Smart factory management method, system and equipment based on industrial Internet of Things, and medium

Abstract

The invention discloses a smart factory management method, system and device based on the industrial Internet of Things and a medium, and relates to the technical field of smart factories, and the method comprises the steps: obtaining the state information of each piece of production equipment in a workshop; according to the state information, risk equipment information is obtained, and the risk equipment information comprises a fault type and a fault probability corresponding to the fault type; obtaining maintenance personnel information, wherein the maintenance personnel information comprises personnel position information and maintenance personnel skill scores; according to the fault type, the fault probability and the maintainer information, the maintainer priority is obtained, and the maintainer priority is used for representing the adaptation degree of the maintainer and the risk equipment; and according to the priority of the maintainer, sending the maintenance work order of the risk equipment to the maintainer meeting a preset condition. According to the method, the industrial Internet of Things technology is utilized, real-time data collection and analysis are combined, the problem that maintenance personnel are not matched with maintenance tasks is solved, the method adapts to complex maintenance tasks for precise maintenance, and the maintenance efficiency is improved.

Description

Technical Field

[0001] The present application relates to the field of smart factory technology, and in particular to a smart factory management method, system, equipment and medium based on the Industrial Internet of Things. Background Art

[0002] In modern industrial manufacturing, with the continuous development of information and intelligent technologies, smart factories have become a key area of ​​industry transformation and upgrading. By integrating advanced information technology, the Internet of Things, artificial intelligence, and big data analytics, smart factories have automated, digitized, and intelligentized production processes, significantly improving production efficiency and product quality while reducing operating costs. However, the actual operation of smart factories still presents several technical and management challenges.

[0003] In existing factory management systems, equipment fault detection mostly relies on threshold alarms or manual experience, and maintenance task allocation is usually done through manual scheduling. There is a mismatch between maintenance personnel and maintenance tasks, especially making it difficult to perform accurate maintenance on complex maintenance tasks, resulting in low maintenance efficiency. Summary of the Invention

[0004] In order to solve the above problems, the present application provides a smart factory management method, system, equipment and medium based on industrial Internet of Things.

[0005] On the one hand, this application provides a smart factory management method based on the Industrial Internet of Things, which adopts the following technical solutions: A smart factory management method based on the Industrial Internet of Things is applied to a smart factory management system. The smart factory management system includes a management platform, a sensor network platform, and an object platform that are sequentially communicatively connected. The method is executed by the management platform and includes: Obtain status information of each production equipment in the workshop; Obtaining risky equipment information according to the state information, the risky equipment information including a fault type and a fault probability corresponding to the fault type; Obtaining maintenance personnel information, including personnel location information and maintenance personnel skill scores; Obtaining a maintenance personnel priority according to the fault type, fault probability, and maintenance personnel information, wherein the maintenance personnel priority is used to characterize the compatibility of the maintenance personnel with the risky equipment; According to the maintenance personnel priority, the maintenance work order of the risky equipment is sent to the maintenance personnel who meet the preset conditions.

[0006] Optionally, obtaining risky device information according to the status information, the risky device information including a fault type and a fault probability corresponding to the fault type, includes: Based on the status information, feature extraction and fusion of multi-dimensional data are performed through a hybrid algorithm to establish an equipment fault model; training the fault model based on historical fault data; The state information is imported into the fault model to obtain the fault type and fault probability.

[0007] Optionally, the obtaining of maintenance personnel information, including personnel location information and maintenance personnel skill scores, includes: The skill matching degree of the maintenance personnel is obtained according to the fault type and the skill score of the maintenance personnel.

[0008] Optionally, the skill matching degree satisfies the following formula:

[0009] in, is the skill matching degree of the jth maintenance worker, indicating his comprehensive ability to deal with all fault types; Is the fault type The weight of , which indicates the importance of the fault type; It is j Maintenance personnel in the fault type Skill rating on P ( fi ): Fault type fi The probability of occurrence of i An index representing the fault type, i ∈{1, 2, ..., n}; :Maintenance personnel j The corresponding skill capability vector represents the maintenance personnel j Proficiency in different fault types.

