Product in-and-out process supervision methods, equipment and media based on the Industrial Internet

Abstract

The embodiments of this specification disclose a method, device, and medium for monitoring product in-and-out warehouse processes based on the industrial Internet. The method includes: obtaining key in-and-out warehouse data of products in the production workshop based on a pre-installed sensor network in the production workshop in the monitored area; linking the key in-and-out warehouse data of products in each production workshop based on a preset digital thread to obtain the product data to be analyzed in the monitored area; performing business decoupling on the product data to be analyzed based on a preset industrial driver to obtain a microservice set corresponding to the product data to be analyzed; orchestrating each microservice combination in the microservice set to form a business combination microservice in the monitored area; obtaining transaction data corresponding to the product if the production of the product in the production workshop is completed, determining a product identification code based on the key in-and-out warehouse data of the product and the data corresponding to the product processing at each stage and the transaction data; and uploading the product identification code to the enterprise point corresponding to the secondary node based on the industrial Internet.

Description

Technical Field

[0001] This specification relates to the field of computer applications, and in particular to a method, equipment, and medium for supervising product inbound and outbound processes based on the industrial Internet. Background Art

[0002] With increasing market demand, the development of social resources, and the rapid growth of the logistics industry, user demands for products are becoming increasingly diverse and intensive in quantity. Furthermore, with the increasing uncertainty in production, the production requirements for various products are becoming increasingly demanding. Consequently, to balance the space utilization of production or sales warehouses with the disposal costs of inventory and provide users with the right products, warehouse management for factories and enterprises has become increasingly complex. Warehouse management encompasses both product shipment and product receipt, and the product shipment and receipt process is a key focus of factory management.

[0003] Currently, the inbound and outbound management of goods is achieved through hierarchical scheduling of different types of workshop workers. However, manual monitoring and management are inefficient and outdated, directly or indirectly affecting production efficiency and market benefits, resulting in lagging production efficiency.

[0004] Therefore, a method is needed to automatically control the product entry and exit processes. Summary of the Invention

[0005] One or more embodiments of this specification provide a method for supervising product entry and exit processes based on the Industrial Internet, which is used to solve the following technical problem: how to provide a method for effectively controlling and managing product entry and exit processes.

[0006] One or more embodiments of this specification adopt the following technical solutions:

[0007] One or more embodiments of this specification provide a method for supervising product inbound and outbound processes based on the Industrial Internet, including:

[0008] Based on the pre-installed sensor network of the production workshop in the monitored area, key inbound and outbound data of the products in the production workshop is obtained; wherein the pre-installed sensor network is composed of sensors pre-installed in the product production equipment and sensors in the workshop environment;

[0009] Based on a preset digital thread, the key inbound and outbound data of the products in each of the production workshops are linked to obtain the product data to be analyzed in the area to be monitored;

[0010] Decoupling the product data to be analyzed from the business according to a preset industrial driver to obtain a microservice set corresponding to the product data to be analyzed;

[0011] According to the current business process of the area to be monitored, the microservices in the microservice set are combined and orchestrated to form a business combination microservice of the area to be monitored, so as to realize the product processing data of the next stage in each of the production workshops based on the business combination microservice;

[0012] If the production of the product in the production workshop is completed, the transaction data corresponding to the product is obtained, and the product identification code of the product is determined based on the key inbound and outbound data of the product, the product processing data of each stage, and the transaction data;

[0013] Based on the industrial Internet, the product identification code is uploaded to the enterprise node corresponding to the secondary node of the identification resolution to realize the monitoring of the product's in and out of storage process.

[0014] Optionally, in one or more embodiments of this specification, obtaining key inbound and outbound data of products in the production workshop based on a pre-installed sensor network in the production workshop in the monitored area specifically includes:

[0015] Collecting product images of products in the production workshop using a first sensor in a preset sensor network, wherein the products include products to be put into storage and products to be shipped out;

[0016] Obtain several sample images with pre-labeled product information areas, extract text information and pixel values ​​from the sample images as training features, and input them into a product classification training model for training to obtain a product classification model that meets the requirements;

[0017] Extracting text information and pixel values ​​from the product image as features to be identified in the product image, and inputting the features to be identified into the product classification model to determine the product name, product type, and product damage value corresponding to the product image;

[0018] Based on the product name, product type and product damage value, the products in the production workshop whose in-and-out weight values ​​in the next preset time period are greater than the preset weight threshold are determined as key products, and the in-and-out data of the key products are used as the key in-and-out data of the products.

[0019] Optionally, in one or more embodiments of the present specification, determining, based on the product name, the product type, and the product damage value, key products in the production workshop whose in / out weight values ​​within the next cycle are greater than a preset weight threshold, and using the in / out data of the key products as the key in / out data of the product, specifically includes:

[0020] If it is determined that the damage value of the product is greater than the preset damage threshold, the product is marked as a product to be re-inspected to prevent the product from being put into or taken out of the warehouse;

[0021] If it is determined that the product damage value is less than the preset damage threshold, then obtaining product in-and-out data corresponding to the product name and product type within a preset time period; wherein the product in-and-out data includes: in-and-out product name, in-and-out quantity, in-and-out interval frequency, out-and-out flow direction, and in-and-out source;

[0022] Obtaining the total in-and-out quantity of the product within a preset time period from a preset database, and determining the remaining in-and-out quantity of the product based on the total in-and-out quantity and the target in-and-out quantity of the product;

[0023] Determine the probability of the product entering or leaving the warehouse within the next preset time period based on the remaining in-and-out quantity of the product and the in-and-out interval frequency, and use the in-and-out probability as a first weight coefficient;

[0024] Obtaining a corresponding target inbound and outbound enterprise based on the outbound flow direction and the inbound source, and using the reputation value of the target inbound and outbound enterprise as a second weight coefficient;

[0025] The weighted value of the first weight coefficient and the second weight coefficient is obtained as the in-and-out weight value of the product. If the in-and-out weight value of the product is greater than the preset weight threshold, the product is regarded as a key product, and the in-and-out data of the key product is obtained as the key in-and-out data of the product.

