of the first aspects above.

[0011] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0013] Figure 1 It is a flowchart of a digital factory data management method provided by an embodiment of the present application; Figure 2 It is a structural diagram of a digital factory data management system provided in an embodiment of the present application; Figure 3 It is a structural diagram of the data center provided in the embodiment of the present application. DETAILED DESCRIPTION

[0014] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0015] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0016] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0017] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0018] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0019] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0020] In the related technology, in the existing data management methods of digital factories, the focus is usually on data communication between different devices, and there is no detailed method for how to manage and use data. The existing factory production process evaluation method often requires quality inspection of each product and each production equipment, which greatly reduces production efficiency, or random quality inspection of products and production equipment, resulting in low efficiency and accuracy. Therefore, a method is needed to manage and use data to complete the evaluation of the entire production process of the digital factory.

[0021] To solve the above problems, the embodiment of the present application provides a digital factory data management method. In the method, first, the equipment production data of all production equipment in the factory is obtained. In this step, the data output by the production equipment is obtained so that the production status of the production equipment can be analyzed according to the equipment production data. Secondly, the equipment scores of all production equipment in the factory are obtained according to the equipment production data of all production equipment in the factory. In this step, the data output by the production equipment is analyzed, and the production equipment is scored. The evaluation of the production status of the production equipment is completed through data analysis. Then, the list of production equipment of the target product is obtained. In this step, all production equipment in the production process of an arbitrary product is obtained, which is conducive to completing the evaluation of product quality through data analysis. Then, according to the equipment scores of all production equipment in the list of production equipment of the target product, the product score of the target product is obtained. In this step, the equipment scores of the production equipment in the product path are analyzed to estimate the product score of the product, and the preliminary evaluation of product quality is completed through data analysis. Finally, a first identifier is generated according to the equipment score and the product score. In this step, the first identifier is set on the product, and the user or business personnel can query the quality and production process of the product through the first identifier. In this method, the equipment score of the production equipment in the factory is first analyzed, and then the product score of any product is analyzed through the equipment score, and a first mark is generated and set on the product so that users and business personnel can query the physical condition of the product and the physical condition of the production equipment during the production process. By managing and using data, the evaluation of the factory's production equipment and products is completed, thereby improving production efficiency.

[0022] The digital factory data management method provided in the embodiment of the present application can be applied to the data middle platform. In this case, the data middle platform is the execution entity of the digital factory data management method provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of the data middle platform.

[0023] For example, the data middle platform is connected to all production equipment in the factory, can receive equipment production data uploaded by all production equipment, and can also obtain a list of production equipment for the target product. The data middle platform can be a single-chip microcomputer, a laptop computer, an ultra-mobile personal computer (UMPC), a desktop computer, a computing device or other processing device connected to a wireless modem, a computer, etc.

[0024] In order to better understand the digital factory data management method provided in the embodiment of the present application, the specific implementation process of the digital factory data management method provided in the embodiment of the present application is exemplarily introduced below.

[0025] Figure 1A schematic flow chart of a digital factory data management method provided in an embodiment of the present application is shown, and the digital factory data management method includes: S100, obtaining equipment production data of all production equipment in the factory, wherein the equipment production data refers to production-related data uploaded by the production equipment during the production process within a preset time period.

[0026] It can be understood that the equipment production data can be data measured and counted by the production equipment itself. For example, the heating production equipment can output internal temperature data through the built-in thermal sensor. It can also be equipment data output by an external sensor. For example, a speed sensor can be installed next to a simple rotary production equipment to output speed data. It can also be quality inspection data of the production equipment or the products produced by maintenance personnel.

[0027] Such an arrangement can diversify the equipment production data, which is conducive to evaluating the production status of the production equipment by analyzing the equipment production data.

[0028] S200, obtaining equipment scores of all production equipment in the factory according to equipment production data of all production equipment in the factory, wherein the equipment scores are used to reflect the production status of the production equipment.