[0010] Optionally, a minimum skill requirement threshold Smin is set for the critical fault type, the calculated skill matching degree is compared with the threshold, and the matching degree is processed according to a preset algorithm.

[0011] Optionally, obtaining the maintenance personnel priority according to the fault type, fault probability and the maintenance personnel information satisfies the following formula:

[0012] in,

[0013] in, represents the priority of the jth maintenance personnel; , Indicates the location coordinates of the equipment to be repaired; , represents the position coordinates of the jth maintenance worker; D(a,b) represents the distance between the equipment to be repaired and the maintenance worker; Mj represents the skill matching degree of the jth maintenance worker; represents the task load of the i-th maintenance personnel; α represents the task load weight coefficient.

[0014] Optionally, the status information is one of device type information, device vibration information, temperature information or current information.

[0015] This application also provides a smart factory management system based on the Industrial Internet of Things, which adopts the following technical solutions: A smart factory management system based on the Industrial Internet of Things includes a management platform, a sensor network platform, and an object platform that are sequentially communicatively connected. The object platform includes a production line and production equipment. The management platform includes: Information acquisition module, used to obtain status information of each production equipment in the workshop; A fault detection module is used to obtain the equipment fault type and fault probability based on the status information; A personnel information acquisition module is used to obtain maintenance personnel information, including personnel location information and maintenance personnel skill scores; A priority calculation module, configured to obtain the maintenance personnel priority according to the fault type, fault probability and maintenance personnel information; The control module is used to perform maintenance operations on the equipment according to the maintenance personnel priority.

[0016] The present application also provides a computer device, comprising one or more processors and a memory; One or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs are configured to perform the method.

[0017] The present application also provides a computer-readable storage medium storing a computer program that can be loaded by a processor and execute the method.

[0018] In summary, this application includes at least one of the following beneficial technical effects: The present application provides a smart factory management method, system, equipment and medium based on the industrial Internet of Things. First, the status information of each production equipment in the workshop is obtained; secondly, based on the status information, risk equipment information is obtained, and the risk equipment information includes the fault type and the fault probability corresponding to the fault type; then maintenance personnel information is obtained, and the personnel information includes personnel location information and maintenance personnel skill score; and based on the fault type, fault probability and maintenance personnel information, the maintenance personnel priority is obtained; finally, based on the maintenance personnel priority, the maintenance work order of the risk equipment is sent to the maintenance personnel who meet the preset conditions. By obtaining the status information of the production equipment, the system can monitor the equipment operation in real time, identify potential fault types and evaluate the probability of fault occurrence. The system can intelligently evaluate the priority of each maintenance personnel and reasonably dispatch maintenance personnel. This dynamic personnel scheduling method ensures that the most suitable personnel perform equipment maintenance, thereby improving maintenance efficiency and maintenance quality. Assigning priorities based on equipment failure type, failure probability, and maintenance personnel skill scores can ensure that high-priority failures are handled promptly, reducing the impact of equipment failures on the production line, reducing downtime, and improving the overall operational efficiency of the production line. This method utilizes industrial Internet of Things technology, combined with real-time data collection and analysis, to provide data support for the management platform, helping decision makers better manage equipment, arrange personnel, and schedule production, thereby achieving the management goals of smart factories. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of a smart factory management method based on the Industrial Internet of Things according to an embodiment of the present application; Figure 2 This is a structural block diagram of a smart factory management system based on the Industrial Internet of Things in an embodiment of the present application. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0021] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0022] In the present invention, unless otherwise specified or limited, the terms "connection" and "fixation" should be understood in a broad sense. For example, "fixation" can mean fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. Those skilled in the art will be able to understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0023] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the meaning of "and / or" appearing throughout the text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A, or scheme B, or a scheme in which A and B are satisfied at the same time. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0024] In existing factory management systems, equipment fault detection often relies on threshold alarms or manual experience, which is unable to predict complex fault types in advance. Maintenance tasks are often assigned manually, which is inefficient and prone to response delays due to information asymmetry. While Industrial Internet of Things technology can collect equipment status data, it lacks the ability to deeply analyze this data and make dynamic decisions.