[0026] Optionally, in one or more embodiments of the present specification, the method further comprises: collecting vibration signals of each of the product production equipment in the production workshop to be monitored based on a pre-installed sensor network in the production workshop to be monitored, so as to perform fault monitoring on the product production equipment based on the vibration signals;

[0027] The performing fault monitoring on the product production equipment based on the vibration signal specifically includes:

[0028] If it is determined that the damage value of the product is greater than a preset damage threshold, obtaining a vibration signal of the production equipment corresponding to the product collected by the preset sensor network;

[0029] Obtaining first power information corresponding to the corresponding production equipment during normal operation;

[0030] Performing a wavelet transform on the first power information based on a preset order to obtain a first wavelet coefficient and a second wavelet coefficient; wherein the first wavelet coefficient is used to represent an approximate feature of the first power information, and the second wavelet coefficient is used to represent a detailed feature of the first power information;

[0031] Performing the wavelet transform on the oscillation signal to obtain a third wavelet coefficient and a fourth wavelet coefficient; wherein the third wavelet coefficient corresponds to the first wavelet coefficient, and the fourth wavelet coefficient corresponds to the second wavelet coefficient;

[0032] Based on the correlation detection algorithm, a first correlation between the first wavelet coefficient and the third wavelet coefficient, and a second correlation between the second wavelet coefficient and the fourth wavelet coefficient are respectively obtained, and the operating status of the product production equipment is determined according to the first correlation and the second correlation; wherein the operating status includes: normal operating status and abnormal operating status.

[0033] In one or more embodiments of this specification, determining the operating status of the product production equipment according to the correlation specifically includes:

[0034] If it is determined that both the first correlation and the second correlation are greater than a preset first threshold, it is determined that the product production equipment is in a normal operating state;

[0035] If it is determined that the first correlation and the second correlation are both less than a preset second threshold and greater than a preset third threshold, it is determined that the product production equipment is in a first abnormal operating state; wherein the preset second threshold is less than the preset first threshold;

[0036] If it is determined that both the first correlation and the second correlation are smaller than the preset third threshold, it is determined that the product production equipment is in a second abnormal operating state.

[0037] Optionally, in one or more embodiments of the present specification, combining and orchestrating the microservices in the microservice set according to the current business process of the area to be monitored to form the business combination microservice of the area to be monitored specifically includes:

[0038] Randomly extract each of the microservices in the microservice set to determine multiple initialization sets of microservices to be combined;

[0039] Determining the fitness value of each microservice in the initialization set based on a preset objective function, and determining the set fitness value of the initialization set based on the sum of the fitness values ​​of the microservices, and taking the initialization set corresponding to the minimum value of the set fitness values ​​as the optimal solution of the business combination microservice;

[0040] Iteratively training each of the initialization sets and obtaining a minimum value among the first set degree values ​​of the business combination microservice after the iterative training; if the minimum value is less than the optimal solution of the business combination microservice, then using the minimum value among the first set degree values ​​as the current optimal solution of the business combination microservice;

[0041] The microservices are combined and orchestrated based on the arrangement order of the microservices in the current optimal solution of the business combination microservice to form the business combination microservice of the area to be monitored.

[0042] Optionally, in one or more embodiments of the present specification, determining the product identification code of the product based on the key inbound and outbound data of the product, the data corresponding to the product processing at each stage, and the transaction data specifically includes:

[0043] Sending a registration request to an enterprise node, wherein the registration information includes workshop information corresponding to the area to be monitored;

[0044] Acquire, according to the workshop information, an enterprise code assigned to the enterprise node, an enterprise node number corresponding to the enterprise node, and a secondary node number and a top node number corresponding to the secondary node and the top node of the enterprise node;

[0045] Based on preset coding rules, determine a preset coding template corresponding to the key inbound and outbound data of the product, the data corresponding to the product processing at each stage, and the transaction data;

[0046] Encode the enterprise code, the enterprise node number, the secondary node number and the top node number based on the preset coding template to generate an initial coding identification for the product;

[0047] The initial coding identifier is bound to the key in-and-out warehouse data of the product, the data corresponding to the product processing at each stage, and the transaction data to obtain the product identification code of the product.

[0048] Optionally, in one or more embodiments of this specification, the method further includes:

[0049] Collecting environmental parameters of the workshop based on a second sensor in the pre-installed sensor network; wherein the environmental parameters include at least one or more of the following: temperature, humidity, and light intensity;

[0050] Obtaining a value of a preset standard environmental parameter of the product, and determining an environmental hazard level of the workshop based on a difference between the value of the environmental parameter of the workshop and the value of the preset standard environmental parameter;

[0051] The environmental hazard level, the key inbound and outbound data of the product, and the operating status of the product production equipment are converted into chart data, and the chart data is uploaded to a display device that matches the area to be monitored to achieve remote monitoring of the product entry and exit processes.

[0052] One or more embodiments of this specification provide a product inbound and outbound process monitoring device based on the Industrial Internet, including:

[0053] at least one processor; and,

[0054] a memory communicatively connected to the at least one processor; wherein,

[0055] The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to:

[0056] Based on the pre-installed sensor network of the production workshop in the monitored area, key inbound and outbound data of the products in the production workshop is obtained; wherein the pre-installed sensor network is composed of sensors pre-installed in the product production equipment and sensors in the workshop environment;

[0057] Based on a preset digital thread, the key inbound and outbound data of the products in each of the production workshops are linked to obtain the product data to be analyzed in the area to be monitored;

[0058] Decoupling the product data to be analyzed from the business according to a preset industrial driver to obtain a microservice set corresponding to the product data to be analyzed;

[0059] According to the current business process of the area to be monitored, the microservices in the microservice set are combined and orchestrated to form a business combination microservice of the area to be monitored, so as to realize the product processing data of the next stage in each of the production workshops based on the business combination microservice;

[0060] If the production of the product in the production workshop is completed, the transaction data corresponding to the product is obtained, and the product identification code of the product is determined based on the key inbound and outbound data of the product, the product processing data of each stage, and the transaction data;

[0061] Based on the industrial Internet, the product identification code is uploaded to the enterprise node corresponding to the secondary node of the identification resolution to realize the monitoring of the product's in and out of storage process.

