Production operation management method and system based on Internet of Things

Abstract

The invention provides a production operation management method and system based on the Internet of Things, and the method comprises the steps: building a simulation model of a whole production process of a product based on production resource data; running the simulation model, simulating a plurality of production processes of the product, and obtaining simulation data of each production process; evaluating the simulation data of each production flow to obtain the production flow with the highest evaluation value as the optimal production flow of the product; acquiring actual production data of the actual production process of the product based on the optimal production flow of the product and the Internet of Things technology; and at least based on the actual production data, the production operation management data of the producer is generated, so that digital and intelligent management of production operation is realized, and the operation efficiency and the management level of a manufacturing enterprise are improved.

Description

Technical Field

[0001] The present invention relates to the field of human-computer interaction technology, and in particular to a production operation management method and system based on the Internet of Things. Background Art

[0002] In manufacturing industries like power equipment, traditional Manufacturing Operations Management (MOM) systems suffer from insufficient data collection capabilities, low production process transparency, inefficient equipment maintenance, insufficient production plan optimization, lagging quality management, extensive energy management, poor system integration, a lack of simulation and optimization capabilities, insufficient scalability, and high costs. These issues severely restrict the operational efficiency and management level of manufacturing companies. Summary of the Invention

[0003] The present invention provides a production operation management method and system based on the Internet of Things, which are used to solve the defects of low operation efficiency and management level of manufacturing enterprises in the prior art.

[0004] In one aspect, the present invention provides a production operation management method based on the Internet of Things, comprising: Build a simulation model of the entire production process based on production resource data; Running the simulation model to simulate multiple production processes of the product and obtain simulation data for each production process; Evaluate the simulation data of each production process, and obtain the production process with the highest evaluation value as the optimal production process for the product; Based on the optimal production process of the product and Internet of Things technology, obtain actual production data of the actual production process of the product; Based at least on the actual production data, the manufacturer's production operation management data is generated.

[0005] According to a production operation management method based on the Internet of Things provided by the present invention, the simulation data of each production process is evaluated, and the production process with the highest evaluation value is obtained as the optimal production process of the product, including: Determining the production time, total cost, quality, resource utilization, and production capacity of each production process based on the simulation data of each production process; Performing a weighted summation on the production time, the total cost, the quality, the resource utilization rate, and the production capacity to obtain an evaluation value for each production process; The production process with the highest evaluation value is regarded as the optimal production process for the product.

[0006] According to a production operation management method based on the Internet of Things provided by the present invention, the simulation data of each production process includes the penalty cost of delayed product delivery, the usage cost of each device in each process, and the labor cost of each person in each process; The process of determining the total cost includes: The sum of the penalty cost, the use cost of each device in each process, and the labor cost of each person in each process is taken as the total cost.

[0007] According to a production operation management method based on the Internet of Things provided by the present invention, the process of obtaining the labor cost of each person in each process includes: Obtain historical behavior data of each person in each process; Determine the cost fluctuation coefficient of each person in each process according to the historical behavior data; The labor cost of each person in each process is obtained according to the cost fluctuation coefficient and the basic cost of each person in each process.

[0008] According to a production operation management method based on the Internet of Things provided by the present invention, the production operation management data includes adjustment data of actual production; Generating production operation management data of the manufacturer based at least on the actual production data, including: Determining a first deviation value between the simulation data of the optimal production process of the product and the actual production data; If the first deviation value is greater than a preset production deviation threshold, actual production adjustment data is generated; wherein the actual production adjustment data includes at least one of equipment parameter adjustment data, environmental parameter adjustment data, product material adjustment data, and personnel behavior adjustment data.

[0009] According to a production operation management method based on the Internet of Things provided by the present invention, before determining the deviation value between the simulation data of the optimal production process of the product and the actual production data, the method further includes: Determining predicted production data during the product production process based on a preset prediction model; wherein the prediction model is trained based on historical production data of the product; Determining a second deviation value between the simulation data of the optimal production process of the product and the predicted production data; If the second deviation value is greater than the preset production deviation threshold, adjustment data of simulated production is generated as the production operation management data; wherein, the adjustment data of simulated production includes at least one of adjustment data of equipment parameters, adjustment data of environmental parameters, adjustment data of product materials and adjustment data of personnel behavior.