[0029] It can be understood that this step analyzes the production data of the equipment to evaluate the score of the corresponding production equipment, and can score the yield rate of the output components, the production stability of the production equipment, and the working life of the production equipment. The three scores are added with a certain weight to obtain the equipment score. First, evaluate the yield rate of the production components of the production equipment (a single production equipment produces a single component, and multiple components are combined into a product). The yield rate of the production components of the production equipment can be determined by regular manual quality inspection of the components; secondly, the non-conforming components produced by the production equipment may be defective components far away from the good line, or they may be semi-conforming components closer to the good line. The two need to be subdivided, so they can be evaluated from the production stability of the production equipment (non-conforming components produced by production equipment with high stability are more likely to be semi-conforming); then, because as the working life of the production equipment is extended, the production equipment will experience various unexpected sudden failures, so it can be evaluated from the working life of the production equipment.

[0030] With this arrangement, the quality of the production equipment can be effectively estimated by analyzing and evaluating the yield rate of the output components, the production stability of the production equipment, and the working life of the production equipment.

[0031] In a possible implementation, in step S200, the equipment production data includes production time data, component spot check yield rate and total equipment working time; the equipment scores of all production equipment in the factory are obtained according to the equipment production data of all production equipment in the factory, including: S210, obtaining a first deduction value according to the production time data, wherein the production time data refers to the time data of the production equipment producing the components within a preset time, and the first deduction value is used to reflect the instability of the production time of the production equipment.

[0032] It can be understood that the average value and variance of multiple groups of time data of the same production equipment can be calculated, and the stability of the production time of the production equipment can be analyzed through the average value and variance, and then the production time stability of the production equipment can be compared with the production time stability of standard similar production equipment to determine the first deduction value.

[0033] With such arrangement, the detection of production time is very simple, and the method of judging the production stability of production equipment from production time is compatible with various product production factories.

[0034] In a possible implementation, in step S210, obtaining a first deduction value according to the production time data includes: S211, calculating the average value and variance of the production time data to obtain a first mean value and a first variance, wherein the first mean value refers to the average time for the target production equipment to produce the component, and the first variance refers to the variance of the time for the target production equipment to produce the component.

[0035] It can be understood that the unstable offset of production time can be divided into global overall offset and local unstable oscillation. The average value and variance of multiple groups of time data recorded in the production time data are calculated. For example, the production time data includes N groups of time data. The N groups of time data are summed and then divided by the number of groups to obtain the first mean value. The variance is equal to (xi refers to the i-th group of duration data, Refers to the first mean), the mean value is used to analyze the global overall offset of the production equipment, and the variance is used to analyze the local unstable oscillation of the production equipment.

[0036] Such a setting can effectively reflect the distribution law of the production time of the time-consuming equipment.

[0037] S212: Obtain a global production duration offset value according to the first mean value and the benchmark production duration of the corresponding component, wherein the global production duration offset value is used to reflect the overall offset of the production duration of the target production equipment.

[0038] It can be understood that the benchmark production time of the corresponding component can be the average time for multiple new similar production equipment to produce the component. The first mean and the benchmark production time are analyzed and compared to obtain a global production time offset value. The global production time offset value can be a unitless proportional value. The global production time offset value = (|first mean - benchmark production time|) / benchmark production time (the global production time offset value is greater than 0 and less than 1).

[0039] Such a setting can effectively reflect the global deviation degree of the production time of the corresponding production equipment.

[0040] S213, obtaining a local production duration offset value according to the first variance and the maximum tolerance of the corresponding component, wherein the local production duration offset value is used to reflect the local oscillation offset of the production duration of the target production equipment.

[0041] It can be understood that the maximum tolerance is first converted into the maximum time tolerance. The maximum time tolerance can be that the component is produced by a brand new production equipment of the same type, and the specification difference of the component is exactly the difference between the production time at the maximum tolerance and the benchmark production time. Multiple measurements can be taken to get the average value. The first variance and the maximum time tolerance are analyzed and compared to obtain the local production time offset value. The local production time offset value can be a unitless proportional value. The local production time offset value = √(first variance) / maximum time tolerance (the local production time offset value is greater than 0 and less than 1).

[0042] Such a setting can effectively reflect the local instability of the production time of the corresponding production equipment.

[0043] S214, obtaining a first deduction value according to the global production duration offset value and the local production duration offset value.