[0062] One or more embodiments of this specification provide a non-volatile computer storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to:

[0063] Based on the pre-installed sensor network of the production workshop in the monitored area, key inbound and outbound data of the products in the production workshop is obtained; wherein the pre-installed sensor network is composed of sensors pre-installed in the product production equipment and sensors in the workshop environment;

[0064] Based on a preset digital thread, the key inbound and outbound data of the products in each of the production workshops are linked to obtain the product data to be analyzed in the area to be monitored;

[0065] Decoupling the product data to be analyzed from the business according to a preset industrial driver to obtain a microservice set corresponding to the product data to be analyzed;

[0066] According to the current business process of the area to be monitored, the microservices in the microservice set are combined and orchestrated to form a business combination microservice of the area to be monitored, so as to realize the product processing data of the next stage in each of the production workshops based on the business combination microservice;

[0067] If the production of the product in the production workshop is completed, the transaction data corresponding to the product is obtained, and the product identification code of the product is determined based on the key inbound and outbound data of the product, the product processing data of each stage, and the transaction data;

[0068] Based on the industrial Internet, the product identification code is uploaded to the enterprise node corresponding to the secondary node of the identification resolution to realize the monitoring of the product's in and out of storage process.

[0069] At least one of the above technical solutions adopted in the embodiments of this specification can achieve the following beneficial effects:

[0070] Connecting the machines, equipment, and sensors in the workshop to form a sensor network, this sensor network is used to collect key product in-and-out data, enabling rapid data acquisition and transmission, accelerating the product in-and-out process. Based on a preset digital thread, the key in-and-out data of products in each production workshop is linked to obtain the product data to be analyzed in the monitored area. This allows the business to decouple the analyzed product data into different microservices through the industrial driver program, and reassemble them into a business combination microservice to support product circulation. The data is uploaded to the enterprise node under the secondary node of the identity resolution, allowing the enterprise node to trace the product in-and-out process, ensuring the traceability of the product circulation process and improving the efficiency of supervision. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are only some of the embodiments described in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without inventive work. In the drawings:

[0072] Figure 1 A flowchart of a method for supervising product inbound and outbound processes based on the Industrial Internet provided in an embodiment of this specification;

[0073] Figure 2 A schematic diagram of the internal structure of a product in-and-out warehousing process monitoring device based on the Industrial Internet provided in an embodiment of this specification;

[0074] Figure 3 A schematic diagram of the internal structure of a non-volatile storage medium provided in an embodiment of this specification. DETAILED DESCRIPTION

[0075] The embodiments of this specification provide a method, device and medium for supervising product in-and-out warehouse processes based on the Industrial Internet.

[0076] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this specification without creative work should fall within the scope of protection of this specification.

[0077] like Figure 1 As shown, the embodiment of this specification provides a method flow chart of a method for monitoring product in and out of warehouse process based on industrial Internet. Figure 1 The known method includes the following steps:

[0078] S101: Based on a pre-installed sensor network of a production workshop in a monitored area, key inbound and outbound data of products in the production workshop is obtained; wherein the pre-installed sensor network is composed of product production equipment in the production workshop and sensors pre-installed on the product production equipment.

[0079] With the development of society, people's demand for commodities and products is getting higher and higher, and the quality requirements for products are also getting higher and higher. In order to avoid product damage caused by mechanical problems or human factors during the process of product delivery or warehousing, which will affect the next step of the warehousing process, as well as the economic losses of the company that performs the warehousing operation and the next-level user after delivery. In one or more embodiments of this specification, in the process of warehousing and warehousing of products, sensors are pre-installed in the environment of the production workshop and on the product production equipment to form a sensor network. According to the pre-installed sensor network of the raw tea workshop in the area to be monitored, the key warehousing and warehousing data of the products in the production workshop are obtained.

[0080] Specifically, in one or more embodiments of this specification, based on a pre-installed sensor network in a production workshop in a monitored area, key inbound and outbound data of products in the production workshop is obtained, which specifically includes the following steps:

[0081] First, a first sensor in a pre-installed sensor network captures images of products in the production workshop. It is understood that products include products waiting to be stored and products waiting to be shipped. To accurately identify relevant product information, several sample images with pre-labeled product information areas are obtained. Textual information and pixel values ​​from these sample images are extracted as training features and input into a product classification training model for training to obtain a qualified product classification model. It is understood that the textual features can include textual information such as the name on the product packaging, thereby determining the product name and type. Pixel values ​​can be used to determine product quality issues and thus determine product damage. For example, if the pixel values ​​in a certain area are significantly lower than historical pixel values, this indicates a product quality issue in that area. The corresponding product damage value can be determined based on the pixel value difference and historical empirical data. For example, when a conventional paper box is eroded by water vapor, the color of the box darkens, causing the corresponding pixel value in that area to decrease. The pixel difference can then be used to determine the extent of damage. After obtaining a qualified product classification model, the text information and pixel values ​​in the product image are extracted as the features to be identified. These features are then input into the qualified product classification model to determine the product name, product type, and product damage value corresponding to the product image. Based on the product name, product type, and product damage value, products in the production workshop with an inbound and outbound weight greater than a preset weight threshold within the next preset time period are identified as key products. The inbound and outbound data of these key products is then used as the key inbound and outbound data of the products.