[0025] To this end, embodiments of the present application provide a smart factory management method, system, device, and medium based on the Industrial Internet of Things.

[0026] Example 1 An embodiment of the present application discloses a smart factory management method based on the Industrial Internet of Things, which is applied to an Industrial Internet of Things system. The Industrial Internet of Things system includes a management platform, a sensor network platform, and an object platform that are communicatively connected in sequence. The method is executed by the management platform.

[0027] The Industrial Internet of Things (IIoT) generally refers to a technology system that integrates various data collection and control sensors or controllers with sensing and monitoring capabilities, as well as mobile communications and intelligent analytics, into every aspect of the industrial production process. The goal of the IIoT is to significantly increase manufacturing efficiency, improve product quality, reduce costs and resource consumption, and ultimately elevate traditional industries to a new stage of intelligence.

[0028] The management platform is a core component in industrial automation systems, responsible for receiving data from various sensors and devices, performing necessary calculations and logical judgments, and sending instructions to actuators to control system operation. In the Industrial Internet of Things (IIoT) environment, the management platform often integrates advanced data processing capabilities and network communication functions, enabling efficient data exchange with other systems.

[0029] The target platform includes a cluster of production equipment deployed in a workshop. The equipment is integrated with heterogeneous sensing units, including a combination of RFID tags, three-axis MEMS accelerometers, fiber Bragg grating temperature sensors, and Rogowski coil current transformers.

[0030] A sensor network platform, a data transmission network constructed by a TSN switch, connects an edge computing node with the object platform, and the edge computing node is configured to perform preprocessing of vibration signal RMS value calculation, temperature threshold comparison, and current harmonic FFT analysis.

[0031] like Figure 1 As shown, the specific process of the method can be as follows: S10, obtaining status information of each production equipment in the workshop; Optionally, status information of the production equipment is collected by a data collection device, where the status information includes equipment type information, equipment vibration information, temperature information, and current information.

[0032] The data acquisition device includes an information acquisition module and a data acquisition module, wherein the information acquisition module includes a temperature sensor, which monitors the temperature of the equipment and the environment to ensure that the production process is within the appropriate temperature range; a vibration sensor: detects the vibration of the equipment, which helps predictive maintenance and identifies mechanical failures; and a sensor for detecting current information, which can be a Hall effect current sensor, a magnetic current sensor, etc.

[0033] Data acquisition modules are devices that collect data from multiple sensors and transmit it to a central system. They can be local embedded systems or edge computing devices. PLCs (Programmable Logic Controllers) are devices widely used in industrial automation with built-in data acquisition capabilities that can connect to a variety of sensors and actuators.

[0034] S20. Obtain risky device information based on the status information, where the risky device information includes a fault type and a fault probability corresponding to the fault type; Specifically, the management platform obtains status information data such as device type information, device vibration information, temperature information, and current information, and pre-processes the status information through steps such as noise removal, data normalization, and feature extraction to facilitate fault analysis.

[0035] Optionally, obtaining a device fault type and a fault probability according to the status information includes: Based on the status information, feature extraction and fusion of multi-dimensional data are performed through a hybrid algorithm to establish an equipment fault model; training the fault model based on historical fault data; The state information is imported into the fault model to obtain the fault type and fault probability.

[0036] Specifically, potential fault types can be identified through algorithms (such as machine learning, pattern recognition, classifiers, etc.), such as model-based diagnostic methods: establishing a mathematical model of the equipment, and judging the fault type by comparing the deviation between the current state and the model prediction value; or data-driven methods: using historical fault data and normal operation data, and training a classifier through supervised learning algorithms (such as decision trees, support vector machines, neural networks, etc.), which can predict the fault type based on the current state information.