[0010] According to a production operation management method based on the Internet of Things provided by the present invention, the production operation management data includes at least one of a product quality assessment result, a prompt message of product failure, and a reason for product failure; Generating production operation management data of the manufacturer based at least on the actual production data, including: Acquiring product quality description data from the actual production data; Extracting features from the product quality description data to obtain product quality feature information; Obtaining a product quality assessment result based on the product quality characteristic information; If the evaluation result indicates that the product is unqualified, a prompt message indicating that the product is unqualified is output, and the product is traced to its source to obtain the reason for the unqualified product.

[0011] According to a production operation management method based on the Internet of Things provided by the present invention, the actual production data includes at least one of the equipment operation status, equipment working parameters, and equipment environment information; The production operation management data includes preventive maintenance prompt information or fault detection information; Generating production operation management data of the manufacturer based at least on the actual production data, including: Determining a performance prediction result of the device according to at least one of the device operating state, the device operating parameters, and the environment information in which the device is located; If the performance prediction result of the equipment indicates a potential failure, outputting a preventive maintenance reminder message; If the performance prediction result of the device indicates that a fault has occurred, fault detection information is generated based on the fault, wherein the fault detection information includes at least one of fault description information, fault location information and fault resolution information.

[0012] According to the present invention, a production operation management method based on the Internet of Things further includes: The production operation management data is obtained by scanning the product barcode or radio frequency reading.

[0013] On the other hand, the present invention also provides a production operation management system based on the Internet of Things, which includes a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, it implements any of the above-mentioned production operation management methods based on the Internet of Things.

[0014] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described Internet of Things-based production operation management methods.

[0015] On the other hand, the present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned production operation management methods based on the Internet of Things.

[0016] The production operation management method based on the Internet of Things provided by the present invention constructs a simulation model of the entire production process of a product based on production resource data; runs the simulation model to simulate multiple production processes of the product to obtain simulation data of each production process; evaluates the simulation data of each production process to obtain the production process with the highest evaluation value as the optimal production process of the product; based on the optimal production process of the product and the Internet of Things technology, obtains actual production data of the actual production process of the product; and generates production operation management data of the manufacturer based at least on the actual production data, thereby realizing digital and intelligent management of production operations and improving the operational efficiency and management level of the manufacturing enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 This is a flowchart of a production operation management method based on the Internet of Things provided by an embodiment of the present invention; Figure 2 yes Figure 1 Flowchart of the implementation method of step 103; Figure 3 It is a structural diagram of a production operation management system based on the Internet of Things provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0020] Figure 1This is a flowchart of a production operation management method based on the Internet of Things provided by an embodiment of the present invention.

[0021] like Figure 1 As shown, the production operation management method based on the Internet of Things provided by the embodiment of the present invention can be executed by a controller in a production operation management system based on the Internet of Things. The method mainly includes the following steps: 101. Based on production resource data, build a simulation model of the entire production process; In a specific implementation process, the MOM system can be seamlessly integrated with Enterprise Resource Planning (ERP), Product Lifecycle Management (PLM), and Supply Chain Management (SCM) systems through standardized data interfaces and protocols. This reduces the difficulty and cost of system integration, promotes efficient system communication, and quickly builds a unified management platform. It breaks down data silos, allowing accurate and timely transmission of various types of production data between departments and systems, providing strong support for collaborative work and decision-making. It standardizes data formats and transmission rules, ensures data consistency and accuracy, and assists in quality control and production planning. It facilitates the integration of new equipment and systems into the existing architecture, reduces the cost and risk of system expansion and upgrades, and ensures the continuity of production operations. It can also strengthen the connection between enterprises and upstream and downstream supply chains, realize information sharing, and improve the collaborative efficiency and competitiveness of the supply chain.

[0022] In this way, the MOM system can obtain various production resource data from other systems. This production resource data can include product demand information (such as product materials and specifications), equipment attributes (such as the operating status of various equipment and processing types), and personnel attributes (such as which process operator is responsible for which operation and their skill level). After obtaining this data, a simulation model of the entire production process can be constructed based on digital twin technology to simulate each link in the entire process. The entire production process can include multiple production processes, each with its own corresponding links, such as order processing, work order generation, process planning, and work order dispatching. The process planning and work order dispatching are the key differences between each generation process.