[0082] Specifically, in one or more embodiments of the present specification, in order to ensure the business interests of the enterprise and the balance of incoming and outgoing products, based on the product name, product type, and product damage value, key products in the production workshop whose incoming and outgoing weight values ​​within the next cycle are greater than a preset weight threshold are determined, and the incoming and outgoing data of the key products are used as the key incoming and outgoing data of the products. Specifically, the following steps are included:

[0083] If the product damage value is determined to be greater than a preset damage threshold, the product is marked as awaiting re-inspection to prevent further economic losses from the product's entry or exit. If the product damage value is determined to be less than the preset damage threshold, product inbound and outbound data corresponding to the product name and product type within a preset time period is obtained. It should be noted that product inbound and outbound data includes: inbound and outbound product name, inbound and outbound quantity, inbound and outbound interval frequency, outbound flow direction, and inbound source. After obtaining the corresponding product inbound and outbound data, the total inbound and outbound volume of the product within the preset time period is obtained from a preset database. Based on the total inbound and outbound volume and the target inbound and outbound volume of the product, the remaining inbound and outbound volume of the product is determined. Based on the remaining inbound and outbound volume and the inbound and outbound interval frequency, the probability of the product's entry and exit within the next preset time period is determined, and this probability is used as the first weighting factor. To prevent the influence of dishonest enterprises on the product inbound and outbound process, the corresponding target inbound and outbound enterprises are obtained based on the outbound flow direction and inbound source, and the reputation of the target inbound and outbound enterprises is used as the second weighting factor. The weighted value of the first weight coefficient and the second weight coefficient is obtained as the in-and-out weight value of the product. If the in-and-out weight value of the product is greater than the preset weight threshold, the product is regarded as a key product, and the in-and-out data of the key product is obtained as the key in-and-out data of the product.

[0084] Furthermore, in order to avoid the problem of interruption of product entry and exit processes caused by failure of product production equipment, in one or more embodiments of this specification, after obtaining the key entry and exit data of products in the production workshop based on the pre-installed sensor network in the production workshop in the monitored area, the method also includes the following steps: first, according to the pre-installed sensor network in the production workshop to be monitored, the vibration signals of each product production equipment in the production workshop are collected, and then the product production equipment is monitored for faults based on the collected vibration signals.

[0085] Specifically, in one or more embodiments of this specification, fault monitoring of product production equipment based on vibration signals specifically includes the following process:

[0086] If the product damage value determined by the above process is greater than a preset damage threshold, a vibration signal from the production equipment corresponding to the product, collected by the preset sensor network, is obtained. First power information corresponding to the corresponding production equipment during normal operation is obtained, and the first power information is subjected to a wavelet transform based on a preset order to obtain first and second wavelet coefficients. It should be noted that the first wavelet coefficients are used to represent the approximate characteristics of the first power information, while the second wavelet coefficients are used to represent the detailed characteristics of the first power information. Similarly, a wavelet transform is performed on the oscillation signal to obtain third and fourth wavelet coefficients. The third wavelet coefficients and the first wavelet coefficients are used to represent the approximate characteristics of the oscillation signal power information, while the fourth wavelet coefficients and the second wavelet coefficients are used to represent the detailed characteristics of the oscillation signal power information. A first correlation between the first and third wavelet coefficients, and a second correlation between the second and fourth wavelet coefficients, are obtained using a correlation detection algorithm. Based on the first and second correlations, the operating status of the product production equipment is determined. It is understood that the operating status includes normal operation and abnormal operation.

[0087] Furthermore, in one or more embodiments of the present specification, determining the operating status of a product production device based on correlation specifically includes the following steps: If it is determined that both the first correlation and the second correlation are greater than a preset first threshold, then the product production device is determined to be in a normal operating state. If it is determined that both the first correlation and the second correlation are less than a preset second threshold and greater than a preset third threshold, then the product production device is determined to be in a first abnormal operating state. It should be noted that the preset second threshold is less than the first preset threshold. If it is determined that both the first correlation and the second correlation are less than a preset third threshold, then the product production device is determined to be in a second abnormal operating state.

[0088] S102: Based on a preset digital thread, key inbound and outbound data of products in each of the production workshops are linked to obtain the product data to be analyzed in the area to be monitored.

[0089] After obtaining key product inbound and outbound data through the pre-configured sensor network in step S101, in order to facilitate comprehensive analysis and monitoring of products in the monitored area, in one or more embodiments of this specification, key product inbound and outbound data from various production workshops is linked via a pre-configured digital thread to obtain the product data to be analyzed in the monitored area. Control based on the digital thread enables more efficient data processing and improves the efficiency of product inbound and outbound management.

[0090] S103: Decoupling the product data to be analyzed from the business according to a preset industrial driver to obtain a microservice set corresponding to the product data to be analyzed.

[0091] In order to make the business processing process of product entry and exit correspond to the production cost and operating benefits expected by the enterprise, in one or more embodiments of this specification, the business decoupling operation is performed on the product data to be analyzed based on the business process according to the pre-set regional program of the industrial Internet, and multiple microservices corresponding to the product data to be analyzed are obtained, wherein the multiple microservice processes are a set of microservices corresponding to the product data to be analyzed.

[0092] S104: Based on the current business process of the area to be monitored, the microservices in the microservice set are combined and orchestrated to form a business combination microservice of the area to be monitored, so as to implement the next stage of product processing in each of the production workshops based on the business combination microservice.