[0037] If the vibration of the equipment exceeds a certain threshold, the possible fault type is "bearing damage"; if the current value is abnormal, the possible fault type is "motor failure".

[0038] S30. Obtain maintenance personnel information, where the personnel information includes personnel location information and maintenance personnel skill scores.

[0039] Specifically, maintenance personnel can be equipped with positioning devices using technologies such as GPS, Wi-Fi positioning, Bluetooth, or RFID tags. These devices can be attached to workers' helmets or worn on their wrists, such as wristbands. In this embodiment, since maintenance personnel are located within the factory, Wi-Fi positioning or Bluetooth Low Energy (BLE) technology can be used. Using the signals sent by the positioning devices, existing tracking systems can be developed or utilized to obtain real-time location data for maintenance personnel.

[0040] The positioning device transmits location information to the management platform via a wireless network (such as Wi-Fi, 4G / 5G, or dedicated wireless communication). The system automatically records and updates the maintenance personnel's location at predetermined intervals and displays it on the management interface. Location information is the maintenance personnel's coordinates, which can include longitude, latitude, floor (for indoor environments), and timestamp.

[0041] Optionally, the obtaining of maintenance personnel information, wherein the personnel information includes personnel location information and maintenance personnel skill scores, includes: The skill matching degree of the maintenance personnel is obtained according to the fault type and the skill score of the maintenance personnel.

[0042] S40. Obtaining a maintenance personnel priority based on the fault type, fault probability, and maintenance personnel information, where the maintenance personnel priority is used to represent the compatibility of the maintenance personnel with the risky equipment. The scheduling priority of maintenance personnel is obtained through comprehensive calculation. The higher the priority value (Wj), the more suitable the maintenance personnel is to be scheduled to handle this fault.

[0043] S50: Send the maintenance work order of the risky equipment to the maintenance personnel who meet the preset conditions according to the maintenance personnel priority.

[0044] According to the calculated maintenance personnel priority, the system assigns the task to the most suitable maintenance personnel, that is, the maintenance personnel with the highest priority.

[0045] In a specific embodiment, maintenance personnel receive task notifications through their own mobile terminals, such as mobile phones, smart bracelets, etc.; after confirming receipt, the system automatically generates an electronic work order; the maintenance personnel arrive at the fault location, complete sign-in, and then perform maintenance work.

[0046] Optionally, if the person fails to sign in within a specified time (e.g., 30 minutes) after receiving the task, the system automatically notifies the maintenance personnel of the next higher priority level.

[0047] In an embodiment of the present application, a smart factory management method based on the Industrial Internet of Things is provided. The method obtains the status information of each piece of production equipment in the workshop, obtains the equipment fault type and fault probability based on the status information, and then obtains maintenance personnel information, including personnel location information and maintenance personnel skill scores. Then, based on the fault type, fault probability, and maintenance personnel information, the maintenance personnel priority is obtained, and the equipment is repaired according to the maintenance personnel priority. By obtaining the status information of each piece of production equipment in the workshop, the system can monitor the equipment operation in real time and promptly identify potential fault hazards. Based on the status information, the equipment fault type and fault probability can be accurately inferred, thereby achieving preventive maintenance and reducing the occurrence rate of equipment failures. Moreover, based on the maintenance personnel's location information and skill scores, the system can intelligently calculate the priority of each maintenance personnel, ensuring that the appropriate maintenance personnel can be assigned to high-priority fault tasks. This not only improves repair efficiency, but also enables the rational scheduling of personnel and optimizes resource utilization.

[0048] Example 2 In the embodiment of the present application, in step S20, obtaining the device fault type and fault probability according to the status information includes: Based on the status information, feature extraction and fusion of multi-dimensional data are performed through a hybrid algorithm to establish an equipment fault model; training the fault model based on historical fault data; The state information is imported into the fault model to obtain the fault type and fault probability.