[0044] It can be understood that the global production time offset value and the local production time offset value are two proportional values. The average proportional value of the two can be taken, and then the average proportional value can be multiplied by the maximum first deduction value to obtain the first deduction value. The maximum first deduction value is related to the properties of the product. For example, for products with high error tolerance, such as fabric products, the maximum first deduction value can be less than 1 / 3×full score, while for products with low error tolerance, such as high-precision optical instruments, the maximum first deduction value can be greater than 1 / 3×full score. A fixed maximum first deduction value table can be set, which stipulates the maximum first deduction values ​​for different types of products, which can standardize the equipment scoring concept proposed in this application.

[0045] Such a setting can standardize the first deduction values ​​of different types of products to improve the compatibility of the device scoring concept and make the device scoring an objective evaluation value.

[0046] S220, obtaining a second deduction value according to the component spot check yield rate, wherein the component spot check yield rate refers to the yield rate of components produced by the production equipment within a preset time period, and the second deduction value is used to reflect the instability of the quality of the components produced by the production equipment.

[0047] It can be understood that the second deduction value = (1-component random inspection yield rate) × maximum second deduction value. The maximum second deduction value is related to the yield threshold set for the product. If there is a fixed and standardized yield evaluation standard in the industry of a product, the standardized yield evaluation standard will be adopted. At this time, the maximum second deduction value of the product is 1 / 3 × the full score. If the product does not have a fixed and standardized yield evaluation standard, the maximum second deduction value can be determined according to the circumstances.

[0048] With such a configuration, the second deduction value can effectively indicate the probability of unqualified quality of components produced by the production equipment.

[0049] S230, obtaining a third deduction value according to the total working time of the equipment, wherein the third deduction value refers to the deduction value corresponding to the working life of the production equipment.

[0050] It can be understood that the third deduction value = ((total working time of equipment - normal life of equipment) / normal life of equipment) × maximum third deduction value (when the third deduction value is less than 0, the third deduction value = 0; when the third deduction value is greater than the maximum third deduction value, the third deduction value = maximum third deduction value). The normal life of the equipment can be the normal continuous working time in the equipment specification. The maximum third deduction value is related to the maximum first deduction value and the maximum second deduction value. The maximum third deduction value is equal to the preset full score minus the maximum first deduction value and the maximum second deduction value.

[0051] With this setting, when the total working time of the equipment is within the range of the normal life of the equipment, the third deduction value = 0. Only when the total working time of the equipment exceeds the normal life of the equipment, the third deduction value > 0, which can effectively and reasonably represent the working life deduction of the production equipment.

[0052] S240, subtract the first deduction value, the second deduction value and the third deduction value from the preset full score to obtain an equipment score for the production equipment.

[0053] It can be understood that the preset full score minus the three deduction values ​​is equal to the equipment score of the production equipment. This evaluation method is applied to all the time equipment in the factory to obtain the equipment score of all the production equipment.

[0054] With such a setting, the equipment score of the production equipment is obtained by analyzing and evaluating the yield rate of the output components, the production stability of the production equipment and the working life of the production equipment, which can effectively represent the production status of the production equipment.

[0055] S300, obtaining a list of production equipment for the target product, wherein the list of production equipment refers to a list of all production equipment that have processed the target product.

[0056] It can be understood that a product can be arbitrarily selected as the target product, and the list of production equipment for the product can be obtained from the data center. A marking device can be set on each production equipment. When the production equipment produces or processes a component, the marking device marks the product with an identifier that uniquely corresponds to the production equipment. Therefore, the identifiers of all production equipment in all routes will be left on a product. When the data center reads the identifier of the product, the list of production equipment can be automatically obtained.

[0057] With this setting, you can easily and quickly obtain the list of production equipment for any product.

[0058] S400: Obtain a product score of the target product according to the equipment scores of all the production equipment in the production equipment list, wherein the product score is used to reflect the quality of the target product.

[0059] It can be understood that after analyzing the data to obtain the equipment scores of all production equipment in the factory, the equipment scores are further analyzed to determine the product score of the product. It can be seen that the product will pass through a series of production equipment to finally become a product, and the equipment scores of the production equipment in the process will affect the quality of the product. Therefore, suppose there are n production equipment in the production equipment list of the target product, and the equipment scores of these n production equipment are Y1, Y2...Yn respectively, then the product score = (Y1×Y2×...×Yn) / preset full score^(n-1).

[0060] With such a configuration, the product rating of the product can be effectively obtained, and the product rating can effectively represent the quality of the product.