[0093] In the above step S103, the business decoupling of the product data to be analyzed is performed through the pre-set industrial driver. In order to improve the efficiency of the outbound and inbound processes, in one or more embodiments of this specification, the microservices in the microservice set are combined and arranged according to the current business process of the area to be monitored to form a business combination microservice of the area to be monitored. In other words, the business combination microservice is formed by re-assembling and applying the microservices. Specifically, according to the current business process of the area to be monitored, the microservices in the microservice set are combined and arranged to form the business combination microservice of the area to be monitored, which includes the following steps:

[0094] First, each microservice in the microservice set is randomly sampled to determine multiple initialization sets of microservices to be combined. For example, the microservice set is randomly sampled so that the number of microservices in each initialization set is 70% of the number of microservices in the microservice set. After obtaining multiple initialization sets, the fitness value of each microservice in the initialization set is determined based on a preset objective function. The fitness values ​​of the microservices are summed to obtain the sum of the fitness values ​​of the microservices in the initialization set, and the set fitness value of the initialization set is determined. The initialization set corresponding to the minimum set fitness value is determined as the optimal solution for the service combination microservice. The initialization sets are re-sampled, and iterative training is performed on each initialization set to obtain the minimum value of the first set fitness value of the service combination microservice after iterative training. If the minimum value is less than the optimal solution of the service combination microservice in the previous iteration, the minimum value of the first set fitness value is determined as the optimal solution for the current service combination microservice. The microservices are combined and orchestrated according to the order of the microservices in the optimal solution of the current service combination microservice to form the service combination microservice for the monitored area.

[0095] S105: If the production of the product in the production workshop is completed, the transaction data corresponding to the product is obtained, and the product identification code of the product is determined based on the key in-and-out data of the product, the data corresponding to the product processing at each stage, and the transaction data.

[0096] In order to link relevant data during the product in and out process, improve communication speed, efficiency, and production speed, maintain the latest status of the production process, and achieve product traceability, in one or more embodiments of this specification, based on the key in and out data of the product and the data corresponding to the product processing at each stage and the transaction data, the product identification code of the product is determined, specifically including the following steps:

[0097] A registration request for a product identification code is sent to the enterprise node, where the registration information includes the workshop information corresponding to the area to be monitored. Based on the workshop information, the enterprise code assigned to the enterprise node, the enterprise node number corresponding to the enterprise node, and the secondary node number and top node number corresponding to the enterprise node in the secondary node and the top node are obtained. Then, based on the pre-set coding rules, a preset coding template corresponding to the key in-and-out data of the product, the data corresponding to the product processing at each stage, and the transaction data is determined. According to the preset coding template, the enterprise code, the enterprise node number corresponding to the enterprise node, and the secondary node number and top node number corresponding to the enterprise node in the secondary node and the top node are coded and identified to obtain the initial coding identification of the product. The initial coding identification is then bound to the key in-and-out data of the product, the data corresponding to the product processing at each stage, and the transaction data to obtain the product identification code of the product, so that the data information in the in-and-out process corresponding to the product can be obtained based on the product identification code of the product, thereby realizing the traceability of the in-and-out process.

[0098] S106: Upload the product identification code to the enterprise point corresponding to the secondary node of the identification resolution based on the industrial Internet to realize the in and out process monitoring of the product.

[0099] After determining the product identification code in step S105, in order to achieve data interoperability based on the Industrial Internet, in one or more embodiments of this specification, the product identification code is uploaded to the enterprise node corresponding to the secondary resolution node via the Industrial Internet, so that the enterprise node corresponding to the secondary node can obtain the product's inbound and outbound data. In one or more embodiments of this specification, after uploading the product identification code to the enterprise node corresponding to the secondary resolution node via the Industrial Internet to monitor the product's inbound and outbound processes, the method further includes the following steps: collecting workshop environmental parameters using a second sensor in a pre-set sensor network; it should be noted that environmental parameters may include: temperature, humidity, and light intensity. The value of the preset standard environmental parameter of the product is obtained, and the workshop's environmental hazard level is determined based on the difference between the workshop's environmental parameter value and the preset standard environmental parameter value. For example, if the temperature in the environmental parameter is 20 degrees Celsius and the preset standard environmental parameter is 15 degrees Celsius, then the difference between the current environmental parameter value and the preset standard environmental parameter value is 5 degrees Celsius. The hazard level corresponding to this temperature difference is searched based on a preset temperature difference level table to determine the workshop's environmental hazard level.

[0100] After obtaining the environmental hazard level, the environmental hazard level, key product entry and exit data, and the operating status of the product production equipment are converted into chart data, and the chart data is uploaded to the display device that matches the area to be monitored to achieve remote monitoring of product entry and exit processes and improve employee productivity.

[0101] like Figure 2 As shown, one or more embodiments of this specification improve the internal structure diagram of a device for monitoring the product in and out of the warehouse process based on the industrial Internet. Figure 2 It can be seen that the equipment includes:

[0102] at least one processor 201; and,

[0103] A memory 202 in communication with the at least one processor 201; wherein,

[0104] The memory 202 stores instructions that can be executed by the at least one processor 201. The instructions are executed by the at least one processor 201 to enable the at least one processor 201 to:

[0105] Based on a pre-installed sensor network in a production workshop in the area to be monitored, key inbound and outbound data of products in the production workshop is obtained; wherein the pre-installed sensor network is composed of product production equipment in the production workshop and sensors pre-installed on the product production equipment;

[0106] Linking key inbound and outbound data of products in each of the production workshops based on a preset digital thread to obtain product data to be analyzed in the area to be monitored;

[0107] Decoupling the product data to be analyzed from the business according to a preset industrial driver to obtain a microservice set corresponding to the product data to be analyzed;

[0108] According to the current business process of the area to be monitored, the microservices in the microservice set are combined and orchestrated to form a business combination microservice of the area to be monitored, so as to realize the next stage of product processing in each of the production workshops based on the business combination microservice;

[0109] If the production of the product in the production workshop is completed, the transaction data corresponding to the product is obtained, and the product identification code of the product is determined based on the key in-and-out data of the product and the data corresponding to the product processing at each stage and the transaction data;

[0110] Based on the industrial Internet, the product identification code is uploaded to the enterprise point corresponding to the secondary node of the identification resolution to realize the monitoring of the product's in and out of storage process.