[0023] Specifically, genetic algorithms, blood relationships, etc. can be used to analyze the acquired production resource data and create multiple production processes for producing products. In the process of creating multiple production processes, bottlenecks in the production process (such as long process waiting time or uneven resource allocation) can be identified, and process plans can be optimized to achieve balanced production.

[0024] 102. Run the simulation model to simulate multiple production processes of the product and obtain simulation data for each production process; After building a simulation model for the entire production process, you can run it to simulate multiple production processes and obtain simulation data for each production process. This data can include equipment operation data, manual operation data, product data, and more.

[0025] 103. Evaluate the simulation data of each production process, and obtain the production process with the highest evaluation value as the optimal production process for the product; In a specific implementation process, the production efficiency, quality, etc. of the multiple production processes created are different in actual applications. Therefore, it is necessary to select the optimal production process from multiple production processes as the optimal production process for the product, so that in the actual production process, the product can be produced according to the optimal production process of the product.

[0026] Specifically, according to Figure 2 The flow chart shown here determines the optimal production process for the product. Figure 2 yes Figure 1 Flowchart of the method for implementing step 103 in FIG. Figure 2 As shown, the method may include the following steps: 201. Determine the production time, total cost, quality, resource utilization, and production capacity of each production process based on the simulation data of the production process; In a specific implementation process, the simulation data of each production process can be analyzed to obtain the production time, total cost, quality, resource utilization and production capacity of each production process.

[0027] The production time of each production process can be calculated according to the following formula (1): (1) in, Indicates the production time of each production process, Indicates the total number of steps in each production process, Indicates the process, Indicates the Theoretical processing time of each process, Indicates the The buffer time of each process (idle time caused by resource competition or delay of the previous process), Indicates the The preparation time of each device.

[0028] The resource utilization rate of each production process can be calculated according to the following formula (2): (2) in, represents the resource utilization of each production process, Indicates the total number of equipment for each production process, Indicates the total number of people in each production process, Indicates the The working time of each device, Indicates the Working hours of individuals.

[0029] The total cost of each production process can be calculated according to the following formula (3): (3) in, represents the total cost of each production process, Indicates the first The cost of using a device, Indicates the first The labor cost of each person, represents the penalty cost of delayed product delivery, where The value of increases as the delay in delivery increases.

[0030] In a specific implementation process, the process of obtaining the labor cost of each person in each process can be achieved as follows: a. Obtain historical behavior data of each person in each process; In a specific implementation process, big data technology can be used to obtain historical behavior data of each person in each process over a period of time. This historical behavior data may include each person's operation data for each process over a period of time (such as operation proficiency, quality of products obtained in each process, etc.), skill training data, etc.

[0031] b. determining the cost fluctuation coefficient of each person in each process based on the historical behavior data; In a specific implementation process, the possible impact of each person on the cost when operating the process can be determined based on the operation data, skill training data, etc. of each person for each process in the past period of time, and the cost fluctuation coefficient of each person in each process can be obtained.

[0032] Specifically, if a person has become proficient in a certain process over the past period of time and has obtained high-quality products from that process, the person's actual cost may be reduced. Therefore, a cost fluctuation coefficient less than 1 can be set. Conversely, if the person has become unproficient in a certain process over the past period of time and has obtained low-quality products from that process, the person's actual cost may be increased. Therefore, a cost fluctuation coefficient greater than 1 can be set.

[0033] Similarly, if the person has received skill training for the process in the past and has a good assessment score, the actual cost of the person may be reduced. Therefore, a cost fluctuation coefficient less than 1 can be set. Conversely, if the person has not received skill training for the process in the past, or has received skill training for the process but has a good assessment score, the actual cost of the person may be increased. Therefore, a cost fluctuation coefficient greater than 1 can be set.

[0034] It should be noted that, in this embodiment, the historical behavior data of the above-mentioned personnel and the cost fluctuation coefficient obtained based on the historical behavior data of the personnel are only exemplary. This embodiment does not limit other methods, and no examples are given one by one here.

[0035] c. Obtain the labor cost of each person in each process based on the cost fluctuation coefficient and the basic cost of each person in each process.

[0036] In a specific implementation, the product of the obtained cost fluctuation coefficient and the basic cost of each person in each process can be used as the labor cost of each person in each process. The basic cost of each person in each process can be determined based on the position salary of each person.