[0061] In a possible implementation, in step S400, the product score of the target product is obtained according to the equipment scores of all the production equipment in the production equipment list, including: S410, dividing all the production equipment in the production equipment list of the target product into functional production equipment and packaging production equipment.

[0062] It can be understood that the components of a product can be divided into functional components and packaging components according to their functions. For example, the function of a refrigerator is refrigeration, and the components that maintain the refrigeration effect are the control electronic components and the compressor. Therefore, the control electronic components and the compressor are functional components, and others such as refrigerator doors and refrigerator bodies are packaging components. The production equipment for producing functional components can be called functional production equipment, and the production equipment for producing packaging components can be called packaging production equipment. For example, if the product is a refrigerator, in the current product scoring method, the refrigerator compressor and refrigerator door have the same scoring weight. This product scoring method is obviously unreasonable (the quality of the refrigerator mainly depends on the quality of the compressor and the control electronic components, not the quality of the refrigerator door). Therefore, the components are subdivided into two categories in advance, so that the scoring weight of the scoring method for product scoring can be adjusted in the subsequent steps.

[0063] This setting divides the component production equipment of the product into two categories, which is conducive to improving the rationality of the product rating.

[0064] S420, dividing the equipment score of the functional production equipment by the preset full score to obtain the full score ratio of the functional production equipment, and dividing the equipment score of the packaging production equipment by the preset full score to obtain the full score ratio of the packaging production equipment.

[0065] It can be understood that the equipment scores of the two categories of production equipment are divided by the preset full marks respectively to obtain the full score ratio of functional production equipment and the full score ratio of packaging production equipment.

[0066] This setting normalizes the device scores, which is beneficial for data processing and calculation in subsequent steps.

[0067] S430, multiply all functions by the full score ratio of the production equipment, and then multiply by the preset full score to obtain a function score.

[0068] It can be understood that the method of obtaining the product score in the main step S400 is followed, but the packaging production equipment is removed, leaving only the functional production equipment, to obtain the functional score. There is also a possibility that the product may be a process or packaging product, without functional components and functional production equipment, in which case the functional score can be set to a preset full score.

[0069] With this arrangement, only the equipment rating of functional production equipment is retained for calculating product ratings, which improves the rationality of product ratings.

[0070] S440, obtaining a packaging deduction value according to the full score ratio of all packaging production equipment.

[0071] It can be understood that the packaging deduction value is the defect deduction value for the product's packaging components. It can be obtained by multiplying the full score ratios of all packaging production equipment, and then multiplying it by a fixed maximum packaging deduction value. For example, if the maximum packaging deduction value is 30% of the preset full score, then the packaging deduction value at this time ∈(0, 30%×preset full score].

[0072] This setting adds the influence of packaging components and improves the rationality of product ratings.

[0073] In a possible implementation, in step S440, obtaining a packaging deduction value according to the full score ratio of all packaging production equipment for the selected product includes: S441, multiply the full score ratios of all packaging production equipment to obtain a first product.

[0074] It can be understood that the first product is used to reflect the defect ratio of the packaging component of the product.

[0075] S442, obtaining a packaging deduction value according to the first product and the maximum packaging score, wherein the maximum packaging score is determined according to the type information and attribute information of the target product.

[0076] It is understandable that the packaging of products of different types and attributes has different effects. For example, the packaging of a vase is its entire value. Another example is a water cup. Although its functional components such as thermal insulation are important, the packaging of the water cup is also its main value. Therefore, it is obviously unreasonable to use a fixed maximum packaging deduction value. The maximum packaging score should be determined based on the type and attribute information of the target product. A fixed maximum packaging score table can be constructed, which standardizes the maximum packaging scores of different types of products.

[0077] This setting improves the rationality of product ratings.

[0078] S450, subtract the packaging penalty from the function score to get the product score.

[0079] It can be understood that the functional score is used as the baseline value, and the packaging deduction value is subtracted to obtain the product score.

[0080] With such settings, product ratings are obtained using different data processing methods, and the product ratings obtained using these methods are more reasonable.

[0081] S500: Generate a first identifier according to the equipment score and the product score, wherein the first identifier is used to allow users or business personnel to query the production process and quality of the product.

[0082] It can be understood that the first identifier can be a barcode or a QR code. When the user or business personnel operates the data center to analyze the first identifier, the product rating of the product and the equipment rating of the production equipment that has processed the target product during the production process can be obtained to understand the production process and quality of the product.