[0111] like Figure 3 As shown, one or more embodiments of this specification provide a schematic diagram of the internal structure of a non-volatile storage medium. Figure 3 As can be seen, a non-volatile storage medium stores computer executable instructions 301, and the computer executable instructions 301 include:

[0112] Based on a pre-installed sensor network in a production workshop in the area to be monitored, key inbound and outbound data of products in the production workshop is obtained; wherein the pre-installed sensor network is composed of product production equipment in the production workshop and sensors pre-installed on the product production equipment;

[0113] Linking key inbound and outbound data of products in each of the production workshops based on a preset digital thread to obtain product data to be analyzed in the area to be monitored;

[0114] Decoupling the product data to be analyzed from the business according to a preset industrial driver to obtain a microservice set corresponding to the product data to be analyzed;

[0115] According to the current business process of the area to be monitored, the microservices in the microservice set are combined and orchestrated to form a business combination microservice of the area to be monitored, so as to realize the next stage of product processing in each of the production workshops based on the business combination microservice;

[0116] If the production of the product in the production workshop is completed, the transaction data corresponding to the product is obtained, and the product identification code of the product is determined based on the key in-and-out data of the product and the data corresponding to the product processing at each stage and the transaction data;

[0117] Based on the industrial Internet, the product identification code is uploaded to the enterprise point corresponding to the secondary node of the identification resolution to realize the monitoring of the product's in and out of storage process.

[0118] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the device, apparatus, and non-volatile computer storage medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simplified. For relevant details, refer to the descriptions of the method embodiments.

[0119] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0120] The foregoing description is merely one or more embodiments of this specification and is not intended to limit this specification. It will be apparent to those skilled in the art that various modifications and variations may be made to one or more embodiments of this specification. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of one or more embodiments of this specification are intended to be within the scope of the claims of this specification.

Claims

1. A product inbound and outbound process supervision method based on the industrial Internet, characterized in that: The method comprises: Based on the pre-installed sensor network of the production workshop in the monitored area, key inbound and outbound data of products in the production workshop is obtained; wherein the pre-installed sensor network is composed of sensors pre-installed in the product production equipment and sensors in the workshop environment; Based on a preset digital thread, the key inbound and outbound data of the products in each of the production workshops are linked to obtain the product data to be analyzed in the area to be monitored; Decoupling the product data to be analyzed from the business according to a preset industrial driver to obtain a microservice set corresponding to the product data to be analyzed; According to the current business process of the area to be monitored, the microservices in the microservice set are combined and orchestrated to form a business combination microservice of the area to be monitored, so as to realize the next stage of products in each of the production workshops based on the business combination microservice; If the production of the product in the production workshop is completed, the transaction data corresponding to the product is obtained, and the product identification code of the product is determined based on the key inbound and outbound data of the product and the processing data of each stage and the transaction data; Upload the product identification code to the enterprise node corresponding to the secondary node of the identity resolution based on the industrial Internet to realize the in and out process monitoring of the product; Based on the pre-installed sensor network in the production workshop in the monitoring area, key inbound and outbound data of products in the production workshop is obtained, including: Collecting product images of products in the production workshop using a first sensor in a preset sensor network, wherein the products include products to be put into storage and products to be shipped out; Obtain several sample images with pre-labeled product information areas, extract text information and pixel values ​​from the sample images as training features, and input them into a product classification training model for training to obtain a product classification model that meets the requirements; Extracting text information and pixel values ​​from the product image as features to be identified in the product image, and inputting the features to be identified into the product classification model to determine the product name, product type, and product damage value corresponding to the product image; Based on the product name, product type, and product damage value, determine the products in the production workshop whose inbound and outbound weight values ​​within a preset next time period are greater than a preset weight threshold as key products, and use the inbound and outbound data of the key products as the key inbound and outbound data of the products; After obtaining key inbound and outbound data of products in the production workshop based on the pre-installed sensor network in the production workshop in the monitored area, the method further includes: Based on a pre-installed sensor network in the production workshop to be monitored, collecting vibration signals of each of the product production equipment in the production workshop, so as to perform fault monitoring on the product production equipment based on the vibration signals; The performing fault monitoring on the product production equipment based on the vibration signal specifically includes: If it is determined that the damage value of the product is greater than a preset damage threshold, obtaining a vibration signal of the production equipment corresponding to the product collected by the preset sensor network; Acquiring first power information corresponding to the corresponding production equipment during normal operation; Performing a wavelet transform on the first power information based on a preset order to obtain a first wavelet coefficient and a second wavelet coefficient; wherein the first wavelet coefficient is used to represent an approximate feature of the first power information, and the second wavelet coefficient is used to represent a detailed feature of the first power information; Performing the wavelet transform on the vibration signal to obtain a third wavelet coefficient and a fourth wavelet coefficient; wherein the third wavelet coefficient corresponds to the first wavelet coefficient, and the fourth wavelet coefficient corresponds to the second wavelet coefficient; Based on the correlation detection algorithm, a first correlation between the first wavelet coefficient and the third wavelet coefficient, and a second correlation between the second wavelet coefficient and the fourth wavelet coefficient are respectively obtained, and the operating status of the product production equipment is determined according to the first correlation and the second correlation; wherein the operating status includes: normal operating status and abnormal operating status.