[0049] Specifically, the collected raw data is subjected to denoising, outlier detection and correction, and missing values ​​are filled to ensure data quality. Data processing techniques (such as signal processing and statistical methods) are then used to extract valuable features from the multi-dimensional status data. Hybrid algorithms, such as genetic algorithms, principal component analysis (PCA), and deep learning, are used to fuse the extracted multi-dimensional features to generate a more accurate feature representation. This feature fusion yields a feature vector that efficiently and effectively characterizes the equipment's operating status. Based on this feature-fused data, a machine learning or deep learning algorithm (such as support vector machines (SVMs), decision trees, random forests, and neural networks) is used to establish an equipment failure prediction model. The goal of this equipment failure model is to identify different fault types and estimate the probability of failure based on the equipment's status characteristics.

[0050] Equipment fault models are trained using historical fault data, which should include known fault types and the conditions in which they occurred. During training, the model is adjusted using supervised or semi-supervised learning methods to continuously optimize the accuracy of fault type classification and failure probability estimation.

[0051] The training process includes: data partitioning (dividing historical data into training and validation sets); model training (adjusting model parameters using the training set data); and validation and optimization (evaluating model performance using the validation set data and adjusting model hyperparameters until the desired accuracy is achieved). This training enables the model to accurately predict the type and probability of equipment failures when faced with unknown status information.

[0052] Real-time device status information is imported into a trained fault model, which then predicts the device's fault type and probability. The system continuously updates the device's real-time status information and feeds it into the fault model for real-time prediction. The model then outputs the fault type and corresponding probability based on the input status information.

[0053] Through the above steps, from the collection and feature extraction of status information, to the establishment, training and real-time application of fault models, and then to the evaluation of fault types and probabilities, the entire process forms a closed-loop system that can efficiently predict and manage equipment failures.

[0054] In one embodiment, step S30: obtaining maintenance personnel information, wherein the personnel information includes personnel location information and maintenance personnel skill scores, includes: The skill matching degree of the maintenance personnel is obtained according to the fault type and the skill score of the maintenance personnel.

[0055] Specifically, obtaining the skill scores of maintenance personnel includes: obtaining basic information such as the maintenance personnel's educational background, work experience, training records, etc.; collecting the maintenance personnel's performance in actual operations, such as the success rate of fault handling, response time, work efficiency, etc.

[0056] Develop skill scoring criteria based on job requirements. The scoring criteria may include: Expertise: such as understanding of the equipment, tools used, ability to diagnose problems, etc. Practical operational capabilities: including operational accuracy, efficiency, troubleshooting capabilities, etc.; Problem-solving ability: solutions and response speed when faced with unexpected problems; Safety awareness: including compliance with safety operating procedures.

[0057] In practice, maintenance personnel's performance is scored to quantitatively assess their skill level. For example, a 1-5 or 1-10 scale can be used to evaluate each dimension item by item. For a specific fault type (e.g., engine failure, circuit failure, mechanical failure, etc.), the skill score can be set on a percentage basis, such as 30%, 50%, 90%, etc.

[0058] Integrating assessed skill scores with the personnel profile system allows real-time access to each maintenance worker's skill score. The management platform displays maintenance workers' skill scores, training records, and actual operational data, allowing administrators to better understand their overall capabilities.

[0059] Optionally, the skill matching degree satisfies the following formula:

[0060] in, is the skill matching degree of the jth maintenance worker, which represents his comprehensive ability to deal with all fault types.

[0061] Is the fault type The weight of the fault type indicates its importance. Different fault types have different urgency levels. For example, some faults may affect critical system functions, while others may affect smaller modules. Therefore, a weight must be set for each fault type. A higher weight indicates a more important fault type. This weight can be preset based on historical maintenance data.