[0083] This setup makes the product quality and production process transparent, which helps gain consumers’ trust and can further increase sales.

[0084] Optionally, the method further includes: S610, obtaining a product number of a target product, wherein a product number uniquely corresponds to one product.

[0085] It can be understood that the product number of the target product can be generated in the format of "production date" + "production equipment identification" + "randomly generated digital sequence", where "production equipment identification" refers to the identification described in step S300, and the product number is verified so that products with the same "production date" and "production equipment identification" will not have the same "randomly generated digital sequence", and the product number can be laser printed on the product surface.

[0086] This arrangement forms a double insurance with the first identification, and even without an Internet connection, the information on the product's production date, product route, and production equipment can be obtained.

[0087] S620, query the product rating of the target product and the equipment rating of the production equipment in the production process by using the product number.

[0088] It can be understood that you can connect to the data center through the network, and after entering the product number, you can obtain the product rating of the product and the equipment rating of the production equipment that has processed the target product during the production process to understand the production process and quality of the product.

[0089] This setup makes the product quality and production process transparent, which helps gain consumers’ trust and can further increase sales.

[0090] Optionally, the method further includes: S630: If the product score of the target product is lower than the first threshold, perform a quality inspection on the target product.

[0091] It can be understood that when the product score is lower than the first threshold, the corresponding product will be marked as a product to be inspected, and then the staff can perform quality inspection on the target product. The size of the first threshold can be the expected yield rate of the product.

[0092] With this setup, by managing and applying data, we can analyze the products that are most likely to be defective among all products, eliminating the need to conduct quality inspection on each product one by one, greatly increasing production efficiency.

[0093] S640: If the equipment score of the target production equipment is lower than the second threshold, the target production equipment is repaired.

[0094] It can be understood that when the equipment score is lower than the second threshold, the corresponding production equipment will be marked as production equipment to be repaired, and then the staff can repair the target production equipment. The size of the second threshold can be the expected yield rate of the components produced by the production equipment.

[0095] With this setup, by managing and using data, we can analyze and obtain the equipment scores of all production equipment, and conduct quality inspection on some production equipment with the lowest equipment scores, thereby improving the quality inspection efficiency of production equipment.

[0096] Optionally, the method further includes: S650, performing random quality inspection on products with a product score higher than a first threshold, and raising the first threshold if the qualified rate is lower than an expected threshold.

[0097] It is understandable that when the number of products produced by the factory is equal to the preset number, a product with a score higher than the first threshold is randomly selected from the preset number of products and quality inspection is carried out, and then the steps of random selection and quality inspection are repeated. When the number of repetitions reaches N times, the results of N random quality inspections are counted to obtain the qualified rate of the product. When the qualified rate is less than the expected threshold, the first threshold is increased by a fixed value. There is also a possibility that when the first threshold reaches the upper limit and cannot be increased, the terminal is prompted to lower the qualified requirements of the product or update the production equipment.

[0098] With such a setting, the first threshold is adjusted dynamically, which can effectively improve the yield rate of the produced products.

[0099] S660, performing random maintenance on production equipment with equipment scores higher than the second threshold, and raising the second threshold if the qualified rate is lower than the expected threshold.

[0100] It can be understood that when the number of products produced by the factory is equal to the preset number, a production equipment with a device score higher than the second threshold can be randomly selected from all the production equipment and repaired, and then the steps of random selection and repair are repeated. When the number of repetitions reaches M times, the results of M random repairs are counted to obtain the qualified rate of the production equipment. When the qualified rate is less than the expected threshold, the second threshold is increased by a fixed value. There is also a possibility that when the second threshold reaches the upper limit and cannot be increased, the terminal is prompted to lower the qualified requirements of the production equipment or update the production equipment.

[0101] By setting in this way, the second threshold is adjusted dynamically, which can effectively improve the yield rate of components produced by the production equipment.