[0037] In this embodiment, by introducing the cost fluctuation coefficient to estimate the cost of each person in each process, the total cost of each production process can be obtained more accurately.

[0038] In a specific implementation process, the labor cost of the personnel can also be compared with the set labor cost. If the labor cost of the personnel is less than the set labor cost, and the difference is greater than the preset difference, a corresponding reward value can be set so that when the first deviation value between the production data and the simulation data of the personnel in the actual production process is less than or equal to the preset production deviation threshold, the corresponding reward value will be issued to the personnel. In this way, the enthusiasm of the personnel can be improved, so that the production cost of the product in the actual production process is relatively low.

[0039] In a specific implementation process, statistics and other methods can be used to obtain the quality and production capacity of each production process, which will not be described in detail here.

[0040] 202. Perform a weighted summation on the production time, the total cost, the quality, the resource utilization rate, and the production capacity to obtain an evaluation value for each production process; In a specific implementation process, it can be obtained according to the following calculation formula (4): (4) in, represents the weight of production time, represents the weight of the total cost, represents the weight of resource utilization, represents the weight of quality, Indicates quality, represents the weight of production capacity, Indicates production capacity. Among them, the various requirements for products may change at different times, so the weights can be adjusted over time. Can be adjusted dynamically. For example, when the delivery time is tight, it can be increased , on the contrary, it can be reduced , can be increased when the budget is tight , on the contrary, it can be reduced , when there is excess capacity, it can be reduced and On the contrary, it can increase and , can be increased when quality requirements are higher , on the contrary, it can be reduced .

[0041] 203. The production process with the highest evaluation value will be regarded as the optimal production process for the product.

[0042] The evaluation values of the production processes can be ranked, and the production process with the highest evaluation value can be selected as the optimal production process for the product. The optimal production process for the product is the most suitable production process for the product that integrates indicators such as production time, total cost, quality, resource utilization and production capacity.

[0043] 104. Based on the optimal production process of the product and Internet of Things technology, obtain actual production data of the actual production process of the product; In a specific implementation process, after the optimal production process of the product is obtained through simulation, the optimal production process of the product can be used as the process benchmark for the actual production of the product. During the actual production process of the product, based on the Internet of Things technology, various sensors, cameras and other data acquisition devices are used to collect the actual production data of the actual production process of the product, thereby realizing the multi-faceted integration of process flow, production management activities and resources, providing a basic basis for accurate and complete production data analysis, and improving the company's decision-making support capabilities; realizing real-time monitoring of the production process, improving the rapid response capability of production; realizing automated control of the production process, minimizing errors as much as possible, and effectively ensuring production quality.

[0044] 105. Generate production operation management data for the manufacturer based at least on the actual production data.

[0045] In a specific implementation, the manufacturer's production operation management data can be generated based on the actual production data of the product's actual production process. Alternatively, the manufacturer's production operation management data can be generated based on the actual production data of the product's actual production process and other relevant data to enable real-time monitoring and automated control of the product's production process, thereby improving the rapid response capability of production, minimizing errors, and effectively ensuring production quality.

[0046] In a specific implementation process, the production operation management data includes actual production adjustment data. The implementation process of the above step 105 can be as follows: Determine a first deviation value between the simulation data of the optimal production process of the product and the actual production data; if the first deviation value is greater than a preset production deviation threshold, generate adjustment data for actual production; wherein the adjustment data for actual production includes at least one of adjustment data for equipment parameters, adjustment data for environmental parameters, adjustment data for product materials, and adjustment data for personnel behavior.

[0047] That is to say, if the actual production data during the actual production process of the product does not match the simulation data of the product's optimal production process, and the deviation between the two is large, it means that the actual production process of the product is affected by various factors, resulting in some data that need to be changed. At this time, data adjustments can be made to at least one of the equipment parameters, environmental parameters, product materials, and personnel behaviors, so that the actual production data of the actual production process of the product is as consistent as possible with the simulation data of the product's optimal production process.