[0062] It is j Maintenance personnel in the fault type Skill scores are based on experience and past performance. Specifically, fault handling history assesses the maintenance personnel's past performance in handling similar faults, including the number of faults handled, the difficulty of resolution, and the efficiency of resolution. Time to resolve a fault: This is based on the time it takes the maintenance personnel to resolve the fault type. A shorter resolution time generally indicates a higher skill level. Success rate: This is the ratio of the number of successful repairs to the total number of faults handled.

[0063] P ( fi ): Fault type fi The probability of occurrence of i An index representing the fault type, i ∈{1, 2, ..., n In this implementation scheme, the fault types can be divided into mechanical fault, electrical fault, software fault and other faults, i=1 represents the first fault type, i.e., mechanical fault, and i=3 represents the third fault type, i.e., software fault.

[0064] When an abnormality in the device status information is detected, the corresponding fault type and fault probability are obtained according to the fault model. The corresponding fault type and probability can be obtained for each abnormality, as shown in the following table:

[0065] :Maintenance personnel j The corresponding skill capability vector represents the maintenance personnel j Proficiency in handling different fault types. This proficiency can be scored based on the maintenance personnel's historical performance. The shorter the time to successfully perform a repair, the higher the score.

[0066] The above technical solution achieves precise matching of maintenance personnel's skills by comprehensively considering the importance and probability of fault types, as well as the maintenance personnel's skill scores. This approach optimizes the fault handling process, improves resource utilization efficiency, and enhances maintenance quality, ultimately enhancing the effectiveness of equipment maintenance management and the stability of production systems. Based on the skill matching, personnel can be efficiently deployed according to each maintenance personnel's expertise. For example, personnel with high skill matching scores can be assigned to handle complex or high-frequency fault types, ensuring rapid response and efficient repair.

[0067] Optionally, a minimum skill requirement threshold Smin is set for the critical fault type, the calculated skill matching degree is compared with the threshold, and the matching degree is processed according to a preset algorithm.

[0068] Optionally, obtaining the maintenance personnel priority according to the fault type, fault probability and the maintenance personnel information satisfies the following formula:

[0069] in,

[0070] in, represents the priority of the jth maintenance personnel; , Indicates the location coordinates of the equipment to be repaired; , represents the position coordinates of the jth maintenance worker; D(a,b) represents the distance between the equipment to be repaired and the maintenance worker; Mj represents the skill matching degree of the jth maintenance worker; represents the task load of the i-th maintenance personnel; α represents the task load weight coefficient.

[0071] Through multi-dimensional priority calculation, maintenance personnel scheduling and task allocation are optimized. This takes into account maintenance personnel's skills and workload, and combines location information and fault urgency to more rationally allocate maintenance resources. This significantly improves fault response speed and repair efficiency, thereby enhancing the overall effectiveness of equipment operation and maintenance management.

[0072] The present application also discloses an industrial IoT-based smart factory management system, including a management platform, a sensor network platform, and an object platform that are sequentially communicatively connected. The object platform includes a production line and production equipment. The management platform includes: Information acquisition module, used to obtain status information of each production equipment in the workshop; A fault detection module is used to obtain the equipment fault type and fault probability based on the status information; A personnel information acquisition module is used to obtain maintenance personnel information, including personnel location information and maintenance personnel skill scores; A priority calculation module, configured to obtain the maintenance personnel priority according to the fault type, fault probability and maintenance personnel information; The control module is used to perform maintenance operations on the equipment according to the maintenance personnel priority.

[0073] The present application also discloses a computer device including one or more processors and a memory; One or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs are configured to execute the above-mentioned method.

[0074] The embodiment of the present application further discloses a computer-readable storage medium storing a computer program that can be loaded by a processor and execute the above method.

[0075] In some embodiments, the computer-readable storage medium may be a memory device such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface mount memory, optical disk, or CD-ROM; or various devices including any one or any combination of the above memories. The computer may be various computing devices including smart terminals and servers.