2. The method for supervising product in-and-out warehousing processes based on the industrial Internet according to claim 1 is characterized in that: The method of determining, based on the product name, the product type, and the product damage value, key products in the production workshop whose inbound and outbound weight values ​​in the next cycle are greater than a preset weight threshold, and using the inbound and outbound data of the key products as the key inbound and outbound data of the products, specifically includes: If it is determined that the damage value of the product is greater than the preset damage threshold, the product is marked as a product to be re-inspected to prevent the product from being put into or taken out of the warehouse; If it is determined that the product damage value is less than the preset damage threshold, then obtaining product in-and-out data corresponding to the product name and product type within a preset time period; wherein the product in-and-out data includes: in-and-out product name, in-and-out quantity, in-and-out interval frequency, out-and-out flow direction, and in-and-out source; Obtaining the total in-and-out quantity of the product within a preset time period from a preset database, and determining the remaining in-and-out quantity of the product based on the total in-and-out quantity and the target in-and-out quantity of the product; Determine the probability of the product entering or leaving the warehouse within the next preset time period based on the remaining in-and-out quantity of the product and the in-and-out interval frequency, and use the in-and-out probability as a first weight coefficient; Obtaining a corresponding target inbound and outbound enterprise based on the outbound flow direction and the inbound source, and using the reputation value of the target inbound and outbound enterprise as a second weight coefficient; The weighted value of the first weight coefficient and the second weight coefficient is obtained as the in-and-out weight value of the product. If the in-and-out weight value of the product is greater than the preset weight threshold, the product is regarded as a key product, and the in-and-out data of the key product is obtained as the key in-and-out data of the product.

3. The method for supervising product in-and-out warehousing processes based on the industrial Internet according to claim 1 is characterized in that: The determining, based on the first correlation and the second correlation, the operating status of the product production equipment specifically includes: If it is determined that both the first correlation and the second correlation are greater than a preset first threshold, it is determined that the product production equipment is in a normal operating state; If it is determined that the first correlation and the second correlation are both less than a preset second threshold and greater than a preset third threshold, it is determined that the product production equipment is in a first abnormal operating state; wherein the preset second threshold is less than the preset first threshold; If it is determined that both the first correlation and the second correlation are less than the preset third threshold, it is determined that the product production equipment is in a second abnormal operating state; wherein the abnormality level of the second abnormal operating state is higher than that of the first abnormal operating state.

4. The method for supervising product in-and-out processes based on the industrial Internet according to claim 1 is characterized in that: Combining and arranging the microservices in the microservice set according to the current business process of the area to be monitored to form the business combination microservice of the area to be monitored specifically includes: Randomly extract each of the microservices in the microservice set to determine multiple initialization sets of microservices to be combined; Determining the fitness value of each microservice in the initialization set based on a preset objective function, and determining the set fitness value of the initialization set based on the sum of the fitness values ​​of the microservices, and taking the initialization set corresponding to the minimum value of the set fitness values ​​as the optimal solution of the business combination microservice; Iteratively training each of the initialization sets and obtaining a minimum value among the first set degree values ​​of the business combination microservice after iterative training; if the minimum value is less than the optimal solution of the business combination microservice, then using the minimum value among the first set degree values ​​as the current optimal solution of the business combination microservice; Based on the arrangement order of the microservices in the current optimal solution of the business combination microservice, the microservices are combined and orchestrated to form the business combination microservice of the area to be monitored.

5. The method for supervising product in-and-out warehousing processes based on the industrial Internet according to claim 4 is characterized in that: Determining the product identification code of the product based on the key inbound and outbound data of the product, the processing data at each stage, and the transaction data specifically includes: Sending a registration request for the product identification code to the enterprise node, wherein the registration information includes workshop information corresponding to the area to be monitored; Acquire, according to the workshop information, an enterprise code assigned to the enterprise node, an enterprise node number corresponding to the enterprise node, and a secondary node number and a top node number corresponding to the enterprise node at the secondary node and the top node, respectively; Based on preset coding rules, determine a preset coding template corresponding to the key inbound and outbound data of the product, the processing data at each stage, and the transaction data; Encode the enterprise code, the enterprise node number, the secondary node number and the top node number based on the preset coding template to generate an initial coding identification for the product; The initial coding identifier is bound to the key in-and-out warehouse data of the product, the processing data of each stage and the transaction data to obtain the product identification code of the product.

6. The method for supervising product in-and-out processes based on the industrial Internet according to claim 1 is characterized in that: After uploading the product identification code to the enterprise point corresponding to the secondary node of the identity resolution based on the industrial Internet, the method further includes: Collecting environmental parameters of the workshop based on a second sensor in the pre-installed sensor network; wherein the environmental parameters include at least one or more of the following: temperature, humidity, and light intensity; Obtaining a value of a preset standard environmental parameter of the product, and determining an environmental hazard level of the workshop based on a difference between the value of the environmental parameter of the workshop and the value of the preset standard environmental parameter; The environmental hazard level, the key in-and-out data of the product, and the operating status of the product production equipment are converted into chart data, and the chart data is uploaded to a display device that matches the area to be monitored to achieve remote monitoring of the product in-and-out data.