[0048] It should be noted that before determining the deviation value between the simulation data of the optimal production process of the product and the actual production data, the following operations may also be performed: a1. Determine predicted production data during the product production process based on a preset prediction model; wherein the prediction model is trained based on historical production data of the product; b1. Determining a second deviation value between the simulation data of the optimal production process of the product and the predicted production data; c1. If the second deviation value is greater than a preset production deviation threshold, generate adjustment data for simulated production as the production operation management data; wherein the adjustment data for simulated production includes at least one of adjustment data for equipment parameters, adjustment data for environmental parameters, adjustment data for product materials, and adjustment data for personnel behavior.

[0049] In other words, machine learning can be used to combine the product's historical production data for training to obtain a prediction model, predict the product's production time, production capacity, etc., and obtain prediction data. If the prediction data does not match the simulation data of the product's optimal production process, and the deviation between the two is large, then during the simulation process, at least one of the equipment parameters, environmental parameters, product materials, personnel behavior, etc. can be adjusted. In this way, the simulation data of the product's optimal production process obtained as benchmark data is more reliable.

[0050] In a specific implementation process, the production operation management data includes at least one of a product quality assessment result, a product failure prompt message, and a reason for the product failure; The implementation process of the above step 105 may include the following methods: Acquire product quality description data from the actual production data; perform feature extraction on the product quality description data to obtain product quality feature information; obtain a product quality assessment result based on the product quality feature information; if the assessment result indicates unqualified, output a prompt message indicating that the product is unqualified, and trace the product to obtain the cause of the product unqualified.

[0051] That is to say, product quality can be supervised based on the actual production data. In this process, sensors and / or detection devices that can be set up to collect various quality data during the production process can be used, and the corresponding data acquisition circuits and software programs can be used to collect quality-related data, and then pre-processing, feature extraction, pattern recognition and other analysis and processing are performed. When the analysis and processing results reach the preset quality threshold, an early warning signal is automatically triggered, including sound, light, text prompts, etc., as well as the right to send early warning information to the mobile device or workstation of the relevant personnel. At the same time, image recognition technology is used to quickly inspect the appearance of semi-finished products in the production process to prevent defective semi-finished products from entering the next production link. For products with quality problems, based on the quality data in the production process, the root cause of the quality problem can be quickly located, and corresponding improvement measures can be taken.

[0052] In a specific implementation process, the actual production data includes at least one of equipment operating status, equipment working parameters, and equipment environment information; the production operation management data includes preventive maintenance prompt information or fault detection information.

[0053] The implementation process of the above step 105 may include the following methods: Determine a performance prediction result of the equipment based on at least one of the equipment operating status, the equipment working parameters, and the environmental information of the equipment; if the performance prediction result of the equipment indicates the existence of a potential fault, output a prompt message for preventive maintenance; if the performance prediction result of the equipment indicates the existence of an existing fault, generate fault detection information based on the existing fault, wherein the fault detection information includes at least one of fault description information, fault location information, and fault resolution information.

[0054] That is to say, the performance of the equipment can be monitored based on the actual production data. In this process, data from various types of equipment in the production environment can be collected and monitored in real time, including but not limited to the equipment's operating status, operating parameters, environmental conditions, etc., and the integrity, accuracy and timeliness of the data can be ensured. The probability of equipment failure and the remaining service life can be predicted, the overall performance and stability of the equipment can be evaluated, and decision support can be provided for preventive maintenance, upgrades and replacements of the equipment. A series of preset rules and algorithms are built in to analyze the collected production data in real time, identify abnormal data and potential failure modes. When a potential failure is detected, the system can promptly issue an early warning signal to relevant personnel to remind them to perform preventive maintenance; when a device fails, the system can automatically analyze the cause of the failure and provide a detailed fault description and location information. Based on the fault diagnosis results, the system can provide targeted maintenance suggestions, including maintenance steps, required tools and spare parts, etc., to reduce the difficulty and time cost of maintenance.

[0055] In a specific implementation process, the manufacturer's production operation management data obtained above is basic data for product production and management, and the production operation management data can be obtained by scanning the product barcode or radio frequency reading.

[0056] Specifically, accurate tracking and management of materials, products, equipment, and personnel throughout the production process can be achieved through product barcoding or radio frequency storage. These technologies can rapidly collect and transmit data, supporting the automation and informatization of production planning, quality traceability, inventory management, and equipment maintenance, thereby improving production efficiency, reducing errors, enhancing data transparency, and providing companies with real-time, accurate production decision support.