[0076] In the above embodiments of the present disclosure, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0077] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0078] 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, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0079] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0080] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a non-volatile storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned non-volatile storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program code.

[0081] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A smart factory management method based on industrial Internet of Things, characterized in that: Applied to a smart factory management system, the smart factory management system includes a management platform, a sensor network platform, and an object platform that are sequentially communicatively connected. The method is executed by the management platform and includes: Obtain status information of each production equipment in the workshop; Obtaining risky equipment information according to the state information, the risky equipment information including a fault type and a fault probability corresponding to the fault type; Obtaining maintenance personnel information, including personnel location information and maintenance personnel skill scores; Obtaining a maintenance personnel priority according to the fault type, fault probability, and maintenance personnel information, wherein the maintenance personnel priority is used to characterize the compatibility of the maintenance personnel with the risky equipment; According to the maintenance personnel priority, the maintenance work order of the risky equipment is sent to the maintenance personnel who meet the preset conditions.

2. The smart factory management method according to claim 1, characterized in that: The step of obtaining risky device information based on the status information, wherein the risky device information includes a fault type and a fault probability corresponding to the fault type, includes: Based on the status information, feature extraction and fusion of multi-dimensional data are performed through a hybrid algorithm to establish an equipment fault model; training the fault model based on historical fault data; The state information is imported into the fault model to obtain the fault type and fault probability.

3. The smart factory management method according to claim 1, characterized in that: The obtaining of maintenance personnel information, including personnel location information and maintenance personnel skill scores, includes: The skill matching degree of the maintenance personnel is obtained according to the fault type and the skill score of the maintenance personnel.

4. The smart factory management method according to claim 3, characterized in that: The skill matching degree satisfies the following formula: in, is the skill matching degree of the jth maintenance worker, indicating his comprehensive ability to deal with all fault types; Is the fault type The weight of , which indicates the importance of the fault type; It is j Maintenance personnel in the fault type Skill rating on P ( fi ): Fault type fi The probability of occurrence of i An index representing the fault type, i ∈{1, 2, ..., n }; :Maintenance personnel j The corresponding skill capability vector represents the maintenance personnel j Proficiency in different fault types.

5. The smart factory management method according to claim 4, characterized in that: A minimum skill requirement threshold Smin is set for key fault types, the calculated skill matching degree is compared with the threshold, and the matching degree is processed according to the preset algorithm.

6. The smart factory management method according to claim 5, characterized in that: The maintenance personnel priority is obtained according to the fault type, fault probability and the maintenance personnel information, and satisfies the following formula: in, in, represents the priority of the jth maintenance personnel; , Indicates the location coordinates of the equipment to be repaired; , represents the position coordinates of the jth maintenance worker; D(a,b) represents the distance between the equipment to be repaired and the maintenance worker; Mj represents the skill matching degree of the jth maintenance personnel; represents the task load of the i-th maintenance personnel; α represents the task load weight coefficient.

7. The smart factory management method according to claim 1, characterized in that: The status information is one of device type information, device vibration information, temperature information or current information.

8. A smart factory management system based on industrial Internet of Things, characterized by: It includes a management platform, a sensor network platform and an object platform that are sequentially connected in communication, wherein the object platform includes a production line and production equipment, and the management platform includes: Information acquisition module, used to obtain status information of each production equipment in the workshop; A fault detection module is used to obtain the equipment fault type and fault probability based on the status information; A personnel information acquisition module is used to obtain maintenance personnel information, including personnel location information and maintenance personnel skill scores; A priority calculation module, configured to obtain the maintenance personnel priority according to the fault type, fault probability and maintenance personnel information; The control module is used to perform maintenance operations on the equipment according to the maintenance personnel priority.

9. A computer device, characterized in that: including one or more processors and memory; One or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs are configured to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer program is stored and can be loaded by a processor to execute the method according to any one of claims 1 to 7.

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