7. An industrial Internet-based product inbound and outbound process monitoring device, comprising: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: Based on the pre-installed sensor network of the production workshop in the monitored area, key inbound and outbound data of products in the production workshop is obtained; wherein the pre-installed sensor network is composed of sensors pre-installed in the product production equipment and sensors in the workshop environment; Based on a preset digital thread, the key inbound and outbound data of the products in each of the production workshops are linked to obtain the product data to be analyzed in the area to be monitored; Decoupling the product data to be analyzed from the business according to a preset industrial driver to obtain a microservice set corresponding to the product data to be analyzed; According to the current business process of the area to be monitored, the microservices in the microservice set are combined and orchestrated to form a business combination microservice of the area to be monitored, so as to realize the next stage of product processing in each of the production workshops based on the business combination microservice; If the production of the product in the production workshop is completed, the transaction data corresponding to the product is obtained, and the product identification code of the product is determined based on the key inbound and outbound data of the product and the processing data of each stage and the transaction data; Upload the product identification code to the enterprise node corresponding to the secondary node of the identity resolution based on the industrial Internet to realize the in and out process monitoring of the product; Based on the pre-installed sensor network in the production workshop in the monitoring area, key inbound and outbound data of products in the production workshop is obtained, including: Collecting product images of products in the production workshop using a first sensor in a preset sensor network, wherein the products include products to be put into storage and products to be shipped out; Obtain several sample images with pre-labeled product information areas, extract text information and pixel values ​​from the sample images as training features, and input them into a product classification training model for training to obtain a product classification model that meets the requirements; Extracting text information and pixel values ​​from the product image as features to be identified in the product image, and inputting the features to be identified into the product classification model to determine the product name, product type, and product damage value corresponding to the product image; Based on the product name, product type, and product damage value, determine the products in the production workshop whose inbound and outbound weight values ​​within a preset next time period are greater than a preset weight threshold as key products, and use the inbound and outbound data of the key products as the key inbound and outbound data of the products; After obtaining key inbound and outbound data of products in the production workshop based on the pre-installed sensor network in the production workshop in the area to be monitored, the method includes: Based on a pre-installed sensor network in the production workshop to be monitored, collecting vibration signals of each of the product production equipment in the production workshop, so as to perform fault monitoring on the product production equipment based on the vibration signals; The performing fault monitoring on the product production equipment based on the vibration signal specifically includes: If it is determined that the damage value of the product is greater than a preset damage threshold, obtaining a vibration signal of the production equipment corresponding to the product collected by the preset sensor network; Acquiring first power information corresponding to the corresponding production equipment during normal operation; Performing a wavelet transform on the first power information based on a preset order to obtain a first wavelet coefficient and a second wavelet coefficient; wherein the first wavelet coefficient is used to represent an approximate feature of the first power information, and the second wavelet coefficient is used to represent a detailed feature of the first power information; Performing the wavelet transform on the vibration signal to obtain a third wavelet coefficient and a fourth wavelet coefficient; wherein the third wavelet coefficient corresponds to the first wavelet coefficient, and the fourth wavelet coefficient corresponds to the second wavelet coefficient; Based on the correlation detection algorithm, a first correlation between the first wavelet coefficient and the third wavelet coefficient, and a second correlation between the second wavelet coefficient and the fourth wavelet coefficient are respectively obtained, and the operating status of the product production equipment is determined according to the first correlation and the second correlation; wherein the operating status includes: normal operating status and abnormal operating status.

8. A non-volatile storage medium storing computer-executable instructions, characterized in that: The computer-executable instructions include: Based on the pre-installed sensor network of the production workshop in the monitored area, key inbound and outbound data of products in the production workshop is obtained; wherein the pre-installed sensor network is composed of sensors pre-installed in the product production equipment and sensors in the workshop environment; Based on a preset digital thread, the key inbound and outbound data of the products in each of the production workshops are linked to obtain the product data to be analyzed in the area to be monitored; Decoupling the product data to be analyzed from the business according to a preset industrial driver to obtain a microservice set corresponding to the product data to be analyzed; According to the current business process of the area to be monitored, the microservices in the microservice set are combined and orchestrated to form a business combination microservice of the area to be monitored, so as to realize the next stage of product processing in each of the production workshops based on the business combination microservice; If the production of the product in the production workshop is completed, the transaction data corresponding to the product is obtained, and the product identification code of the product is determined based on the key inbound and outbound data of the product and the processing data of each stage and the transaction data; Upload the product identification code to the enterprise node corresponding to the secondary node of the identity resolution based on the industrial Internet to realize the in and out process monitoring of the product; Based on the pre-installed sensor network in the production workshop in the monitoring area, key inbound and outbound data of products in the production workshop is obtained, including: Collecting product images of products in the production workshop using a first sensor in a preset sensor network, wherein the products include products to be put into storage and products to be shipped out; Obtain several sample images with pre-labeled product information areas, extract text information and pixel values ​​from the sample images as training features, and input them into a product classification training model for training to obtain a product classification model that meets the requirements; Extracting text information and pixel values ​​from the product image as features to be identified in the product image, and inputting the features to be identified into the product classification model to determine the product name, product type, and product damage value corresponding to the product image; Based on the product name, product type, and product damage value, determine the products in the production workshop whose inbound and outbound weight values ​​within a preset next time period are greater than a preset weight threshold as key products, and use the inbound and outbound data of the key products as the key inbound and outbound data of the products; After obtaining key inbound and outbound data of products in the production workshop based on the pre-installed sensor network in the production workshop in the area to be monitored, the method further includes: Based on a pre-installed sensor network in the production workshop to be monitored, collecting vibration signals of each of the product production equipment in the production workshop, so as to perform fault monitoring on the product production equipment based on the vibration signals; The performing fault monitoring on the product production equipment based on the vibration signal specifically includes: If it is determined that the damage value of the product is greater than a preset damage threshold, obtaining a vibration signal of the production equipment corresponding to the product collected by the preset sensor network; Acquiring first power information corresponding to the corresponding production equipment during normal operation; Performing a wavelet transform on the first power information based on a preset order to obtain a first wavelet coefficient and a second wavelet coefficient; wherein the first wavelet coefficient is used to represent an approximate feature of the first power information, and the second wavelet coefficient is used to represent a detailed feature of the first power information; Performing the wavelet transform on the vibration signal to obtain a third wavelet coefficient and a fourth wavelet coefficient; wherein the third wavelet coefficient corresponds to the first wavelet coefficient, and the fourth wavelet coefficient corresponds to the second wavelet coefficient; Based on the correlation detection algorithm, a first correlation between the first wavelet coefficient and the third wavelet coefficient, and a second correlation between the second wavelet coefficient and the fourth wavelet coefficient are respectively obtained, and the operating status of the product production equipment is determined according to the first correlation and the second correlation; wherein the operating status includes: normal operating status and abnormal operating status.

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