[0057] In a specific implementation process, it can automatically generate unique barcodes based on the information of raw materials, products, equipment and personnel, and support the printing and pasting of barcodes to ensure that each entity has a unique identity. It supports interfaces with various data collection devices (such as barcode scanners, RFID readers, etc.) to achieve fast and accurate data collection, and process and analyze the collected barcode data, including data cleaning, format conversion, statistical analysis, etc. It automatically generates or adjusts production plans based on order requirements, inventory status, production capacity and other data to ensure the smooth progress of the production process; records the quality data of each raw material, product, equipment and personnel in the production process, and quickly traces the root cause of the problem through barcodes, including raw material batches, production equipment, operators, etc., to provide a basis for quality improvement and a reliable data foundation for subsequent decision support; updates inventory data in real time, including the quantity, location and other information of raw materials, semi-finished products and finished products, to ensure the accuracy of inventory data. When the inventory quantity falls below the preset threshold, the system can automatically trigger an early warning signal and provide inventory optimization suggestions, such as replenishment plans and inventory layout adjustments; record the basic information, usage history, maintenance records, etc. of the equipment, and automatically generate equipment maintenance plans based on the equipment usage and maintenance records, and track the implementation of the maintenance plans.

[0058] The above production operation management method can achieve the following effects: Improve production efficiency: Through simulation, real-time monitoring and intelligent scheduling, it reduces waiting time and resource waste in the production process and improves production efficiency.

[0059] Improve product quality: Through quality control and traceability, timely discover and solve quality problems and improve product quality.

[0060] Reduce production costs: By optimizing production planning and resource allocation, reducing material inventory and equipment failure rate, and reducing production costs.

[0061] Enhance enterprise competitiveness: By realizing digital and intelligent management of production operations, the company's response speed and decision-making level can be improved, thereby enhancing its competitiveness.

[0062] The IoT-based production operation management method of this embodiment constructs a simulation model of the entire product production process based on production resource data; runs the simulation model to simulate multiple production processes of the product to obtain simulation data for each production process; evaluates the simulation data of each production process to obtain the production process with the highest evaluation value as the optimal production process for the product; based on the optimal production process of the product and IoT technology, obtains actual production data of the actual production process of the product; and generates production operation management data for the manufacturer based at least on the actual production data, thereby realizing digital and intelligent management of production operations and improving the operational efficiency and management level of the manufacturing enterprise.

[0063] Based on the same general inventive concept, the present invention also protects a production operation and management system based on the Internet of Things. The production operation and management system based on the Internet of Things provided by the present invention is described below. The production operation and management system based on the Internet of Things described below and the production operation and management method based on the Internet of Things described above can be referenced to each other.

[0064] Figure 3 3 is a schematic diagram of the structure of a production operation management system based on the Internet of Things provided by an embodiment of the present invention. The production operation management system based on the Internet of Things may include: a processor (processor) 310, a communication interface (Communications Interface) 320, a memory (memory) 330 and a communication bus 330, wherein the processor 310, the communication interface 320, and the memory 330 communicate with each other via the communication bus 330. The processor 310 can call the logic instructions in the memory 330 to execute the production operation management method based on the Internet of Things. Furthermore, the logic instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0065] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the production operation management method based on the Internet of Things provided by the above methods.

[0066] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the production operation management method based on the Internet of Things provided by the above methods.

[0067] It should be noted that the relevant user personal information that may be involved in the various embodiments of this application is strictly in accordance with the requirements of laws and regulations, follows the principles of legality, legitimacy and necessity, and is based on the reasonable purposes of business scenarios to process personal information that users actively provide during the use of products / services or generated due to the use of products / services, as well as personal information obtained with the user's authorization.

[0068] The user personal information processed by this application will vary depending on the specific product / service scenario and must be based on the specific scenario in which the user uses the product / service. This may involve the user's account information, device information, driving information, vehicle information, or other related information. This application will treat the user's personal information and its processing with a high degree of diligence.

[0069] This application attaches great importance to the security of user personal information and has taken reasonable and feasible security protection measures that comply with industry standards to protect user information and prevent personal information from being accessed, disclosed, used, modified, damaged or lost without authorization.