[0102] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0103] Corresponding to the digital factory data management method described in the above embodiment, the embodiment of the present application also provides a digital factory data management system, and each module of the system can implement each step of the digital factory data management method. Figure 2 A structural block diagram of a digital factory data management system provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0104] Reference Figure 2 The system comprises: A data acquisition unit is used to obtain equipment production data of all production equipment in the factory; wherein equipment production data refers to production-related data uploaded by production equipment during the production process within a preset time period; A first scoring unit is used to obtain equipment scores of all production equipment in the factory according to equipment production data of all production equipment in the factory; wherein the equipment scores are used to reflect the production status of the production equipment; The data collection unit is also used to obtain a list of production equipment for the target product; wherein the list of production equipment refers to a list of all production equipment that have processed the target product; The second scoring unit is used to obtain a product score of the target product according to the equipment scores of all the production equipment in the production equipment list; wherein the product score is used to reflect the quality of the target product; The marking unit is used to generate a first identifier according to the equipment score and the product score; wherein the first identifier is used to allow users or business personnel to query the production process and quality of the product.

[0105] It should be noted that the information interaction, execution process and other contents between the above-mentioned units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0106] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the division of the above-mentioned functional units is used as an example for illustration. In practical applications, the above-mentioned functions can be assigned to different functional units as needed, that is, the internal structure of the device can be divided into different functional units to complete all or part of the functions described above. The functional units in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0107] The present application embodiment also provides a data middle platform, Figure 3 This is a schematic diagram of the structure of the data center provided in one embodiment of the present application. Figure 3 As shown, the data center 3 of this embodiment includes: at least one processor 30 ( Figure 3 Only one is shown), at least one memory 31 ( Figure 3 Only one is shown in the figure) and a computer program 32 stored in the at least one memory 31 and executable on the at least one processor 30. When the processor 30 executes the computer program 32, the data middle platform 3 implements the steps in any of the above-mentioned digital factory data management method embodiments, or the data middle platform 3 implements the functions of each unit in the above-mentioned device embodiments.

[0108] Exemplarily, the computer program 32 may be divided into one or more units, which are stored in the memory 31 and executed by the processor 30 to complete the present application. The one or more units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 32 in the data center 3.

[0109] The data center 3 may be a single-chip microcomputer, a microprocessor, a mobile phone, a tablet computer, a wearable device, a vehicle-mounted device, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), a desktop computer, a smart screen, a smart TV, or a handheld device with wireless communication function. The data center 3 may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will understand that Figure 3 It is only an example of the data middle platform 3 and does not constitute a limitation of the data middle platform 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, buses, etc.

[0110] The processor 30 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0111] In some embodiments, the memory 31 may be an internal storage unit of the data platform 3, such as a hard disk or memory of the data platform 3. In other embodiments, the memory 31 may also be an external storage device of the data platform 3, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the data platform 3. Furthermore, the memory 31 may also include both an internal storage unit and an external storage device of the data platform 3. The memory 31 is used to store operating systems, applications, boot loaders (BootLoader), data and other programs, such as the program code of the computer program. The memory 31 can also be used to temporarily store data that has been output or is to be output.

[0112] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0113] An embodiment of the present application provides a computer program product. When the computer program product runs on a data middle platform, the data middle platform implements the steps in any of the above-mentioned method embodiments.

[0114] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the data platform, a recording medium, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electrical carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0115] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0116] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0117] In the embodiments provided in the present application, it should be understood that the disclosed digital factory data management method, digital factory data management system and data middle platform can be implemented in other ways. For example, the digital factory data management method, digital factory data management system and data middle platform embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0118] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0119] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A digital factory data management method, characterized in that: Applied to the data center, the method includes: Obtaining equipment production data of all production equipment in the factory; wherein the equipment production data refers to production-related data uploaded by the production equipment during the production process within a preset time period; Obtaining equipment scores for all production equipment in the factory according to the equipment production data of all production equipment in the factory; wherein the equipment scores are used to reflect the production status of the production equipment; Obtaining a list of production equipment for the target product; wherein the list of production equipment refers to a list of all production equipment that have processed the target product; Obtaining a product score of the target product according to the equipment scores of all the production equipment in the production equipment list; wherein the product score is used to reflect the quality of the target product; A first identifier is generated according to the equipment score and the product score; wherein the first identifier is used to allow users or business personnel to query the production process and quality of the product.