[0070] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0071] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A production operation management method based on the Internet of Things, characterized in that: include: Build a simulation model of the entire production process based on production resource data; Running the simulation model to simulate multiple production processes of the product and obtain simulation data for each production process; Evaluate the simulation data of each production process, and obtain the production process with the highest evaluation value as the optimal production process for the product; Based on the optimal production process of the product and Internet of Things technology, obtain actual production data of the actual production process of the product; Based at least on the actual production data, the manufacturer's production operation management data is generated.

2. The production operation management method based on the Internet of Things according to claim 1, characterized in that: Evaluate the simulation data of each production process and obtain the production process with the highest evaluation value as the optimal production process for the product, including: Determining the production time, total cost, quality, resource utilization, and production capacity of each production process based on the simulation data of each production process; Performing a weighted summation on the production time, the total cost, the quality, the resource utilization rate, and the production capacity to obtain an evaluation value for each production process; The production process with the highest evaluation value is regarded as the optimal production process for the product.

3. The production operation management method based on the Internet of Things according to claim 2, characterized in that: The simulation data of each production process includes the penalty cost of delayed product delivery, the usage cost of each device in each process, and the labor cost of each person in each process; The process of determining the total cost includes: The sum of the penalty cost, the use cost of each device in each process, and the labor cost of each person in each process is taken as the total cost.

4. The production operation management method based on the Internet of Things according to claim 3 is characterized in that: The process of obtaining the labor cost of each person in each process includes: Obtain historical behavior data of each person in each process; Determine the cost fluctuation coefficient of each person in each process according to the historical behavior data; The labor cost of each person in each process is obtained according to the cost fluctuation coefficient and the basic cost of each person in each process.

5. The production operation management method based on the Internet of Things according to claim 1, characterized in that: The production operation management data includes actual production adjustment data; Generating production operation management data of the manufacturer based at least on the actual production data, including: Determining a first deviation value between the simulation data of the optimal production process of the product and the actual production data; If the first deviation value is greater than a preset production deviation threshold, actual production adjustment data is generated; wherein the actual production adjustment data includes at least one of equipment parameter adjustment data, environmental parameter adjustment data, product material adjustment data, and personnel behavior adjustment data.

6. The production operation management method based on the Internet of Things according to claim 5, characterized in that: Before determining the deviation value between the simulation data of the optimal production process of the product and the actual production data, the method further includes: Determining predicted production data during the product production process based on a preset prediction model; wherein the prediction model is trained based on historical production data of the product; Determining a second deviation value between the simulation data of the optimal production process of the product and the predicted production data; If the second deviation value is greater than the preset production deviation threshold, adjustment data of simulated production is generated as the production operation management data; wherein, the adjustment data of simulated production includes at least one of adjustment data of equipment parameters, adjustment data of environmental parameters, adjustment data of product materials and adjustment data of personnel behavior.

7. The production operation management method based on the Internet of Things according to claim 1, characterized in that: The production operation management data includes at least one of a product quality assessment result, a product failure prompt message, and a reason for the product failure; Generating production operation management data of the manufacturer based at least on the actual production data, including: Acquiring product quality description data from the actual production data; Extracting features from the product quality description data to obtain product quality feature information; Obtaining a product quality assessment result based on the product quality characteristic information; If the evaluation result indicates that the product is unqualified, a prompt message indicating that the product is unqualified is output, and the product is traced to its source to obtain the reason for the unqualified product.

8. The production operation management method based on the Internet of Things according to claim 1, characterized in that: The actual production data includes at least one of the equipment operating status, equipment working parameters, and equipment environment information; The production operation management data includes preventive maintenance prompt information or fault detection information; Generating production operation management data of the manufacturer based at least on the actual production data, including: Determining a performance prediction result of the device according to at least one of the device operating state, the device operating parameters, and the environment information in which the device is located; If the performance prediction result of the equipment indicates a potential failure, outputting a preventive maintenance reminder message; If the performance prediction result of the device indicates that a fault has occurred, fault detection information is generated based on the fault, wherein the fault detection information includes at least one of fault description information, fault location information and fault resolution information.

9. The method for production operation management based on the Internet of Things according to any one of claims 1 to 8, characterized in that: Also includes: The production operation management data is obtained by scanning the product barcode or radio frequency reading.

10. A production operation management system based on the Internet of Things, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, any one of the above-described Internet of Things-based production and operation management methods is implemented.

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