2. The digital factory data management method according to claim 1, characterized in that The equipment production data includes production time data, component spot check yield rate and total equipment working time; The equipment scores of all production equipment in the factory are obtained according to the equipment production data of all production equipment in the factory, including: A first deduction value is obtained according to the production time data; wherein the production time data refers to the time data of the production equipment producing the components within a preset time, and the first deduction value is used to reflect the instability of the production time of the production equipment; A second deduction value is obtained according to the component spot check yield rate; wherein the component spot check yield rate refers to the yield rate of components produced by the production equipment within a preset time period, and the second deduction value is used to reflect the instability of the quality of the components produced by the production equipment; Obtain a third deduction value according to the total working time of the equipment; wherein the third deduction value is a deduction value corresponding to the working life of the production equipment; The equipment score of the production equipment is obtained by subtracting the first deduction value, the second deduction value and the third deduction value from the preset full score.

3. The digital factory data management method according to claim 2, characterized in that: The obtaining a first deduction value according to the production time data includes: Calculate the average value and variance of the production time data to obtain a first mean value and a first variance; wherein the first mean value refers to the average time for the target production equipment to produce the component, and the first variance refers to the variance of the time for the target production equipment to produce the component; According to the first mean value and the benchmark production time of the corresponding component, a global production time offset value is obtained; wherein the global production time offset value is used to reflect the overall offset of the production time of the target production equipment; According to the first variance and the maximum tolerance of the corresponding element, a local production time offset value is obtained; wherein the local production time offset value is used to reflect the local oscillation offset of the production time of the target production equipment; The first deduction value is obtained according to the global production duration offset value and the local production duration offset value.

4. The digital factory data management method according to claim 1, characterized in that: The obtaining of the product score of the target product according to the equipment scores of all the production equipment in the production equipment list includes: Divide all the production equipment in the list of production equipment for the target product into functional production equipment and packaging production equipment; The equipment score of the functional production equipment is divided by the preset full score to obtain the full score ratio of functional equipment, and the equipment score of the packaging production equipment is divided by the preset full score to obtain the full score ratio of packaging equipment; Multiply all the functions by the device full score ratio, and then multiply by the preset full score to obtain the function score; Determine the packaging deduction value according to the full score ratio of all the packaging equipment; The product score is obtained by subtracting the packaging deduction value from the function score.

5. The digital factory data management method according to claim 4, characterized in that: The packaging deduction value obtained according to the full score ratio of all the packaging equipment includes: Multiplying the full score ratios of all the packaging equipment to obtain a first product; The packaging deduction value is obtained according to the first product and the maximum packaging score; wherein the maximum packaging score is determined according to the type information and attribute information of the target product.

6. The digital factory data management method according to claim 1, characterized in that: The method further comprises: Obtain the product number of the target product; wherein the product number uniquely corresponds to one product; The product rating of the target product and the equipment rating of the production equipment in the production process are queried through the product number.

7. The digital factory data management method according to claim 1, characterized in that: The method further comprises: If the product score of the target product is lower than a first threshold, performing a quality inspection on the target product; If the equipment score of the target production equipment is lower than a second threshold, the target production equipment is repaired.

8. The digital factory data management method according to claim 7, characterized in that: The method further comprises: Performing random quality inspection on products with a product score higher than the first threshold, and raising the first threshold if the qualified rate is lower than the expected threshold; Random maintenance is performed regularly at the first frequency on the production equipment whose equipment score is higher than a second threshold, and if the qualified rate is lower than the expected threshold, the second threshold is increased.

9. A digital factory data management system, characterized in that: Used to implement the method according to any one of claims 1 to 8, the system is applied to a data middle station, and the system includes: A data acquisition unit, used to obtain equipment production data of all production equipment in the factory; wherein the equipment production data refers to production-related data uploaded by the production equipment during the production process within a preset time period; A first scoring unit, configured to obtain equipment scores of all production equipment in the factory according to the equipment production data of all production equipment in the factory; wherein the equipment scores are used to reflect the production status of the production equipment; The data collection unit is also used to obtain a list of production equipment for the target product; wherein the list of production equipment refers to a list of all production equipment that have processed the target product; A second scoring unit is used to obtain a product score of the target product according to the equipment scores of all the production equipment in the production equipment list; wherein the product score is used to reflect the quality of the target product; A marking unit is used to generate a first identifier according to the device score and the product score; wherein the first identifier is used to allow users or business personnel to query the production process and quality of the product.

10. A data middle platform, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

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